EDA_Production_data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/silvya-github/silvya-github/blob/main/EDA_Production_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Llmnb14YcLZs"
      },
      "outputs": [],
      "source": [
        "#Data preprocessing - EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9zUgEvzFcaFr"
      },
      "outputs": [],
      "source": [
        "#importing libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "k6ZrsCZfykSR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a90edb96-2aee-4ceb-8f7e-8702cb59c094"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading the data"
      ],
      "metadata": {
        "id": "aBV4CmBN72Ni"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#csv path and read\n",
        "df = pd.read_csv('/content/drive/MyDrive/production_data_train.csv')"
      ],
      "metadata": {
        "id": "eAu_NIa6zK-n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print the dataset\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yMsMUZ_P7QQ_",
        "outputId": "d5439bfb-f64b-4e52-b06d-a20c9cef19cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   API  Year  Month  Liquid    Gas  RatioGasOil  Water  \\\n",
              "0       33053063590000  2015      3   17186  26624     1.549168   8213   \n",
              "1       33053063590000  2015      4   18796  21796     1.159608   7255   \n",
              "2       33053063590000  2015      5   14626  15988     1.093122   4213   \n",
              "3       33053063590000  2015      6   10421  12311     1.181365   2717   \n",
              "4       33053063590000  2015      7    9777  11254     1.151069   2399   \n",
              "...                ...   ...    ...     ...    ...          ...    ...   \n",
              "394069  42301324920100  2018      5    2387  10603     4.441977  21452   \n",
              "394070  42301324920100  2018      6    4531   8700     1.920106  40840   \n",
              "394071  42301324920100  2018      7    7432   9536     1.283100  66864   \n",
              "394072  42301324920100  2018      8    4614   8625     1.869311  41465   \n",
              "394073  42301324920100  2018      9    2156   3354     1.555659  19285   \n",
              "\n",
              "        PercentWater  DaysOn              _LastUpdate  \n",
              "0           0.477889      16  2016-08-11 14:03:14.000  \n",
              "1           0.385986      30  2016-08-11 14:03:14.000  \n",
              "2           0.288049      31  2016-08-11 14:03:14.000  \n",
              "3           0.260724      30  2016-04-06 13:47:11.383  \n",
              "4           0.245372      31  2016-04-06 13:47:11.383  \n",
              "...              ...     ...                      ...  \n",
              "394069      8.987013       0  2019-03-10 13:58:15.553  \n",
              "394070      9.013463       0  2019-03-10 13:58:15.553  \n",
              "394071      8.996771       0  2019-03-10 13:58:15.553  \n",
              "394072      8.986779       0  2019-03-10 13:58:15.553  \n",
              "394073      8.944805       0  2019-03-10 13:58:15.553  \n",
              "\n",
              "[394074 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5138e2c0-3791-4389-a078-8b09bbaef48c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>API</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>RatioGasOil</th>\n",
              "      <th>Water</th>\n",
              "      <th>PercentWater</th>\n",
              "      <th>DaysOn</th>\n",
              "      <th>_LastUpdate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>3</td>\n",
              "      <td>17186</td>\n",
              "      <td>26624</td>\n",
              "      <td>1.549168</td>\n",
              "      <td>8213</td>\n",
              "      <td>0.477889</td>\n",
              "      <td>16</td>\n",
              "      <td>2016-08-11 14:03:14.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>4</td>\n",
              "      <td>18796</td>\n",
              "      <td>21796</td>\n",
              "      <td>1.159608</td>\n",
              "      <td>7255</td>\n",
              "      <td>0.385986</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-08-11 14:03:14.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>14626</td>\n",
              "      <td>15988</td>\n",
              "      <td>1.093122</td>\n",
              "      <td>4213</td>\n",
              "      <td>0.288049</td>\n",
              "      <td>31</td>\n",
              "      <td>2016-08-11 14:03:14.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>6</td>\n",
              "      <td>10421</td>\n",
              "      <td>12311</td>\n",
              "      <td>1.181365</td>\n",
              "      <td>2717</td>\n",
              "      <td>0.260724</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-04-06 13:47:11.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>9777</td>\n",
              "      <td>11254</td>\n",
              "      <td>1.151069</td>\n",
              "      <td>2399</td>\n",
              "      <td>0.245372</td>\n",
              "      <td>31</td>\n",
              "      <td>2016-04-06 13:47:11.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394069</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>5</td>\n",
              "      <td>2387</td>\n",
              "      <td>10603</td>\n",
              "      <td>4.441977</td>\n",
              "      <td>21452</td>\n",
              "      <td>8.987013</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394070</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>6</td>\n",
              "      <td>4531</td>\n",
              "      <td>8700</td>\n",
              "      <td>1.920106</td>\n",
              "      <td>40840</td>\n",
              "      <td>9.013463</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394071</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>7432</td>\n",
              "      <td>9536</td>\n",
              "      <td>1.283100</td>\n",
              "      <td>66864</td>\n",
              "      <td>8.996771</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394072</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>8</td>\n",
              "      <td>4614</td>\n",
              "      <td>8625</td>\n",
              "      <td>1.869311</td>\n",
              "      <td>41465</td>\n",
              "      <td>8.986779</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394073</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "      <td>2156</td>\n",
              "      <td>3354</td>\n",
              "      <td>1.555659</td>\n",
              "      <td>19285</td>\n",
              "      <td>8.944805</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>394074 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5138e2c0-3791-4389-a078-8b09bbaef48c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5138e2c0-3791-4389-a078-8b09bbaef48c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5138e2c0-3791-4389-a078-8b09bbaef48c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYpU_S7fY1oh",
        "outputId": "85480db8-6402-4694-cbc1-65f13fb36d79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(394074, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Basic Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "rBoa0KLk6770"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Prints column header \n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7nN6hJTT9eQW",
        "outputId": "58891d15-c586-483f-cf30-528c4670e48d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['API', 'Year', 'Month', 'Liquid', 'Gas', 'RatioGasOil', 'Water',\n",
              "       'PercentWater', 'DaysOn', '_LastUpdate'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Explanations about the columns**\n",
        "\n",
        "*   **API:** specifies the unique Well number\n",
        "*   **Year, Month:** Specifies the year and month which the hydrocarbons produced\n",
        "*   **Liquid, Gas, Water:** The amount of oil, gas and water produced from the well\n",
        "*   **RatioGasOil:** Measure of volume of gas produced along with  oil\n",
        "*   **PercentWater:** Water produced per liquid production in the well\n",
        "*   **DaysOn:** No of days of hydrocarbon production in the well\n",
        "*   **_LastUpdate:** Date and time of last updation of data of the specific well"
      ],
      "metadata": {
        "id": "Ux3qovlB-HzT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#prints first 5 rows of the database\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IykLFYSK0X2U",
        "outputId": "42b352f0-00c2-414f-e3de-ee74cd92890a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              API  Year  Month  Liquid    Gas  RatioGasOil  Water  \\\n",
              "0  33053063590000  2015      3   17186  26624     1.549168   8213   \n",
              "1  33053063590000  2015      4   18796  21796     1.159608   7255   \n",
              "2  33053063590000  2015      5   14626  15988     1.093122   4213   \n",
              "3  33053063590000  2015      6   10421  12311     1.181365   2717   \n",
              "4  33053063590000  2015      7    9777  11254     1.151069   2399   \n",
              "\n",
              "   PercentWater  DaysOn              _LastUpdate  \n",
              "0      0.477889      16  2016-08-11 14:03:14.000  \n",
              "1      0.385986      30  2016-08-11 14:03:14.000  \n",
              "2      0.288049      31  2016-08-11 14:03:14.000  \n",
              "3      0.260724      30  2016-04-06 13:47:11.383  \n",
              "4      0.245372      31  2016-04-06 13:47:11.383  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-683e018e-f0f7-4281-9e83-f3c6d4572663\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>API</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>RatioGasOil</th>\n",
              "      <th>Water</th>\n",
              "      <th>PercentWater</th>\n",
              "      <th>DaysOn</th>\n",
              "      <th>_LastUpdate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>3</td>\n",
              "      <td>17186</td>\n",
              "      <td>26624</td>\n",
              "      <td>1.549168</td>\n",
              "      <td>8213</td>\n",
              "      <td>0.477889</td>\n",
              "      <td>16</td>\n",
              "      <td>2016-08-11 14:03:14.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>4</td>\n",
              "      <td>18796</td>\n",
              "      <td>21796</td>\n",
              "      <td>1.159608</td>\n",
              "      <td>7255</td>\n",
              "      <td>0.385986</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-08-11 14:03:14.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>14626</td>\n",
              "      <td>15988</td>\n",
              "      <td>1.093122</td>\n",
              "      <td>4213</td>\n",
              "      <td>0.288049</td>\n",
              "      <td>31</td>\n",
              "      <td>2016-08-11 14:03:14.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>6</td>\n",
              "      <td>10421</td>\n",
              "      <td>12311</td>\n",
              "      <td>1.181365</td>\n",
              "      <td>2717</td>\n",
              "      <td>0.260724</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-04-06 13:47:11.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>9777</td>\n",
              "      <td>11254</td>\n",
              "      <td>1.151069</td>\n",
              "      <td>2399</td>\n",
              "      <td>0.245372</td>\n",
              "      <td>31</td>\n",
              "      <td>2016-04-06 13:47:11.383</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-683e018e-f0f7-4281-9e83-f3c6d4572663')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-683e018e-f0f7-4281-9e83-f3c6d4572663 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-683e018e-f0f7-4281-9e83-f3c6d4572663');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#prints last 5 rows of the database\n",
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "G8R2WPqm0iOf",
        "outputId": "2fd49771-b44e-42d4-c96a-d5f38066a534"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   API  Year  Month  Liquid    Gas  RatioGasOil  Water  \\\n",
              "394069  42301324920100  2018      5    2387  10603     4.441977  21452   \n",
              "394070  42301324920100  2018      6    4531   8700     1.920106  40840   \n",
              "394071  42301324920100  2018      7    7432   9536     1.283100  66864   \n",
              "394072  42301324920100  2018      8    4614   8625     1.869311  41465   \n",
              "394073  42301324920100  2018      9    2156   3354     1.555659  19285   \n",
              "\n",
              "        PercentWater  DaysOn              _LastUpdate  \n",
              "394069      8.987013       0  2019-03-10 13:58:15.553  \n",
              "394070      9.013463       0  2019-03-10 13:58:15.553  \n",
              "394071      8.996771       0  2019-03-10 13:58:15.553  \n",
              "394072      8.986779       0  2019-03-10 13:58:15.553  \n",
              "394073      8.944805       0  2019-03-10 13:58:15.553  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea1fe535-09d5-4d0c-adce-cf27e70df5d6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>API</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>RatioGasOil</th>\n",
              "      <th>Water</th>\n",
              "      <th>PercentWater</th>\n",
              "      <th>DaysOn</th>\n",
              "      <th>_LastUpdate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>394069</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>5</td>\n",
              "      <td>2387</td>\n",
              "      <td>10603</td>\n",
              "      <td>4.441977</td>\n",
              "      <td>21452</td>\n",
              "      <td>8.987013</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394070</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>6</td>\n",
              "      <td>4531</td>\n",
              "      <td>8700</td>\n",
              "      <td>1.920106</td>\n",
              "      <td>40840</td>\n",
              "      <td>9.013463</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394071</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>7432</td>\n",
              "      <td>9536</td>\n",
              "      <td>1.283100</td>\n",
              "      <td>66864</td>\n",
              "      <td>8.996771</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394072</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>8</td>\n",
              "      <td>4614</td>\n",
              "      <td>8625</td>\n",
              "      <td>1.869311</td>\n",
              "      <td>41465</td>\n",
              "      <td>8.986779</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394073</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "      <td>2156</td>\n",
              "      <td>3354</td>\n",
              "      <td>1.555659</td>\n",
              "      <td>19285</td>\n",
              "      <td>8.944805</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-03-10 13:58:15.553</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea1fe535-09d5-4d0c-adce-cf27e70df5d6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ea1fe535-09d5-4d0c-adce-cf27e70df5d6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ea1fe535-09d5-4d0c-adce-cf27e70df5d6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Basic statistics of numerical data (count, mean, minimum, maximum etc)\n",
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-MLe2ZNX0miA",
        "outputId": "d9ba07a4-8e17-4413-e65d-bf97f7cc96c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                API           Year          Month         Liquid  \\\n",
              "count  3.940740e+05  394074.000000  394074.000000  394074.000000   \n",
              "mean   3.060603e+13    2016.297221       6.683620    3058.839477   \n",
              "std    1.330614e+13       1.761555       3.448168    3823.349212   \n",
              "min    5.001098e+12    1977.000000       1.000000       0.000000   \n",
              "25%    3.002541e+13    2015.000000       4.000000     891.000000   \n",
              "50%    3.306103e+13    2016.000000       7.000000    1848.000000   \n",
              "75%    4.223535e+13    2017.000000      10.000000    3640.000000   \n",
              "max    4.902123e+13    2019.000000      12.000000  115876.000000   \n",
              "\n",
              "                 Gas    RatioGasOil         Water   PercentWater  \\\n",
              "count  394074.000000  394074.000000  3.940740e+05  394074.000000   \n",
              "mean    10345.621449      21.223017  4.436955e+03       2.381140   \n",
              "std     15535.037753     822.474584  1.063366e+04      61.958723   \n",
              "min         0.000000       0.000000  0.000000e+00       0.000000   \n",
              "25%      2427.000000       1.206981  3.200000e+02       0.252230   \n",
              "50%      5591.000000       2.458482  1.639000e+03       0.859604   \n",
              "75%     12346.000000       5.942927  4.404000e+03       1.922737   \n",
              "max    396506.000000  178095.000000  1.451478e+06   31860.000000   \n",
              "\n",
              "              DaysOn  \n",
              "count  394074.000000  \n",
              "mean       16.918008  \n",
              "std        14.473768  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%        27.000000  \n",
              "75%        31.000000  \n",
              "max        31.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4fab7593-6a03-47bf-b024-5ad27f1f1a39\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>API</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>RatioGasOil</th>\n",
              "      <th>Water</th>\n",
              "      <th>PercentWater</th>\n",
              "      <th>DaysOn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.940740e+05</td>\n",
              "      <td>394074.000000</td>\n",
              "      <td>394074.000000</td>\n",
              "      <td>394074.000000</td>\n",
              "      <td>394074.000000</td>\n",
              "      <td>394074.000000</td>\n",
              "      <td>3.940740e+05</td>\n",
              "      <td>394074.000000</td>\n",
              "      <td>394074.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.060603e+13</td>\n",
              "      <td>2016.297221</td>\n",
              "      <td>6.683620</td>\n",
              "      <td>3058.839477</td>\n",
              "      <td>10345.621449</td>\n",
              "      <td>21.223017</td>\n",
              "      <td>4.436955e+03</td>\n",
              "      <td>2.381140</td>\n",
              "      <td>16.918008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.330614e+13</td>\n",
              "      <td>1.761555</td>\n",
              "      <td>3.448168</td>\n",
              "      <td>3823.349212</td>\n",
              "      <td>15535.037753</td>\n",
              "      <td>822.474584</td>\n",
              "      <td>1.063366e+04</td>\n",
              "      <td>61.958723</td>\n",
              "      <td>14.473768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.001098e+12</td>\n",
              "      <td>1977.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.002541e+13</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>2427.000000</td>\n",
              "      <td>1.206981</td>\n",
              "      <td>3.200000e+02</td>\n",
              "      <td>0.252230</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.306103e+13</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1848.000000</td>\n",
              "      <td>5591.000000</td>\n",
              "      <td>2.458482</td>\n",
              "      <td>1.639000e+03</td>\n",
              "      <td>0.859604</td>\n",
              "      <td>27.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.223535e+13</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3640.000000</td>\n",
              "      <td>12346.000000</td>\n",
              "      <td>5.942927</td>\n",
              "      <td>4.404000e+03</td>\n",
              "      <td>1.922737</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.902123e+13</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>115876.000000</td>\n",
              "      <td>396506.000000</td>\n",
              "      <td>178095.000000</td>\n",
              "      <td>1.451478e+06</td>\n",
              "      <td>31860.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4fab7593-6a03-47bf-b024-5ad27f1f1a39')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4fab7593-6a03-47bf-b024-5ad27f1f1a39 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4fab7593-6a03-47bf-b024-5ad27f1f1a39');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Identify the rows and columns and datatype and memory storage\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btUlIMDp8pe4",
        "outputId": "776f4554-3a31-42fb-f6e0-f27540cddd48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 394074 entries, 0 to 394073\n",
            "Data columns (total 10 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
            " 0   API           394074 non-null  int64  \n",
            " 1   Year          394074 non-null  int64  \n",
            " 2   Month         394074 non-null  int64  \n",
            " 3   Liquid        394074 non-null  int64  \n",
            " 4   Gas           394074 non-null  int64  \n",
            " 5   RatioGasOil   394074 non-null  float64\n",
            " 6   Water         394074 non-null  int64  \n",
            " 7   PercentWater  394074 non-null  float64\n",
            " 8   DaysOn        394074 non-null  int64  \n",
            " 9   _LastUpdate   394074 non-null  object \n",
            "dtypes: float64(2), int64(7), object(1)\n",
            "memory usage: 30.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for null values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3KhAyCX0r4n",
        "outputId": "002ad1f7-b68f-4a9a-fa67-9a077bab3740"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "API             0\n",
              "Year            0\n",
              "Month           0\n",
              "Liquid          0\n",
              "Gas             0\n",
              "RatioGasOil     0\n",
              "Water           0\n",
              "PercentWater    0\n",
              "DaysOn          0\n",
              "_LastUpdate     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check for duplicated rows\n",
        "df[df.duplicated()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "NrEDV2py1ME_",
        "outputId": "446a3598-aa53-4452-f0cb-87020104b96c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [API, Year, Month, Liquid, Gas, RatioGasOil, Water, PercentWater, DaysOn, _LastUpdate]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c532c22-6036-453a-961d-ae96a9f8e1b2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>API</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>RatioGasOil</th>\n",
              "      <th>Water</th>\n",
              "      <th>PercentWater</th>\n",
              "      <th>DaysOn</th>\n",
              "      <th>_LastUpdate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c532c22-6036-453a-961d-ae96a9f8e1b2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1c532c22-6036-453a-961d-ae96a9f8e1b2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1c532c22-6036-453a-961d-ae96a9f8e1b2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the no of unique values in each column\n",
        "df.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vq1KGOp91oZp",
        "outputId": "6dae6967-d4ac-4b44-bc0c-d61a4a32aa2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "API               7844\n",
              "Year                43\n",
              "Month               12\n",
              "Liquid           20469\n",
              "Gas              48462\n",
              "RatioGasOil     365934\n",
              "Water            32161\n",
              "PercentWater    321440\n",
              "DaysOn              32\n",
              "_LastUpdate     195033\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.API.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vffom3Wtpsu1",
        "outputId": "f884982c-d111-4b9c-c7e4-de0b6b51947d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7844"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the unique values in the column \"Year\"\n",
        "df['Year'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nWyaFHfSdAP5",
        "outputId": "de99a6ff-1e2c-432d-cc29-4dbf84c84ceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2015, 2016, 2017, 2018, 2019, 2014, 2013, 2008, 2009, 2010, 2011,\n",
              "       2012, 2007, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989,\n",
              "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000,\n",
              "       2001, 2002, 2003, 2004, 2005, 2006, 1977, 1978, 1979, 1980])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Remove the columns \"_LastUpdate\" and \"DaysOn\"\n",
        "drop_df = df.drop(['_LastUpdate'], axis=1)\n",
        "drop_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "cKLfdqSfeKgl",
        "outputId": "1635a757-f9c8-447f-9f80-1892840b3dd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   API  Year  Month  Liquid    Gas  RatioGasOil  Water  \\\n",
              "0       33053063590000  2015      3   17186  26624     1.549168   8213   \n",
              "1       33053063590000  2015      4   18796  21796     1.159608   7255   \n",
              "2       33053063590000  2015      5   14626  15988     1.093122   4213   \n",
              "3       33053063590000  2015      6   10421  12311     1.181365   2717   \n",
              "4       33053063590000  2015      7    9777  11254     1.151069   2399   \n",
              "...                ...   ...    ...     ...    ...          ...    ...   \n",
              "394069  42301324920100  2018      5    2387  10603     4.441977  21452   \n",
              "394070  42301324920100  2018      6    4531   8700     1.920106  40840   \n",
              "394071  42301324920100  2018      7    7432   9536     1.283100  66864   \n",
              "394072  42301324920100  2018      8    4614   8625     1.869311  41465   \n",
              "394073  42301324920100  2018      9    2156   3354     1.555659  19285   \n",
              "\n",
              "        PercentWater  DaysOn  \n",
              "0           0.477889      16  \n",
              "1           0.385986      30  \n",
              "2           0.288049      31  \n",
              "3           0.260724      30  \n",
              "4           0.245372      31  \n",
              "...              ...     ...  \n",
              "394069      8.987013       0  \n",
              "394070      9.013463       0  \n",
              "394071      8.996771       0  \n",
              "394072      8.986779       0  \n",
              "394073      8.944805       0  \n",
              "\n",
              "[394074 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e7f5260c-da72-465c-823d-66425caab665\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>API</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>RatioGasOil</th>\n",
              "      <th>Water</th>\n",
              "      <th>PercentWater</th>\n",
              "      <th>DaysOn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>3</td>\n",
              "      <td>17186</td>\n",
              "      <td>26624</td>\n",
              "      <td>1.549168</td>\n",
              "      <td>8213</td>\n",
              "      <td>0.477889</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>4</td>\n",
              "      <td>18796</td>\n",
              "      <td>21796</td>\n",
              "      <td>1.159608</td>\n",
              "      <td>7255</td>\n",
              "      <td>0.385986</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>5</td>\n",
              "      <td>14626</td>\n",
              "      <td>15988</td>\n",
              "      <td>1.093122</td>\n",
              "      <td>4213</td>\n",
              "      <td>0.288049</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>6</td>\n",
              "      <td>10421</td>\n",
              "      <td>12311</td>\n",
              "      <td>1.181365</td>\n",
              "      <td>2717</td>\n",
              "      <td>0.260724</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>9777</td>\n",
              "      <td>11254</td>\n",
              "      <td>1.151069</td>\n",
              "      <td>2399</td>\n",
              "      <td>0.245372</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394069</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>5</td>\n",
              "      <td>2387</td>\n",
              "      <td>10603</td>\n",
              "      <td>4.441977</td>\n",
              "      <td>21452</td>\n",
              "      <td>8.987013</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394070</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>6</td>\n",
              "      <td>4531</td>\n",
              "      <td>8700</td>\n",
              "      <td>1.920106</td>\n",
              "      <td>40840</td>\n",
