BDC-team-1/1_Descriptive_Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3f41343f-7205-41d9-89dd-88039e301413",
   "metadata": {},
   "source": [
    "# Statistiques descriptives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abfaf341-7b35-4407-9133-d21336c04027",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import s3fs\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from datetime import datetime, date, timedelta\n",
    "from dateutil.relativedelta import relativedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7fb72fa3-7940-496f-ac78-c2837f65eefa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create filesystem object\n",
    "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
    "fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d5261f-4d46-49cb-8582-dd2121122b05",
   "metadata": {},
   "source": [
    "# 1 - Comportement d'achat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3479960c-0d23-45f1-8fff-d87395205731",
   "metadata": {},
   "source": [
    "## Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9376af51-4320-44b6-8f30-1e1234371556",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chargement des données temporaires\n",
    "BUCKET = \"projet-bdc2324-team1\"\n",
    "FILE_KEY_S3 = \"0_Temp/Company 1 - Purchasing behaviour.csv\"\n",
    "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n",
    "\n",
    "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
    "    tickets_kpi = pd.read_csv(file_in, sep=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1855dcca-cfce-4c54-90ae-55d9a1ab5d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>event_type_id</th>\n",
       "      <th>nb_tickets</th>\n",
       "      <th>nb_purchases</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>nb_suppliers</th>\n",
       "      <th>vente_internet_max</th>\n",
       "      <th>purchase_date_min</th>\n",
       "      <th>purchase_date_max</th>\n",
       "      <th>time_between_purchase</th>\n",
       "      <th>nb_tickets_internet</th>\n",
       "      <th>name_event_types</th>\n",
       "      <th>avg_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>384226</td>\n",
       "      <td>194790</td>\n",
       "      <td>2686540.5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3262.190868</td>\n",
       "      <td>4.179306</td>\n",
       "      <td>3258.011562</td>\n",
       "      <td>51.0</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>6.150659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>453242</td>\n",
       "      <td>228945</td>\n",
       "      <td>3248965.5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3698.198229</td>\n",
       "      <td>5.221840</td>\n",
       "      <td>3692.976389</td>\n",
       "      <td>2988.0</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>7.762474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>201750</td>\n",
       "      <td>107110</td>\n",
       "      <td>1459190.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3803.369792</td>\n",
       "      <td>0.146331</td>\n",
       "      <td>3803.223461</td>\n",
       "      <td>9.0</td>\n",
       "      <td>offre muséale groupe</td>\n",
       "      <td>4.452618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>217356</td>\n",
       "      <td>111786</td>\n",
       "      <td>1435871.5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2502.715509</td>\n",
       "      <td>1408.715532</td>\n",
       "      <td>1093.999977</td>\n",
       "      <td>5.0</td>\n",
       "      <td>formule adhésion</td>\n",
       "      <td>6.439463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2041.274549</td>\n",
       "      <td>1340.308160</td>\n",
       "      <td>700.966389</td>\n",
       "      <td>0.0</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>6.150659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  event_type_id  nb_tickets  nb_purchases  total_amount  \\\n",
       "0            1              2      384226        194790     2686540.5   \n",
       "1            1              4      453242        228945     3248965.5   \n",
       "2            1              5      201750        107110     1459190.0   \n",
       "3            1              6      217356        111786     1435871.5   \n",
       "4            2              2         143           143           0.0   \n",
       "\n",
       "   nb_suppliers  vente_internet_max  purchase_date_min  purchase_date_max  \\\n",
       "0             7                   1        3262.190868           4.179306   \n",
       "1             6                   1        3698.198229           5.221840   \n",
       "2             6                   1        3803.369792           0.146331   \n",
       "3             5                   1        2502.715509        1408.715532   \n",
       "4             1                   0        2041.274549        1340.308160   \n",
