Traffic accident analysis in France.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project : France Traffic DataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T18:18:52.169726Z",
     "start_time": "2022-09-20T18:18:52.159750Z"
    },
    "heading_collapsed": true
   },
   "source": [
    "## Introduction\n",
    "\n",
    "#### DataSet Columns:\n",
    "- This dataset presents the number of passenger validations per day per station name and per ticket on the rail network.\n",
    "     * DATE : Date of validation.\n",
    "     * STATION_NAME : validation's station name.\n",
    "     * ID_REFA_LDA : Stop identifier in the STIF reference database.\n",
    "     * TITLE_CATEGORY : Transport tickets( IMAGINE R,NAVIGO, AMETHYSTE, FGT , NOT DEFINED)\n",
    "     * NB_VALID : The number of passenger validations\n",
    "     \n",
    "#### Title_categories Column: \n",
    "\n",
    "   * IMAGINE R: combines the annual Imagine R School and Imagine R Student packages reserved for pupils, apprentices           and students which allows to travel at will all year round and in all of Ile-de-France.<br>\n",
    "   \n",
    "   * NAVIGO : includes the Navigo Annuel, Navigo Mois and Navigo Semaine packages.<br>\n",
    "   \n",
    "   * AMETHYSTE : includes the Amethyst packages: package reserved for seniors or disabled under conditions of means or          status, and residing in the Île-de-France region.<br>\n",
    "   \n",
    "   * TST : groups together weekly and monthly reduced fare packages granted to beneficiaries of the Transportation              Solidarity Reduction program, to travel within the selected zones in all the modes of transport in the Île-de-France        region.<br>\n",
    "   \n",
    "   * FGT : accounts for the Navigo Gratuité Transport Packages, a package that allows certain receiving social assistance        to travel free of charge throughout the Paris Region.<br>\n",
    "   \n",
    "   * OTHER TITLE : accounts for special packages.<br><br>\n",
    "   \n",
    "   * NON DEFINED : records validations for which the type of ticket is not defined (anomalies)<br>\n",
    "     \n",
    "     NB_VALID : Number of validations. 1 validation = 1 person <br>\n",
    "     ID REFA LDA : -1 means that the data is not defined\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:07.149703Z",
     "start_time": "2022-09-20T21:14:07.130756Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:09.784960Z",
     "start_time": "2022-09-20T21:14:09.347097Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>DATE</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>ID_REFA_LDA</th>\n",
       "      <th>TITLE_CATEGORY</th>\n",
       "      <th>NB_VALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21/07/2019</td>\n",
       "      <td>LA TOUR MAUBOURG</td>\n",
       "      <td>71242.0</td>\n",
       "      <td>NAVIGO</td>\n",
       "      <td>1141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21/07/2019</td>\n",
       "      <td>PARMENTIER</td>\n",
       "      <td>71801.0</td>\n",
       "      <td>NOT DEFINED</td>\n",
       "      <td>Less than 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21/07/2019</td>\n",
       "      <td>PARMENTIER</td>\n",
       "      <td>71801.0</td>\n",
       "      <td>TST</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21/07/2019</td>\n",
       "      <td>PEREIRE-LEVALLOIS</td>\n",
       "      <td>71453.0</td>\n",
       "      <td>FGT</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21/07/2019</td>\n",
       "      <td>PERNETY</td>\n",
       "      <td>412687.0</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE       STATION_NAME  ID_REFA_LDA TITLE_CATEGORY     NB_VALID\n",
       "0  21/07/2019   LA TOUR MAUBOURG      71242.0         NAVIGO         1141\n",
       "1  21/07/2019         PARMENTIER      71801.0    NOT DEFINED  Less than 5\n",
       "2  21/07/2019         PARMENTIER      71801.0            TST           97\n",
       "3  21/07/2019  PEREIRE-LEVALLOIS      71453.0            FGT           53\n",
       "4  21/07/2019            PERNETY     412687.0          OTHER           36"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:/Users/SHUBHAM SAINI/Desktop/Python/archive/Travel_titles_validations_in_Paris_and_suburbs.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring  Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:11.408619Z",
     "start_time": "2022-09-20T21:14:11.290934Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 883958 entries, 0 to 883957\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   DATE            883958 non-null  object \n",
      " 1   STATION_NAME    883958 non-null  object \n",
      " 2   ID_REFA_LDA     882459 non-null  float64\n",
      " 3   TITLE_CATEGORY  883958 non-null  object \n",
      " 4   NB_VALID        883958 non-null  object \n",
      "dtypes: float64(1), object(4)\n",
      "memory usage: 33.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>DATE</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>ID_REFA_LDA</th>\n",