              "      <td>9.013463</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394071</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>7</td>\n",
              "      <td>7432</td>\n",
              "      <td>9536</td>\n",
              "      <td>1.283100</td>\n",
              "      <td>66864</td>\n",
              "      <td>8.996771</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394072</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>8</td>\n",
              "      <td>4614</td>\n",
              "      <td>8625</td>\n",
              "      <td>1.869311</td>\n",
              "      <td>41465</td>\n",
              "      <td>8.986779</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394073</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "      <td>2156</td>\n",
              "      <td>3354</td>\n",
              "      <td>1.555659</td>\n",
              "      <td>19285</td>\n",
              "      <td>8.944805</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>394074 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e7f5260c-da72-465c-823d-66425caab665')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e7f5260c-da72-465c-823d-66425caab665 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e7f5260c-da72-465c-823d-66425caab665');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Analysis and Visualization"
      ],
      "metadata": {
        "id": "9gBymNehtjo1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating total Oil, gas and water produced from the Raisa Well\n",
        "Total_oil = df.Liquid.sum()\n",
        "Total_gas = df.Gas.sum()\n",
        "Total_water = df.Water.sum()\n",
        "\n",
        "Total_production = Total_oil + Total_gas + Total_water"
      ],
      "metadata": {
        "id": "aN9zu9tJtqIL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"The total oil produced is {Total_oil} cubic metres\")\n",
        "print(f\"The total gas produced is  {Total_gas} cubic metres\")\n",
        "print(f\"The total water produced is {Total_water} cubic metres\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XAw4UNowJrf",
        "outputId": "23db5949-7b44-4952-f1b0-ade8503e8238"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The total oil produced is 1205409108 cubic metres\n",
            "The total gas produced is  4076940427 cubic metres\n",
            "The total water produced is 1748488645 cubic metres\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting pie chart using Plotly library px.pie\n",
        "values = [Total_oil, Total_gas, Total_water]\n",
        "labels = ['Oil', 'Gas', 'Water']\n",
        "\n",
        "fig = px.pie(values=values, names=labels, labels=labels, color=labels, color_discrete_map={'Oil':'purple', 'Gas':'lime', 'Water':'blue'},title= \"Fluid Production Fraction\")\n",
        "fig.update_layout(font_size = 14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ZeiiDKuPyTyQ",
        "outputId": "bb8c60c6-66ca-4094-8619-8044050b5ce1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"2d2bacdc-f3e3-49c4-94a4-af1810298dd8\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"2d2bacdc-f3e3-49c4-94a4-af1810298dd8\")) {                    Plotly.newPlot(                        \"2d2bacdc-f3e3-49c4-94a4-af1810298dd8\",                        [{\"customdata\":[[\"Oil\"],[\"Gas\"],[\"Water\"]],\"domain\":{\"x\":[0.0,1.0],\"y\":[0.0,1.0]},\"hovertemplate\":\"label=%{label}<br>value=%{value}<br>color=%{customdata[0]}<extra></extra>\",\"labels\":[\"Oil\",\"Gas\",\"Water\"],\"legendgroup\":\"\",\"marker\":{\"colors\":[\"purple\",\"lime\",\"blue\"]},\"name\":\"\",\"showlegend\":true,\"values\":[1205409108,4076940427,1748488645],\"type\":\"pie\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Fluid Production Fraction\"},\"font\":{\"size\":14}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('2d2bacdc-f3e3-49c4-94a4-af1810298dd8');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import calendar\n",
        "\n",
        "abbrevations = dict(enumerate(calendar.month_abbr))\n",
        "drop_df['Month']=drop_df['Month'].map(abbrevations)\n"
      ],
      "metadata": {
        "id": "5by8COUZmi03"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drop_df['Month']= pd.Categorical(drop_df['Month'], categories=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'], ordered=True)\n",
        "\n",
        "drop_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "1B8yTvpRmr6u",
        "outputId": "6f9bf7d6-2e5f-490c-a2b9-6cd1ce8449cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   API  Year Month  Liquid    Gas  RatioGasOil  Water  \\\n",
              "0       33053063590000  2015   NaN   17186  26624     1.549168   8213   \n",
              "1       33053063590000  2015   NaN   18796  21796     1.159608   7255   \n",
              "2       33053063590000  2015   NaN   14626  15988     1.093122   4213   \n",
              "3       33053063590000  2015   NaN   10421  12311     1.181365   2717   \n",
              "4       33053063590000  2015   NaN    9777  11254     1.151069   2399   \n",
              "...                ...   ...   ...     ...    ...          ...    ...   \n",
              "394069  42301324920100  2018   NaN    2387  10603     4.441977  21452   \n",
              "394070  42301324920100  2018   NaN    4531   8700     1.920106  40840   \n",
              "394071  42301324920100  2018   NaN    7432   9536     1.283100  66864   \n",
              "394072  42301324920100  2018   NaN    4614   8625     1.869311  41465   \n",
              "394073  42301324920100  2018   NaN    2156   3354     1.555659  19285   \n",
              "\n",
              "        PercentWater  DaysOn  \n",
              "0           0.477889      16  \n",
              "1           0.385986      30  \n",
              "2           0.288049      31  \n",
              "3           0.260724      30  \n",
              "4           0.245372      31  \n",
              "...              ...     ...  \n",
              "394069      8.987013       0  \n",
              "394070      9.013463       0  \n",
              "394071      8.996771       0  \n",
              "394072      8.986779       0  \n",
              "394073      8.944805       0  \n",
              "\n",
              "[394074 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae94a8d6-71e7-448d-880e-9967569e1f6a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>API</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>RatioGasOil</th>\n",
              "      <th>Water</th>\n",
              "      <th>PercentWater</th>\n",
              "      <th>DaysOn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17186</td>\n",
              "      <td>26624</td>\n",
              "      <td>1.549168</td>\n",
              "      <td>8213</td>\n",
              "      <td>0.477889</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18796</td>\n",
              "      <td>21796</td>\n",
              "      <td>1.159608</td>\n",
              "      <td>7255</td>\n",
              "      <td>0.385986</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14626</td>\n",
              "      <td>15988</td>\n",
              "      <td>1.093122</td>\n",
              "      <td>4213</td>\n",
              "      <td>0.288049</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10421</td>\n",
              "      <td>12311</td>\n",
              "      <td>1.181365</td>\n",
              "      <td>2717</td>\n",
              "      <td>0.260724</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33053063590000</td>\n",
              "      <td>2015</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9777</td>\n",
              "      <td>11254</td>\n",
              "      <td>1.151069</td>\n",
              "      <td>2399</td>\n",
              "      <td>0.245372</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394069</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2387</td>\n",
              "      <td>10603</td>\n",
              "      <td>4.441977</td>\n",
              "      <td>21452</td>\n",
              "      <td>8.987013</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394070</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4531</td>\n",
              "      <td>8700</td>\n",
              "      <td>1.920106</td>\n",
              "      <td>40840</td>\n",
              "      <td>9.013463</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394071</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7432</td>\n",
              "      <td>9536</td>\n",
              "      <td>1.283100</td>\n",
              "      <td>66864</td>\n",
              "      <td>8.996771</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394072</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4614</td>\n",
              "      <td>8625</td>\n",
              "      <td>1.869311</td>\n",
              "      <td>41465</td>\n",
              "      <td>8.986779</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394073</th>\n",
              "      <td>42301324920100</td>\n",
              "      <td>2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2156</td>\n",
              "      <td>3354</td>\n",
              "      <td>1.555659</td>\n",
              "      <td>19285</td>\n",
              "      <td>8.944805</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>394074 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae94a8d6-71e7-448d-880e-9967569e1f6a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae94a8d6-71e7-448d-880e-9967569e1f6a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ae94a8d6-71e7-448d-880e-9967569e1f6a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sort the dataframe by column name \"Year\" then by \"Month\" in ascending order\n",
        "Sort= drop_df.sort_values([\"Year\", \"Month\"])\n",
        "\n",
        "Sort"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "u71JOflMsnu1",
        "outputId": "6384c636-24c7-4fa3-fc3a-2023f1b97334"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   API  Year Month  Liquid    Gas  RatioGasOil  Water  \\\n",
              "379202  42371323150300  1977   NaN   21711   1213     0.055870      0   \n",
              "379203  42371323150300  1977   NaN   18744   1174     0.062633      0   \n",
              "379204  42371323150300  1977   NaN   17224   1164     0.067580      0   \n",
              "379205  42371323150300  1978   NaN   14937   1072     0.071768      0   \n",
              "379206  42371323150300  1978   NaN   11971    955     0.079776      0   \n",
              "...                ...   ...   ...     ...    ...          ...    ...   \n",
              "222855  49021226570000  2019   NaN     340    940     2.764706    746   \n",
              "222894  33053063130000  2019   NaN    1641  11284     6.876295   4190   \n",
              "222933  33053068450000  2019   NaN    2635  13035     4.946869   3401   \n",
              "232129  33053047870000  2019   NaN    3630   3629     0.999725   1414   \n",
              "383214  33053072000100  2019   NaN    1744  16818     9.643349   2894   \n",
              "\n",
              "        PercentWater  DaysOn  \n",
              "379202      0.000000       0  \n",
              "379203      0.000000       0  \n",
              "379204      0.000000       0  \n",
              "379205      0.000000       0  \n",
              "379206      0.000000       0  \n",
              "...              ...     ...  \n",
              "222855      2.194118      31  \n",
              "222894      2.553321      27  \n",
              "222933      1.290702      31  \n",
              "232129      0.389532      31  \n",
              "383214      1.659404      31  \n",
              "\n",
              "[394074 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b62589c-0db6-4862-a92c-325da31b8fc9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>API</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>RatioGasOil</th>\n",
              "      <th>Water</th>\n",
              "      <th>PercentWater</th>\n",
              "      <th>DaysOn</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>379202</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21711</td>\n",
              "      <td>1213</td>\n",
              "      <td>0.055870</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379203</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18744</td>\n",
              "      <td>1174</td>\n",
              "      <td>0.062633</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379204</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17224</td>\n",
              "      <td>1164</td>\n",
              "      <td>0.067580</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379205</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1978</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14937</td>\n",
              "      <td>1072</td>\n",
              "      <td>0.071768</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379206</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1978</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11971</td>\n",
              "      <td>955</td>\n",
              "      <td>0.079776</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222855</th>\n",
              "      <td>49021226570000</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>340</td>\n",
              "      <td>940</td>\n",
              "      <td>2.764706</td>\n",
              "      <td>746</td>\n",
              "      <td>2.194118</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222894</th>\n",
              "      <td>33053063130000</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1641</td>\n",
              "      <td>11284</td>\n",
              "      <td>6.876295</td>\n",
              "      <td>4190</td>\n",
              "      <td>2.553321</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222933</th>\n",
              "      <td>33053068450000</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2635</td>\n",
              "      <td>13035</td>\n",
              "      <td>4.946869</td>\n",
              "      <td>3401</td>\n",
              "      <td>1.290702</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232129</th>\n",
              "      <td>33053047870000</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3630</td>\n",
              "      <td>3629</td>\n",
              "      <td>0.999725</td>\n",
              "      <td>1414</td>\n",
              "      <td>0.389532</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383214</th>\n",
              "      <td>33053072000100</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1744</td>\n",
              "      <td>16818</td>\n",
              "      <td>9.643349</td>\n",
              "      <td>2894</td>\n",
              "      <td>1.659404</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>394074 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b62589c-0db6-4862-a92c-325da31b8fc9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6b62589c-0db6-4862-a92c-325da31b8fc9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6b62589c-0db6-4862-a92c-325da31b8fc9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Calculate cumulative production of oil, gas and water\n",
        "#adding new columns to calculate the cumulative oil production, cumulative gas production, cumulative water production\n",
        "\n",
        "Sort['Cum_Oil'] = Sort.Liquid.cumsum()\n",
        "Sort['Cum_Gas'] = Sort.Gas.cumsum()\n",
        "Sort['Cum_Water'] = Sort.Water.cumsum()\n",
        "\n",
        "Sort['Total_Production'] = Sort['Cum_Oil'] + Sort['Cum_Gas'] + Sort['Cum_Water']"
      ],
      "metadata": {
        "id": "Acn_whVaYBsO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Sort"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "CsLsP8eTtART",
        "outputId": "c038e37d-95ec-4861-e2ec-e0d9b7d6b4d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   API  Year Month  Liquid    Gas  RatioGasOil  Water  \\\n",
              "379202  42371323150300  1977   NaN   21711   1213     0.055870      0   \n",
              "379203  42371323150300  1977   NaN   18744   1174     0.062633      0   \n",
              "379204  42371323150300  1977   NaN   17224   1164     0.067580      0   \n",
              "379205  42371323150300  1978   NaN   14937   1072     0.071768      0   \n",
              "379206  42371323150300  1978   NaN   11971    955     0.079776      0   \n",
              "...                ...   ...   ...     ...    ...          ...    ...   \n",
              "222855  49021226570000  2019   NaN     340    940     2.764706    746   \n",
              "222894  33053063130000  2019   NaN    1641  11284     6.876295   4190   \n",
              "222933  33053068450000  2019   NaN    2635  13035     4.946869   3401   \n",
              "232129  33053047870000  2019   NaN    3630   3629     0.999725   1414   \n",
              "383214  33053072000100  2019   NaN    1744  16818     9.643349   2894   \n",
              "\n",
              "        PercentWater  DaysOn     Cum_Oil     Cum_Gas   Cum_Water  \\\n",
              "379202      0.000000       0       21711        1213           0   \n",
              "379203      0.000000       0       40455        2387           0   \n",
              "379204      0.000000       0       57679        3551           0   \n",
              "379205      0.000000       0       72616        4623           0   \n",
              "379206      0.000000       0       84587        5578           0   \n",
              "...              ...     ...         ...         ...         ...   \n",
              "222855      2.194118      31  1205399458  4076895661  1748476746   \n",
              "222894      2.553321      27  1205401099  4076906945  1748480936   \n",
              "222933      1.290702      31  1205403734  4076919980  1748484337   \n",
              "232129      0.389532      31  1205407364  4076923609  1748485751   \n",
              "383214      1.659404      31  1205409108  4076940427  1748488645   \n",
              "\n",
              "        Total_Production  \n",
              "379202             22924  \n",
              "379203             42842  \n",
              "379204             61230  \n",
              "379205             77239  \n",
              "379206             90165  \n",
              "...                  ...  \n",
              "222855        7030771865  \n",
              "222894        7030788980  \n",
              "222933        7030808051  \n",
              "232129        7030816724  \n",
              "383214        7030838180  \n",
              "\n",
              "[394074 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-abb641b8-40e6-4f91-bdac-f8a89a7233e4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>API</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>RatioGasOil</th>\n",
              "      <th>Water</th>\n",
              "      <th>PercentWater</th>\n",
              "      <th>DaysOn</th>\n",
              "      <th>Cum_Oil</th>\n",
              "      <th>Cum_Gas</th>\n",
              "      <th>Cum_Water</th>\n",
              "      <th>Total_Production</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>379202</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21711</td>\n",
              "      <td>1213</td>\n",
              "      <td>0.055870</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>21711</td>\n",
              "      <td>1213</td>\n",
              "      <td>0</td>\n",
              "      <td>22924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379203</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18744</td>\n",
              "      <td>1174</td>\n",
              "      <td>0.062633</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>40455</td>\n",
              "      <td>2387</td>\n",
              "      <td>0</td>\n",
              "      <td>42842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379204</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1977</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17224</td>\n",
              "      <td>1164</td>\n",
              "      <td>0.067580</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>57679</td>\n",
              "      <td>3551</td>\n",
              "      <td>0</td>\n",
              "      <td>61230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379205</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1978</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14937</td>\n",
              "      <td>1072</td>\n",
              "      <td>0.071768</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>72616</td>\n",
              "      <td>4623</td>\n",
              "      <td>0</td>\n",
              "      <td>77239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>379206</th>\n",
              "      <td>42371323150300</td>\n",
              "      <td>1978</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11971</td>\n",
              "      <td>955</td>\n",
              "      <td>0.079776</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>84587</td>\n",
              "      <td>5578</td>\n",
              "      <td>0</td>\n",
              "      <td>90165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222855</th>\n",
              "      <td>49021226570000</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>340</td>\n",
              "      <td>940</td>\n",
              "      <td>2.764706</td>\n",
              "      <td>746</td>\n",
              "      <td>2.194118</td>\n",
              "      <td>31</td>\n",
              "      <td>1205399458</td>\n",
              "      <td>4076895661</td>\n",
              "      <td>1748476746</td>\n",
              "      <td>7030771865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222894</th>\n",
              "      <td>33053063130000</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1641</td>\n",
              "      <td>11284</td>\n",
              "      <td>6.876295</td>\n",
              "      <td>4190</td>\n",
              "      <td>2.553321</td>\n",
              "      <td>27</td>\n",
              "      <td>1205401099</td>\n",
              "      <td>4076906945</td>\n",
              "      <td>1748480936</td>\n",
              "      <td>7030788980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222933</th>\n",
              "      <td>33053068450000</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2635</td>\n",
              "      <td>13035</td>\n",
              "      <td>4.946869</td>\n",
              "      <td>3401</td>\n",
              "      <td>1.290702</td>\n",
              "      <td>31</td>\n",
              "      <td>1205403734</td>\n",
              "      <td>4076919980</td>\n",
              "      <td>1748484337</td>\n",
              "      <td>7030808051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232129</th>\n",
              "      <td>33053047870000</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3630</td>\n",
              "      <td>3629</td>\n",
              "      <td>0.999725</td>\n",
              "      <td>1414</td>\n",
              "      <td>0.389532</td>\n",
              "      <td>31</td>\n",
              "      <td>1205407364</td>\n",
              "      <td>4076923609</td>\n",
              "      <td>1748485751</td>\n",
              "      <td>7030816724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383214</th>\n",
              "      <td>33053072000100</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1744</td>\n",
              "      <td>16818</td>\n",
              "      <td>9.643349</td>\n",
              "      <td>2894</td>\n",
              "      <td>1.659404</td>\n",
              "      <td>31</td>\n",
              "      <td>1205409108</td>\n",
              "      <td>4076940427</td>\n",
              "      <td>1748488645</td>\n",
              "      <td>7030838180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>394074 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abb641b8-40e6-4f91-bdac-f8a89a7233e4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-abb641b8-40e6-4f91-bdac-f8a89a7233e4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-abb641b8-40e6-4f91-bdac-f8a89a7233e4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Production Analysis\n",
        "\n",
        "#Group data of \"Liquid\", \"Gas\", \"Water\", \"Cum_Oil\", \"Cum_Gas\", \"Cum_Water\" by \"Year\" using pd.groupby and pd.sum\n",
        "\n",
        "annual_df = Sort.groupby('Year')[['Liquid', 'Gas', 'Water', 'Cum_Oil', 'Cum_Gas', 'Cum_Water']].sum()\n",
        "annual_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6TeuE1HqkFfv",
        "outputId": "cfbef102-713a-4c4d-a269-5f1616c63d28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Liquid         Gas      Water          Cum_Oil          Cum_Gas  \\\n",
              "Year                                                                       \n",
              "1977      57679        3551          0           119845             7151   \n",
              "1978     120066       10575          0          1512856           113631   \n",
              "1979      93339        8677          0          2791742           229152   \n",
              "1980      60339        5235          0          3648866           310414   \n",
              "1981     114608        6173          0          7033836           563511   \n",
              "1982     156018       12211          0         15479410          1158813   \n",
              "1983     125372       22872        190         24277439          2012438   \n",
              "1984     125870       33021       2863         28834659          3156406   \n",
              "1985     145607       56307      16499         33370525          4441465   \n",
              "1986     128352      127793      22717         38574877          8018263   \n",
              "1987      68292      127598      16609         41938176         12971202   \n",
              "1988      59230      156077       4782         44175010         17562553   \n",
              "1989      42158      223533       1568         45979197         24410286   \n",
              "1990      35114      291761       1574         47354994         33515167   \n",
              "1991      38873      335572       2826         48697351         45667325   \n",
              "1992      50002      473391      12369         50152947         59311126   \n",
              "1993      60876      289384      53626         52363692         74066886   \n",
              "1994      55292      150343     114509         54353241         80740173   \n",
              "1995     107420      185986     419132         57234269         88586532   \n",
              "1996     125336      248669     873827         61513658         95467193   \n",
              "1997     134542      127571    1102824         66300248        102066333   \n",
              "1998     104307      108871    1192188         70760564        106397172   \n",
              "1999      45274      118004    1083794         73230711        110546814   \n",
              "2000      43829      126351    1208908         74812650        114873045   \n",
              "2001      45405      108445    1041133         76391652        119232156   \n",
              "2002      49347       86773     983026         78063195        122565267   \n",
              "2003      83274      228431    1911341         80568161        127789055   \n",
              "2004      63738      122300    1820479         83121400        134716599   \n",
              "2005      61713      114199    1692460         85359431        138766006   \n",
              "2006      54606       98835    1372683         87471672        142666715   \n",
              "2007     158065      280048    1141298        116107406        190502638   \n",
              "2008     293509      495617    1106612        252445752        413576644   \n",
              "2009     233010      396867     808760        327373183        538068457   \n",
              "2010     250746      451091     593646        386615916        638351142   \n",
              "2011     174189      387546     298185        449867701        753051356   \n",
              "2012     175849      465826     364236        645933754       1099596766   \n",
              "2013    1537954     4481512    1383704       2125503480       4068547057   \n",
              "2014  207485594   483988063  288099499    3293944062497    7491559164974   \n",
              "2015  425565982  1191187762  551740238   35337696873006   88501979487290   \n",
              "2016  270407642  1044361377  374570930   74159798421417  210192242047047   \n",
              "2017  169250544   766955097  281938851   93889477892670  294232283605245   \n",
              "2018  123702876   571069773  227309397  104228008018170  345990252376384   \n",
              "2019    3717270     8411339    4181362    2762075462935    9346833498743   \n",
              "\n",
              "            Cum_Water  \n",
              "Year                   \n",
              "1977                0  \n",
              "1978                0  \n",
              "1979                0  \n",
              "1980                0  \n",
              "1981                0  \n",
              "1982                0  \n",
              "1983             2329  \n",