       "\n",
       "   time_between_purchase  nb_tickets_internet          name_event_types  \\\n",
       "0            3258.011562                 51.0  offre muséale individuel   \n",
       "1            3692.976389               2988.0          spectacle vivant   \n",
       "2            3803.223461                  9.0      offre muséale groupe   \n",
       "3            1093.999977                  5.0          formule adhésion   \n",
       "4             700.966389                  0.0  offre muséale individuel   \n",
       "\n",
       "   avg_amount  \n",
       "0    6.150659  \n",
       "1    7.762474  \n",
       "2    4.452618  \n",
       "3    6.439463  \n",
       "4    6.150659  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets_kpi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0e5d3b2e-1a75-4d46-80e6-c306e9f8de84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'event_type_id', 'nb_tickets', 'nb_purchases',\n",
       "       'total_amount', 'nb_suppliers', 'vente_internet_max',\n",
       "       'purchase_date_min', 'purchase_date_max', 'time_between_purchase',\n",
       "       'nb_tickets_internet', 'name_event_types', 'avg_amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets_kpi.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7667e8eb-9a1e-4216-96f4-bf987c6e30b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>event_type_id</th>\n",
       "      <th>nb_tickets</th>\n",
       "      <th>nb_purchases</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>nb_suppliers</th>\n",
       "      <th>vente_internet_max</th>\n",
       "      <th>purchase_date_min</th>\n",
       "      <th>purchase_date_max</th>\n",
       "      <th>time_between_purchase</th>\n",
       "      <th>nb_tickets_internet</th>\n",
       "      <th>name_event_types</th>\n",
       "      <th>avg_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>453242</td>\n",
       "      <td>228945</td>\n",
       "      <td>3248965.5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3698.198229</td>\n",
       "      <td>5.221840</td>\n",
       "      <td>3692.976389</td>\n",
       "      <td>2988.0</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>7.762474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>384226</td>\n",
       "      <td>194790</td>\n",
       "      <td>2686540.5</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3262.190868</td>\n",
       "      <td>4.179306</td>\n",
       "      <td>3258.011562</td>\n",
       "      <td>51.0</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>6.150659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>217356</td>\n",
       "      <td>111786</td>\n",
       "      <td>1435871.5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2502.715509</td>\n",
       "      <td>1408.715532</td>\n",
       "      <td>1093.999977</td>\n",
       "      <td>5.0</td>\n",
       "      <td>formule adhésion</td>\n",
       "      <td>6.439463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>201750</td>\n",
       "      <td>107110</td>\n",
       "      <td>1459190.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3803.369792</td>\n",
       "      <td>0.146331</td>\n",
       "      <td>3803.223461</td>\n",
       "      <td>9.0</td>\n",
       "      <td>offre muséale groupe</td>\n",
       "      <td>4.452618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5032</th>\n",
       "      <td>6733</td>\n",
       "      <td>6</td>\n",
       "      <td>14208</td>\n",
       "      <td>114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2492.187199</td>\n",
       "      <td>1442.405116</td>\n",
       "      <td>1049.782083</td>\n",
       "      <td>13497.0</td>\n",
       "      <td>formule adhésion</td>\n",
       "      <td>6.439463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5029</th>\n",
       "      <td>6733</td>\n",
       "      <td>2</td>\n",
       "      <td>11656</td>\n",
       "      <td>158</td>\n",
       "      <td>471.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2982.237384</td>\n",
       "      <td>489.495324</td>\n",
       "      <td>2492.742060</td>\n",
       "      <td>9815.0</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>6.150659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5030</th>\n",
       "      <td>6733</td>\n",
       "      <td>4</td>\n",
       "      <td>7440</td>\n",
       "      <td>162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1036.392674</td>\n",
       "      <td>426.201944</td>\n",
       "      <td>610.190729</td>\n",
       "      <td>7419.0</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>7.762474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>41</td>\n",
       "      <td>6</td>\n",
       "      <td>6583</td>\n",
       "      <td>634</td>\n",
       "      <td>12546.5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2501.337905</td>\n",