       "      <th>TITLE_CATEGORY</th>\n",
       "      <th>NB_VALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>883958</td>\n",
       "      <td>883958</td>\n",
       "      <td>882459.000000</td>\n",
       "      <td>883958</td>\n",
       "      <td>883958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>184</td>\n",
       "      <td>686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>17497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>13/11/2019</td>\n",
       "      <td>GARE DE LYON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NAVIGO</td>\n",
       "      <td>Less than 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5704</td>\n",
       "      <td>4765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118260</td>\n",
       "      <td>128827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69150.277691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27606.821859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66338.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71158.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71756.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>415852.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              DATE  STATION_NAME    ID_REFA_LDA TITLE_CATEGORY     NB_VALID\n",
       "count       883958        883958  882459.000000         883958       883958\n",
       "unique         184           686            NaN              9        17497\n",
       "top     13/11/2019  GARE DE LYON            NaN         NAVIGO  Less than 5\n",
       "freq          5704          4765            NaN         118260       128827\n",
       "mean           NaN           NaN   69150.277691            NaN          NaN\n",
       "std            NaN           NaN   27606.821859            NaN          NaN\n",
       "min            NaN           NaN      -1.000000            NaN          NaN\n",
       "25%            NaN           NaN   66338.000000            NaN          NaN\n",
       "50%            NaN           NaN   71158.000000            NaN          NaN\n",
       "75%            NaN           NaN   71756.000000            NaN          NaN\n",
       "max            NaN           NaN  415852.000000            NaN          NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:14.414271Z",
     "start_time": "2022-09-20T21:14:14.405295Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(883958, 5)\n"
     ]
    }
   ],
   "source": [
    "originalShape = df.shape\n",
    "print(originalShape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:15.149786Z",
     "start_time": "2022-09-20T21:14:15.131838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DATE', 'STATION_NAME', 'ID_REFA_LDA', 'TITLE_CATEGORY', 'NB_VALID']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ColumnNames=df.columns.to_list()\n",
    "ColumnNames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:16.043598Z",
     "start_time": "2022-09-20T21:14:16.032661Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE               object\n",
       "STATION_NAME       object\n",
       "ID_REFA_LDA       float64\n",
       "TITLE_CATEGORY     object\n",
       "NB_VALID           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:18.262296Z",
     "start_time": "2022-09-20T21:14:18.064438Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE                 0\n",
       "STATION_NAME         0\n",
       "ID_REFA_LDA       1499\n",
       "TITLE_CATEGORY       0\n",
       "NB_VALID             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:19.379795Z",
     "start_time": "2022-09-20T21:14:19.159557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.isna().sum().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:20.286540Z",
     "start_time": "2022-09-20T21:14:20.114913Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TITLE_CATEGORY'].value_counts().sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:21.146320Z",
     "start_time": "2022-09-20T21:14:21.041494Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE              False\n",
       "STATION_NAME      False\n",
       "ID_REFA_LDA        True\n",
       "TITLE_CATEGORY    False\n",
       "NB_VALID          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cleaning Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:23.404138Z",
     "start_time": "2022-09-20T21:14:23.099075Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1241"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check Duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:28.657659Z",
     "start_time": "2022-09-20T21:14:28.318599Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(882717, 5)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove duplicate but leave one \n",
    "df.drop_duplicates(keep='first',inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:34.161346Z",
     "start_time": "2022-09-20T21:14:34.070997Z"
    }
   },
   "outputs": [],
   "source": [
    "#Drop -1 as it is a undefind value for the data so it looks like null value\n",