              "1984            55242  \n",
              "1985           337687  \n",
              "1986          1141292  \n",
              "1987          1871069  \n",
              "1988          2224959  \n",
              "1989          2322034  \n",
              "1990          2379468  \n",
              "1991          2446888  \n",
              "1992          2711090  \n",
              "1993          3820223  \n",
              "1994          6861763  \n",
              "1995         15181815  \n",
              "1996         39787025  \n",
              "1997         75566621  \n",
              "1998        118683920  \n",
              "1999        158330562  \n",
              "2000        201517173  \n",
              "2001        241306734  \n",
              "2002        276278500  \n",
              "2003        328827207  \n",
              "2004        396041693  \n",
              "2005        460520449  \n",
              "2006        515919806  \n",
              "2007        717761233  \n",
              "2008       1519705004  \n",
              "2009       1901860850  \n",
              "2010       2161358699  \n",
              "2011       2422821238  \n",
              "2012       3369198132  \n",
              "2013       9685845648  \n",
              "2014    4962482497822  \n",
              "2015   48231252870717  \n",
              "2016   99436214195965  \n",
              "2017  129631247791255  \n",
              "2018  148777270000073  \n",
              "2019    4007832514422  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ab58640a-2211-4c76-aa5f-e3a6e9ae788a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Liquid</th>\n",
              "      <th>Gas</th>\n",
              "      <th>Water</th>\n",
              "      <th>Cum_Oil</th>\n",
              "      <th>Cum_Gas</th>\n",
              "      <th>Cum_Water</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>57679</td>\n",
              "      <td>3551</td>\n",
              "      <td>0</td>\n",
              "      <td>119845</td>\n",
              "      <td>7151</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1978</th>\n",
              "      <td>120066</td>\n",
              "      <td>10575</td>\n",
              "      <td>0</td>\n",
              "      <td>1512856</td>\n",
              "      <td>113631</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1979</th>\n",
              "      <td>93339</td>\n",
              "      <td>8677</td>\n",
              "      <td>0</td>\n",
              "      <td>2791742</td>\n",
              "      <td>229152</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1980</th>\n",
              "      <td>60339</td>\n",
              "      <td>5235</td>\n",
              "      <td>0</td>\n",
              "      <td>3648866</td>\n",
              "      <td>310414</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1981</th>\n",
              "      <td>114608</td>\n",
              "      <td>6173</td>\n",
              "      <td>0</td>\n",
              "      <td>7033836</td>\n",
              "      <td>563511</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1982</th>\n",
              "      <td>156018</td>\n",
              "      <td>12211</td>\n",
              "      <td>0</td>\n",
              "      <td>15479410</td>\n",
              "      <td>1158813</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1983</th>\n",
              "      <td>125372</td>\n",
              "      <td>22872</td>\n",
              "      <td>190</td>\n",
              "      <td>24277439</td>\n",
              "      <td>2012438</td>\n",
              "      <td>2329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1984</th>\n",
              "      <td>125870</td>\n",
              "      <td>33021</td>\n",
              "      <td>2863</td>\n",
              "      <td>28834659</td>\n",
              "      <td>3156406</td>\n",
              "      <td>55242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1985</th>\n",
              "      <td>145607</td>\n",
              "      <td>56307</td>\n",
              "      <td>16499</td>\n",
              "      <td>33370525</td>\n",
              "      <td>4441465</td>\n",
              "      <td>337687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1986</th>\n",
              "      <td>128352</td>\n",
              "      <td>127793</td>\n",
              "      <td>22717</td>\n",
              "      <td>38574877</td>\n",
              "      <td>8018263</td>\n",
              "      <td>1141292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1987</th>\n",
              "      <td>68292</td>\n",
              "      <td>127598</td>\n",
              "      <td>16609</td>\n",
              "      <td>41938176</td>\n",
              "      <td>12971202</td>\n",
              "      <td>1871069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1988</th>\n",
              "      <td>59230</td>\n",
              "      <td>156077</td>\n",
              "      <td>4782</td>\n",
              "      <td>44175010</td>\n",
              "      <td>17562553</td>\n",
              "      <td>2224959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1989</th>\n",
              "      <td>42158</td>\n",
              "      <td>223533</td>\n",
              "      <td>1568</td>\n",
              "      <td>45979197</td>\n",
              "      <td>24410286</td>\n",
              "      <td>2322034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>35114</td>\n",
              "      <td>291761</td>\n",
              "      <td>1574</td>\n",
              "      <td>47354994</td>\n",
              "      <td>33515167</td>\n",
              "      <td>2379468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>38873</td>\n",
              "      <td>335572</td>\n",
              "      <td>2826</td>\n",
              "      <td>48697351</td>\n",
              "      <td>45667325</td>\n",
              "      <td>2446888</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>50002</td>\n",
              "      <td>473391</td>\n",
              "      <td>12369</td>\n",
              "      <td>50152947</td>\n",
              "      <td>59311126</td>\n",
              "      <td>2711090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>60876</td>\n",
              "      <td>289384</td>\n",
              "      <td>53626</td>\n",
              "      <td>52363692</td>\n",
              "      <td>74066886</td>\n",
              "      <td>3820223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>55292</td>\n",
              "      <td>150343</td>\n",
              "      <td>114509</td>\n",
              "      <td>54353241</td>\n",
              "      <td>80740173</td>\n",
              "      <td>6861763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>107420</td>\n",
              "      <td>185986</td>\n",
              "      <td>419132</td>\n",
              "      <td>57234269</td>\n",
              "      <td>88586532</td>\n",
              "      <td>15181815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>125336</td>\n",
              "      <td>248669</td>\n",
              "      <td>873827</td>\n",
              "      <td>61513658</td>\n",
              "      <td>95467193</td>\n",
              "      <td>39787025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>134542</td>\n",
              "      <td>127571</td>\n",
              "      <td>1102824</td>\n",
              "      <td>66300248</td>\n",
              "      <td>102066333</td>\n",
              "      <td>75566621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>104307</td>\n",
              "      <td>108871</td>\n",
              "      <td>1192188</td>\n",
              "      <td>70760564</td>\n",
              "      <td>106397172</td>\n",
              "      <td>118683920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>45274</td>\n",
              "      <td>118004</td>\n",
              "      <td>1083794</td>\n",
              "      <td>73230711</td>\n",
              "      <td>110546814</td>\n",
              "      <td>158330562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>43829</td>\n",
              "      <td>126351</td>\n",
              "      <td>1208908</td>\n",
              "      <td>74812650</td>\n",
              "      <td>114873045</td>\n",
              "      <td>201517173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>45405</td>\n",
              "      <td>108445</td>\n",
              "      <td>1041133</td>\n",
              "      <td>76391652</td>\n",
              "      <td>119232156</td>\n",
              "      <td>241306734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>49347</td>\n",
              "      <td>86773</td>\n",
              "      <td>983026</td>\n",
              "      <td>78063195</td>\n",
              "      <td>122565267</td>\n",
              "      <td>276278500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>83274</td>\n",
              "      <td>228431</td>\n",
              "      <td>1911341</td>\n",
              "      <td>80568161</td>\n",
              "      <td>127789055</td>\n",
              "      <td>328827207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>63738</td>\n",
              "      <td>122300</td>\n",
              "      <td>1820479</td>\n",
              "      <td>83121400</td>\n",
              "      <td>134716599</td>\n",
              "      <td>396041693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>61713</td>\n",
              "      <td>114199</td>\n",
              "      <td>1692460</td>\n",
              "      <td>85359431</td>\n",
              "      <td>138766006</td>\n",
              "      <td>460520449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>54606</td>\n",
              "      <td>98835</td>\n",
              "      <td>1372683</td>\n",
              "      <td>87471672</td>\n",
              "      <td>142666715</td>\n",
              "      <td>515919806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>158065</td>\n",
              "      <td>280048</td>\n",
              "      <td>1141298</td>\n",
              "      <td>116107406</td>\n",
              "      <td>190502638</td>\n",
              "      <td>717761233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>293509</td>\n",
              "      <td>495617</td>\n",
              "      <td>1106612</td>\n",
              "      <td>252445752</td>\n",
              "      <td>413576644</td>\n",
              "      <td>1519705004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>233010</td>\n",
              "      <td>396867</td>\n",
              "      <td>808760</td>\n",
              "      <td>327373183</td>\n",
              "      <td>538068457</td>\n",
              "      <td>1901860850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>250746</td>\n",
              "      <td>451091</td>\n",
              "      <td>593646</td>\n",
              "      <td>386615916</td>\n",
              "      <td>638351142</td>\n",
              "      <td>2161358699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>174189</td>\n",
              "      <td>387546</td>\n",
              "      <td>298185</td>\n",
              "      <td>449867701</td>\n",
              "      <td>753051356</td>\n",
              "      <td>2422821238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>175849</td>\n",
              "      <td>465826</td>\n",
              "      <td>364236</td>\n",
              "      <td>645933754</td>\n",
              "      <td>1099596766</td>\n",
              "      <td>3369198132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>1537954</td>\n",
              "      <td>4481512</td>\n",
              "      <td>1383704</td>\n",
              "      <td>2125503480</td>\n",
              "      <td>4068547057</td>\n",
              "      <td>9685845648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>207485594</td>\n",
              "      <td>483988063</td>\n",
              "      <td>288099499</td>\n",
              "      <td>3293944062497</td>\n",
              "      <td>7491559164974</td>\n",
              "      <td>4962482497822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>425565982</td>\n",
              "      <td>1191187762</td>\n",
              "      <td>551740238</td>\n",
              "      <td>35337696873006</td>\n",
              "      <td>88501979487290</td>\n",
              "      <td>48231252870717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>270407642</td>\n",
              "      <td>1044361377</td>\n",
              "      <td>374570930</td>\n",
              "      <td>74159798421417</td>\n",
              "      <td>210192242047047</td>\n",
              "      <td>99436214195965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>169250544</td>\n",
              "      <td>766955097</td>\n",
              "      <td>281938851</td>\n",
              "      <td>93889477892670</td>\n",
              "      <td>294232283605245</td>\n",
              "      <td>129631247791255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>123702876</td>\n",
              "      <td>571069773</td>\n",
              "      <td>227309397</td>\n",
              "      <td>104228008018170</td>\n",
              "      <td>345990252376384</td>\n",
              "      <td>148777270000073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>3717270</td>\n",
              "      <td>8411339</td>\n",
              "      <td>4181362</td>\n",
              "      <td>2762075462935</td>\n",
              "      <td>9346833498743</td>\n",
              "      <td>4007832514422</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab58640a-2211-4c76-aa5f-e3a6e9ae788a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ab58640a-2211-4c76-aa5f-e3a6e9ae788a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ab58640a-2211-4c76-aa5f-e3a6e9ae788a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "layout = go.Layout(title = 'Annual Gas Production', xaxis_title = 'Year', yaxis_title = 'Gas Production', yaxis_type='log', font_size = 14)\n",
        "\n",
        "fig = go.Figure(layout = layout)\n",
        "\n",
        "fig.add_traces(data = go.Bar (x=annual_df.index, y=annual_df.Gas, name='Distribution', marker=dict(color='lime', line=dict(color='white', width=1.2))) )\n",
        "fig.add_traces(data = go.Scatter(x=annual_df.index, y=annual_df.Gas, name='Profile', line=dict(dash='dash', color='red'), marker_size=10))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "copWKR60Fi9w",
        "outputId": "b429eba6-2293-436b-cc8e-aa1458687a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"0fb6ffc4-75c0-42f5-b7f2-bd4d69e70d5e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"0fb6ffc4-75c0-42f5-b7f2-bd4d69e70d5e\")) {                    Plotly.newPlot(                        \"0fb6ffc4-75c0-42f5-b7f2-bd4d69e70d5e\",                        [{\"marker\":{\"color\":\"lime\",\"line\":{\"color\":\"white\",\"width\":1.2}},\"name\":\"Distribution\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[3551,10575,8677,5235,6173,12211,22872,33021,56307,127793,127598,156077,223533,291761,335572,473391,289384,150343,185986,248669,127571,108871,118004,126351,108445,86773,228431,122300,114199,98835,280048,495617,396867,451091,387546,465826,4481512,483988063,1191187762,1044361377,766955097,571069773,8411339],\"type\":\"bar\"},{\"line\":{\"color\":\"red\",\"dash\":\"dash\"},\"marker\":{\"size\":10},\"name\":\"Profile\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[3551,10575,8677,5235,6173,12211,22872,33021,56307,127793,127598,156077,223533,291761,335572,473391,289384,150343,185986,248669,127571,108871,118004,126351,108445,86773,228431,122300,114199,98835,280048,495617,396867,451091,387546,465826,4481512,483988063,1191187762,1044361377,766955097,571069773,8411339],\"type\":\"scatter\"}],                        {\"font\":{\"size\":14},\"title\":{\"text\":\"Annual Gas Production\"},\"xaxis\":{\"title\":{\"text\":\"Year\"}},\"yaxis\":{\"title\":{\"text\":\"Gas Production\"},\"type\":\"log\"},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('0fb6ffc4-75c0-42f5-b7f2-bd4d69e70d5e');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "layout = go.Layout(title = 'Annual Water Production', xaxis_title = 'Year', yaxis_title = \"Water Production\", yaxis_type='log', font_size = 14)\n",
        "\n",
        "fig = go.Figure(layout = layout)\n",
        "\n",
        "fig.add_traces(data = go.Bar (x=annual_df.index, y=annual_df.Water, name='Distribution', marker=dict(color='orange', line=dict(color='white', width=1.2))) )\n",
        "fig.add_traces(data = go.Scatter(x=annual_df.index, y=annual_df.Water, line=dict(dash='dash', color='red'), marker_size=10, name='Profile'))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "qDcoo2roKdE6",
        "outputId": "e3bcea32-915c-47d5-8f86-0df0f0fa37d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"aa526b1c-8b2f-4150-aee2-af6948d55803\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"aa526b1c-8b2f-4150-aee2-af6948d55803\")) {                    Plotly.newPlot(                        \"aa526b1c-8b2f-4150-aee2-af6948d55803\",                        [{\"marker\":{\"color\":\"orange\",\"line\":{\"color\":\"white\",\"width\":1.2}},\"name\":\"Distribution\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[0,0,0,0,0,0,190,2863,16499,22717,16609,4782,1568,1574,2826,12369,53626,114509,419132,873827,1102824,1192188,1083794,1208908,1041133,983026,1911341,1820479,1692460,1372683,1141298,1106612,808760,593646,298185,364236,1383704,288099499,551740238,374570930,281938851,227309397,4181362],\"type\":\"bar\"},{\"line\":{\"color\":\"red\",\"dash\":\"dash\"},\"marker\":{\"size\":10},\"name\":\"Profile\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[0,0,0,0,0,0,190,2863,16499,22717,16609,4782,1568,1574,2826,12369,53626,114509,419132,873827,1102824,1192188,1083794,1208908,1041133,983026,1911341,1820479,1692460,1372683,1141298,1106612,808760,593646,298185,364236,1383704,288099499,551740238,374570930,281938851,227309397,4181362],\"type\":\"scatter\"}],                        {\"font\":{\"size\":14},\"title\":{\"text\":\"Annual Water Production\"},\"xaxis\":{\"title\":{\"text\":\"Year\"}},\"yaxis\":{\"title\":{\"text\":\"Water Production\"},\"type\":\"log\"},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('aa526b1c-8b2f-4150-aee2-af6948d55803');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "layout = go.Layout(title = 'Cumulative Oil vs Cumulative Water Production', xaxis_title = 'Year', yaxis_title = \"Cumulative Production\", yaxis_type='log', font_size = 14)\n",
        "\n",
        "fig = go.Figure(layout = layout)\n",
        "\n",
        "fig.add_traces(data = go.Scatter (x=annual_df.index, y=annual_df.Cum_Oil, line=dict(color='green'), name='Cum_Oil') )\n",
        "fig.add_traces(data = go.Scatter(x=annual_df.index, y=annual_df.Cum_Water, line=dict(dash='dash', color='red'), name='Cum_Water'))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "WAK3tqafLviz",
        "outputId": "dcf93cf5-b9d1-4dbd-fede-2f048b23e590"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"6a4f8282-6ef1-4e75-9a8b-b9a8b8f87700\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6a4f8282-6ef1-4e75-9a8b-b9a8b8f87700\")) {                    Plotly.newPlot(                        \"6a4f8282-6ef1-4e75-9a8b-b9a8b8f87700\",                        [{\"line\":{\"color\":\"green\"},\"name\":\"Cum_Oil\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[119845,1512856,2791742,3648866,7033836,15479410,24277439,28834659,33370525,38574877,41938176,44175010,45979197,47354994,48697351,50152947,52363692,54353241,57234269,61513658,66300248,70760564,73230711,74812650,76391652,78063195,80568161,83121400,85359431,87471672,116107406,252445752,327373183,386615916,449867701,645933754,2125503480,3293944062497,35337696873006,74159798421417,93889477892670,104228008018170,2762075462935],\"type\":\"scatter\"},{\"line\":{\"color\":\"red\",\"dash\":\"dash\"},\"name\":\"Cum_Water\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[0,0,0,0,0,0,2329,55242,337687,1141292,1871069,2224959,2322034,2379468,2446888,2711090,3820223,6861763,15181815,39787025,75566621,118683920,158330562,201517173,241306734,276278500,328827207,396041693,460520449,515919806,717761233,1519705004,1901860850,2161358699,2422821238,3369198132,9685845648,4962482497822,48231252870717,99436214195965,129631247791255,148777270000073,4007832514422],\"type\":\"scatter\"}],                        {\"font\":{\"size\":14},\"title\":{\"text\":\"Cumulative Oil vs Cumulative Water Production\"},\"xaxis\":{\"title\":{\"text\":\"Year\"}},\"yaxis\":{\"title\":{\"text\":\"Cumulative Production\"},\"type\":\"log\"},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('6a4f8282-6ef1-4e75-9a8b-b9a8b8f87700');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "layout = go.Layout(title = 'Cumulative Gas vs Cumulative Water Production', xaxis_title = 'Year', yaxis_title = \"Cumulative Production\", yaxis_type='log', font_size = 14)\n",
        "\n",
        "fig = go.Figure(layout = layout)\n",
        "\n",
        "fig.add_traces(data = go.Scatter (x=annual_df.index, y=annual_df.Cum_Gas, line=dict(color='blue'), name='Cum_Gas') )\n",
        "fig.add_traces(data = go.Scatter(x=annual_df.index, y=annual_df.Cum_Water, line=dict(dash='dash', color='orange'), name='Cum_Water'))\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "OeO1o7ywMIAj",
        "outputId": "733b7511-4180-4169-ea09-a33d5fec45ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"014cb9b9-ac53-4a15-b0f4-9e6afc3a10f8\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"014cb9b9-ac53-4a15-b0f4-9e6afc3a10f8\")) {                    Plotly.newPlot(                        \"014cb9b9-ac53-4a15-b0f4-9e6afc3a10f8\",                        [{\"line\":{\"color\":\"blue\"},\"name\":\"Cum_Gas\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[7151,113631,229152,310414,563511,1158813,2012438,3156406,4441465,8018263,12971202,17562553,24410286,33515167,45667325,59311126,74066886,80740173,88586532,95467193,102066333,106397172,110546814,114873045,119232156,122565267,127789055,134716599,138766006,142666715,190502638,413576644,538068457,638351142,753051356,1099596766,4068547057,7491559164974,88501979487290,210192242047047,294232283605245,345990252376384,9346833498743],\"type\":\"scatter\"},{\"line\":{\"color\":\"orange\",\"dash\":\"dash\"},\"name\":\"Cum_Water\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[0,0,0,0,0,0,2329,55242,337687,1141292,1871069,2224959,2322034,2379468,2446888,2711090,3820223,6861763,15181815,39787025,75566621,118683920,158330562,201517173,241306734,276278500,328827207,396041693,460520449,515919806,717761233,1519705004,1901860850,2161358699,2422821238,3369198132,9685845648,4962482497822,48231252870717,99436214195965,129631247791255,148777270000073,4007832514422],\"type\":\"scatter\"}],                        {\"font\":{\"size\":14},\"title\":{\"text\":\"Cumulative Gas vs Cumulative Water Production\"},\"xaxis\":{\"title\":{\"text\":\"Year\"}},\"yaxis\":{\"title\":{\"text\":\"Cumulative Production\"},\"type\":\"log\"},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('014cb9b9-ac53-4a15-b0f4-9e6afc3a10f8');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "layout = go.Layout(title='Oil vs Total Production', xaxis_title='Year', yaxis_title='Total Production', font_size=14, yaxis_type='log')\n",
        "\n",
        "fig = go.Figure(layout=layout)\n",
        "fig.add_traces(data=go.Scatter(x=annual_df.index, y=annual_df.Liquid, line=dict(color='brown'), name='Oil'))\n",
        "fig.add_traces(data=go.Scatter(x=annual_df.index, y=annual_df.Gas, line=dict(color='tomato'), name='Gas'))\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "vZmwq240ZNqy",
        "outputId": "2d78c75b-cf08-43a4-8cfb-9ca98a968bad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"c100f2c3-f359-433b-a0f2-0057aa543814\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"c100f2c3-f359-433b-a0f2-0057aa543814\")) {                    Plotly.newPlot(                        \"c100f2c3-f359-433b-a0f2-0057aa543814\",                        [{\"line\":{\"color\":\"brown\"},\"name\":\"Oil\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[57679,120066,93339,60339,114608,156018,125372,125870,145607,128352,68292,59230,42158,35114,38873,50002,60876,55292,107420,125336,134542,104307,45274,43829,45405,49347,83274,63738,61713,54606,158065,293509,233010,250746,174189,175849,1537954,207485594,425565982,270407642,169250544,123702876,3717270],\"type\":\"scatter\"},{\"line\":{\"color\":\"tomato\"},\"name\":\"Gas\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[3551,10575,8677,5235,6173,12211,22872,33021,56307,127793,127598,156077,223533,291761,335572,473391,289384,150343,185986,248669,127571,108871,118004,126351,108445,86773,228431,122300,114199,98835,280048,495617,396867,451091,387546,465826,4481512,483988063,1191187762,1044361377,766955097,571069773,8411339],\"type\":\"scatter\"}],                        {\"font\":{\"size\":14},\"title\":{\"text\":\"Oil vs Total Production\"},\"xaxis\":{\"title\":{\"text\":\"Year\"}},\"yaxis\":{\"title\":{\"text\":\"Total Production\"},\"type\":\"log\"},\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('c100f2c3-f359-433b-a0f2-0057aa543814');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Monthly production of Oil\n",
        "\n",
        "monthly_oil_df= Sort.pivot_table('Liquid', 'Month', 'Year', sort=False)\n",
        "\n",
        "monthly_oil_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "r6NF-VtUdITH",
        "outputId": "926e1adf-904e-40bd-d096-64b58758a5d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year      1977     1978    1979    1980    1981         1982         1983  \\\n",
              "Month                                                                       \n",
              "Jan        NaN  14937.0  9853.0  5617.0  5409.0  6434.000000  4483.666667   \n",
              "Feb        NaN  11971.0  8723.0  5111.0  5039.0  5799.500000  4032.333333   \n",
              "Mar        NaN  11544.0  9613.0  5289.0  5758.0  6453.000000  4444.000000   \n",
              "Apr        NaN   9491.0  8560.0  4937.0  5772.0  6329.000000  3756.000000   \n",
              "May        NaN   8067.0  8490.0  4925.0  6922.0  6676.500000  3574.333333   \n",
              "Jun        NaN   7819.0  7990.0  4588.0  7258.0  6353.500000  3257.333333   \n",
              "Jul        NaN   8132.0  7828.0  4855.0  6741.5  6497.000000  3321.666667   \n",
              "Aug        NaN   8208.0  7259.0  4946.0  6704.5  4590.333333  3197.333333   \n",
              "Sep        NaN   9883.0  6757.0  4796.0  6478.5  4406.666667  3050.666667   \n",
              "Oct    21711.0  10206.0  6589.0  5055.0  6558.0  4494.333333  3032.333333   \n",
              "Nov    18744.0   9865.0  5858.0  4965.0  6281.5  4332.666667  2853.333333   \n",
              "Dec    17224.0   9943.0  5819.0  5255.0  6461.0  4487.000000  2787.666667   \n",
              "\n",
              "Year          1984         1985         1986  ...         2010         2011  \\\n",
              "Month                                         ...                             \n",
              "Jan    6752.333333  3429.333333  4300.000000  ...  2078.444444  1734.100000   \n",
              "Feb    4964.000000  3175.000000  3972.333333  ...  1821.333333  1413.500000   \n",
              "Mar    3969.666667  3809.666667  4416.666667  ...  2237.700000  1716.500000   \n",
              "Apr    2384.666667  4360.666667  4050.333333  ...  3101.800000  1475.300000   \n",
              "May    2628.666667  4345.666667  3897.000000  ...  2563.500000  1435.818182   \n",
              "Jun    2653.333333  4109.333333  3552.666667  ...  2266.400000  1378.363636   \n",
              "Jul    2860.666667  4213.666667  3546.000000  ...  2295.000000  1254.727273   \n",
              "Aug    2931.000000  4224.000000  3404.333333  ...  2045.700000  1268.545455   \n",
              "Sep    2899.333333  4059.666667  3103.333333  ...  1794.100000  1219.454545   \n",
              "Oct    3198.666667  4305.000000  3046.333333  ...  1625.400000  1116.545455   \n",
              "Nov    3232.666667  4186.333333  2782.000000  ...  1734.200000  1092.454545   \n",
              "Dec    3481.666667  4317.333333  2713.000000  ...  1901.000000  1197.500000   \n",
              "\n",
              "Year          2012         2013         2014         2015         2016  \\\n",
              "Month                                                                    \n",
              "Jan     978.000000   858.937500  6337.379070  6134.414109  3986.826214   \n",
              "Feb     819.500000   733.812500  6598.695771  5371.360284  3471.257555   \n",
              "Mar     756.916667   849.823529  7745.566602  5968.212766  3475.668834   \n",
              "Apr     773.866667  1204.636364  7506.142652  5633.166721  3145.284075   \n",
              "May     969.933333  1995.360000  7902.630205  5748.670218  3068.940074   \n",
              "Jun     754.933333  2733.571429  7679.148456  5426.269038  2798.160653   \n",
              "Jul     899.437500  3493.103448  7783.071926  5379.676454  2753.346679   \n",
              "Aug    1537.250000  2791.787879  7420.494480  5292.728710  2583.233074   \n",
              "Sep    1079.187500  3040.682927  6897.492362  4959.357067  2388.116665   \n",