       "      <td>1409.370521</td>\n",
       "      <td>1091.967384</td>\n",
       "      <td>6391.0</td>\n",
       "      <td>formule adhésion</td>\n",
       "      <td>6.439463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>6514</td>\n",
       "      <td>812</td>\n",
       "      <td>22423.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3576.106609</td>\n",
       "      <td>247.232697</td>\n",
       "      <td>3328.873912</td>\n",
       "      <td>5321.0</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>6.150659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36376</th>\n",
       "      <td>63488</td>\n",
       "      <td>4</td>\n",
       "      <td>5750</td>\n",
       "      <td>9</td>\n",
       "      <td>63250.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>887.298484</td>\n",
       "      <td>440.265162</td>\n",
       "      <td>447.033322</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>7.762474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       customer_id  event_type_id  nb_tickets  nb_purchases  total_amount  \\\n",
       "1                1              4      453242        228945     3248965.5   \n",
       "0                1              2      384226        194790     2686540.5   \n",
       "3                1              6      217356        111786     1435871.5   \n",
       "2                1              5      201750        107110     1459190.0   \n",
       "5032          6733              6       14208           114           0.0   \n",
       "5029          6733              2       11656           158         471.0   \n",
       "5030          6733              4        7440           162           0.0   \n",
       "60              41              6        6583           634       12546.5   \n",
       "57              41              2        6514           812       22423.0   \n",
       "36376        63488              4        5750             9       63250.0   \n",
       "\n",
       "       nb_suppliers  vente_internet_max  purchase_date_min  purchase_date_max  \\\n",
       "1                 6                   1        3698.198229           5.221840   \n",
       "0                 7                   1        3262.190868           4.179306   \n",
       "3                 5                   1        2502.715509        1408.715532   \n",
       "2                 6                   1        3803.369792           0.146331   \n",
       "5032              3                   1        2492.187199        1442.405116   \n",
       "5029              3                   1        2982.237384         489.495324   \n",
       "5030              2                   1        1036.392674         426.201944   \n",
       "60                4                   1        2501.337905        1409.370521   \n",
       "57                6                   1        3576.106609         247.232697   \n",
       "36376             1                   1         887.298484         440.265162   \n",
       "\n",
       "       time_between_purchase  nb_tickets_internet          name_event_types  \\\n",
       "1                3692.976389               2988.0          spectacle vivant   \n",
       "0                3258.011562                 51.0  offre muséale individuel   \n",
       "3                1093.999977                  5.0          formule adhésion   \n",
       "2                3803.223461                  9.0      offre muséale groupe   \n",
       "5032             1049.782083              13497.0          formule adhésion   \n",
       "5029             2492.742060               9815.0  offre muséale individuel   \n",
       "5030              610.190729               7419.0          spectacle vivant   \n",
       "60               1091.967384               6391.0          formule adhésion   \n",
       "57               3328.873912               5321.0  offre muséale individuel   \n",
       "36376             447.033322               5750.0          spectacle vivant   \n",
       "\n",
       "       avg_amount  \n",
       "1        7.762474  \n",
       "0        6.150659  \n",
       "3        6.439463  \n",
       "2        4.452618  \n",
       "5032     6.439463  \n",
       "5029     6.150659  \n",
       "5030     7.762474  \n",
       "60       6.439463  \n",
       "57       6.150659  \n",
       "36376    7.762474  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Présence d'outlier\n",
    "tickets_kpi.sort_values(by = ['nb_tickets'], axis = 0, ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9b2e27f2-703d-465b-a0f9-76e996de617c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part du CA par customer\n",
    "total_amount_share = tickets_kpi.groupby('customer_id')['total_amount'].sum().reset_index()\n",
    "total_amount_share['total_amount_entreprise'] = total_amount_share['total_amount'].sum()\n",
