Copy_of_Pandas_Task_3.ipynb

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    {
      "cell_type": "markdown",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/dmarinere/10academysolution/blob/master/Copy_of_Pandas_Task_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p7mcx5GODp1i",
        "colab_type": "text"
      },
      "source": [
        "# Task 3\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNNHH4AHDp1k",
        "colab_type": "text"
      },
      "source": [
        "The **pandas_tasks.csv** dataset contains demographic information and what financial services are used by approximately 10,000 individuals across Tanzania. This data was extracted from the FSDT Finscope 2017 survey. \n",
        "Data Source: zindi.africa. The dataset is in the same folder as this notebook. In the same folder you will find another file which contains an explanation to the variables in the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4WO_NKpDp1l",
        "colab_type": "text"
      },
      "source": [
        "#### Question 1. Group the pandas_tasks.csv by Q2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_PNvGk6MHTM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGRO5z2qNksE",
        "colab_type": "code",
        "colab": {
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          "height": 122
        },
        "outputId": "19b50fc9-0612-48d4-af94-5c45aeec4675"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyEu9LkzNzsC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df =  pd.read_csv(\"/content/drive/My Drive/Thursday-Week0/pandas_tasks.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJYXSWQ7Dp1l",
        "colab_type": "text"
      },
      "source": [
        "#### Question 2. Show the sum of all null values and drop all null values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqxAUGmqSX7j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mbXXH66nNJOD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "df.dropna(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOlK-ophO0gl",
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          "height": 224
        },
        "outputId": "c6dbce95-9607-461e-a8be-06b7061ead31"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "     ID  Q1  Q2  Q3  ...  savings  borrowing  insurance  mobile_money_classification\n",
              "0  5086  98   2   3  ...        0          0          0                            0\n",
              "1  1258  40   1   1  ...        1          1          0                            3\n",
              "2   331  18   2   4  ...        0          0          0                            2\n",
              "3  6729  50   1   1  ...        0          1          0                            3\n",
              "4  8671  34   1   1  ...        1          0          1                            3\n",
              "\n",
              "[5 rows x 37 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMP01lRNDp1m",
        "colab_type": "text"
      },
      "source": [
        "#### Question 3. Select the columns, [1:5], and 7 rows.                                                                                                    "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9wLgJFj2NJ4K",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "bdb397a1-aa22-4cf1-cef5-6665bcc79770"
      },
      "source": [
        "df_edit =df.iloc[1:8, 1:5]\n",
        "df_edit"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "   Q1  Q2  Q3  Q4\n",
              "1  40   1   1   3\n",
              "2  18   2   4   6\n",
              "3  50   1   1   3\n",
              "4  34   1   1   1\n",
              "5  35   1   1   3\n",
              "6  31   2   1   3\n",
              "7  23   2   4   5"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K6RJA2beDp1n",
        "colab_type": "text"
      },
      "source": [
        "#### Question 4. Using the melt() function, unpivot the result from question 3. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VUjOFv4zDp1o",
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          "height": 917
        },
        "outputId": "0b4ae73b-aaee-42d9-dcb5-6cf64e70bd05"
      },
      "source": [
        "df_edit.melt()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Q1</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Q1</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Q1</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Q2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Q2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Q2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Q2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Q2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Q2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Q2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Q3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Q3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Q3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Q3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Q3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Q3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Q3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Q4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Q4</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Q4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Q4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Q4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Q4</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Q4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   variable  value\n",
              "0        Q1     40\n",
              "1        Q1     18\n",
              "2        Q1     50\n",
              "3        Q1     34\n",
              "4        Q1     35\n",
              "5        Q1     31\n",
              "6        Q1     23\n",
              "7        Q2      1\n",
              "8        Q2      2\n",
              "9        Q2      1\n",
              "10       Q2      1\n",
              "11       Q2      1\n",
              "12       Q2      2\n",
              "13       Q2      2\n",
              "14       Q3      1\n",
              "15       Q3      4\n",
              "16       Q3      1\n",
              "17       Q3      1\n",
              "18       Q3      1\n",
              "19       Q3      1\n",
              "20       Q3      4\n",
              "21       Q4      3\n",
              "22       Q4      6\n",
              "23       Q4      3\n",
              "24       Q4      1\n",
              "25       Q4      3\n",
              "26       Q4      3\n",
              "27       Q4      5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnH0VZmnRjom",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}