              "Oct    1072.375000  4016.959184  6893.067797  4951.283810  2437.002550   \n",
              "Nov    1080.000000  3892.396825  6555.018510  4674.794174  2247.862698   \n",
              "Dec    1063.937500  3952.351351  6602.663368  4289.045263  2122.035191   \n",
              "\n",
              "Year          2017         2018         2019  \n",
              "Month                                         \n",
              "Jan    2122.795104  1502.774070  1619.084568  \n",
              "Feb    1924.345404  1357.375384  3090.000000  \n",
              "Mar    2048.702155  1456.521053          NaN  \n",
              "Apr    1926.184623  1398.359465          NaN  \n",
              "May    1918.201326  1402.986991          NaN  \n",
              "Jun    1762.432870  1309.798037          NaN  \n",
              "Jul    1771.961229  1336.859009          NaN  \n",
              "Aug    1721.387706  1319.836166          NaN  \n",
              "Sep    1623.744738  1279.014602          NaN  \n",
              "Oct    1658.418709  1302.601442          NaN  \n",
              "Nov    1565.228225  1236.482297          NaN  \n",
              "Dec    1545.366041  1343.667299          NaN  \n",
              "\n",
              "[12 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ad2b6184-aadc-44ba-80bc-853d40ce6a19\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>NaN</td>\n",
              "      <td>14937.0</td>\n",
              "      <td>9853.0</td>\n",
              "      <td>5617.0</td>\n",
              "      <td>5409.0</td>\n",
              "      <td>6434.000000</td>\n",
              "      <td>4483.666667</td>\n",
              "      <td>6752.333333</td>\n",
              "      <td>3429.333333</td>\n",
              "      <td>4300.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2078.444444</td>\n",
              "      <td>1734.100000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>858.937500</td>\n",
              "      <td>6337.379070</td>\n",
              "      <td>6134.414109</td>\n",
              "      <td>3986.826214</td>\n",
              "      <td>2122.795104</td>\n",
              "      <td>1502.774070</td>\n",
              "      <td>1619.084568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11971.0</td>\n",
              "      <td>8723.0</td>\n",
              "      <td>5111.0</td>\n",
              "      <td>5039.0</td>\n",
              "      <td>5799.500000</td>\n",
              "      <td>4032.333333</td>\n",
              "      <td>4964.000000</td>\n",
              "      <td>3175.000000</td>\n",
              "      <td>3972.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>1821.333333</td>\n",
              "      <td>1413.500000</td>\n",
              "      <td>819.500000</td>\n",
              "      <td>733.812500</td>\n",
              "      <td>6598.695771</td>\n",
              "      <td>5371.360284</td>\n",
              "      <td>3471.257555</td>\n",
              "      <td>1924.345404</td>\n",
              "      <td>1357.375384</td>\n",
              "      <td>3090.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>NaN</td>\n",
              "      <td>11544.0</td>\n",
              "      <td>9613.0</td>\n",
              "      <td>5289.0</td>\n",
              "      <td>5758.0</td>\n",
              "      <td>6453.000000</td>\n",
              "      <td>4444.000000</td>\n",
              "      <td>3969.666667</td>\n",
              "      <td>3809.666667</td>\n",
              "      <td>4416.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>2237.700000</td>\n",
              "      <td>1716.500000</td>\n",
              "      <td>756.916667</td>\n",
              "      <td>849.823529</td>\n",
              "      <td>7745.566602</td>\n",
              "      <td>5968.212766</td>\n",
              "      <td>3475.668834</td>\n",
              "      <td>2048.702155</td>\n",
              "      <td>1456.521053</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9491.0</td>\n",
              "      <td>8560.0</td>\n",
              "      <td>4937.0</td>\n",
              "      <td>5772.0</td>\n",
              "      <td>6329.000000</td>\n",
              "      <td>3756.000000</td>\n",
              "      <td>2384.666667</td>\n",
              "      <td>4360.666667</td>\n",
              "      <td>4050.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3101.800000</td>\n",
              "      <td>1475.300000</td>\n",
              "      <td>773.866667</td>\n",
              "      <td>1204.636364</td>\n",
              "      <td>7506.142652</td>\n",
              "      <td>5633.166721</td>\n",
              "      <td>3145.284075</td>\n",
              "      <td>1926.184623</td>\n",
              "      <td>1398.359465</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8067.0</td>\n",
              "      <td>8490.0</td>\n",
              "      <td>4925.0</td>\n",
              "      <td>6922.0</td>\n",
              "      <td>6676.500000</td>\n",
              "      <td>3574.333333</td>\n",
              "      <td>2628.666667</td>\n",
              "      <td>4345.666667</td>\n",
              "      <td>3897.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2563.500000</td>\n",
              "      <td>1435.818182</td>\n",
              "      <td>969.933333</td>\n",
              "      <td>1995.360000</td>\n",
              "      <td>7902.630205</td>\n",
              "      <td>5748.670218</td>\n",
              "      <td>3068.940074</td>\n",
              "      <td>1918.201326</td>\n",
              "      <td>1402.986991</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jun</th>\n",
              "      <td>NaN</td>\n",
              "      <td>7819.0</td>\n",
              "      <td>7990.0</td>\n",
              "      <td>4588.0</td>\n",
              "      <td>7258.0</td>\n",
              "      <td>6353.500000</td>\n",
              "      <td>3257.333333</td>\n",
              "      <td>2653.333333</td>\n",
              "      <td>4109.333333</td>\n",
              "      <td>3552.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>2266.400000</td>\n",
              "      <td>1378.363636</td>\n",
              "      <td>754.933333</td>\n",
              "      <td>2733.571429</td>\n",
              "      <td>7679.148456</td>\n",
              "      <td>5426.269038</td>\n",
              "      <td>2798.160653</td>\n",
              "      <td>1762.432870</td>\n",
              "      <td>1309.798037</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8132.0</td>\n",
              "      <td>7828.0</td>\n",
              "      <td>4855.0</td>\n",
              "      <td>6741.5</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>3321.666667</td>\n",
              "      <td>2860.666667</td>\n",
              "      <td>4213.666667</td>\n",
              "      <td>3546.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2295.000000</td>\n",
              "      <td>1254.727273</td>\n",
              "      <td>899.437500</td>\n",
              "      <td>3493.103448</td>\n",
              "      <td>7783.071926</td>\n",
              "      <td>5379.676454</td>\n",
              "      <td>2753.346679</td>\n",
              "      <td>1771.961229</td>\n",
              "      <td>1336.859009</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8208.0</td>\n",
              "      <td>7259.0</td>\n",
              "      <td>4946.0</td>\n",
              "      <td>6704.5</td>\n",
              "      <td>4590.333333</td>\n",
              "      <td>3197.333333</td>\n",
              "      <td>2931.000000</td>\n",
              "      <td>4224.000000</td>\n",
              "      <td>3404.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>2045.700000</td>\n",
              "      <td>1268.545455</td>\n",
              "      <td>1537.250000</td>\n",
              "      <td>2791.787879</td>\n",
              "      <td>7420.494480</td>\n",
              "      <td>5292.728710</td>\n",
              "      <td>2583.233074</td>\n",
              "      <td>1721.387706</td>\n",
              "      <td>1319.836166</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <td>NaN</td>\n",
              "      <td>9883.0</td>\n",
              "      <td>6757.0</td>\n",
              "      <td>4796.0</td>\n",
              "      <td>6478.5</td>\n",
              "      <td>4406.666667</td>\n",
              "      <td>3050.666667</td>\n",
              "      <td>2899.333333</td>\n",
              "      <td>4059.666667</td>\n",
              "      <td>3103.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>1794.100000</td>\n",
              "      <td>1219.454545</td>\n",
              "      <td>1079.187500</td>\n",
              "      <td>3040.682927</td>\n",
              "      <td>6897.492362</td>\n",
              "      <td>4959.357067</td>\n",
              "      <td>2388.116665</td>\n",
              "      <td>1623.744738</td>\n",
              "      <td>1279.014602</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>21711.0</td>\n",
              "      <td>10206.0</td>\n",
              "      <td>6589.0</td>\n",
              "      <td>5055.0</td>\n",
              "      <td>6558.0</td>\n",
              "      <td>4494.333333</td>\n",
              "      <td>3032.333333</td>\n",
              "      <td>3198.666667</td>\n",
              "      <td>4305.000000</td>\n",
              "      <td>3046.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>1625.400000</td>\n",
              "      <td>1116.545455</td>\n",
              "      <td>1072.375000</td>\n",
              "      <td>4016.959184</td>\n",
              "      <td>6893.067797</td>\n",
              "      <td>4951.283810</td>\n",
              "      <td>2437.002550</td>\n",
              "      <td>1658.418709</td>\n",
              "      <td>1302.601442</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>18744.0</td>\n",
              "      <td>9865.0</td>\n",
              "      <td>5858.0</td>\n",
              "      <td>4965.0</td>\n",
              "      <td>6281.5</td>\n",
              "      <td>4332.666667</td>\n",
              "      <td>2853.333333</td>\n",
              "      <td>3232.666667</td>\n",
              "      <td>4186.333333</td>\n",
              "      <td>2782.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1734.200000</td>\n",
              "      <td>1092.454545</td>\n",
              "      <td>1080.000000</td>\n",
              "      <td>3892.396825</td>\n",
              "      <td>6555.018510</td>\n",
              "      <td>4674.794174</td>\n",
              "      <td>2247.862698</td>\n",
              "      <td>1565.228225</td>\n",
              "      <td>1236.482297</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>17224.0</td>\n",
              "      <td>9943.0</td>\n",
              "      <td>5819.0</td>\n",
              "      <td>5255.0</td>\n",
              "      <td>6461.0</td>\n",
              "      <td>4487.000000</td>\n",
              "      <td>2787.666667</td>\n",
              "      <td>3481.666667</td>\n",
              "      <td>4317.333333</td>\n",
              "      <td>2713.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1901.000000</td>\n",
              "      <td>1197.500000</td>\n",
              "      <td>1063.937500</td>\n",
              "      <td>3952.351351</td>\n",
              "      <td>6602.663368</td>\n",
              "      <td>4289.045263</td>\n",
              "      <td>2122.035191</td>\n",
              "      <td>1545.366041</td>\n",
              "      <td>1343.667299</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad2b6184-aadc-44ba-80bc-853d40ce6a19')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ad2b6184-aadc-44ba-80bc-853d40ce6a19 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ad2b6184-aadc-44ba-80bc-853d40ce6a19');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Replace NAN values to 0 (zero)\n",
        "\n",
        "monthly_oil_df.fillna(0, inplace=True)\n",
        "\n",
        "monthly_oil_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "M6QX20wba7Lu",
        "outputId": "1112be8e-6918-4354-ed3f-ef7e90887117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year      1977     1978    1979    1980    1981         1982         1983  \\\n",
              "Month                                                                       \n",
              "Jan        0.0  14937.0  9853.0  5617.0  5409.0  6434.000000  4483.666667   \n",
              "Feb        0.0  11971.0  8723.0  5111.0  5039.0  5799.500000  4032.333333   \n",
              "Mar        0.0  11544.0  9613.0  5289.0  5758.0  6453.000000  4444.000000   \n",
              "Apr        0.0   9491.0  8560.0  4937.0  5772.0  6329.000000  3756.000000   \n",
              "May        0.0   8067.0  8490.0  4925.0  6922.0  6676.500000  3574.333333   \n",
              "Jun        0.0   7819.0  7990.0  4588.0  7258.0  6353.500000  3257.333333   \n",
              "Jul        0.0   8132.0  7828.0  4855.0  6741.5  6497.000000  3321.666667   \n",
              "Aug        0.0   8208.0  7259.0  4946.0  6704.5  4590.333333  3197.333333   \n",
              "Sep        0.0   9883.0  6757.0  4796.0  6478.5  4406.666667  3050.666667   \n",
              "Oct    21711.0  10206.0  6589.0  5055.0  6558.0  4494.333333  3032.333333   \n",
              "Nov    18744.0   9865.0  5858.0  4965.0  6281.5  4332.666667  2853.333333   \n",
              "Dec    17224.0   9943.0  5819.0  5255.0  6461.0  4487.000000  2787.666667   \n",
              "\n",
              "Year          1984         1985         1986  ...         2010         2011  \\\n",
              "Month                                         ...                             \n",
              "Jan    6752.333333  3429.333333  4300.000000  ...  2078.444444  1734.100000   \n",
              "Feb    4964.000000  3175.000000  3972.333333  ...  1821.333333  1413.500000   \n",
              "Mar    3969.666667  3809.666667  4416.666667  ...  2237.700000  1716.500000   \n",
              "Apr    2384.666667  4360.666667  4050.333333  ...  3101.800000  1475.300000   \n",
              "May    2628.666667  4345.666667  3897.000000  ...  2563.500000  1435.818182   \n",
              "Jun    2653.333333  4109.333333  3552.666667  ...  2266.400000  1378.363636   \n",
              "Jul    2860.666667  4213.666667  3546.000000  ...  2295.000000  1254.727273   \n",
              "Aug    2931.000000  4224.000000  3404.333333  ...  2045.700000  1268.545455   \n",
              "Sep    2899.333333  4059.666667  3103.333333  ...  1794.100000  1219.454545   \n",
              "Oct    3198.666667  4305.000000  3046.333333  ...  1625.400000  1116.545455   \n",
              "Nov    3232.666667  4186.333333  2782.000000  ...  1734.200000  1092.454545   \n",
              "Dec    3481.666667  4317.333333  2713.000000  ...  1901.000000  1197.500000   \n",
              "\n",
              "Year          2012         2013         2014         2015         2016  \\\n",
              "Month                                                                    \n",
              "Jan     978.000000   858.937500  6337.379070  6134.414109  3986.826214   \n",
              "Feb     819.500000   733.812500  6598.695771  5371.360284  3471.257555   \n",
              "Mar     756.916667   849.823529  7745.566602  5968.212766  3475.668834   \n",
              "Apr     773.866667  1204.636364  7506.142652  5633.166721  3145.284075   \n",
              "May     969.933333  1995.360000  7902.630205  5748.670218  3068.940074   \n",
              "Jun     754.933333  2733.571429  7679.148456  5426.269038  2798.160653   \n",
              "Jul     899.437500  3493.103448  7783.071926  5379.676454  2753.346679   \n",
              "Aug    1537.250000  2791.787879  7420.494480  5292.728710  2583.233074   \n",
              "Sep    1079.187500  3040.682927  6897.492362  4959.357067  2388.116665   \n",
              "Oct    1072.375000  4016.959184  6893.067797  4951.283810  2437.002550   \n",
              "Nov    1080.000000  3892.396825  6555.018510  4674.794174  2247.862698   \n",
              "Dec    1063.937500  3952.351351  6602.663368  4289.045263  2122.035191   \n",
              "\n",
              "Year          2017         2018         2019  \n",
              "Month                                         \n",
              "Jan    2122.795104  1502.774070  1619.084568  \n",
              "Feb    1924.345404  1357.375384  3090.000000  \n",
              "Mar    2048.702155  1456.521053     0.000000  \n",
              "Apr    1926.184623  1398.359465     0.000000  \n",
              "May    1918.201326  1402.986991     0.000000  \n",
              "Jun    1762.432870  1309.798037     0.000000  \n",
              "Jul    1771.961229  1336.859009     0.000000  \n",
              "Aug    1721.387706  1319.836166     0.000000  \n",
              "Sep    1623.744738  1279.014602     0.000000  \n",
              "Oct    1658.418709  1302.601442     0.000000  \n",
              "Nov    1565.228225  1236.482297     0.000000  \n",
              "Dec    1545.366041  1343.667299     0.000000  \n",
              "\n",
              "[12 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70af8d2b-c136-4f4a-a6f5-1d7f308d1c79\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.0</td>\n",
              "      <td>14937.0</td>\n",
              "      <td>9853.0</td>\n",
              "      <td>5617.0</td>\n",
              "      <td>5409.0</td>\n",
              "      <td>6434.000000</td>\n",
              "      <td>4483.666667</td>\n",
              "      <td>6752.333333</td>\n",
              "      <td>3429.333333</td>\n",
              "      <td>4300.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2078.444444</td>\n",
              "      <td>1734.100000</td>\n",
              "      <td>978.000000</td>\n",
              "      <td>858.937500</td>\n",
              "      <td>6337.379070</td>\n",
              "      <td>6134.414109</td>\n",
              "      <td>3986.826214</td>\n",
              "      <td>2122.795104</td>\n",
              "      <td>1502.774070</td>\n",
              "      <td>1619.084568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.0</td>\n",
              "      <td>11971.0</td>\n",
              "      <td>8723.0</td>\n",
              "      <td>5111.0</td>\n",
              "      <td>5039.0</td>\n",
              "      <td>5799.500000</td>\n",
              "      <td>4032.333333</td>\n",
              "      <td>4964.000000</td>\n",
              "      <td>3175.000000</td>\n",
              "      <td>3972.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>1821.333333</td>\n",
              "      <td>1413.500000</td>\n",
              "      <td>819.500000</td>\n",
              "      <td>733.812500</td>\n",
              "      <td>6598.695771</td>\n",
              "      <td>5371.360284</td>\n",
              "      <td>3471.257555</td>\n",
              "      <td>1924.345404</td>\n",
              "      <td>1357.375384</td>\n",
              "      <td>3090.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.0</td>\n",
              "      <td>11544.0</td>\n",
              "      <td>9613.0</td>\n",
              "      <td>5289.0</td>\n",
              "      <td>5758.0</td>\n",
              "      <td>6453.000000</td>\n",
              "      <td>4444.000000</td>\n",
              "      <td>3969.666667</td>\n",
              "      <td>3809.666667</td>\n",
              "      <td>4416.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>2237.700000</td>\n",
              "      <td>1716.500000</td>\n",
              "      <td>756.916667</td>\n",
              "      <td>849.823529</td>\n",
              "      <td>7745.566602</td>\n",
              "      <td>5968.212766</td>\n",
              "      <td>3475.668834</td>\n",
              "      <td>2048.702155</td>\n",
              "      <td>1456.521053</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>0.0</td>\n",
              "      <td>9491.0</td>\n",
              "      <td>8560.0</td>\n",
              "      <td>4937.0</td>\n",
              "      <td>5772.0</td>\n",
              "      <td>6329.000000</td>\n",
              "      <td>3756.000000</td>\n",
              "      <td>2384.666667</td>\n",
              "      <td>4360.666667</td>\n",
              "      <td>4050.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3101.800000</td>\n",
              "      <td>1475.300000</td>\n",
              "      <td>773.866667</td>\n",
              "      <td>1204.636364</td>\n",
              "      <td>7506.142652</td>\n",
              "      <td>5633.166721</td>\n",
              "      <td>3145.284075</td>\n",
              "      <td>1926.184623</td>\n",
              "      <td>1398.359465</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>0.0</td>\n",
              "      <td>8067.0</td>\n",
              "      <td>8490.0</td>\n",
              "      <td>4925.0</td>\n",
              "      <td>6922.0</td>\n",
              "      <td>6676.500000</td>\n",
              "      <td>3574.333333</td>\n",
              "      <td>2628.666667</td>\n",
              "      <td>4345.666667</td>\n",
              "      <td>3897.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2563.500000</td>\n",
              "      <td>1435.818182</td>\n",
              "      <td>969.933333</td>\n",
              "      <td>1995.360000</td>\n",
              "      <td>7902.630205</td>\n",
              "      <td>5748.670218</td>\n",
              "      <td>3068.940074</td>\n",
              "      <td>1918.201326</td>\n",
              "      <td>1402.986991</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jun</th>\n",
              "      <td>0.0</td>\n",
              "      <td>7819.0</td>\n",
              "      <td>7990.0</td>\n",
              "      <td>4588.0</td>\n",
              "      <td>7258.0</td>\n",
              "      <td>6353.500000</td>\n",
              "      <td>3257.333333</td>\n",
              "      <td>2653.333333</td>\n",
              "      <td>4109.333333</td>\n",
              "      <td>3552.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>2266.400000</td>\n",
              "      <td>1378.363636</td>\n",
              "      <td>754.933333</td>\n",
              "      <td>2733.571429</td>\n",
              "      <td>7679.148456</td>\n",
              "      <td>5426.269038</td>\n",
              "      <td>2798.160653</td>\n",
              "      <td>1762.432870</td>\n",
              "      <td>1309.798037</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>0.0</td>\n",
              "      <td>8132.0</td>\n",
              "      <td>7828.0</td>\n",
              "      <td>4855.0</td>\n",
              "      <td>6741.5</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>3321.666667</td>\n",
              "      <td>2860.666667</td>\n",
              "      <td>4213.666667</td>\n",
              "      <td>3546.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>2295.000000</td>\n",
              "      <td>1254.727273</td>\n",
              "      <td>899.437500</td>\n",
              "      <td>3493.103448</td>\n",
              "      <td>7783.071926</td>\n",
              "      <td>5379.676454</td>\n",
              "      <td>2753.346679</td>\n",
              "      <td>1771.961229</td>\n",
              "      <td>1336.859009</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>0.0</td>\n",
              "      <td>8208.0</td>\n",
              "      <td>7259.0</td>\n",
              "      <td>4946.0</td>\n",
              "      <td>6704.5</td>\n",
              "      <td>4590.333333</td>\n",
              "      <td>3197.333333</td>\n",
              "      <td>2931.000000</td>\n",
              "      <td>4224.000000</td>\n",
              "      <td>3404.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>2045.700000</td>\n",
              "      <td>1268.545455</td>\n",
              "      <td>1537.250000</td>\n",
              "      <td>2791.787879</td>\n",
              "      <td>7420.494480</td>\n",
              "      <td>5292.728710</td>\n",
              "      <td>2583.233074</td>\n",
              "      <td>1721.387706</td>\n",
              "      <td>1319.836166</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <td>0.0</td>\n",
              "      <td>9883.0</td>\n",
              "      <td>6757.0</td>\n",
              "      <td>4796.0</td>\n",
              "      <td>6478.5</td>\n",
              "      <td>4406.666667</td>\n",
              "      <td>3050.666667</td>\n",
              "      <td>2899.333333</td>\n",
              "      <td>4059.666667</td>\n",
              "      <td>3103.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>1794.100000</td>\n",
              "      <td>1219.454545</td>\n",
              "      <td>1079.187500</td>\n",
              "      <td>3040.682927</td>\n",
              "      <td>6897.492362</td>\n",
              "      <td>4959.357067</td>\n",
              "      <td>2388.116665</td>\n",
              "      <td>1623.744738</td>\n",
              "      <td>1279.014602</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>21711.0</td>\n",
              "      <td>10206.0</td>\n",
              "      <td>6589.0</td>\n",
              "      <td>5055.0</td>\n",
              "      <td>6558.0</td>\n",
              "      <td>4494.333333</td>\n",
              "      <td>3032.333333</td>\n",
              "      <td>3198.666667</td>\n",
              "      <td>4305.000000</td>\n",
              "      <td>3046.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>1625.400000</td>\n",
              "      <td>1116.545455</td>\n",
              "      <td>1072.375000</td>\n",
              "      <td>4016.959184</td>\n",
              "      <td>6893.067797</td>\n",
              "      <td>4951.283810</td>\n",
              "      <td>2437.002550</td>\n",
              "      <td>1658.418709</td>\n",
              "      <td>1302.601442</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>18744.0</td>\n",
              "      <td>9865.0</td>\n",
              "      <td>5858.0</td>\n",
              "      <td>4965.0</td>\n",
              "      <td>6281.5</td>\n",
              "      <td>4332.666667</td>\n",
              "      <td>2853.333333</td>\n",
              "      <td>3232.666667</td>\n",
              "      <td>4186.333333</td>\n",
              "      <td>2782.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1734.200000</td>\n",
              "      <td>1092.454545</td>\n",
              "      <td>1080.000000</td>\n",
              "      <td>3892.396825</td>\n",
              "      <td>6555.018510</td>\n",
              "      <td>4674.794174</td>\n",
              "      <td>2247.862698</td>\n",
              "      <td>1565.228225</td>\n",
              "      <td>1236.482297</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>17224.0</td>\n",
              "      <td>9943.0</td>\n",
              "      <td>5819.0</td>\n",
              "      <td>5255.0</td>\n",
              "      <td>6461.0</td>\n",
              "      <td>4487.000000</td>\n",
              "      <td>2787.666667</td>\n",
              "      <td>3481.666667</td>\n",
              "      <td>4317.333333</td>\n",
              "      <td>2713.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1901.000000</td>\n",
              "      <td>1197.500000</td>\n",
              "      <td>1063.937500</td>\n",
              "      <td>3952.351351</td>\n",
              "      <td>6602.663368</td>\n",
              "      <td>4289.045263</td>\n",
              "      <td>2122.035191</td>\n",
              "      <td>1545.366041</td>\n",
              "      <td>1343.667299</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-70af8d2b-c136-4f4a-a6f5-1d7f308d1c79')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-70af8d2b-c136-4f4a-a6f5-1d7f308d1c79 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-70af8d2b-c136-4f4a-a6f5-1d7f308d1c79');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig=px.imshow(monthly_oil_df, color_continuous_scale='twilight',labels=dict(x='Year', y='Month'), title='Monthly Oil Production')\n",
        "fig.update_traces(hovertemplate='<br>'.join(['Year:%{x}', 'Month:%{y}', 'Produced Oil:%{z}']), name='')\n",
        "fig.update_layout(font_size=14)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "oyWGYBoCZaUN",