    "total_amount_share['share_total_amount'] = total_amount_share['total_amount']/total_amount_share['total_amount_entreprise']\n",
    "\n",
    "total_amount_share_index = total_amount_share.set_index('customer_id')\n",
    "df_circulaire = total_amount_share_index['total_amount'].sort_values(axis = 0, ascending = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "36141803-8865-4210-bd39-0a980301fd0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Costumer 1 vs others customers\n",
    "coupure = 1\n",
    "\n",
    "top = df_circulaire[:coupure]\n",
    "rest = df_circulaire[coupure:]\n",
    "\n",
    "# Calculez la somme du reste\n",
    "rest_sum = rest.sum()\n",
    "\n",
    "# Créez une nouvelle série avec les cinq plus grandes parts et 'Autre'\n",
    "new_series = pd.concat([top, pd.Series([rest_sum], index=['Autre'])])\n",
    "\n",
    "# Créez le graphique circulaire\n",
    "plt.figure(figsize=(3, 3))\n",
    "plt.pie(new_series, labels=new_series.index, autopct='%1.1f%%', startangle=140, pctdistance=0.5)\n",
    "plt.axis('equal')  # Assurez-vous que le graphique est un cercle\n",
    "plt.title('Répartition des montants totaux')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "94cf1a25-9ded-48f2-b1b2-75225bdaf49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tickets_kpi_filtered  = tickets_kpi[tickets_kpi['customer_id'] != 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbebfa92-310a-417b-a7fa-36ac3593db06",
   "metadata": {},
   "source": [
    "## Evolution des commandes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "06137694-7f50-47ba-8749-68471ececc1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importation - Chargement des données temporaires\n",
    "\n",
    "# Achat\n",
    "BUCKET = \"projet-bdc2324-team1\"\n",
    "FILE_KEY_S3 = \"0_Temp/Company 1 - Purchases.csv\"\n",
    "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n",
    "\n",
    "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
    "    purchases = pd.read_csv(file_in, sep=\",\")\n",
    "    \n",
    "purchases['purchase_date'] = pd.to_datetime(purchases['purchase_date'], utc = True, format = 'ISO8601')\n",
    "\n",
    "# Emails\n",
    "BUCKET = \"projet-bdc2324-team1\"\n",
    "FILE_KEY_S3 = \"0_Temp/Company 1 - Campaigns dataset clean.csv\"\n",
    "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n",
    "\n",
    "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
    "    campaigns = pd.read_csv(file_in, sep=\",\")\n",
    "\n",
    "campaigns['sent_at'] = pd.to_datetime(campaigns['sent_at'], utc = True, format = 'ISO8601')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e6b962d4-1a30-4133-ac0f-359f7afef42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mois du premier achat\n",
    "purchase_min = purchases.groupby(['customer_id', 'event_type_id'])['purchase_date'].min().reset_index()\n",
    "purchase_min.rename(columns = {'purchase_date' : 'first_purchase_event'}, inplace = True)\n",
    "purchase_min['first_purchase_event'] = pd.to_datetime(purchase_min['first_purchase_event'])\n",
    "purchase_min['first_purchase_month'] = pd.to_datetime(purchase_min['first_purchase_event'].dt.strftime('%Y-%m'))\n",
    "\n",
    "# Mois du premier mails\n",
    "first_mail_received = campaigns.groupby('customer_id')['sent_at'].min().reset_index()\n",
    "first_mail_received.rename(columns = {'sent_at' : 'first_email_reception'}, inplace = True)\n",
    "first_mail_received['first_email_reception'] = pd.to_datetime(first_mail_received['first_email_reception'])\n",
    "first_mail_received['first_email_month'] = pd.to_datetime(first_mail_received['first_email_reception'].dt.strftime('%Y-%m'))\n",
    "\n",
    "# Fusion \n",
    "known_customer = pd.merge(purchase_min[['customer_id', 'event_type_id', 'first_purchase_month']], \n",
    "                          first_mail_received[['customer_id', 'first_email_month']], on = 'customer_id', how = 'outer')\n",
    "\n",
    "# Mois à partir duquel le client est considere comme connu\n",
    "known_customer['known_date'] = pd.to_datetime(known_customer[['first_email_month', 'first_purchase_month']].min(axis = 1), utc = True, format = 'ISO8601')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c56e5ac-cbf4-4343-80ba-be2ab8b60eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nombre de commande par mois\n",
    "purchases_count = pd.merge(purchases[['customer_id', 'event_type_id', 'purchase_id', 'purchase_date']].drop_duplicates(), known_customer[['customer_id', 'event_type_id', 'known_date']], on = ['customer_id', 'event_type_id'], how = 'inner')\n",
    "purchases_count['is_customer_known'] = purchases_count['purchase_date'] > purchases_count['known_date'] + pd.DateOffset(months=1)\n",
    "purchases_count['purchase_date_month'] = pd.to_datetime(purchases_count['purchase_date'].dt.strftime('%Y-%m'))\n",