    "df.drop(df[df.ID_REFA_LDA == -1].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:35.958332Z",
     "start_time": "2022-09-20T21:14:35.801721Z"
    }
   },
   "outputs": [],
   "source": [
    "#Remove nulls row first\n",
    "df.dropna(axis=0,how='any',thresh=None,subset=None,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:38.980488Z",
     "start_time": "2022-09-20T21:14:38.941504Z"
    }
   },
   "outputs": [],
   "source": [
    "# Remove ID_REFA_LDA Column\n",
    "df=df.drop(['ID_REFA_LDA'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:44.892190Z",
     "start_time": "2022-09-20T21:14:44.833521Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NAVIGO\n",
       "Name: TITLE_CATEGORY, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most frequence of TITLE_CATEGORY Column\n",
    "df['TITLE_CATEGORY'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:46.736125Z",
     "start_time": "2022-09-20T21:14:46.648061Z"
    }
   },
   "outputs": [],
   "source": [
    "# fit TITLE_CATEGORY cloumn with most frequent\n",
    "imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')\n",
    "imp = imp.fit(df[['TITLE_CATEGORY']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:49.010353Z",
     "start_time": "2022-09-20T21:14:48.967014Z"
    }
   },
   "outputs": [],
   "source": [
    "df['TITLE_CATEGORY']= imp.transform(df[['TITLE_CATEGORY']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:51.184502Z",
     "start_time": "2022-09-20T21:14:51.081849Z"
    }
   },
   "outputs": [],
   "source": [
    "# replace value \"?\" with most frequent\n",
    "imp = SimpleImputer(missing_values='?', strategy='most_frequent')\n",
    "imp = imp.fit(df[['TITLE_CATEGORY']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:52.446066Z",
     "start_time": "2022-09-20T21:14:52.398537Z"
    }
   },
   "outputs": [],
   "source": [
    "df['TITLE_CATEGORY']= imp.transform(df[['TITLE_CATEGORY']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:53.917954Z",
     "start_time": "2022-09-20T21:14:53.837728Z"
    }
   },
   "outputs": [],
   "source": [
    "# Replace value \"less than 5\" with Constant value '2' in NB_VALID column\n",
    "df = df.replace('Less than 5', value='2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:55.119629Z",
     "start_time": "2022-09-20T21:14:54.914057Z"
    }
   },
   "outputs": [],
   "source": [
    "# fit NB_VALID cloumn with median\n",
    "imp = SimpleImputer(missing_values=np.nan, strategy='median')\n",
    "imp = imp.fit(df[['NB_VALID']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:56.210547Z",
     "start_time": "2022-09-20T21:14:56.075869Z"
    }
   },
   "outputs": [],
   "source": [
    "df['NB_VALID']= imp.transform(df[['NB_VALID']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:57.115387Z",
     "start_time": "2022-09-20T21:14:57.036588Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATE              0\n",
      "STATION_NAME      0\n",
      "TITLE_CATEGORY    0\n",
      "NB_VALID          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check missing values\n",
    "missing_values_count = df.isnull().sum()\n",
    "print(missing_values_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:14:58.004777Z",
     "start_time": "2022-09-20T21:14:57.988819Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(857008, 4)\n"
     ]
    }
   ],
   "source": [
    "newShape = df.shape\n",
    "print(newShape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:00.324738Z",
     "start_time": "2022-09-20T21:15:00.318736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26950"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rowDropped = originalShape[0] - newShape[0]\n",
    "rowDropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:01.637416Z",
     "start_time": "2022-09-20T21:15:01.633429Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0487873858260235"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of dropped data\n",
    "rowDropped*100/originalShape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:04.345387Z",
     "start_time": "2022-09-20T21:15:04.261691Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATE              0\n",
      "STATION_NAME      0\n",
      "TITLE_CATEGORY    0\n",
      "NB_VALID          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check missing values\n",
    "missing_values_count = df.isnull().sum()\n",
    "print(missing_values_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statstics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:10.968623Z",
     "start_time": "2022-09-20T21:15:10.726347Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>DATE</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>TITLE_CATEGORY</th>\n",
       "      <th>NB_VALID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>857008</td>\n",
       "      <td>857008</td>\n",
       "      <td>857008</td>\n",
       "      <td>857008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>184</td>\n",