        "outputId": "01d5f884-294f-4e3a-80f1-aba216489b3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"9bbc9078-a388-49d5-a85e-72533047dcc7\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"9bbc9078-a388-49d5-a85e-72533047dcc7\")) {                    Plotly.newPlot(                        \"9bbc9078-a388-49d5-a85e-72533047dcc7\",                        [{\"coloraxis\":\"coloraxis\",\"name\":\"\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],\"z\":[[0.0,14937.0,9853.0,5617.0,5409.0,6434.0,4483.666666666667,6752.333333333333,3429.3333333333335,4300.0,2511.0,1886.0,1396.6666666666667,1123.3333333333333,1155.3333333333333,1027.6666666666667,2118.0,1520.0,2567.6666666666665,3400.6666666666665,3950.3333333333335,3727.6666666666665,1656.6666666666667,1267.3333333333333,1223.3333333333333,1369.3333333333333,2282.3333333333335,1691.6666666666667,1839.3333333333333,1832.3333333333333,1612.6666666666667,4128.833333333333,2574.222222222222,2078.4444444444443,1734.1,978.0,858.9375,6337.379069767442,6134.414109206856,3986.826214458753,2122.7951039143186,1502.7740698120444,1619.0845684394071],[0.0,11971.0,8723.0,5111.0,5039.0,5799.5,4032.3333333333335,4964.0,3175.0,3972.3333333333335,2148.3333333333335,1709.3333333333333,1175.6666666666667,1006.3333333333334,1058.0,1009.6666666666666,1852.3333333333333,1340.3333333333333,2443.6666666666665,3243.3333333333335,3668.3333333333335,3318.3333333333335,1354.3333333333333,1163.3333333333333,1120.3333333333333,1245.0,2161.6666666666665,1594.3333333333333,1709.6666666666667,1648.6666666666667,1781.0,3481.6666666666665,2244.222222222222,1821.3333333333333,1413.5,819.5,733.8125,6598.695770804911,5371.360284484372,3471.257554507204,1924.345403544562,1357.3753842213114,3090.0],[0.0,11544.0,9613.0,5289.0,5758.0,6453.0,4444.0,3969.6666666666665,3809.6666666666665,4416.666666666667,2171.6666666666665,1762.6666666666667,1242.6666666666667,1086.6666666666667,1206.3333333333333,1133.3333333333333,1931.6666666666667,1462.3333333333333,2812.0,3544.3333333333335,4073.6666666666665,3613.0,1385.3333333333333,1303.6666666666667,1259.3333333333333,1366.0,2577.6666666666665,1784.3333333333333,1769.3333333333333,1608.3333333333333,2269.0,3365.1666666666665,2330.1111111111113,2237.7,1716.5,756.9166666666666,849.8235294117648,7745.566602316602,5968.212765957447,3475.6688344809995,2048.702154787709,1456.5210526315789,0.0],[0.0,9491.0,8560.0,4937.0,5772.0,6329.0,3756.0,2384.6666666666665,4360.666666666667,4050.3333333333335,1876.0,1652.0,1136.0,1025.6666666666667,1181.3333333333333,1117.0,1724.0,1414.3333333333333,2879.3333333333335,3444.3333333333335,4001.3333333333335,3398.0,1310.3333333333333,1245.0,1228.6666666666667,1308.0,2366.0,1770.0,1715.3333333333333,1571.0,2557.6666666666665,3592.4285714285716,2166.3333333333335,3101.8,1475.3,773.8666666666667,1204.6363636363637,7506.142652329749,5633.16672083198,3145.284074971312,1926.1846232309065,1398.359465364349,0.0],[0.0,8067.0,8490.0,4925.0,6922.0,6676.5,3574.3333333333335,2628.6666666666665,4345.666666666667,3897.0,1868.0,1717.0,1169.6666666666667,963.3333333333334,1225.0,1199.3333333333333,1632.3333333333333,1466.6666666666667,3065.3333333333335,3499.0,4081.3333333333335,3453.6666666666665,1207.0,1289.3333333333333,1283.6666666666667,1281.3333333333333,2614.0,1781.3333333333333,1724.3333333333333,1613.3333333333333,3044.3333333333335,3286.375,2161.4444444444443,2563.5,1435.8181818181818,969.9333333333333,1995.36,7902.630205441422,5748.670217693377,3068.940073951294,1918.2013260232054,1402.986991241628,0.0],[0.0,7819.0,7990.0,4588.0,7258.0,6353.5,3257.3333333333335,2653.3333333333335,4109.333333333333,3552.6666666666665,1784.3333333333333,1632.3333333333333,1114.3333333333333,786.6666666666666,1134.6666666666667,1100.3333333333333,1625.3333333333333,1387.3333333333333,3059.6666666666665,3350.0,3541.0,2899.3333333333335,1039.0,1246.0,1235.3333333333333,1206.0,2684.3333333333335,1793.3333333333333,1554.3333333333333,1505.3333333333333,3170.3333333333335,3730.625,2205.1111111111113,2266.4,1378.3636363636363,754.9333333333333,2733.5714285714284,7679.148455509451,5426.269037780401,2798.1606528114244,1762.4328700752262,1309.7980371900826,0.0],[0.0,8132.0,7828.0,4855.0,6741.5,6497.0,3321.6666666666665,2860.6666666666665,4213.666666666667,3546.0,1778.3333333333333,1641.0,1161.6666666666667,821.3333333333334,1117.6666666666667,1259.6666666666667,1657.6666666666667,1469.6666666666667,3155.0,3473.6666666666665,3577.6666666666665,2803.0,1142.3333333333333,1288.0,1295.0,1222.6666666666667,2590.0,1955.3333333333333,1575.0,1561.6666666666667,2972.6666666666665,3462.5,2239.3333333333335,2295.0,1254.7272727272727,899.4375,3493.103448275862,7783.07192575406,5379.676453980802,2753.3466785668747,1771.961229435021,1336.8590091838055,0.0],[0.0,8208.0,7259.0,4946.0,6704.5,4590.333333333333,3197.3333333333335,2931.0,4224.0,3404.3333333333335,1774.0,1600.3333333333333,1166.3333333333333,881.0,1040.6666666666667,1362.6666666666667,1611.6666666666667,1397.6666666666667,3087.3333333333335,3485.3333333333335,3552.3333333333335,2737.0,1169.0,1297.0,1295.0,1221.0,2694.0,1952.0,1619.0,1495.3333333333333,2894.3333333333335,3145.125,2185.222222222222,2045.7,1268.5454545454545,1537.25,2791.787878787879,7420.494479759117,5292.728709633465,2583.2330740787966,1721.3877056497895,1319.8361655208198,0.0],[0.0,9883.0,6757.0,4796.0,6478.5,4406.666666666667,3050.6666666666665,2899.3333333333335,4059.6666666666665,3103.3333333333335,1711.6666666666667,1559.0,1132.3333333333333,911.0,967.6666666666666,1494.3333333333333,1551.6666666666667,1950.3333333333333,3104.0,3428.6666666666665,3491.0,2521.6666666666665,1156.0,1093.6666666666667,1303.3333333333333,1195.6666666666667,2148.3333333333335,1702.3333333333333,1639.3333333333333,1312.3333333333333,3294.0,2944.875,1982.6666666666667,1794.1,1219.4545454545455,1079.1875,3040.682926829268,6897.492361927145,4959.357067231339,2388.1166645416297,1623.7447378492154,1279.0146023468058,0.0],[21711.0,10206.0,6589.0,5055.0,6558.0,4494.333333333333,3032.3333333333335,3198.6666666666665,4305.0,3046.3333333333335,1756.6666666666667,1591.6666666666667,1163.0,999.0,925.6666666666666,1727.3333333333333,1590.3333333333333,1366.0,3204.6666666666665,3585.6666666666665,3627.0,2331.0,1216.0,1122.3333333333333,1293.3333333333333,1409.0,2110.3333333333335,1782.6666666666667,1801.0,1349.6666666666667,4976.0,2877.625,1973.888888888889,1625.4,1116.5454545454545,1072.375,4016.9591836734694,6893.067796610169,4951.283810016824,2437.002550044626,1658.4187085247574,1302.6014416775884,0.0],[18744.0,9865.0,5858.0,4965.0,6281.5,4332.666666666667,2853.3333333333335,3232.6666666666665,4186.333333333333,2782.0,1682.6666666666667,1502.0,1120.6666666666667,1000.3333333333334,937.6666666666666,1981.3333333333333,1484.3333333333333,1274.0,3161.0,3569.0,3600.0,2084.6666666666665,1198.3333333333333,1121.0,1273.6666666666667,1293.6666666666667,1843.3333333333333,1657.0,1795.3333333333333,1324.6666666666667,5080.0,2385.4444444444443,1805.888888888889,1734.2,1092.4545454545455,1080.0,3892.3968253968255,6555.018509729473,4674.794174013032,2247.8626976032638,1565.2282247765006,1236.4822967776156,0.0],[17224.0,9943.0,5819.0,5255.0,6461.0,4487.0,2787.6666666666665,3481.6666666666665,4317.333333333333,2713.0,1701.3333333333333,1490.0,1073.6666666666667,1100.0,1007.6666666666666,2254.6666666666665,1512.6666666666667,2382.0,3267.0,3754.6666666666665,3683.3333333333335,1881.6666666666667,1257.0,1173.0,1324.0,2331.3333333333335,1686.0,1781.6666666666667,1829.0,1379.3333333333333,4221.5,2829.1111111111113,2021.5555555555557,1901.0,1197.5,1063.9375,3952.3513513513512,6602.663368466337,4289.045263292108,2122.0351906158357,1545.3660406285933,1343.667298578199,0.0]],\"type\":\"heatmap\",\"xaxis\":\"x\",\"yaxis\":\"y\",\"hovertemplate\":\"Year:%{x}<br>Month:%{y}<br>Produced Oil:%{z}\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"scaleanchor\":\"y\",\"constrain\":\"domain\",\"title\":{\"text\":\"Year\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"autorange\":\"reversed\",\"constrain\":\"domain\",\"title\":{\"text\":\"Month\"}},\"coloraxis\":{\"colorscale\":[[0.0,\"#e2d9e2\"],[0.1111111111111111,\"#9ebbc9\"],[0.2222222222222222,\"#6785be\"],[0.3333333333333333,\"#5e43a5\"],[0.4444444444444444,\"#421257\"],[0.5555555555555556,\"#471340\"],[0.6666666666666666,\"#8e2c50\"],[0.7777777777777778,\"#ba6657\"],[0.8888888888888888,\"#ceac94\"],[1.0,\"#e2d9e2\"]]},\"title\":{\"text\":\"Monthly Oil Production\"},\"font\":{\"size\":14}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('9bbc9078-a388-49d5-a85e-72533047dcc7');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Monthly production of Gas\n",
        "\n",
        "monthly_gas_df= Sort.pivot_table('Gas', 'Month', 'Year', sort=False)\n",
        "\n",
        "monthly_gas_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "MKY_NVS16I_e",
        "outputId": "af67602d-798b-42d1-f567-fe2a0ddb2b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year     1977    1978   1979   1980   1981        1982         1983  \\\n",
              "Month                                                                 \n",
              "Jan       NaN  1072.0  822.0  566.0  347.0  332.000000   514.000000   \n",
              "Feb       NaN   955.0  714.0  498.0  287.0  308.000000   421.666667   \n",
              "Mar       NaN  1049.0  772.0  520.0  316.0  356.000000   445.666667   \n",
              "Apr       NaN   920.0  863.0  473.0  299.0  359.500000   457.000000   \n",
              "May       NaN   798.0  811.0  466.0  325.0  357.500000   385.666667   \n",
              "Jun       NaN   747.0  785.0  431.0  318.0  467.000000   258.666667   \n",
              "Jul       NaN   751.0  774.0  434.0  409.5  565.000000   658.666667   \n",
              "Aug       NaN   779.0  729.0  411.0  380.0  411.000000   856.000000   \n",
              "Sep       NaN   903.0  653.0  369.0  352.0  421.000000   956.000000   \n",
              "Oct    1213.0   894.0  622.0  374.0  344.0  454.000000  1030.666667   \n",
              "Nov    1174.0   865.0  553.0  356.0  324.5  469.000000   949.333333   \n",
              "Dec    1164.0   842.0  579.0  337.0  330.5  485.333333   690.666667   \n",
              "\n",
              "Year          1984         1985         1986  ...         2010         2011  \\\n",
              "Month                                         ...                             \n",
              "Jan     854.333333   784.000000  2904.666667  ...  4253.777778  3863.000000   \n",
              "Feb    1521.000000   629.666667  3222.000000  ...  3924.222222  3044.000000   \n",
              "Mar    1621.666667   617.000000  4179.666667  ...  4000.900000  3736.200000   \n",
              "Apr     560.333333   819.000000  3828.000000  ...  4053.200000  3450.300000   \n",
              "May     721.666667  1299.333333  3573.000000  ...  4355.600000  3212.272727   \n",
              "Jun     739.000000  1440.000000  3298.333333  ...  3998.400000  3012.272727   \n",
              "Jul     824.666667  1722.000000  3233.000000  ...  3454.500000  2926.636364   \n",
              "Aug     796.333333  1962.000000  3431.333333  ...  3577.300000  2546.000000   \n",
              "Sep     814.000000  1876.000000  3377.333333  ...  3480.100000  2663.636364   \n",
              "Oct     881.333333  2364.333333  3442.333333  ...  3413.000000  2627.090909   \n",
              "Nov     816.333333  2567.333333  3967.333333  ...  3317.600000  2570.818182   \n",
              "Dec     856.333333  2688.333333  4140.666667  ...  4098.300000  2622.083333   \n",
              "\n",
              "Year          2012          2013          2014          2015          2016  \\\n",
              "Month                                                                        \n",
              "Jan    2254.000000   3494.062500  14064.476744  14806.068553  12966.334566   \n",
              "Feb    1878.666667   2981.937500  14482.384720  13461.300019  12068.519317   \n",
              "Mar    1940.583333   3097.411765  17517.267375  15216.119184  12097.028947   \n",
              "Apr    1933.133333   4311.318182  17435.731900  15189.490900  11790.124315   \n",
              "May    2324.266667   5451.480000  18115.015547  15820.134939  11783.664542   \n",
              "Jun    1771.600000   8470.678571  17731.654219  15206.789699  11056.494454   \n",
              "Jul    2048.125000  10591.482759  18135.080820  15040.308865  11179.465766   \n",
              "Aug    3505.625000   9757.393939  17553.714286  15180.176318  10885.284967   \n",
              "Sep    2265.375000   8875.146341  15803.512926  14480.391212  10134.776106   \n",
              "Oct    2316.062500  11377.938776  16125.555411  14818.029119  10325.612648   \n",
              "Nov    3926.875000  11260.047619  15438.691979  14268.734637   9657.446583   \n",
              "Dec    4844.937500  10790.506757  15854.642289  13320.343746   9213.964682   \n",
              "\n",
              "Year          2017         2018         2019  \n",
              "Month                                         \n",
              "Jan    9044.950529  6816.953586  3666.669137  \n",
              "Feb    8308.615326  6193.424052     0.000000  \n",
              "Mar    9078.281907  6857.387163          NaN  \n",
              "Apr    8670.759913  6528.547745          NaN  \n",
              "May    8764.311615  6556.395544          NaN  \n",
              "Jun    8180.789366  6026.474948          NaN  \n",
              "Jul    8342.170004  6260.411978          NaN  \n",
              "Aug    8019.668792  6237.614087          NaN  \n",
              "Sep    7551.965174  5998.452282          NaN  \n",
              "Oct    7578.720138  6048.431848          NaN  \n",
              "Nov    7181.526692  5780.686514          NaN  \n",
              "Dec    7107.918743  5587.794313          NaN  \n",
              "\n",
              "[12 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f77a42b-4529-4aef-9185-2514bcbff4e4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1072.0</td>\n",
              "      <td>822.0</td>\n",
              "      <td>566.0</td>\n",
              "      <td>347.0</td>\n",
              "      <td>332.000000</td>\n",
              "      <td>514.000000</td>\n",
              "      <td>854.333333</td>\n",
              "      <td>784.000000</td>\n",
              "      <td>2904.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>4253.777778</td>\n",
              "      <td>3863.000000</td>\n",
              "      <td>2254.000000</td>\n",
              "      <td>3494.062500</td>\n",
              "      <td>14064.476744</td>\n",
              "      <td>14806.068553</td>\n",
              "      <td>12966.334566</td>\n",
              "      <td>9044.950529</td>\n",
              "      <td>6816.953586</td>\n",
              "      <td>3666.669137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>NaN</td>\n",
              "      <td>955.0</td>\n",
              "      <td>714.0</td>\n",
              "      <td>498.0</td>\n",
              "      <td>287.0</td>\n",
              "      <td>308.000000</td>\n",
              "      <td>421.666667</td>\n",
              "      <td>1521.000000</td>\n",
              "      <td>629.666667</td>\n",
              "      <td>3222.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3924.222222</td>\n",
              "      <td>3044.000000</td>\n",
              "      <td>1878.666667</td>\n",
              "      <td>2981.937500</td>\n",
              "      <td>14482.384720</td>\n",
              "      <td>13461.300019</td>\n",
              "      <td>12068.519317</td>\n",
              "      <td>8308.615326</td>\n",
              "      <td>6193.424052</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1049.0</td>\n",
              "      <td>772.0</td>\n",
              "      <td>520.0</td>\n",
              "      <td>316.0</td>\n",
              "      <td>356.000000</td>\n",
              "      <td>445.666667</td>\n",
              "      <td>1621.666667</td>\n",
              "      <td>617.000000</td>\n",
              "      <td>4179.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>4000.900000</td>\n",
              "      <td>3736.200000</td>\n",
              "      <td>1940.583333</td>\n",
              "      <td>3097.411765</td>\n",
              "      <td>17517.267375</td>\n",
              "      <td>15216.119184</td>\n",
              "      <td>12097.028947</td>\n",
              "      <td>9078.281907</td>\n",
              "      <td>6857.387163</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>NaN</td>\n",
              "      <td>920.0</td>\n",
              "      <td>863.0</td>\n",
              "      <td>473.0</td>\n",
              "      <td>299.0</td>\n",
              "      <td>359.500000</td>\n",
              "      <td>457.000000</td>\n",
              "      <td>560.333333</td>\n",
              "      <td>819.000000</td>\n",
              "      <td>3828.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4053.200000</td>\n",
              "      <td>3450.300000</td>\n",
              "      <td>1933.133333</td>\n",
              "      <td>4311.318182</td>\n",
              "      <td>17435.731900</td>\n",
              "      <td>15189.490900</td>\n",
              "      <td>11790.124315</td>\n",
              "      <td>8670.759913</td>\n",
              "      <td>6528.547745</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>NaN</td>\n",
              "      <td>798.0</td>\n",
              "      <td>811.0</td>\n",
              "      <td>466.0</td>\n",
              "      <td>325.0</td>\n",
              "      <td>357.500000</td>\n",
              "      <td>385.666667</td>\n",
              "      <td>721.666667</td>\n",
              "      <td>1299.333333</td>\n",
              "      <td>3573.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4355.600000</td>\n",
              "      <td>3212.272727</td>\n",
              "      <td>2324.266667</td>\n",
              "      <td>5451.480000</td>\n",
              "      <td>18115.015547</td>\n",
              "      <td>15820.134939</td>\n",
              "      <td>11783.664542</td>\n",
              "      <td>8764.311615</td>\n",
              "      <td>6556.395544</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jun</th>\n",
              "      <td>NaN</td>\n",
              "      <td>747.0</td>\n",
              "      <td>785.0</td>\n",
              "      <td>431.0</td>\n",
              "      <td>318.0</td>\n",
              "      <td>467.000000</td>\n",
              "      <td>258.666667</td>\n",
              "      <td>739.000000</td>\n",
              "      <td>1440.000000</td>\n",
              "      <td>3298.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3998.400000</td>\n",
              "      <td>3012.272727</td>\n",
              "      <td>1771.600000</td>\n",
              "      <td>8470.678571</td>\n",
              "      <td>17731.654219</td>\n",
              "      <td>15206.789699</td>\n",
              "      <td>11056.494454</td>\n",
              "      <td>8180.789366</td>\n",
              "      <td>6026.474948</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>NaN</td>\n",
              "      <td>751.0</td>\n",
              "      <td>774.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>409.5</td>\n",
              "      <td>565.000000</td>\n",
              "      <td>658.666667</td>\n",
              "      <td>824.666667</td>\n",
              "      <td>1722.000000</td>\n",
              "      <td>3233.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3454.500000</td>\n",
              "      <td>2926.636364</td>\n",
              "      <td>2048.125000</td>\n",
              "      <td>10591.482759</td>\n",
              "      <td>18135.080820</td>\n",
              "      <td>15040.308865</td>\n",
              "      <td>11179.465766</td>\n",
              "      <td>8342.170004</td>\n",
              "      <td>6260.411978</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>NaN</td>\n",
              "      <td>779.0</td>\n",
              "      <td>729.0</td>\n",
              "      <td>411.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>411.000000</td>\n",
              "      <td>856.000000</td>\n",
              "      <td>796.333333</td>\n",
              "      <td>1962.000000</td>\n",
              "      <td>3431.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3577.300000</td>\n",
              "      <td>2546.000000</td>\n",
              "      <td>3505.625000</td>\n",
              "      <td>9757.393939</td>\n",
              "      <td>17553.714286</td>\n",
              "      <td>15180.176318</td>\n",
              "      <td>10885.284967</td>\n",
              "      <td>8019.668792</td>\n",
              "      <td>6237.614087</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <td>NaN</td>\n",
              "      <td>903.0</td>\n",
              "      <td>653.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>421.000000</td>\n",
              "      <td>956.000000</td>\n",
              "      <td>814.000000</td>\n",
              "      <td>1876.000000</td>\n",
              "      <td>3377.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3480.100000</td>\n",
              "      <td>2663.636364</td>\n",
              "      <td>2265.375000</td>\n",
              "      <td>8875.146341</td>\n",
              "      <td>15803.512926</td>\n",
              "      <td>14480.391212</td>\n",
              "      <td>10134.776106</td>\n",
              "      <td>7551.965174</td>\n",
              "      <td>5998.452282</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>1213.0</td>\n",
              "      <td>894.0</td>\n",
              "      <td>622.0</td>\n",
              "      <td>374.0</td>\n",
              "      <td>344.0</td>\n",
              "      <td>454.000000</td>\n",
              "      <td>1030.666667</td>\n",
              "      <td>881.333333</td>\n",
              "      <td>2364.333333</td>\n",
              "      <td>3442.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3413.000000</td>\n",
              "      <td>2627.090909</td>\n",
              "      <td>2316.062500</td>\n",
              "      <td>11377.938776</td>\n",
              "      <td>16125.555411</td>\n",
              "      <td>14818.029119</td>\n",
              "      <td>10325.612648</td>\n",
              "      <td>7578.720138</td>\n",
              "      <td>6048.431848</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>1174.0</td>\n",
              "      <td>865.0</td>\n",
              "      <td>553.0</td>\n",
              "      <td>356.0</td>\n",
              "      <td>324.5</td>\n",
              "      <td>469.000000</td>\n",
              "      <td>949.333333</td>\n",
              "      <td>816.333333</td>\n",
              "      <td>2567.333333</td>\n",
              "      <td>3967.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3317.600000</td>\n",
              "      <td>2570.818182</td>\n",
              "      <td>3926.875000</td>\n",
              "      <td>11260.047619</td>\n",
              "      <td>15438.691979</td>\n",
              "      <td>14268.734637</td>\n",
              "      <td>9657.446583</td>\n",
              "      <td>7181.526692</td>\n",
              "      <td>5780.686514</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>1164.0</td>\n",
              "      <td>842.0</td>\n",
              "      <td>579.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>330.5</td>\n",
              "      <td>485.333333</td>\n",
              "      <td>690.666667</td>\n",
              "      <td>856.333333</td>\n",
              "      <td>2688.333333</td>\n",
              "      <td>4140.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>4098.300000</td>\n",
              "      <td>2622.083333</td>\n",
              "      <td>4844.937500</td>\n",
              "      <td>10790.506757</td>\n",
              "      <td>15854.642289</td>\n",
              "      <td>13320.343746</td>\n",
              "      <td>9213.964682</td>\n",
              "      <td>7107.918743</td>\n",
              "      <td>5587.794313</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f77a42b-4529-4aef-9185-2514bcbff4e4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5f77a42b-4529-4aef-9185-2514bcbff4e4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5f77a42b-4529-4aef-9185-2514bcbff4e4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Replace NAN values to 0 (zero)\n",
        "\n",
        "monthly_gas_df.fillna(0, inplace=True)\n",
        "\n",
        "monthly_gas_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "Fxd4Z8eh6Uat",
        "outputId": "c6e4eed7-537c-4952-b546-11ec4c3dde83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year     1977    1978   1979   1980   1981        1982         1983  \\\n",
              "Month                                                                 \n",
              "Jan       0.0  1072.0  822.0  566.0  347.0  332.000000   514.000000   \n",
              "Feb       0.0   955.0  714.0  498.0  287.0  308.000000   421.666667   \n",
              "Mar       0.0  1049.0  772.0  520.0  316.0  356.000000   445.666667   \n",
              "Apr       0.0   920.0  863.0  473.0  299.0  359.500000   457.000000   \n",
              "May       0.0   798.0  811.0  466.0  325.0  357.500000   385.666667   \n",
              "Jun       0.0   747.0  785.0  431.0  318.0  467.000000   258.666667   \n",
              "Jul       0.0   751.0  774.0  434.0  409.5  565.000000   658.666667   \n",
              "Aug       0.0   779.0  729.0  411.0  380.0  411.000000   856.000000   \n",
              "Sep       0.0   903.0  653.0  369.0  352.0  421.000000   956.000000   \n",
              "Oct    1213.0   894.0  622.0  374.0  344.0  454.000000  1030.666667   \n",
              "Nov    1174.0   865.0  553.0  356.0  324.5  469.000000   949.333333   \n",
              "Dec    1164.0   842.0  579.0  337.0  330.5  485.333333   690.666667   \n",
              "\n",
              "Year          1984         1985         1986  ...         2010         2011  \\\n",
              "Month                                         ...                             \n",
              "Jan     854.333333   784.000000  2904.666667  ...  4253.777778  3863.000000   \n",
              "Feb    1521.000000   629.666667  3222.000000  ...  3924.222222  3044.000000   \n",
              "Mar    1621.666667   617.000000  4179.666667  ...  4000.900000  3736.200000   \n",
              "Apr     560.333333   819.000000  3828.000000  ...  4053.200000  3450.300000   \n",
              "May     721.666667  1299.333333  3573.000000  ...  4355.600000  3212.272727   \n",
              "Jun     739.000000  1440.000000  3298.333333  ...  3998.400000  3012.272727   \n",
              "Jul     824.666667  1722.000000  3233.000000  ...  3454.500000  2926.636364   \n",
              "Aug     796.333333  1962.000000  3431.333333  ...  3577.300000  2546.000000   \n",
              "Sep     814.000000  1876.000000  3377.333333  ...  3480.100000  2663.636364   \n",
              "Oct     881.333333  2364.333333  3442.333333  ...  3413.000000  2627.090909   \n",
              "Nov     816.333333  2567.333333  3967.333333  ...  3317.600000  2570.818182   \n",
              "Dec     856.333333  2688.333333  4140.666667  ...  4098.300000  2622.083333   \n",
              "\n",
              "Year          2012          2013          2014          2015          2016  \\\n",
              "Month                                                                        \n",
              "Jan    2254.000000   3494.062500  14064.476744  14806.068553  12966.334566   \n",
              "Feb    1878.666667   2981.937500  14482.384720  13461.300019  12068.519317   \n",
              "Mar    1940.583333   3097.411765  17517.267375  15216.119184  12097.028947   \n",
              "Apr    1933.133333   4311.318182  17435.731900  15189.490900  11790.124315   \n",
              "May    2324.266667   5451.480000  18115.015547  15820.134939  11783.664542   \n",
              "Jun    1771.600000   8470.678571  17731.654219  15206.789699  11056.494454   \n",
              "Jul    2048.125000  10591.482759  18135.080820  15040.308865  11179.465766   \n",
              "Aug    3505.625000   9757.393939  17553.714286  15180.176318  10885.284967   \n",
              "Sep    2265.375000   8875.146341  15803.512926  14480.391212  10134.776106   \n",
              "Oct    2316.062500  11377.938776  16125.555411  14818.029119  10325.612648   \n",
              "Nov    3926.875000  11260.047619  15438.691979  14268.734637   9657.446583   \n",
              "Dec    4844.937500  10790.506757  15854.642289  13320.343746   9213.964682   \n",
              "\n",
              "Year          2017         2018         2019  \n",
              "Month                                         \n",
              "Jan    9044.950529  6816.953586  3666.669137  \n",
              "Feb    8308.615326  6193.424052     0.000000  \n",
              "Mar    9078.281907  6857.387163     0.000000  \n",
              "Apr    8670.759913  6528.547745     0.000000  \n",
              "May    8764.311615  6556.395544     0.000000  \n",
              "Jun    8180.789366  6026.474948     0.000000  \n",
              "Jul    8342.170004  6260.411978     0.000000  \n",
              "Aug    8019.668792  6237.614087     0.000000  \n",
              "Sep    7551.965174  5998.452282     0.000000  \n",
              "Oct    7578.720138  6048.431848     0.000000  \n",
              "Nov    7181.526692  5780.686514     0.000000  \n",
              "Dec    7107.918743  5587.794313     0.000000  \n",
              "\n",
              "[12 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-95e82432-d69d-4840-a9ee-c08ebfe0b4aa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jan</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1072.0</td>\n",
              "      <td>822.0</td>\n",
              "      <td>566.0</td>\n",
              "      <td>347.0</td>\n",
              "      <td>332.000000</td>\n",
              "      <td>514.000000</td>\n",
              "      <td>854.333333</td>\n",
              "      <td>784.000000</td>\n",