    "purchases_count = purchases_count[purchases_count['customer_id'] != 1]\n",
    "\n",
    "# Nombre de commande par mois par type de client\n",
    "nb_purchases_graph = purchases_count.groupby(['purchase_date_month', 'event_type_id', 'is_customer_known'])['purchase_id'].count().reset_index()\n",
    "nb_purchases_graph.rename(columns = {'purchase_id' : 'nb_purchases'}, inplace = True)\n",
    "\n",
    "nb_purchases_graph_2 = purchases_count.groupby(['purchase_date_month', 'event_type_id', 'is_customer_known'])['customer_id'].nunique().reset_index()\n",
    "nb_purchases_graph_2.rename(columns = {'customer_id' : 'nb_new_customer'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c1aed44-03d3-49f9-b96c-b06a0df03dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphique en nombre de commande\n",
    "event_type = 5\n",
    "purchases_graph = nb_purchases_graph[nb_purchases_graph['event_type_id'] == event_type]\n",
    "\n",
    "purchases_graph_used = purchases_graph[purchases_graph[\"purchase_date_month\"] >= datetime(2022,1,1)]\n",
    "purchases_graph_used_0 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==False]\n",
    "purchases_graph_used_1 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==True]\n",
    "\n",
    "\n",
    "# Création du barplot\n",
    "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_0[\"nb_purchases\"], width=12, label = \"Nouveau client\")\n",
    "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_1[\"nb_purchases\"], \n",
    "        bottom = purchases_graph_used_0[\"nb_purchases\"], width=12, label = \"Ancien client\")\n",
    "\n",
    "\n",
    "# commande pr afficher slt\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%y'))\n",
    "\n",
    "\n",
    "# Ajout de titres et d'étiquettes\n",
    "plt.xlabel('Mois')\n",
    "plt.ylabel(\"Nombre d'achats\")\n",
    "plt.title(\"Nombre d'achats au cours de l'année 2023 pour l'offre 'muséale groupe'\")\n",
    "plt.legend()\n",
    "\n",
    "# Affichage du barplot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d312276c-4c46-4d29-b6d6-ed110f59890d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graphique en nombre de client ayant commandé\n",
    "event_type = 5\n",
    "purchases_graph = nb_purchases_graph_2[nb_purchases_graph_2['event_type_id'] == event_type]\n",
    "\n",
    "purchases_graph_used = purchases_graph[purchases_graph[\"purchase_date_month\"] >= datetime(2022,1,1)]\n",
    "purchases_graph_used_0 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==False]\n",
    "purchases_graph_used_1 = purchases_graph_used[purchases_graph_used[\"is_customer_known\"]==True]\n",
    "\n",
    "\n",
    "# Création du barplot\n",
    "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_0[\"nb_new_customer\"], width=12, label = \"Nouveau client\")\n",
    "plt.bar(purchases_graph_used_0[\"purchase_date_month\"], purchases_graph_used_1[\"nb_new_customer\"], \n",
    "        bottom = purchases_graph_used_0[\"nb_new_customer\"], width=12, label = \"Ancien client\")\n",
    "\n",
    "\n",
    "# commande pr afficher slt\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%y'))\n",
    "\n",
    "\n",
    "# Ajout de titres et d'étiquettes\n",
    "plt.xlabel('Mois')\n",
    "plt.ylabel(\"Nombre de client ayant commandé\")\n",
    "plt.title(\"Nombre de client ayant commandé un ticket pour l'offre 'muséale groupe'\")\n",
    "plt.legend()\n",
    "\n",
    "# Affichage du barplot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8a90eaa-c383-4f73-9fd6-6fbbe8eeefb8",
   "metadata": {},
   "source": [
    "# 2 - Comportement d'achat bis (Alexis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dc45c1cd-2a78-48a6-aa2b-6a501254b6f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(156289, 40)\n"
     ]
    },
    {
     "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>customer_id</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>street_id</th>\n",
       "      <th>is_partner</th>\n",
       "      <th>gender</th>\n",
       "      <th>is_email_true</th>\n",
       "      <th>opt_in</th>\n",
       "      <th>structure_id</th>\n",
       "      <th>profession</th>\n",
       "      <th>language</th>\n",
       "      <th>...</th>\n",
       "      <th>vente_internet_max</th>\n",
       "      <th>purchase_date_min</th>\n",
       "      <th>purchase_date_max</th>\n",
       "      <th>time_between_purchase</th>\n",
       "      <th>nb_tickets_internet</th>\n",
       "      <th>name_event_types</th>\n",
       "      <th>avg_amount</th>\n",
       "      <th>nb_campaigns</th>\n",
       "      <th>nb_campaigns_opened</th>\n",
       "      <th>time_to_open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3262.190868</td>\n",
       "      <td>4.179306</td>\n",
       "      <td>3258.011562</td>\n",
       "      <td>51.0</td>\n",
       "      <td>offre muséale individuel</td>\n",
       "      <td>6.150659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2502.715509</td>\n",