       "      <td>668</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>28/11/2019</td>\n",
       "      <td>GARE DE LYON</td>\n",
       "      <td>NAVIGO</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5461</td>\n",
       "      <td>4735</td>\n",
       "      <td>215114</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>781.669664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2849.521297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>382.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125007.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              DATE  STATION_NAME TITLE_CATEGORY       NB_VALID\n",
       "count       857008        857008         857008  857008.000000\n",
       "unique         184           668              8            NaN\n",
       "top     28/11/2019  GARE DE LYON         NAVIGO            NaN\n",
       "freq          5461          4735         215114            NaN\n",
       "mean           NaN           NaN            NaN     781.669664\n",
       "std            NaN           NaN            NaN    2849.521297\n",
       "min            NaN           NaN            NaN       2.000000\n",
       "25%            NaN           NaN            NaN      14.000000\n",
       "50%            NaN           NaN            NaN      88.000000\n",
       "75%            NaN           NaN            NaN     382.000000\n",
       "max            NaN           NaN            NaN  125007.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:12.575859Z",
     "start_time": "2022-09-20T21:15:12.401014Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TITLE_CATEGORY  STATION_NAME           \n",
       "NAVIGO          LA DEFENSE-GRANDE ARCHE    1094\n",
       "                GARE DE LYON               1091\n",
       "                GARE DU NORD                736\n",
       "Name: STATION_NAME, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show the most frequent category depending on name station\n",
    "df.groupby([df['TITLE_CATEGORY'], df['STATION_NAME']])['STATION_NAME'].count().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:13.496040Z",
     "start_time": "2022-09-20T21:15:13.331446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['TITLE_CATEGORY'].value_counts().sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:15.058105Z",
     "start_time": "2022-09-20T21:15:14.981347Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION_NAME\n",
       "GARE DE LYON                        4735\n",
       "LA DEFENSE-GRANDE ARCHE             4713\n",
       "BIBLIOTHEQUE FRANCOIS MITTERRAND    3179\n",
       "SAINT-LAZARE                        3164\n",
       "GARE DU NORD                        3130\n",
       "PORTE MAILLOT                       3010\n",
       "GARE DE L'EST                       3009\n",
       "MONTPARNASSE                        2974\n",
       "NATION                              2888\n",
       "AUSTERLITZ                          2866\n",
       "Name: NB_VALID, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Showing the most recorded stations\n",
    "df.groupby(df['STATION_NAME'])['NB_VALID'].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:16.446874Z",
     "start_time": "2022-09-20T21:15:16.265356Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['STATION_NAME'].value_counts()[:10].sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:17.135194Z",
     "start_time": "2022-09-20T21:15:17.058100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION_NAME\n",
       "SAINT-LAZARE                        32477180.0\n",
       "GARE DE L'EST                       14853063.0\n",
       "GARE DU NORD                        14651759.0\n",
       "MONTPARNASSE                        13675157.0\n",
       "LA DEFENSE-GRANDE ARCHE             12303445.0\n",
       "GARE DE LYON                         7758245.0\n",
       "LES HALLES                           5833889.0\n",
       "BIBLIOTHEQUE FRANCOIS MITTERRAND     5747671.0\n",
       "REPUBLIQUE                           5549203.0\n",
       "CHATELET-LES HALLES                  5272189.0\n",
       "Name: NB_VALID, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Showing the sum of NB_VALID for every STATION_NAME\n",
    "df.groupby(df['STATION_NAME'])['NB_VALID'].sum().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract new columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T23:30:09.913501Z",
     "start_time": "2022-09-20T23:30:09.774872Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATE              datetime64[ns]\n",
      "STATION_NAME              object\n",
      "TITLE_CATEGORY            object\n",
      "NB_VALID                 float64\n",