              "      <td>2904.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>4253.777778</td>\n",
              "      <td>3863.000000</td>\n",
              "      <td>2254.000000</td>\n",
              "      <td>3494.062500</td>\n",
              "      <td>14064.476744</td>\n",
              "      <td>14806.068553</td>\n",
              "      <td>12966.334566</td>\n",
              "      <td>9044.950529</td>\n",
              "      <td>6816.953586</td>\n",
              "      <td>3666.669137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <td>0.0</td>\n",
              "      <td>955.0</td>\n",
              "      <td>714.0</td>\n",
              "      <td>498.0</td>\n",
              "      <td>287.0</td>\n",
              "      <td>308.000000</td>\n",
              "      <td>421.666667</td>\n",
              "      <td>1521.000000</td>\n",
              "      <td>629.666667</td>\n",
              "      <td>3222.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3924.222222</td>\n",
              "      <td>3044.000000</td>\n",
              "      <td>1878.666667</td>\n",
              "      <td>2981.937500</td>\n",
              "      <td>14482.384720</td>\n",
              "      <td>13461.300019</td>\n",
              "      <td>12068.519317</td>\n",
              "      <td>8308.615326</td>\n",
              "      <td>6193.424052</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1049.0</td>\n",
              "      <td>772.0</td>\n",
              "      <td>520.0</td>\n",
              "      <td>316.0</td>\n",
              "      <td>356.000000</td>\n",
              "      <td>445.666667</td>\n",
              "      <td>1621.666667</td>\n",
              "      <td>617.000000</td>\n",
              "      <td>4179.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>4000.900000</td>\n",
              "      <td>3736.200000</td>\n",
              "      <td>1940.583333</td>\n",
              "      <td>3097.411765</td>\n",
              "      <td>17517.267375</td>\n",
              "      <td>15216.119184</td>\n",
              "      <td>12097.028947</td>\n",
              "      <td>9078.281907</td>\n",
              "      <td>6857.387163</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <td>0.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>863.0</td>\n",
              "      <td>473.0</td>\n",
              "      <td>299.0</td>\n",
              "      <td>359.500000</td>\n",
              "      <td>457.000000</td>\n",
              "      <td>560.333333</td>\n",
              "      <td>819.000000</td>\n",
              "      <td>3828.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4053.200000</td>\n",
              "      <td>3450.300000</td>\n",
              "      <td>1933.133333</td>\n",
              "      <td>4311.318182</td>\n",
              "      <td>17435.731900</td>\n",
              "      <td>15189.490900</td>\n",
              "      <td>11790.124315</td>\n",
              "      <td>8670.759913</td>\n",
              "      <td>6528.547745</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <td>0.0</td>\n",
              "      <td>798.0</td>\n",
              "      <td>811.0</td>\n",
              "      <td>466.0</td>\n",
              "      <td>325.0</td>\n",
              "      <td>357.500000</td>\n",
              "      <td>385.666667</td>\n",
              "      <td>721.666667</td>\n",
              "      <td>1299.333333</td>\n",
              "      <td>3573.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4355.600000</td>\n",
              "      <td>3212.272727</td>\n",
              "      <td>2324.266667</td>\n",
              "      <td>5451.480000</td>\n",
              "      <td>18115.015547</td>\n",
              "      <td>15820.134939</td>\n",
              "      <td>11783.664542</td>\n",
              "      <td>8764.311615</td>\n",
              "      <td>6556.395544</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jun</th>\n",
              "      <td>0.0</td>\n",
              "      <td>747.0</td>\n",
              "      <td>785.0</td>\n",
              "      <td>431.0</td>\n",
              "      <td>318.0</td>\n",
              "      <td>467.000000</td>\n",
              "      <td>258.666667</td>\n",
              "      <td>739.000000</td>\n",
              "      <td>1440.000000</td>\n",
              "      <td>3298.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3998.400000</td>\n",
              "      <td>3012.272727</td>\n",
              "      <td>1771.600000</td>\n",
              "      <td>8470.678571</td>\n",
              "      <td>17731.654219</td>\n",
              "      <td>15206.789699</td>\n",
              "      <td>11056.494454</td>\n",
              "      <td>8180.789366</td>\n",
              "      <td>6026.474948</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <td>0.0</td>\n",
              "      <td>751.0</td>\n",
              "      <td>774.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>409.5</td>\n",
              "      <td>565.000000</td>\n",
              "      <td>658.666667</td>\n",
              "      <td>824.666667</td>\n",
              "      <td>1722.000000</td>\n",
              "      <td>3233.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3454.500000</td>\n",
              "      <td>2926.636364</td>\n",
              "      <td>2048.125000</td>\n",
              "      <td>10591.482759</td>\n",
              "      <td>18135.080820</td>\n",
              "      <td>15040.308865</td>\n",
              "      <td>11179.465766</td>\n",
              "      <td>8342.170004</td>\n",
              "      <td>6260.411978</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <td>0.0</td>\n",
              "      <td>779.0</td>\n",
              "      <td>729.0</td>\n",
              "      <td>411.0</td>\n",
              "      <td>380.0</td>\n",
              "      <td>411.000000</td>\n",
              "      <td>856.000000</td>\n",
              "      <td>796.333333</td>\n",
              "      <td>1962.000000</td>\n",
              "      <td>3431.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3577.300000</td>\n",
              "      <td>2546.000000</td>\n",
              "      <td>3505.625000</td>\n",
              "      <td>9757.393939</td>\n",
              "      <td>17553.714286</td>\n",
              "      <td>15180.176318</td>\n",
              "      <td>10885.284967</td>\n",
              "      <td>8019.668792</td>\n",
              "      <td>6237.614087</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <td>0.0</td>\n",
              "      <td>903.0</td>\n",
              "      <td>653.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>352.0</td>\n",
              "      <td>421.000000</td>\n",
              "      <td>956.000000</td>\n",
              "      <td>814.000000</td>\n",
              "      <td>1876.000000</td>\n",
              "      <td>3377.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3480.100000</td>\n",
              "      <td>2663.636364</td>\n",
              "      <td>2265.375000</td>\n",
              "      <td>8875.146341</td>\n",
              "      <td>15803.512926</td>\n",
              "      <td>14480.391212</td>\n",
              "      <td>10134.776106</td>\n",
              "      <td>7551.965174</td>\n",
              "      <td>5998.452282</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <td>1213.0</td>\n",
              "      <td>894.0</td>\n",
              "      <td>622.0</td>\n",
              "      <td>374.0</td>\n",
              "      <td>344.0</td>\n",
              "      <td>454.000000</td>\n",
              "      <td>1030.666667</td>\n",
              "      <td>881.333333</td>\n",
              "      <td>2364.333333</td>\n",
              "      <td>3442.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3413.000000</td>\n",
              "      <td>2627.090909</td>\n",
              "      <td>2316.062500</td>\n",
              "      <td>11377.938776</td>\n",
              "      <td>16125.555411</td>\n",
              "      <td>14818.029119</td>\n",
              "      <td>10325.612648</td>\n",
              "      <td>7578.720138</td>\n",
              "      <td>6048.431848</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <td>1174.0</td>\n",
              "      <td>865.0</td>\n",
              "      <td>553.0</td>\n",
              "      <td>356.0</td>\n",
              "      <td>324.5</td>\n",
              "      <td>469.000000</td>\n",
              "      <td>949.333333</td>\n",
              "      <td>816.333333</td>\n",
              "      <td>2567.333333</td>\n",
              "      <td>3967.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>3317.600000</td>\n",
              "      <td>2570.818182</td>\n",
              "      <td>3926.875000</td>\n",
              "      <td>11260.047619</td>\n",
              "      <td>15438.691979</td>\n",
              "      <td>14268.734637</td>\n",
              "      <td>9657.446583</td>\n",
              "      <td>7181.526692</td>\n",
              "      <td>5780.686514</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <td>1164.0</td>\n",
              "      <td>842.0</td>\n",
              "      <td>579.0</td>\n",
              "      <td>337.0</td>\n",
              "      <td>330.5</td>\n",
              "      <td>485.333333</td>\n",
              "      <td>690.666667</td>\n",
              "      <td>856.333333</td>\n",
              "      <td>2688.333333</td>\n",
              "      <td>4140.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>4098.300000</td>\n",
              "      <td>2622.083333</td>\n",
              "      <td>4844.937500</td>\n",
              "      <td>10790.506757</td>\n",
              "      <td>15854.642289</td>\n",
              "      <td>13320.343746</td>\n",
              "      <td>9213.964682</td>\n",
              "      <td>7107.918743</td>\n",
              "      <td>5587.794313</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-95e82432-d69d-4840-a9ee-c08ebfe0b4aa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-95e82432-d69d-4840-a9ee-c08ebfe0b4aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-95e82432-d69d-4840-a9ee-c08ebfe0b4aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig=px.imshow(monthly_gas_df, color_continuous_scale='peach',labels=dict(x='Year', y='Month'), title='Monthly Gas Production')\n",
        "fig.update_traces(hovertemplate='<br>'.join(['Year:%{x}', 'Month:%{y}', 'Produced Gas:%{z}']), name='')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "fig.update_layout(font_size=14)\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "JpSVOgoC5gEJ",
        "outputId": "b6dd6446-2d7e-4dcc-f9ba-01de851c3320"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"6d732f40-9065-4f4d-a04a-0a13d4597ba9\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6d732f40-9065-4f4d-a04a-0a13d4597ba9\")) {                    Plotly.newPlot(                        \"6d732f40-9065-4f4d-a04a-0a13d4597ba9\",                        [{\"coloraxis\":\"coloraxis\",\"name\":\"\",\"x\":[1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019],\"y\":[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],\"z\":[[0.0,1072.0,822.0,566.0,347.0,332.0,514.0,854.3333333333334,784.0,2904.6666666666665,4242.333333333333,4288.666666666667,5795.666666666667,7174.666666666667,11983.0,9575.0,10464.333333333334,3561.3333333333335,12521.0,4168.333333333333,4740.333333333333,3733.3333333333335,3460.3333333333335,2972.0,3526.0,2657.0,3624.0,4550.333333333333,2279.0,4390.666666666667,4552.666666666667,6830.0,4139.888888888889,4253.777777777777,3863.0,2254.0,3494.0625,14064.476744186046,14806.06855320845,12966.334565854902,9044.95052913426,6816.953586497891,3666.6691368788142],[0.0,955.0,714.0,498.0,287.0,308.0,421.6666666666667,1521.0,629.6666666666666,3222.0,3872.0,3876.3333333333335,4962.333333333333,6627.666666666667,10290.333333333334,10131.0,9392.0,2696.0,10408.0,4979.666666666667,3631.3333333333335,3247.0,3093.6666666666665,2914.6666666666665,3094.6666666666665,2571.0,3344.3333333333335,3910.6666666666665,2803.6666666666665,4527.666666666667,4803.666666666667,6413.166666666667,3542.3333333333335,3924.222222222222,3044.0,1878.6666666666667,2981.9375,14482.384720327422,13461.300018716078,12068.519316588041,8308.615325768202,6193.424052254099,0.0],[0.0,1049.0,772.0,520.0,316.0,356.0,445.6666666666667,1621.6666666666667,617.0,4179.666666666667,4624.666666666667,3849.0,5713.0,7400.333333333333,11257.333333333334,12349.0,9904.333333333334,2747.0,9977.333333333334,6717.666666666667,3859.6666666666665,3471.3333333333335,3599.6666666666665,3575.6666666666665,3493.6666666666665,2776.3333333333335,4644.0,4336.666666666667,3314.6666666666665,2419.0,6126.333333333333,4847.0,3667.4444444444443,4000.9,3736.2,1940.5833333333333,3097.4117647058824,17517.267374517374,15216.11918353226,12097.028946697272,9078.28190743338,6857.387163029525,0.0],[0.0,920.0,863.0,473.0,299.0,359.5,457.0,560.3333333333334,819.0,3828.0,4744.666666666667,3566.0,5688.666666666667,7175.0,10485.333333333334,12203.0,9204.333333333334,2538.3333333333335,7580.0,8243.666666666666,3403.0,3392.0,3540.0,3705.6666666666665,3299.0,2435.0,4972.333333333333,4442.333333333333,5179.0,2229.6666666666665,7004.666666666667,4815.571428571428,3332.6666666666665,4053.2,3450.3,1933.1333333333334,4311.318181818182,17435.731899641578,15189.490900227494,11790.124314675506,8670.759913298483,6528.547744505848,0.0],[0.0,798.0,811.0,466.0,325.0,357.5,385.6666666666667,721.6666666666666,1299.3333333333333,3573.0,4123.0,3941.6666666666665,5989.0,7907.0,9516.0,13808.333333333334,9806.666666666666,2551.3333333333335,5027.0,10110.333333333334,3104.0,3140.0,3459.6666666666665,3946.6666666666665,3408.6666666666665,2327.3333333333335,6153.0,3567.6666666666665,3826.3333333333335,2023.0,8864.666666666666,4970.5,3628.777777777778,4355.6,3212.2727272727275,2324.266666666667,5451.48,18115.015546918377,15820.134939014975,11783.664541629478,8764.31161545327,6556.3955435342605,0.0],[0.0,747.0,785.0,431.0,318.0,467.0,258.6666666666667,739.0,1440.0,3298.3333333333335,3561.3333333333335,3779.0,5598.0,6708.666666666667,9016.0,13622.0,10050.0,2338.3333333333335,2151.6666666666665,9174.666666666666,2832.3333333333335,2784.6666666666665,3247.0,3756.6666666666665,3253.0,2181.3333333333335,7531.666666666667,2928.0,3202.6666666666665,1752.6666666666667,4514.333333333333,6207.0,4121.0,3998.4,3012.2727272727275,1771.6,8470.67857142857,17731.654218533888,15206.789698937426,11056.494453652938,8180.78936631391,6026.474948347108,0.0],[0.0,751.0,774.0,434.0,409.5,565.0,658.6666666666666,824.6666666666666,1722.0,3233.0,3418.6666666666665,4031.3333333333335,6226.0,6316.666666666667,8968.333333333334,14637.666666666666,9158.333333333334,2395.3333333333335,2112.0,8385.333333333334,2986.3333333333335,2687.6666666666665,3580.3333333333335,3784.6666666666665,3243.0,2081.6666666666665,8752.333333333334,2990.3333333333335,2378.0,2343.6666666666665,4711.0,6514.875,3845.777777777778,3454.5,2926.6363636363635,2048.125,10591.48275862069,18135.080819798917,15040.308865047995,11179.465765650899,8342.170003826042,6260.411977751908,0.0],[0.0,779.0,729.0,411.0,380.0,411.0,856.0,796.3333333333334,1962.0,3431.3333333333335,3007.6666666666665,4180.333333333333,6808.333333333333,7908.0,8655.333333333334,14638.0,7652.333333333333,2371.3333333333335,2022.3333333333333,7328.666666666667,3264.6666666666665,2693.3333333333335,3460.3333333333335,4006.3333333333335,2759.6666666666665,1979.6666666666667,9114.0,2985.3333333333335,2471.6666666666665,2350.3333333333335,5158.0,6736.75,3797.5555555555557,3577.3,2546.0,3505.625,9757.39393939394,17553.714285714286,15180.176318299496,10885.28496748693,8019.668792245887,6237.614087430276,0.0],[0.0,903.0,653.0,369.0,352.0,421.0,956.0,814.0,1876.0,3377.3333333333335,2982.6666666666665,4651.0,6873.0,9021.666666666666,7644.0,17171.0,6697.666666666667,12478.0,2207.6666666666665,6777.0,3410.3333333333335,2644.6666666666665,3103.0,3372.6666666666665,2680.0,1847.3333333333333,7330.0,2299.0,3008.3333333333335,2107.3333333333335,4954.0,5423.5,3845.222222222222,3480.1,2663.6363636363635,2265.375,8875.146341463415,15803.512925969448,14480.391212281631,10134.776106081856,7551.965174129353,5998.452281616688,0.0],[1213.0,894.0,622.0,374.0,344.0,454.0,1030.6666666666667,881.3333333333334,2364.3333333333335,3442.3333333333335,2876.0,5045.333333333333,7156.333333333333,9728.666666666666,7191.666666666667,15114.333333333334,5764.0,2313.0,2609.6666666666665,6404.0,3564.0,2765.0,2877.6666666666665,3408.0,2572.0,2169.0,7583.666666666667,2467.6666666666665,2923.0,2557.3333333333335,6484.8,5085.25,3578.0,3413.0,2627.090909090909,2316.0625,11377.938775510203,16125.555410691004,14818.029118674776,10325.612648221344,7578.720137825421,6048.431847968545,0.0],[1174.0,865.0,553.0,356.0,324.5,469.0,949.3333333333334,816.3333333333334,2567.3333333333335,3967.3333333333335,2658.6666666666665,5216.0,7100.0,9555.666666666666,7739.0,14135.0,4456.666666666667,2049.3333333333335,2647.0,5604.666666666667,3741.3333333333335,2806.6666666666665,2736.3333333333335,3140.3333333333335,2381.0,1906.3333333333333,6648.0,2937.6666666666665,3398.0,3291.0,7991.0,4075.3333333333335,3474.222222222222,3317.6,2570.818181818182,3926.875,11260.047619047618,15438.691979117228,14268.73463651463,9657.44658337583,7181.526692209451,5780.686513724971,0.0],[1164.0,842.0,579.0,337.0,330.5,485.3333333333333,690.6666666666666,856.3333333333334,2688.3333333333335,4140.666666666667,2421.0,5601.0,6600.666666666667,11729.666666666666,9111.0,10412.666666666666,3910.6666666666665,12075.0,2731.6666666666665,4995.666666666667,3986.3333333333335,2924.6666666666665,3176.6666666666665,3533.6666666666665,2437.6666666666665,3992.3333333333335,6446.333333333333,3351.0,3282.0,2952.6666666666665,6283.666666666667,4131.777777777777,3123.4444444444443,4098.3,2622.0833333333335,4844.9375,10790.506756756757,15854.642288664229,13320.343746015555,9213.964681881933,7107.918742813338,5587.794312796209,0.0]],\"type\":\"heatmap\",\"xaxis\":\"x\",\"yaxis\":\"y\",\"hovertemplate\":\"Year:%{x}<br>Month:%{y}<br>Produced Gas:%{z}\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"scaleanchor\":\"y\",\"constrain\":\"domain\",\"title\":{\"text\":\"Year\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"autorange\":\"reversed\",\"constrain\":\"domain\",\"title\":{\"text\":\"Month\"}},\"coloraxis\":{\"colorscale\":[[0.0,\"rgb(253, 224, 197)\"],[0.16666666666666666,\"rgb(250, 203, 166)\"],[0.3333333333333333,\"rgb(248, 181, 139)\"],[0.5,\"rgb(245, 158, 114)\"],[0.6666666666666666,\"rgb(242, 133, 93)\"],[0.8333333333333334,\"rgb(239, 106, 76)\"],[1.0,\"rgb(235, 74, 64)\"]]},\"title\":{\"text\":\"Monthly Gas Production\"},\"font\":{\"size\":14}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('6d732f40-9065-4f4d-a04a-0a13d4597ba9');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style('whitegrid')"
      ],
      "metadata": {
        "id": "cjH7rAve2xJW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib\n",
        "matplotlib.rcParams['font.size']=14\n",
        "matplotlib.rcParams['figure.facecolor']='#000'"
      ],
      "metadata": {
        "id": "YkpZVDzh29mC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#combine all the graph in a single plot\n",
        "\n",
        "fig, ax =plt.subplots(2,3, figsize=(18,9)) #create a figure with 2 rows and 3 columns of subplots\n",
        "\n",
        "#Annual Oil Production\n",
        "ax[0,0].bar(annual_df.index, annual_df.Liquid, color='blue')\n",
        "ax[0,0].plot(annual_df.index, annual_df.Liquid, marker='.', ms=8, ls='--', c='red')\n",
        "ax[0,0].set_yscale('log')\n",
        "ax[0,0].set_xlabel('Year')\n",
        "ax[0,0].set_ylabel('Oil Production')\n",
        "ax[0,0].set_title('Annual Oil Production')\n",
        "\n",
        "#Annual Gas Production\n",
        "ax[1,0].bar(annual_df.index, annual_df.Gas, color='lime')\n",
        "ax[1,0].plot(annual_df.index, annual_df.Gas, marker='.', ms=8, ls='--', c='red')\n",
        "ax[1,0].set_yscale('log')\n",
        "ax[1,0].set_xlabel('Year')\n",
        "ax[1,0].set_ylabel('Oil Production')\n",
        "ax[1,0].set_title('Annual Oil Production')\n",
        "\n",
        "#Monthly Oil Production\n",
        "ax[0,1].set_title('Monthly Oil production')\n",
        "sns.heatmap(monthly_oil_df, cmap='twilight', ax=ax[0,1])\n",
        "\n",
        "#Monthly Gas Production\n",
        "ax[1,1].set_title('Monthly Gas production')\n",
        "sns.heatmap(monthly_oil_df, cmap='Oranges', ax=ax[1,1])\n",
        "\n",
        "#Cum Oil Vs. Cum Water\n",
        "ax[0, 2].plot(annual_df.index, annual_df.Cum_Oil, 'o-g')\n",
        "ax[0, 2].plot(annual_df.index, annual_df.Cum_Water, 's--b')\n",
        "ax[0,2].set_yscale('log')\n",
        "ax[0, 2].set_xlabel('Year')\n",
        "ax[0, 2].set_ylabel('Cumulative production')\n",
        "ax[0, 2].set_title('Cum Oil Vs. Cum Water production')\n",
        "ax[0, 2].legend(['Oil', 'Water'])\n",
        "\n",
        "#Cum Gas Vs. Cum Water\n",
        "ax[1, 2].plot(annual_df.index, annual_df.Cum_Gas, 'o-g')\n",
        "ax[1, 2].plot(annual_df.index, annual_df.Cum_Water, 's--b')\n",
        "ax[1,2].set_yscale('log')\n",
        "ax[1, 2].set_xlabel('Year')\n",
        "ax[1, 2].set_ylabel('Cumulative production')\n",
        "ax[1, 2].set_title('Cum Gas Vs. Cum Water production')\n",
        "ax[1, 2].legend(['Gas', 'Water'])\n",
        "\n",
        "plt.tight_layout(pad=2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "JDyVviLj5jtb",
        "outputId": "8a9d401d-1c15-4732-f002-09b5bac4d4f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x648 with 8 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "API_Tot_Liq_Prod = Sort.pivot_table('Liquid', 'API', 'Year', sort = False)\n",
        "\n",
        "API_Tot_Liq_Prod"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "odEuXle8l2r2",
        "outputId": "e02e76a7-9923-405d-b685-6d5ccdc2af76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                    1977     1978     1979     1980         1981  \\\n",
              "API                                                                    \n",
              "42371323150300  19226.333333  10005.5  7778.25  5028.25  6272.083333   \n",
              "42371334530200           NaN      NaN      NaN      NaN  6557.166667   \n",
              "42371337890300           NaN      NaN      NaN      NaN          NaN   \n",
              "33025006260100           NaN      NaN      NaN      NaN          NaN   \n",
              "33025006080100           NaN      NaN      NaN      NaN          NaN   \n",
              "...                      ...      ...      ...      ...          ...   \n",
              "42495317050100           NaN      NaN      NaN      NaN          NaN   \n",
              "42389348680000           NaN      NaN      NaN      NaN          NaN   \n",
              "42389348690000           NaN      NaN      NaN      NaN          NaN   \n",
              "42389348390000           NaN      NaN      NaN      NaN          NaN   \n",
              "42301324920100           NaN      NaN      NaN      NaN          NaN   \n",
              "\n",
              "Year                   1982         1983         1984         1985  \\\n",
              "API                                                                  \n",
              "42371323150300  6116.666667  1225.500000    13.583333   935.166667   \n",
              "42371334530200  6464.000000  8513.333333  6613.500000  5176.166667   \n",
              "42371337890300  1010.000000   708.833333  3862.083333  6022.583333   \n",
              "33025006260100          NaN          NaN          NaN          NaN   \n",
              "33025006080100          NaN          NaN          NaN          NaN   \n",
              "...                     ...          ...          ...          ...   \n",
              "42495317050100          NaN          NaN          NaN          NaN   \n",
              "42389348680000          NaN          NaN          NaN          NaN   \n",
              "42389348690000          NaN          NaN          NaN          NaN   \n",
              "42389348390000          NaN          NaN          NaN          NaN   \n",
              "42301324920100          NaN          NaN          NaN          NaN   \n",
              "\n",
              "Year                   1986  ...         2010         2011         2012  \\\n",
              "API                          ...                                          \n",
              "42371323150300   389.750000  ...  1257.833333   434.583333   504.000000   \n",
              "42371334530200  3899.583333  ...  1602.916667  1215.666667   701.000000   \n",
              "42371337890300  6406.666667  ...   587.416667   425.916667   356.333333   \n",
              "33025006260100          NaN  ...  1463.916667  1215.666667  1059.083333   \n",
              "33025006080100          NaN  ...  1088.583333   967.583333   686.333333   \n",
              "...                     ...  ...          ...          ...          ...   \n",
              "42495317050100          NaN  ...          NaN          NaN          NaN   \n",
              "42389348680000          NaN  ...          NaN          NaN          NaN   \n",
              "42389348690000          NaN  ...          NaN          NaN          NaN   \n",
              "42389348390000          NaN  ...          NaN          NaN          NaN   \n",
              "42301324920100          NaN  ...          NaN          NaN          NaN   \n",
              "\n",
              "Year                  2013         2014          2015          2016  \\\n",
              "API                                                                   \n",
              "42371323150300  683.500000  1530.333333    447.666667    892.583333   \n",
              "42371334530200  378.000000   114.500000    216.583333    172.750000   \n",
              "42371337890300  386.000000  1272.416667   3114.666667      0.000000   \n",
              "33025006260100  956.083333   889.000000   6919.083333   6032.750000   \n",
              "33025006080100  691.500000   647.000000   3962.750000   5139.000000   \n",
              "...                    ...          ...           ...           ...   \n",
              "42495317050100         NaN          NaN   6561.000000   3467.583333   \n",
              "42389348680000         NaN          NaN  22935.600000   9824.250000   \n",
              "42389348690000         NaN          NaN  16845.800000   4026.583333   \n",
              "42389348390000         NaN          NaN  33203.500000  13456.000000   \n",
              "42301324920100         NaN          NaN   7537.142857   3740.666667   \n",
              "\n",
              "Year                   2017         2018    2019  \n",
              "API                                               \n",
              "42371323150300  1196.583333   231.000000     NaN  \n",
              "42371334530200   216.750000  2093.000000     NaN  \n",
              "42371337890300     0.000000   966.333333     NaN  \n",
              "33025006260100  4575.083333  3118.333333  3143.0  \n",
              "33025006080100  3265.416667  1896.750000  2178.0  \n",
              "...                     ...          ...     ...  \n",
              "42495317050100  1348.230769  1005.833333     NaN  \n",
              "42389348680000  5501.916667  1370.333333     NaN  \n",
              "42389348690000  2621.000000  1492.583333     NaN  \n",
              "42389348390000  7560.666667  3014.000000     NaN  \n",
              "42301324920100  2206.083333  3874.583333     NaN  \n",
              "\n",
              "[7844 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45295cb6-79f5-4db5-ab33-5ef81de6b86f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>API</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42371323150300</th>\n",
              "      <td>19226.333333</td>\n",
              "      <td>10005.5</td>\n",
              "      <td>7778.25</td>\n",
              "      <td>5028.25</td>\n",
              "      <td>6272.083333</td>\n",
              "      <td>6116.666667</td>\n",
              "      <td>1225.500000</td>\n",
              "      <td>13.583333</td>\n",
              "      <td>935.166667</td>\n",
              "      <td>389.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>1257.833333</td>\n",
              "      <td>434.583333</td>\n",
              "      <td>504.000000</td>\n",
              "      <td>683.500000</td>\n",
              "      <td>1530.333333</td>\n",
              "      <td>447.666667</td>\n",
              "      <td>892.583333</td>\n",
              "      <td>1196.583333</td>\n",
              "      <td>231.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371334530200</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6557.166667</td>\n",
              "      <td>6464.000000</td>\n",
              "      <td>8513.333333</td>\n",
              "      <td>6613.500000</td>\n",
              "      <td>5176.166667</td>\n",
              "      <td>3899.583333</td>\n",
              "      <td>...</td>\n",
              "      <td>1602.916667</td>\n",
              "      <td>1215.666667</td>\n",
              "      <td>701.000000</td>\n",
              "      <td>378.000000</td>\n",
              "      <td>114.500000</td>\n",
              "      <td>216.583333</td>\n",