       "      <td>1408.715532</td>\n",
       "      <td>1093.999977</td>\n",
       "      <td>5.0</td>\n",
       "      <td>formule adhésion</td>\n",
       "      <td>6.439463</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3698.198229</td>\n",
       "      <td>5.221840</td>\n",
       "      <td>3692.976389</td>\n",
       "      <td>2988.0</td>\n",
       "      <td>spectacle vivant</td>\n",
       "      <td>7.762474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3803.369792</td>\n",
       "      <td>0.146331</td>\n",
       "      <td>3803.223461</td>\n",
       "      <td>9.0</td>\n",
       "      <td>offre muséale groupe</td>\n",
       "      <td>4.452618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1705.261192</td>\n",
       "      <td>1456.333715</td>\n",
       "      <td>248.927477</td>\n",
       "      <td>0.0</td>\n",
       "      <td>formule adhésion</td>\n",
       "      <td>6.439463</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id birthdate  street_id  is_partner  gender  is_email_true  \\\n",
       "0            1       NaN          2       False       2           True   \n",
       "1            1       NaN          2       False       2           True   \n",
       "2            1       NaN          2       False       2           True   \n",
       "3            1       NaN          2       False       2           True   \n",
       "4            2       NaN          2       False       1           True   \n",
       "\n",
       "   opt_in  structure_id profession language  ...  vente_internet_max  \\\n",
       "0   False           NaN        NaN      NaN  ...                 1.0   \n",
       "1   False           NaN        NaN      NaN  ...                 1.0   \n",
       "2   False           NaN        NaN      NaN  ...                 1.0   \n",
       "3   False           NaN        NaN      NaN  ...                 1.0   \n",
       "4    True           NaN        NaN      NaN  ...                 0.0   \n",
       "\n",
       "  purchase_date_min  purchase_date_max  time_between_purchase  \\\n",
       "0       3262.190868           4.179306            3258.011562   \n",
       "1       2502.715509        1408.715532            1093.999977   \n",
       "2       3698.198229           5.221840            3692.976389   \n",
       "3       3803.369792           0.146331            3803.223461   \n",
       "4       1705.261192        1456.333715             248.927477   \n",
       "\n",
       "   nb_tickets_internet          name_event_types  avg_amount  nb_campaigns  \\\n",
       "0                 51.0  offre muséale individuel    6.150659           NaN   \n",
       "1                  5.0          formule adhésion    6.439463           NaN   \n",
       "2               2988.0          spectacle vivant    7.762474           NaN   \n",
       "3                  9.0      offre muséale groupe    4.452618           NaN   \n",
       "4                  0.0          formule adhésion    6.439463           4.0   \n",
       "\n",
       "   nb_campaigns_opened  time_to_open  \n",
       "0                  NaN           NaN  \n",
       "1                  NaN           NaN  \n",
       "2                  NaN           NaN  \n",
       "3                  NaN           NaN  \n",
       "4                  NaN           NaN  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chargement des données temporaires\n",
    "BUCKET = \"projet-bdc2324-team1\"\n",
    "FILE_KEY_S3 = \"0_Temp/Company 1 - customer_event.csv\"\n",
    "FILE_PATH_S3 = BUCKET + \"/\" + FILE_KEY_S3\n",
    "\n",
    "with fs.open(FILE_PATH_S3, mode=\"rb\") as file_in:\n",
    "    customer = pd.read_csv(file_in, sep=\",\")\n",
    "\n",
    "print(customer.shape)\n",
    "customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89fcb455-efb4-4ad4-ab88-efd6c8a76287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'birthdate', 'street_id', 'is_partner', 'gender',\n",
       "       'is_email_true', 'opt_in', 'structure_id', 'profession', 'language',\n",
       "       'mcp_contact_id', 'last_buying_date', 'max_price', 'ticket_sum',\n",
       "       'average_price', 'fidelity', 'average_purchase_delay',\n",
       "       'average_price_basket', 'average_ticket_basket', 'total_price',\n",
       "       'purchase_count', 'first_buying_date', 'country', 'age', 'tenant_id',\n",
       "       'event_type_id', 'nb_tickets', 'nb_purchases', 'total_amount',\n",
       "       'nb_suppliers', 'vente_internet_max', 'purchase_date_min',\n",
       "       'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet',\n",
       "       'name_event_types', 'avg_amount', 'nb_campaigns', 'nb_campaigns_opened',\n",
       "       'time_to_open'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d7b2356a-d5fc-4547-b3ff-fded0e304fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>customer_id</th>\n",