      "dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/08/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/10/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/09/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/11/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '17/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '16/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '13/07/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '26/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '30/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '31/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '28/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '29/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '22/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '23/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '14/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '25/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '19/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '20/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\SHUBHAM SAINI\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/12/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n"
     ]
    }
   ],
   "source": [
    "#Convert DATE Column to datetime DataType\n",
    "df[\"DATE\"] = pd.to_datetime(df[\"DATE\"])\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:26.336385Z",
     "start_time": "2022-09-20T21:15:26.218982Z"
    }
   },
   "outputs": [],
   "source": [
    "#Extract MONTH and DAY columns from DATE column\n",
    "df['MONTH'] = df['DATE'].dt.month\n",
    "df['DAY'] = df['DATE'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:28.540766Z",
     "start_time": "2022-09-20T21:15:28.529793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>DATE</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>TITLE_CATEGORY</th>\n",
       "      <th>NB_VALID</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>LA TOUR MAUBOURG</td>\n",
       "      <td>NAVIGO</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PARMENTIER</td>\n",
       "      <td>NOT DEFINED</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PARMENTIER</td>\n",
       "      <td>TST</td>\n",
       "      <td>97.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PEREIRE-LEVALLOIS</td>\n",
       "      <td>FGT</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PERNETY</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE       STATION_NAME TITLE_CATEGORY  NB_VALID  MONTH  DAY\n",
       "0 2019-07-21   LA TOUR MAUBOURG         NAVIGO    1141.0      7   21\n",
       "1 2019-07-21         PARMENTIER    NOT DEFINED       2.0      7   21\n",
       "2 2019-07-21         PARMENTIER            TST      97.0      7   21\n",
       "3 2019-07-21  PEREIRE-LEVALLOIS            FGT      53.0      7   21\n",
       "4 2019-07-21            PERNETY          OTHER      36.0      7   21"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:32.705181Z",
     "start_time": "2022-09-20T21:15:32.634451Z"
    }
   },
   "outputs": [],
   "source": [
    "#Add DAY_OF_WEEK column\n",
    "df['DAY_OF_WEEK'] = df['DATE'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:34.030439Z",
     "start_time": "2022-09-20T21:15:33.865384Z"
    }
   },
   "outputs": [],
   "source": [
    "#Add DAY_NAME column\n",
    "df['DAY_NAME'] = df['DATE'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:15:34.871286Z",
     "start_time": "2022-09-20T21:15:34.857330Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>DATE</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>TITLE_CATEGORY</th>\n",
       "      <th>NB_VALID</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>DAY_NAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>LA TOUR MAUBOURG</td>\n",
       "      <td>NAVIGO</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PARMENTIER</td>\n",
       "      <td>NOT DEFINED</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PARMENTIER</td>\n",
       "      <td>TST</td>\n",
       "      <td>97.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PEREIRE-LEVALLOIS</td>\n",
       "      <td>FGT</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PERNETY</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PICPUS</td>\n",
       "      <td>FGT</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PIERRE CURIE</td>\n",
       "      <td>IMAGINE R</td>\n",
       "      <td>264.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PIGALLE</td>\n",
       "      <td>DAILY NAVIGO</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PLACE DES FETES</td>\n",
       "      <td>NAVIGO</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PLACE DES FETES</td>\n",
       "      <td>AMETHYSTE</td>\n",
       "      <td>297.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE       STATION_NAME TITLE_CATEGORY  NB_VALID  MONTH  DAY  \\\n",
       "0 2019-07-21   LA TOUR MAUBOURG         NAVIGO    1141.0      7   21   \n",
       "1 2019-07-21         PARMENTIER    NOT DEFINED       2.0      7   21   \n",
       "2 2019-07-21         PARMENTIER            TST      97.0      7   21   \n",
       "3 2019-07-21  PEREIRE-LEVALLOIS            FGT      53.0      7   21   \n",
       "4 2019-07-21            PERNETY          OTHER      36.0      7   21   \n",
       "5 2019-07-21             PICPUS            FGT      25.0      7   21   \n",