              "      <td>172.750000</td>\n",
              "      <td>216.750000</td>\n",
              "      <td>2093.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371337890300</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1010.000000</td>\n",
              "      <td>708.833333</td>\n",
              "      <td>3862.083333</td>\n",
              "      <td>6022.583333</td>\n",
              "      <td>6406.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>587.416667</td>\n",
              "      <td>425.916667</td>\n",
              "      <td>356.333333</td>\n",
              "      <td>386.000000</td>\n",
              "      <td>1272.416667</td>\n",
              "      <td>3114.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>966.333333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006260100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1463.916667</td>\n",
              "      <td>1215.666667</td>\n",
              "      <td>1059.083333</td>\n",
              "      <td>956.083333</td>\n",
              "      <td>889.000000</td>\n",
              "      <td>6919.083333</td>\n",
              "      <td>6032.750000</td>\n",
              "      <td>4575.083333</td>\n",
              "      <td>3118.333333</td>\n",
              "      <td>3143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006080100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1088.583333</td>\n",
              "      <td>967.583333</td>\n",
              "      <td>686.333333</td>\n",
              "      <td>691.500000</td>\n",
              "      <td>647.000000</td>\n",
              "      <td>3962.750000</td>\n",
              "      <td>5139.000000</td>\n",
              "      <td>3265.416667</td>\n",
              "      <td>1896.750000</td>\n",
              "      <td>2178.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42495317050100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6561.000000</td>\n",
              "      <td>3467.583333</td>\n",
              "      <td>1348.230769</td>\n",
              "      <td>1005.833333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348680000</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22935.600000</td>\n",
              "      <td>9824.250000</td>\n",
              "      <td>5501.916667</td>\n",
              "      <td>1370.333333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348690000</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16845.800000</td>\n",
              "      <td>4026.583333</td>\n",
              "      <td>2621.000000</td>\n",
              "      <td>1492.583333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348390000</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33203.500000</td>\n",
              "      <td>13456.000000</td>\n",
              "      <td>7560.666667</td>\n",
              "      <td>3014.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42301324920100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7537.142857</td>\n",
              "      <td>3740.666667</td>\n",
              "      <td>2206.083333</td>\n",
              "      <td>3874.583333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7844 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45295cb6-79f5-4db5-ab33-5ef81de6b86f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-45295cb6-79f5-4db5-ab33-5ef81de6b86f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-45295cb6-79f5-4db5-ab33-5ef81de6b86f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "API_Tot_Liq_Prod.fillna(0, inplace=True)\n",
        "\n",
        "API_Tot_Liq_Prod "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "kqzWhei21q7w",
        "outputId": "42b5e4a2-ed5f-4f72-a854-1e7ce1ba9fa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                    1977     1978     1979     1980         1981  \\\n",
              "API                                                                    \n",
              "42371323150300  19226.333333  10005.5  7778.25  5028.25  6272.083333   \n",
              "42371334530200      0.000000      0.0     0.00     0.00  6557.166667   \n",
              "42371337890300      0.000000      0.0     0.00     0.00     0.000000   \n",
              "33025006260100      0.000000      0.0     0.00     0.00     0.000000   \n",
              "33025006080100      0.000000      0.0     0.00     0.00     0.000000   \n",
              "...                      ...      ...      ...      ...          ...   \n",
              "42495317050100      0.000000      0.0     0.00     0.00     0.000000   \n",
              "42389348680000      0.000000      0.0     0.00     0.00     0.000000   \n",
              "42389348690000      0.000000      0.0     0.00     0.00     0.000000   \n",
              "42389348390000      0.000000      0.0     0.00     0.00     0.000000   \n",
              "42301324920100      0.000000      0.0     0.00     0.00     0.000000   \n",
              "\n",
              "Year                   1982         1983         1984         1985  \\\n",
              "API                                                                  \n",
              "42371323150300  6116.666667  1225.500000    13.583333   935.166667   \n",
              "42371334530200  6464.000000  8513.333333  6613.500000  5176.166667   \n",
              "42371337890300  1010.000000   708.833333  3862.083333  6022.583333   \n",
              "33025006260100     0.000000     0.000000     0.000000     0.000000   \n",
              "33025006080100     0.000000     0.000000     0.000000     0.000000   \n",
              "...                     ...          ...          ...          ...   \n",
              "42495317050100     0.000000     0.000000     0.000000     0.000000   \n",
              "42389348680000     0.000000     0.000000     0.000000     0.000000   \n",
              "42389348690000     0.000000     0.000000     0.000000     0.000000   \n",
              "42389348390000     0.000000     0.000000     0.000000     0.000000   \n",
              "42301324920100     0.000000     0.000000     0.000000     0.000000   \n",
              "\n",
              "Year                   1986  ...         2010         2011         2012  \\\n",
              "API                          ...                                          \n",
              "42371323150300   389.750000  ...  1257.833333   434.583333   504.000000   \n",
              "42371334530200  3899.583333  ...  1602.916667  1215.666667   701.000000   \n",
              "42371337890300  6406.666667  ...   587.416667   425.916667   356.333333   \n",
              "33025006260100     0.000000  ...  1463.916667  1215.666667  1059.083333   \n",
              "33025006080100     0.000000  ...  1088.583333   967.583333   686.333333   \n",
              "...                     ...  ...          ...          ...          ...   \n",
              "42495317050100     0.000000  ...     0.000000     0.000000     0.000000   \n",
              "42389348680000     0.000000  ...     0.000000     0.000000     0.000000   \n",
              "42389348690000     0.000000  ...     0.000000     0.000000     0.000000   \n",
              "42389348390000     0.000000  ...     0.000000     0.000000     0.000000   \n",
              "42301324920100     0.000000  ...     0.000000     0.000000     0.000000   \n",
              "\n",
              "Year                  2013         2014          2015          2016  \\\n",
              "API                                                                   \n",
              "42371323150300  683.500000  1530.333333    447.666667    892.583333   \n",
              "42371334530200  378.000000   114.500000    216.583333    172.750000   \n",
              "42371337890300  386.000000  1272.416667   3114.666667      0.000000   \n",
              "33025006260100  956.083333   889.000000   6919.083333   6032.750000   \n",
              "33025006080100  691.500000   647.000000   3962.750000   5139.000000   \n",
              "...                    ...          ...           ...           ...   \n",
              "42495317050100    0.000000     0.000000   6561.000000   3467.583333   \n",
              "42389348680000    0.000000     0.000000  22935.600000   9824.250000   \n",
              "42389348690000    0.000000     0.000000  16845.800000   4026.583333   \n",
              "42389348390000    0.000000     0.000000  33203.500000  13456.000000   \n",
              "42301324920100    0.000000     0.000000   7537.142857   3740.666667   \n",
              "\n",
              "Year                   2017         2018    2019  \n",
              "API                                               \n",
              "42371323150300  1196.583333   231.000000     0.0  \n",
              "42371334530200   216.750000  2093.000000     0.0  \n",
              "42371337890300     0.000000   966.333333     0.0  \n",
              "33025006260100  4575.083333  3118.333333  3143.0  \n",
              "33025006080100  3265.416667  1896.750000  2178.0  \n",
              "...                     ...          ...     ...  \n",
              "42495317050100  1348.230769  1005.833333     0.0  \n",
              "42389348680000  5501.916667  1370.333333     0.0  \n",
              "42389348690000  2621.000000  1492.583333     0.0  \n",
              "42389348390000  7560.666667  3014.000000     0.0  \n",
              "42301324920100  2206.083333  3874.583333     0.0  \n",
              "\n",
              "[7844 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3926c88e-6f4c-4134-b9af-432342c99589\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>API</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42371323150300</th>\n",
              "      <td>19226.333333</td>\n",
              "      <td>10005.5</td>\n",
              "      <td>7778.25</td>\n",
              "      <td>5028.25</td>\n",
              "      <td>6272.083333</td>\n",
              "      <td>6116.666667</td>\n",
              "      <td>1225.500000</td>\n",
              "      <td>13.583333</td>\n",
              "      <td>935.166667</td>\n",
              "      <td>389.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>1257.833333</td>\n",
              "      <td>434.583333</td>\n",
              "      <td>504.000000</td>\n",
              "      <td>683.500000</td>\n",
              "      <td>1530.333333</td>\n",
              "      <td>447.666667</td>\n",
              "      <td>892.583333</td>\n",
              "      <td>1196.583333</td>\n",
              "      <td>231.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371334530200</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6557.166667</td>\n",
              "      <td>6464.000000</td>\n",
              "      <td>8513.333333</td>\n",
              "      <td>6613.500000</td>\n",
              "      <td>5176.166667</td>\n",
              "      <td>3899.583333</td>\n",
              "      <td>...</td>\n",
              "      <td>1602.916667</td>\n",
              "      <td>1215.666667</td>\n",
              "      <td>701.000000</td>\n",
              "      <td>378.000000</td>\n",
              "      <td>114.500000</td>\n",
              "      <td>216.583333</td>\n",
              "      <td>172.750000</td>\n",
              "      <td>216.750000</td>\n",
              "      <td>2093.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371337890300</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1010.000000</td>\n",
              "      <td>708.833333</td>\n",
              "      <td>3862.083333</td>\n",
              "      <td>6022.583333</td>\n",
              "      <td>6406.666667</td>\n",
              "      <td>...</td>\n",
              "      <td>587.416667</td>\n",
              "      <td>425.916667</td>\n",
              "      <td>356.333333</td>\n",
              "      <td>386.000000</td>\n",
              "      <td>1272.416667</td>\n",
              "      <td>3114.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>966.333333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006260100</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1463.916667</td>\n",
              "      <td>1215.666667</td>\n",
              "      <td>1059.083333</td>\n",
              "      <td>956.083333</td>\n",
              "      <td>889.000000</td>\n",
              "      <td>6919.083333</td>\n",
              "      <td>6032.750000</td>\n",
              "      <td>4575.083333</td>\n",
              "      <td>3118.333333</td>\n",
              "      <td>3143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006080100</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1088.583333</td>\n",
              "      <td>967.583333</td>\n",
              "      <td>686.333333</td>\n",
              "      <td>691.500000</td>\n",
              "      <td>647.000000</td>\n",
              "      <td>3962.750000</td>\n",
              "      <td>5139.000000</td>\n",
              "      <td>3265.416667</td>\n",
              "      <td>1896.750000</td>\n",
              "      <td>2178.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42495317050100</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6561.000000</td>\n",
              "      <td>3467.583333</td>\n",
              "      <td>1348.230769</td>\n",
              "      <td>1005.833333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348680000</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22935.600000</td>\n",
              "      <td>9824.250000</td>\n",
              "      <td>5501.916667</td>\n",
              "      <td>1370.333333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348690000</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16845.800000</td>\n",
              "      <td>4026.583333</td>\n",
              "      <td>2621.000000</td>\n",
              "      <td>1492.583333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348390000</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>33203.500000</td>\n",
              "      <td>13456.000000</td>\n",
              "      <td>7560.666667</td>\n",
              "      <td>3014.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42301324920100</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7537.142857</td>\n",
              "      <td>3740.666667</td>\n",
              "      <td>2206.083333</td>\n",
              "      <td>3874.583333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7844 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3926c88e-6f4c-4134-b9af-432342c99589')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3926c88e-6f4c-4134-b9af-432342c99589 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3926c88e-6f4c-4134-b9af-432342c99589');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "API_Tot_Gas_Prod = Sort.pivot_table('Gas', 'API', 'Year', sort = False)\n",
        "\n",
        "API_Tot_Gas_Prod"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "KOLU8Vck24qN",
        "outputId": "8eaa7e80-228e-4b75-a130-917ef05edafb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                   1977    1978        1979    1980        1981  \\\n",
              "API                                                                   \n",
              "42371323150300  1183.666667  881.25  723.083333  436.25  293.416667   \n",
              "42371334530200          NaN     NaN         NaN     NaN  442.000000   \n",
              "42371337890300          NaN     NaN         NaN     NaN         NaN   \n",
              "33025006260100          NaN     NaN         NaN     NaN         NaN   \n",
              "33025006080100          NaN     NaN         NaN     NaN         NaN   \n",
              "...                     ...     ...         ...     ...         ...   \n",
              "42495317050100          NaN     NaN         NaN     NaN         NaN   \n",
              "42389348680000          NaN     NaN         NaN     NaN         NaN   \n",
              "42389348690000          NaN     NaN         NaN     NaN         NaN   \n",
              "42389348390000          NaN     NaN         NaN     NaN         NaN   \n",
              "42301324920100          NaN     NaN         NaN     NaN         NaN   \n",
              "\n",
              "Year                  1982         1983         1984         1985  \\\n",
              "API                                                                 \n",
              "42371323150300  727.333333  1511.250000   134.166667  2501.833333   \n",
              "42371334530200  290.250000   347.416667   657.000000   687.166667   \n",
              "42371337890300    0.000000    47.333333  1960.583333  1503.250000   \n",
              "33025006260100         NaN          NaN          NaN          NaN   \n",
              "33025006080100         NaN          NaN          NaN          NaN   \n",
              "...                    ...          ...          ...          ...   \n",
              "42495317050100         NaN          NaN          NaN          NaN   \n",
              "42389348680000         NaN          NaN          NaN          NaN   \n",
              "42389348690000         NaN          NaN          NaN          NaN   \n",
              "42389348390000         NaN          NaN          NaN          NaN   \n",
              "42301324920100         NaN          NaN          NaN          NaN   \n",
              "\n",
              "Year                   1986  ...          2010          2011         2012  \\\n",
              "API                          ...                                            \n",
              "42371323150300  2924.750000  ...  10608.916667   6722.583333  6908.250000   \n",
              "42371334530200  2882.750000  ...  10543.000000  10583.250000  6540.500000   \n",
              "42371337890300  4841.916667  ...   2200.500000   1547.083333  1716.666667   \n",
              "33025006260100          NaN  ...   1245.000000   1091.583333   977.666667   \n",
              "33025006080100          NaN  ...    840.750000    753.750000   512.583333   \n",
              "...                     ...  ...           ...           ...          ...   \n",
              "42495317050100          NaN  ...           NaN           NaN          NaN   \n",
              "42389348680000          NaN  ...           NaN           NaN          NaN   \n",
              "42389348690000          NaN  ...           NaN           NaN          NaN   \n",
              "42389348390000          NaN  ...           NaN           NaN          NaN   \n",
              "42301324920100          NaN  ...           NaN           NaN          NaN   \n",
              "\n",
              "Year                   2013          2014           2015          2016  \\\n",
              "API                                                                      \n",
              "42371323150300  7911.916667   5851.500000    2488.916667   1908.000000   \n",
              "42371334530200  2581.583333    390.750000     976.000000    955.750000   \n",
              "42371337890300  2551.000000  16995.916667   38615.083333      0.000000   \n",
              "33025006260100   910.083333    845.583333    5480.666667   4931.000000   \n",
              "33025006080100   523.750000    439.083333    2162.333333   3700.833333   \n",
              "...                     ...           ...            ...           ...   \n",
              "42495317050100          NaN           NaN   36537.200000  28463.166667   \n",
              "42389348680000          NaN           NaN  101652.200000  45271.333333   \n",
              "42389348690000          NaN           NaN   72174.600000  16189.583333   \n",
              "42389348390000          NaN           NaN  151218.000000  63754.833333   \n",
              "42301324920100          NaN           NaN   11201.857143   4786.333333   \n",
              "\n",
              "Year                    2017          2018    2019  \n",
              "API                                                 \n",
              "42371323150300   1947.083333    786.916667     NaN  \n",
              "42371334530200   3693.916667   5503.250000     NaN  \n",
              "42371337890300      0.000000   7483.416667     NaN  \n",
              "33025006260100   5002.750000   2877.666667  2217.0  \n",
              "33025006080100   4313.583333   1963.583333  2391.0  \n",
              "...                      ...           ...     ...  \n",
              "42495317050100   5971.384615   2818.083333     NaN  \n",
              "42389348680000  28294.250000  11762.500000     NaN  \n",
              "42389348690000  17192.833333   9998.250000     NaN  \n",
              "42389348390000  38071.333333  14867.916667     NaN  \n",
              "42301324920100   3998.583333   6177.583333     NaN  \n",
              "\n",
              "[7844 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-267e9037-c4ae-4d8a-a530-7387e943d56b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>API</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42371323150300</th>\n",
              "      <td>1183.666667</td>\n",
              "      <td>881.25</td>\n",
              "      <td>723.083333</td>\n",
              "      <td>436.25</td>\n",
              "      <td>293.416667</td>\n",
              "      <td>727.333333</td>\n",
              "      <td>1511.250000</td>\n",
              "      <td>134.166667</td>\n",
              "      <td>2501.833333</td>\n",
              "      <td>2924.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>10608.916667</td>\n",
              "      <td>6722.583333</td>\n",
              "      <td>6908.250000</td>\n",
              "      <td>7911.916667</td>\n",
              "      <td>5851.500000</td>\n",
              "      <td>2488.916667</td>\n",
              "      <td>1908.000000</td>\n",
              "      <td>1947.083333</td>\n",
              "      <td>786.916667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371334530200</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>442.000000</td>\n",
              "      <td>290.250000</td>\n",
              "      <td>347.416667</td>\n",
              "      <td>657.000000</td>\n",
              "      <td>687.166667</td>\n",
              "      <td>2882.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>10543.000000</td>\n",
              "      <td>10583.250000</td>\n",
              "      <td>6540.500000</td>\n",
              "      <td>2581.583333</td>\n",
              "      <td>390.750000</td>\n",
              "      <td>976.000000</td>\n",
              "      <td>955.750000</td>\n",
              "      <td>3693.916667</td>\n",
              "      <td>5503.250000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371337890300</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>47.333333</td>\n",
              "      <td>1960.583333</td>\n",
              "      <td>1503.250000</td>\n",
              "      <td>4841.916667</td>\n",
              "      <td>...</td>\n",
              "      <td>2200.500000</td>\n",
              "      <td>1547.083333</td>\n",
              "      <td>1716.666667</td>\n",
              "      <td>2551.000000</td>\n",
              "      <td>16995.916667</td>\n",
              "      <td>38615.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7483.416667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006260100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1245.000000</td>\n",
              "      <td>1091.583333</td>\n",
              "      <td>977.666667</td>\n",
              "      <td>910.083333</td>\n",
              "      <td>845.583333</td>\n",
              "      <td>5480.666667</td>\n",
              "      <td>4931.000000</td>\n",
              "      <td>5002.750000</td>\n",
              "      <td>2877.666667</td>\n",
              "      <td>2217.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006080100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>840.750000</td>\n",
              "      <td>753.750000</td>\n",
              "      <td>512.583333</td>\n",
              "      <td>523.750000</td>\n",
              "      <td>439.083333</td>\n",
              "      <td>2162.333333</td>\n",
              "      <td>3700.833333</td>\n",
              "      <td>4313.583333</td>\n",
              "      <td>1963.583333</td>\n",
              "      <td>2391.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42495317050100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36537.200000</td>\n",
              "      <td>28463.166667</td>\n",
              "      <td>5971.384615</td>\n",
              "      <td>2818.083333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348680000</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>101652.200000</td>\n",
              "      <td>45271.333333</td>\n",
              "      <td>28294.250000</td>\n",
              "      <td>11762.500000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348690000</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72174.600000</td>\n",
              "      <td>16189.583333</td>\n",
              "      <td>17192.833333</td>\n",
              "      <td>9998.250000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348390000</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>151218.000000</td>\n",
              "      <td>63754.833333</td>\n",
              "      <td>38071.333333</td>\n",
              "      <td>14867.916667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42301324920100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11201.857143</td>\n",
              "      <td>4786.333333</td>\n",
              "      <td>3998.583333</td>\n",
              "      <td>6177.583333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7844 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-267e9037-c4ae-4d8a-a530-7387e943d56b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-267e9037-c4ae-4d8a-a530-7387e943d56b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-267e9037-c4ae-4d8a-a530-7387e943d56b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "API_Tot_Gas_Prod.fillna(0, inplace=True)\n",
        "\n",
        "API_Tot_Gas_Prod"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "ThN-2c3w3UvB",
        "outputId": "7760d579-3f66-4e1d-aa9c-664f91162bde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                   1977    1978        1979    1980        1981  \\\n",
              "API                                                                   \n",
              "42371323150300  1183.666667  881.25  723.083333  436.25  293.416667   \n",
              "42371334530200     0.000000    0.00    0.000000    0.00  442.000000   \n",
              "42371337890300     0.000000    0.00    0.000000    0.00    0.000000   \n",
              "33025006260100     0.000000    0.00    0.000000    0.00    0.000000   \n",
              "33025006080100     0.000000    0.00    0.000000    0.00    0.000000   \n",
              "...                     ...     ...         ...     ...         ...   \n",
              "42495317050100     0.000000    0.00    0.000000    0.00    0.000000   \n",
              "42389348680000     0.000000    0.00    0.000000    0.00    0.000000   \n",
              "42389348690000     0.000000    0.00    0.000000    0.00    0.000000   \n",
              "42389348390000     0.000000    0.00    0.000000    0.00    0.000000   \n",
              "42301324920100     0.000000    0.00    0.000000    0.00    0.000000   \n",
              "\n",
              "Year                  1982         1983         1984         1985  \\\n",
              "API                                                                 \n",
              "42371323150300  727.333333  1511.250000   134.166667  2501.833333   \n",
              "42371334530200  290.250000   347.416667   657.000000   687.166667   \n",
              "42371337890300    0.000000    47.333333  1960.583333  1503.250000   \n",
              "33025006260100    0.000000     0.000000     0.000000     0.000000   \n",
              "33025006080100    0.000000     0.000000     0.000000     0.000000   \n",
              "...                    ...          ...          ...          ...   \n",
              "42495317050100    0.000000     0.000000     0.000000     0.000000   \n",
              "42389348680000    0.000000     0.000000     0.000000     0.000000   \n",
              "42389348690000    0.000000     0.000000     0.000000     0.000000   \n",
              "42389348390000    0.000000     0.000000     0.000000     0.000000   \n",
              "42301324920100    0.000000     0.000000     0.000000     0.000000   \n",
              "\n",
              "Year                   1986  ...          2010          2011         2012  \\\n",
              "API                          ...                                            \n",
              "42371323150300  2924.750000  ...  10608.916667   6722.583333  6908.250000   \n",
              "42371334530200  2882.750000  ...  10543.000000  10583.250000  6540.500000   \n",
              "42371337890300  4841.916667  ...   2200.500000   1547.083333  1716.666667   \n",
              "33025006260100     0.000000  ...   1245.000000   1091.583333   977.666667   \n",
              "33025006080100     0.000000  ...    840.750000    753.750000   512.583333   \n",
              "...                     ...  ...           ...           ...          ...   \n",
              "42495317050100     0.000000  ...      0.000000      0.000000     0.000000   \n",
              "42389348680000     0.000000  ...      0.000000      0.000000     0.000000   \n",
              "42389348690000     0.000000  ...      0.000000      0.000000     0.000000   \n",
              "42389348390000     0.000000  ...      0.000000      0.000000     0.000000   \n",
              "42301324920100     0.000000  ...      0.000000      0.000000     0.000000   \n",
              "\n",
              "Year                   2013          2014           2015          2016  \\\n",
              "API                                                                      \n",
              "42371323150300  7911.916667   5851.500000    2488.916667   1908.000000   \n",
              "42371334530200  2581.583333    390.750000     976.000000    955.750000   \n",
              "42371337890300  2551.000000  16995.916667   38615.083333      0.000000   \n",
              "33025006260100   910.083333    845.583333    5480.666667   4931.000000   \n",
              "33025006080100   523.750000    439.083333    2162.333333   3700.833333   \n",
              "...                     ...           ...            ...           ...   \n",
              "42495317050100     0.000000      0.000000   36537.200000  28463.166667   \n",
              "42389348680000     0.000000      0.000000  101652.200000  45271.333333   \n",
              "42389348690000     0.000000      0.000000   72174.600000  16189.583333   \n",
              "42389348390000     0.000000      0.000000  151218.000000  63754.833333   \n",
              "42301324920100     0.000000      0.000000   11201.857143   4786.333333   \n",
              "\n",
              "Year                    2017          2018    2019  \n",
              "API                                                 \n",
              "42371323150300   1947.083333    786.916667     0.0  \n",
              "42371334530200   3693.916667   5503.250000     0.0  \n",
              "42371337890300      0.000000   7483.416667     0.0  \n",
              "33025006260100   5002.750000   2877.666667  2217.0  \n",
              "33025006080100   4313.583333   1963.583333  2391.0  \n",
              "...                      ...           ...     ...  \n",
              "42495317050100   5971.384615   2818.083333     0.0  \n",
              "42389348680000  28294.250000  11762.500000     0.0  \n",
              "42389348690000  17192.833333   9998.250000     0.0  \n",
              "42389348390000  38071.333333  14867.916667     0.0  \n",
              "42301324920100   3998.583333   6177.583333     0.0  \n",
              "\n",
              "[7844 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d7938842-204c-4023-8328-f906bd95d9ae\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>API</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42371323150300</th>\n",
              "      <td>1183.666667</td>\n",
              "      <td>881.25</td>\n",
              "      <td>723.083333</td>\n",
              "      <td>436.25</td>\n",
              "      <td>293.416667</td>\n",
              "      <td>727.333333</td>\n",
              "      <td>1511.250000</td>\n",
              "      <td>134.166667</td>\n",
              "      <td>2501.833333</td>\n",
              "      <td>2924.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>10608.916667</td>\n",
              "      <td>6722.583333</td>\n",
              "      <td>6908.250000</td>\n",
              "      <td>7911.916667</td>\n",
              "      <td>5851.500000</td>\n",
              "      <td>2488.916667</td>\n",
              "      <td>1908.000000</td>\n",
              "      <td>1947.083333</td>\n",
              "      <td>786.916667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371334530200</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>442.000000</td>\n",
              "      <td>290.250000</td>\n",
              "      <td>347.416667</td>\n",
              "      <td>657.000000</td>\n",
              "      <td>687.166667</td>\n",
              "      <td>2882.750000</td>\n",
              "      <td>...</td>\n",
              "      <td>10543.000000</td>\n",
              "      <td>10583.250000</td>\n",
              "      <td>6540.500000</td>\n",
              "      <td>2581.583333</td>\n",
              "      <td>390.750000</td>\n",
              "      <td>976.000000</td>\n",
              "      <td>955.750000</td>\n",
              "      <td>3693.916667</td>\n",
              "      <td>5503.250000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371337890300</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>47.333333</td>\n",
              "      <td>1960.583333</td>\n",
              "      <td>1503.250000</td>\n",
              "      <td>4841.916667</td>\n",
              "      <td>...</td>\n",
              "      <td>2200.500000</td>\n",
              "      <td>1547.083333</td>\n",
              "      <td>1716.666667</td>\n",
              "      <td>2551.000000</td>\n",
              "      <td>16995.916667</td>\n",
              "      <td>38615.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7483.416667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006260100</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1245.000000</td>\n",
              "      <td>1091.583333</td>\n",
              "      <td>977.666667</td>\n",
              "      <td>910.083333</td>\n",
              "      <td>845.583333</td>\n",
              "      <td>5480.666667</td>\n",
              "      <td>4931.000000</td>\n",
              "      <td>5002.750000</td>\n",
              "      <td>2877.666667</td>\n",
              "      <td>2217.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006080100</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>840.750000</td>\n",
              "      <td>753.750000</td>\n",
              "      <td>512.583333</td>\n",
              "      <td>523.750000</td>\n",
              "      <td>439.083333</td>\n",
              "      <td>2162.333333</td>\n",
              "      <td>3700.833333</td>\n",
              "      <td>4313.583333</td>\n",
              "      <td>1963.583333</td>\n",
              "      <td>2391.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42495317050100</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>36537.200000</td>\n",
              "      <td>28463.166667</td>\n",
              "      <td>5971.384615</td>\n",
              "      <td>2818.083333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348680000</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>101652.200000</td>\n",
              "      <td>45271.333333</td>\n",
              "      <td>28294.250000</td>\n",
              "      <td>11762.500000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348690000</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>72174.600000</td>\n",
              "      <td>16189.583333</td>\n",
              "      <td>17192.833333</td>\n",
              "      <td>9998.250000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348390000</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>151218.000000</td>\n",
              "      <td>63754.833333</td>\n",
              "      <td>38071.333333</td>\n",
              "      <td>14867.916667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42301324920100</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11201.857143</td>\n",
              "      <td>4786.333333</td>\n",
              "      <td>3998.583333</td>\n",
              "      <td>6177.583333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7844 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7938842-204c-4023-8328-f906bd95d9ae')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d7938842-204c-4023-8328-f906bd95d9ae button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d7938842-204c-4023-8328-f906bd95d9ae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "API_Tot_Water_Prod = Sort.pivot_table('Water', 'API', 'Year', sort=False)\n",
        "\n",
        "API_Tot_Water_Prod"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "2YaQZ05-3lgg",
        "outputId": "77f5163d-f5e8-4621-a30d-795bc66ac173"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year            1977  1978  1979  1980  1981  1982       1983        1984  \\\n",
              "API                                                                         \n",
              "42371323150300   0.0   0.0   0.0   0.0   0.0   0.0  15.833333    0.000000   \n",
              "42371334530200   NaN   NaN   NaN   NaN   0.0   0.0   0.000000  238.583333   \n",
              "42371337890300   NaN   NaN   NaN   NaN   NaN   0.0   0.000000    0.000000   \n",
              "33025006260100   NaN   NaN   NaN   NaN   NaN   NaN        NaN         NaN   \n",
              "33025006080100   NaN   NaN   NaN   NaN   NaN   NaN        NaN         NaN   \n",
              "...              ...   ...   ...   ...   ...   ...        ...         ...   \n",
              "42495317050100   NaN   NaN   NaN   NaN   NaN   NaN        NaN         NaN   \n",
              "42389348680000   NaN   NaN   NaN   NaN   NaN   NaN        NaN         NaN   \n",
              "42389348690000   NaN   NaN   NaN   NaN   NaN   NaN        NaN         NaN   \n",
              "42389348390000   NaN   NaN   NaN   NaN   NaN   NaN        NaN         NaN   \n",
              "42301324920100   NaN   NaN   NaN   NaN   NaN   NaN        NaN         NaN   \n",
              "\n",
              "Year                   1985         1986  ...          2010          2011  \\\n",
              "API                                       ...                               \n",
              "42371323150300    23.916667     7.500000  ...   5450.583333    714.666667   \n",
              "42371334530200    36.666667     0.000000  ...  38626.083333  21239.083333   \n",
              "42371337890300  1314.333333  1885.583333  ...   1329.333333    110.666667   \n",
              "33025006260100          NaN          NaN  ...    331.166667    280.166667   \n",
              "33025006080100          NaN          NaN  ...    207.916667    203.666667   \n",
              "...                     ...          ...  ...           ...           ...   \n",
              "42495317050100          NaN          NaN  ...           NaN           NaN   \n",
              "42389348680000          NaN          NaN  ...           NaN           NaN   \n",
              "42389348690000          NaN          NaN  ...           NaN           NaN   \n",
              "42389348390000          NaN          NaN  ...           NaN           NaN   \n",
              "42301324920100          NaN          NaN  ...           NaN           NaN   \n",
              "\n",
              "Year                   2012         2013          2014           2015  \\\n",
              "API                                                                     \n",
              "42371323150300     6.583333   497.666667  11187.666667   21519.000000   \n",
              "42371334530200  5307.833333  9758.750000   8179.333333   23538.916667   \n",
              "42371337890300    15.083333    13.666667      2.166667       0.000000   \n",
              "33025006260100   244.416667   338.083333    212.000000    4181.666667   \n",
              "33025006080100   162.250000   155.250000    122.166667    2981.250000   \n",
              "...                     ...          ...           ...            ...   \n",
              "42495317050100          NaN          NaN           NaN       0.000000   \n",
              "42389348680000          NaN          NaN           NaN   94981.000000   \n",
              "42389348690000          NaN          NaN           NaN   71909.800000   \n",
              "42389348390000          NaN          NaN           NaN  211980.500000   \n",
              "42301324920100          NaN          NaN           NaN   37973.714286   \n",
              "\n",
              "Year                    2016          2017          2018    2019  \n",
              "API                                                               \n",
              "42371323150300  18305.750000  12492.583333  11000.416667     NaN  \n",
              "42371334530200  23635.083333  15210.083333  16504.916667     NaN  \n",
              "42371337890300      0.000000      0.000000   5333.833333     NaN  \n",
              "33025006260100   3049.250000   2167.750000   1987.833333  2720.0  \n",
              "33025006080100   2769.250000   1912.750000   1216.750000  1653.0  \n",
              "...                      ...           ...           ...     ...  \n",
              "42495317050100    958.083333   4093.230769   1931.833333     NaN  \n",
              "42389348680000  43688.083333  27304.750000  11351.083333     NaN  \n",
              "42389348690000  16951.416667  18001.833333  10468.666667     NaN  \n",
              "42389348390000  89372.833333  53369.166667  20842.166667     NaN  \n",
              "42301324920100  23387.916667  17390.583333  34764.916667     NaN  \n",
              "\n",
              "[7844 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-37f76d17-21f6-4e8e-b989-a715a98c9a47\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>API</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42371323150300</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.833333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.916667</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>...</td>\n",
              "      <td>5450.583333</td>\n",
              "      <td>714.666667</td>\n",
              "      <td>6.583333</td>\n",
              "      <td>497.666667</td>\n",
              "      <td>11187.666667</td>\n",
              "      <td>21519.000000</td>\n",
              "      <td>18305.750000</td>\n",
              "      <td>12492.583333</td>\n",
              "      <td>11000.416667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371334530200</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>238.583333</td>\n",
              "      <td>36.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>38626.083333</td>\n",
              "      <td>21239.083333</td>\n",
              "      <td>5307.833333</td>\n",
              "      <td>9758.750000</td>\n",
              "      <td>8179.333333</td>\n",
              "      <td>23538.916667</td>\n",
              "      <td>23635.083333</td>\n",
              "      <td>15210.083333</td>\n",
              "      <td>16504.916667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371337890300</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1314.333333</td>\n",
              "      <td>1885.583333</td>\n",
              "      <td>...</td>\n",
              "      <td>1329.333333</td>\n",
              "      <td>110.666667</td>\n",
              "      <td>15.083333</td>\n",
              "      <td>13.666667</td>\n",
              "      <td>2.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5333.833333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006260100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>331.166667</td>\n",
              "      <td>280.166667</td>\n",
              "      <td>244.416667</td>\n",
              "      <td>338.083333</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>4181.666667</td>\n",
              "      <td>3049.250000</td>\n",
              "      <td>2167.750000</td>\n",
              "      <td>1987.833333</td>\n",
              "      <td>2720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006080100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>207.916667</td>\n",
              "      <td>203.666667</td>\n",
              "      <td>162.250000</td>\n",
              "      <td>155.250000</td>\n",
              "      <td>122.166667</td>\n",
              "      <td>2981.250000</td>\n",
              "      <td>2769.250000</td>\n",
              "      <td>1912.750000</td>\n",
              "      <td>1216.750000</td>\n",
              "      <td>1653.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42495317050100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>958.083333</td>\n",
              "      <td>4093.230769</td>\n",
              "      <td>1931.833333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348680000</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>94981.000000</td>\n",
              "      <td>43688.083333</td>\n",
              "      <td>27304.750000</td>\n",
              "      <td>11351.083333</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348690000</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>71909.800000</td>\n",
              "      <td>16951.416667</td>\n",
              "      <td>18001.833333</td>\n",
              "      <td>10468.666667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348390000</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>211980.500000</td>\n",
              "      <td>89372.833333</td>\n",
              "      <td>53369.166667</td>\n",
              "      <td>20842.166667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42301324920100</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37973.714286</td>\n",
              "      <td>23387.916667</td>\n",
              "      <td>17390.583333</td>\n",
              "      <td>34764.916667</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7844 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37f76d17-21f6-4e8e-b989-a715a98c9a47')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-37f76d17-21f6-4e8e-b989-a715a98c9a47 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-37f76d17-21f6-4e8e-b989-a715a98c9a47');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "API_Tot_Water_Prod.fillna(0, inplace=True)\n",
        "\n",
        "API_Tot_Water_Prod"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "o9oFqnGn4Iqb",
        "outputId": "8201fed0-7e0a-4e51-d3dc-adde7e356024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year            1977  1978  1979  1980  1981  1982       1983        1984  \\\n",
              "API                                                                         \n",
              "42371323150300   0.0   0.0   0.0   0.0   0.0   0.0  15.833333    0.000000   \n",
              "42371334530200   0.0   0.0   0.0   0.0   0.0   0.0   0.000000  238.583333   \n",
              "42371337890300   0.0   0.0   0.0   0.0   0.0   0.0   0.000000    0.000000   \n",
              "33025006260100   0.0   0.0   0.0   0.0   0.0   0.0   0.000000    0.000000   \n",
              "33025006080100   0.0   0.0   0.0   0.0   0.0   0.0   0.000000    0.000000   \n",
              "...              ...   ...   ...   ...   ...   ...        ...         ...   \n",
              "42495317050100   0.0   0.0   0.0   0.0   0.0   0.0   0.000000    0.000000   \n",
              "42389348680000   0.0   0.0   0.0   0.0   0.0   0.0   0.000000    0.000000   \n",
              "42389348690000   0.0   0.0   0.0   0.0   0.0   0.0   0.000000    0.000000   \n",
              "42389348390000   0.0   0.0   0.0   0.0   0.0   0.0   0.000000    0.000000   \n",
              "42301324920100   0.0   0.0   0.0   0.0   0.0   0.0   0.000000    0.000000   \n",
              "\n",
              "Year                   1985         1986  ...          2010          2011  \\\n",
              "API                                       ...                               \n",
              "42371323150300    23.916667     7.500000  ...   5450.583333    714.666667   \n",
              "42371334530200    36.666667     0.000000  ...  38626.083333  21239.083333   \n",
              "42371337890300  1314.333333  1885.583333  ...   1329.333333    110.666667   \n",
              "33025006260100     0.000000     0.000000  ...    331.166667    280.166667   \n",
              "33025006080100     0.000000     0.000000  ...    207.916667    203.666667   \n",
              "...                     ...          ...  ...           ...           ...   \n",
              "42495317050100     0.000000     0.000000  ...      0.000000      0.000000   \n",
              "42389348680000     0.000000     0.000000  ...      0.000000      0.000000   \n",
              "42389348690000     0.000000     0.000000  ...      0.000000      0.000000   \n",
              "42389348390000     0.000000     0.000000  ...      0.000000      0.000000   \n",
              "42301324920100     0.000000     0.000000  ...      0.000000      0.000000   \n",
              "\n",
              "Year                   2012         2013          2014           2015  \\\n",
              "API                                                                     \n",
              "42371323150300     6.583333   497.666667  11187.666667   21519.000000   \n",
              "42371334530200  5307.833333  9758.750000   8179.333333   23538.916667   \n",
              "42371337890300    15.083333    13.666667      2.166667       0.000000   \n",
              "33025006260100   244.416667   338.083333    212.000000    4181.666667   \n",
              "33025006080100   162.250000   155.250000    122.166667    2981.250000   \n",
              "...                     ...          ...           ...            ...   \n",
              "42495317050100     0.000000     0.000000      0.000000       0.000000   \n",
              "42389348680000     0.000000     0.000000      0.000000   94981.000000   \n",
              "42389348690000     0.000000     0.000000      0.000000   71909.800000   \n",
              "42389348390000     0.000000     0.000000      0.000000  211980.500000   \n",
              "42301324920100     0.000000     0.000000      0.000000   37973.714286   \n",
              "\n",
              "Year                    2016          2017          2018    2019  \n",
              "API                                                               \n",
              "42371323150300  18305.750000  12492.583333  11000.416667     0.0  \n",
              "42371334530200  23635.083333  15210.083333  16504.916667     0.0  \n",
              "42371337890300      0.000000      0.000000   5333.833333     0.0  \n",
              "33025006260100   3049.250000   2167.750000   1987.833333  2720.0  \n",
              "33025006080100   2769.250000   1912.750000   1216.750000  1653.0  \n",
              "...                      ...           ...           ...     ...  \n",
              "42495317050100    958.083333   4093.230769   1931.833333     0.0  \n",
              "42389348680000  43688.083333  27304.750000  11351.083333     0.0  \n",
              "42389348690000  16951.416667  18001.833333  10468.666667     0.0  \n",
              "42389348390000  89372.833333  53369.166667  20842.166667     0.0  \n",
              "42301324920100  23387.916667  17390.583333  34764.916667     0.0  \n",
              "\n",
              "[7844 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8b3ba0a-0eb2-48cc-abe4-1fc08d1cc07c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Year</th>\n",
              "      <th>1977</th>\n",
              "      <th>1978</th>\n",
              "      <th>1979</th>\n",
              "      <th>1980</th>\n",
              "      <th>1981</th>\n",
              "      <th>1982</th>\n",
              "      <th>1983</th>\n",
              "      <th>1984</th>\n",
              "      <th>1985</th>\n",
              "      <th>1986</th>\n",
              "      <th>...</th>\n",
              "      <th>2010</th>\n",
              "      <th>2011</th>\n",
              "      <th>2012</th>\n",
              "      <th>2013</th>\n",
              "      <th>2014</th>\n",
              "      <th>2015</th>\n",
              "      <th>2016</th>\n",
              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>API</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42371323150300</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.833333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.916667</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>...</td>\n",
              "      <td>5450.583333</td>\n",
              "      <td>714.666667</td>\n",
              "      <td>6.583333</td>\n",
              "      <td>497.666667</td>\n",
              "      <td>11187.666667</td>\n",
              "      <td>21519.000000</td>\n",
              "      <td>18305.750000</td>\n",
              "      <td>12492.583333</td>\n",
              "      <td>11000.416667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371334530200</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>238.583333</td>\n",
              "      <td>36.666667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>38626.083333</td>\n",
              "      <td>21239.083333</td>\n",
              "      <td>5307.833333</td>\n",
              "      <td>9758.750000</td>\n",
              "      <td>8179.333333</td>\n",
              "      <td>23538.916667</td>\n",
              "      <td>23635.083333</td>\n",
              "      <td>15210.083333</td>\n",
              "      <td>16504.916667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42371337890300</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1314.333333</td>\n",
              "      <td>1885.583333</td>\n",
              "      <td>...</td>\n",
              "      <td>1329.333333</td>\n",
              "      <td>110.666667</td>\n",
              "      <td>15.083333</td>\n",
              "      <td>13.666667</td>\n",
              "      <td>2.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5333.833333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006260100</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>331.166667</td>\n",
              "      <td>280.166667</td>\n",
              "      <td>244.416667</td>\n",
              "      <td>338.083333</td>\n",
              "      <td>212.000000</td>\n",
              "      <td>4181.666667</td>\n",
              "      <td>3049.250000</td>\n",
              "      <td>2167.750000</td>\n",
              "      <td>1987.833333</td>\n",
              "      <td>2720.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33025006080100</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>207.916667</td>\n",
              "      <td>203.666667</td>\n",
              "      <td>162.250000</td>\n",
              "      <td>155.250000</td>\n",
              "      <td>122.166667</td>\n",
              "      <td>2981.250000</td>\n",
              "      <td>2769.250000</td>\n",
              "      <td>1912.750000</td>\n",
              "      <td>1216.750000</td>\n",
              "      <td>1653.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42495317050100</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>958.083333</td>\n",
              "      <td>4093.230769</td>\n",
              "      <td>1931.833333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348680000</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94981.000000</td>\n",
              "      <td>43688.083333</td>\n",
              "      <td>27304.750000</td>\n",
              "      <td>11351.083333</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348690000</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71909.800000</td>\n",
              "      <td>16951.416667</td>\n",
              "      <td>18001.833333</td>\n",
              "      <td>10468.666667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42389348390000</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>211980.500000</td>\n",
              "      <td>89372.833333</td>\n",
              "      <td>53369.166667</td>\n",
              "      <td>20842.166667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42301324920100</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>37973.714286</td>\n",
              "      <td>23387.916667</td>\n",
              "      <td>17390.583333</td>\n",
              "      <td>34764.916667</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7844 rows × 43 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8b3ba0a-0eb2-48cc-abe4-1fc08d1cc07c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8b3ba0a-0eb2-48cc-abe4-1fc08d1cc07c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8b3ba0a-0eb2-48cc-abe4-1fc08d1cc07c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "t4mBQawc4hvD",
        "outputId": "5f38ee86-52b5-41dd-cfc2-c9735c4c2ef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-52-02a506c29c24>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mGroup_API\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAPI_Tot_Liq_Prod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'API'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Liquid'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1536\u001b[0m                 \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1537\u001b[0m             )\n\u001b[0;32m-> 1538\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1539\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    220\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m                 \u001b[0mbad_keys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifference\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Columns not found: {str(bad_keys)[1:-1]}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gotitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"Columns not found: 'Liquid'\""
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1DDLxhzoPlswIJASDcWKN9v4W8s7YRWq4",
      "authorship_tag": "ABX9TyOVFcGj54cRGu3QziKQQZ3n",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}