       "6 2019-07-21       PIERRE CURIE      IMAGINE R     264.0      7   21   \n",
       "7 2019-07-21            PIGALLE   DAILY NAVIGO       7.0      7   21   \n",
       "8 2019-07-21    PLACE DES FETES         NAVIGO      20.0      7   21   \n",
       "9 2019-07-21    PLACE DES FETES      AMETHYSTE     297.0      7   21   \n",
       "\n",
       "   DAY_OF_WEEK DAY_NAME  \n",
       "0            6   Sunday  \n",
       "1            6   Sunday  \n",
       "2            6   Sunday  \n",
       "3            6   Sunday  \n",
       "4            6   Sunday  \n",
       "5            6   Sunday  \n",
       "6            6   Sunday  \n",
       "7            6   Sunday  \n",
       "8            6   Sunday  \n",
       "9            6   Sunday  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:18:28.942847Z",
     "start_time": "2022-09-20T21:18:28.922910Z"
    }
   },
   "outputs": [],
   "source": [
    "df['NB_VALID'] = pd.to_numeric(df.NB_VALID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:19:14.673982Z",
     "start_time": "2022-09-20T21:19:14.315945Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Showing The most frequent day for traveling\n",
    "df['DAY_NAME'].value_counts().sort_values().plot(kind='barh')\n",
    "plt.xlabel(\"Count of Travel\")\n",
    "plt.ylabel(\"Day Name\")\n",
    "plt.title(\"Count of Travel By Day Name\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:26:51.709059Z",
     "start_time": "2022-09-20T21:26:51.622008Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY_NAME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>Friday</th>\n",
       "      <td>4993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>5071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>5034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count\n",
       "MONTH DAY_NAME       \n",
       "1     Friday     4993\n",
       "      Monday     5071\n",
       "      Saturday   5034"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of trips based on the day of the week\n",
    "df.groupby([df['MONTH'], df['DAY_NAME']])['NB_VALID'].agg({'count'}).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:27:28.094176Z",
     "start_time": "2022-09-20T21:27:28.084195Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>DATE</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>TITLE_CATEGORY</th>\n",
       "      <th>NB_VALID</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>DAY_NAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>LA TOUR MAUBOURG</td>\n",
       "      <td>NAVIGO</td>\n",
       "      <td>1141.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PARMENTIER</td>\n",
       "      <td>NOT DEFINED</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PARMENTIER</td>\n",
       "      <td>TST</td>\n",
       "      <td>97.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PEREIRE-LEVALLOIS</td>\n",
       "      <td>FGT</td>\n",
       "      <td>53.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-21</td>\n",
       "      <td>PERNETY</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>36.0</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE       STATION_NAME TITLE_CATEGORY  NB_VALID  MONTH  DAY  \\\n",
       "0 2019-07-21   LA TOUR MAUBOURG         NAVIGO    1141.0      7   21   \n",
       "1 2019-07-21         PARMENTIER    NOT DEFINED       2.0      7   21   \n",
       "2 2019-07-21         PARMENTIER            TST      97.0      7   21   \n",
       "3 2019-07-21  PEREIRE-LEVALLOIS            FGT      53.0      7   21   \n",
       "4 2019-07-21            PERNETY          OTHER      36.0      7   21   \n",
       "\n",
       "   DAY_OF_WEEK DAY_NAME  \n",
       "0            6   Sunday  \n",
       "1            6   Sunday  \n",
       "2            6   Sunday  \n",
       "3            6   Sunday  \n",
       "4            6   Sunday  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:34:32.787803Z",
     "start_time": "2022-09-20T21:34:32.758881Z"
    }
   },
   "outputs": [],
   "source": [
    "date_by_month=df.groupby(df['MONTH'])[['NB_VALID']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:34:48.706051Z",
     "start_time": "2022-09-20T21:34:48.470648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Showing the number of NB_VALID for every month\n",
    "sns.set()\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('NB_VALID')\n",
    "plt.title('NB_VALID by Date')\n",
    "plt.plot(date_by_month.NB_VALID,color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:40:00.481215Z",
     "start_time": "2022-09-20T21:40:00.221690Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='MONTH'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Showing the most frequent month\n",
    "df.groupby(df['MONTH'])['NB_VALID'].sum().sort_values(ascending=True).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:40:52.092355Z",
     "start_time": "2022-09-20T21:40:52.052345Z"
    }
   },
   "outputs": [],
   "source": [
    "date_by_day=df.groupby(df['DAY'])[['NB_VALID']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:43:20.935479Z",
     "start_time": "2022-09-20T21:43:20.712072Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Showing days with a lot of trips\n",
    "sns.set()\n",
    "plt.xlabel('Day Name')\n",
    "plt.ylabel('NB_VALID')\n",
    "plt.title('NB_VALID by Date')\n",
    "plt.plot(date_by_day.NB_VALID,color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:44:14.940393Z",
     "start_time": "2022-09-20T21:44:14.654159Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Count of Travel By Day Name')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['DAY_NAME'].value_counts().sort_values().plot(kind='barh')\n",
    "plt.xlabel(\"Count of Travel\")\n",
    "plt.ylabel(\"Day Name\")\n",
    "plt.title(\"Count of Travel By Day Name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:33:18.502146Z",
     "start_time": "2022-09-20T21:33:18.080250Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>NB_VALID</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TITLE_CATEGORY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMETHYSTE</th>\n",
       "      <td>158.304172</td>\n",
       "      <td>8.119242</td>\n",
       "      <td>16.894615</td>\n",
       "      <td>2.962815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAILY NAVIGO</th>\n",
       "      <td>7.833240</td>\n",
       "      <td>8.003522</td>\n",
       "      <td>16.760896</td>\n",
       "      <td>2.948231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FGT</th>\n",
       "      <td>217.056736</td>\n",
       "      <td>8.132801</td>\n",
       "      <td>16.899104</td>\n",
       "      <td>2.964246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IMAGINE R</th>\n",
       "      <td>903.715926</td>\n",
       "      <td>8.147022</td>\n",
       "      <td>16.911137</td>\n",
       "      <td>2.968808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAVIGO</th>\n",
       "      <td>2211.967203</td>\n",
       "      <td>8.166409</td>\n",
       "      <td>16.906394</td>\n",
       "      <td>2.950045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOT DEFINED</th>\n",
       "      <td>61.114384</td>\n",
       "      <td>8.515480</td>\n",
       "      <td>17.611680</td>\n",
       "      <td>2.918328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER</th>\n",
       "      <td>120.258109</td>\n",
       "      <td>8.046119</td>\n",
       "      <td>16.740590</td>\n",
       "      <td>2.921774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TST</th>\n",
       "      <td>367.736848</td>\n",
       "      <td>8.138142</td>\n",
       "      <td>16.905364</td>\n",
       "      <td>2.963340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   NB_VALID     MONTH        DAY  DAY_OF_WEEK\n",
       "TITLE_CATEGORY                                               \n",
       "AMETHYSTE        158.304172  8.119242  16.894615     2.962815\n",
       "DAILY NAVIGO       7.833240  8.003522  16.760896     2.948231\n",
       "FGT              217.056736  8.132801  16.899104     2.964246\n",
       "IMAGINE R        903.715926  8.147022  16.911137     2.968808\n",
       "NAVIGO          2211.967203  8.166409  16.906394     2.950045\n",
       "NOT DEFINED       61.114384  8.515480  17.611680     2.918328\n",
       "OTHER            120.258109  8.046119  16.740590     2.921774\n",
       "TST              367.736848  8.138142  16.905364     2.963340"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(['NB_VALID'],ascending=True).groupby(df['TITLE_CATEGORY']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-20T21:47:56.785018Z",
     "start_time": "2022-09-20T21:47:56.759087Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-11-28    5461\n",
       "2019-11-29    5457\n",
       "2019-11-13    5455\n",
       "2019-11-22    5451\n",
       "2019-02-12    5450\n",
       "              ... \n",
       "2019-07-12    1989\n",
       "2019-12-22    1979\n",
       "2019-12-25    1909\n",
       "2019-12-15    1890\n",
       "2019-08-12    1648\n",
       "Name: DATE, Length: 184, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Showing the most freqent date\n",
    "df['DATE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Colleration between columns\n",
    "df.corr()\n",
    "sns.heatmap(df.corr(),cmap='Reds',annot=True)\n",
    "plt.rcParams['figure.figsize']=(10,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "df[[\"ScaledNB_VALID\"]] = scaler.fit_transform(df[['NB_VALID']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.009112\n",
       "1         0.000000\n",
       "2         0.000760\n",
       "3         0.000408\n",
       "4         0.000272\n",
       "            ...   \n",
       "883953    0.002240\n",
       "883954    0.022423\n",
       "883955    0.114995\n",
       "883956    0.036887\n",
       "883957    0.008136\n",
       "Name: ScaledNB_VALID, Length: 857008, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScaledNB_VALID']\n",
    "   "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}