       "      <th>average_price</th>\n",
       "      <th>average_purchase_delay</th>\n",
       "      <th>average_price_basket</th>\n",
       "      <th>average_ticket_basket</th>\n",
       "      <th>purchase_count</th>\n",
       "      <th>total_price</th>\n",
       "      <th>nb_campaigns</th>\n",
       "      <th>nb_campaigns_opened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7.030122</td>\n",
       "      <td>-67.790969</td>\n",
       "      <td>13.751530</td>\n",
       "      <td>1.956087</td>\n",
       "      <td>641472</td>\n",
       "      <td>8821221.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>30.666667</td>\n",
       "      <td>36.666667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>110.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id  average_price  average_purchase_delay  average_price_basket  \\\n",
       "0            1       7.030122              -67.790969             13.751530   \n",
       "4            2       0.000000                0.000000              0.000000   \n",
       "6            3      18.333333               30.666667             36.666667   \n",
       "7            4      10.250000                5.000000             20.500000   \n",
       "9            5       9.500000                0.000000             19.000000   \n",
       "\n",
       "   average_ticket_basket  purchase_count  total_price  nb_campaigns  \\\n",
       "0               1.956087          641472    8821221.5           0.0   \n",
       "4               1.000000             307          0.0           4.0   \n",
       "6               2.000000               3        110.0         222.0   \n",
       "7               2.000000               2         41.0           7.0   \n",
       "9               2.000000               1         19.0           4.0   \n",
       "\n",
       "   nb_campaigns_opened  \n",
       "0                  0.0  \n",
       "4                  0.0  \n",
       "6                124.0  \n",
       "7                  7.0  \n",
       "9                  0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "achat = ['customer_id', 'average_price', 'average_purchase_delay', 'average_price_basket',\n",
    "          'average_ticket_basket', 'purchase_count', 'total_price', 'nb_campaigns',\n",
    "         'nb_campaigns_opened']\n",
    "\n",
    "customer_achat = customer[achat].drop_duplicates(subset = ['customer_id'])\n",
    "customer_achat['nb_campaigns'] = customer_achat['nb_campaigns'].fillna(0)\n",
    "customer_achat['nb_campaigns_opened'] = customer_achat['nb_campaigns_opened'].fillna(0)\n",
    "customer_achat = customer_achat.fillna(0)\n",
    "customer_achat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5559748f-1745-4651-a9f6-94702c7ee66f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>average_price</th>\n",
       "      <th>average_purchase_delay</th>\n",
       "      <th>average_price_basket</th>\n",
       "      <th>average_ticket_basket</th>\n",
       "      <th>purchase_count</th>\n",
       "      <th>total_price</th>\n",
       "      <th>nb_campaigns</th>\n",
       "      <th>nb_campaigns_opened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>151865.000000</td>\n",
       "      <td>151865.000000</td>\n",
       "      <td>151865.000000</td>\n",
       "      <td>151865.000000</td>\n",
       "      <td>151865.000000</td>\n",
       "      <td>151865.000000</td>\n",
       "      <td>151865.000000</td>\n",
       "      <td>151865.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.252070</td>\n",
       "      <td>-206.581486</td>\n",
       "      <td>11.451596</td>\n",
       "      <td>1.723372</td>\n",
       "      <td>0.655148</td>\n",
       "      <td>16.994064</td>\n",
       "      <td>40.923241</td>\n",
       "      <td>7.870681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.915955</td>\n",
       "      <td>2996.743657</td>\n",
       "      <td>48.271194</td>\n",
       "      <td>7.045950</td>\n",
       "      <td>5.694038</td>\n",
       "      <td>313.099102</td>\n",
       "      <td>70.445724</td>\n",
       "      <td>23.119061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-44863.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>320.000000</td>\n",
       "      <td>1914.000000</td>\n",
       "      <td>9900.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1508.000000</td>\n",
       "      <td>64350.000000</td>\n",
       "      <td>439.000000</td>\n",
       "      <td>434.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_price  average_purchase_delay  average_price_basket  \\\n",
       "count  151865.000000           151865.000000         151865.000000   \n",
       "mean        5.252070             -206.581486             11.451596   \n",
       "std         7.915955             2996.743657             48.271194   \n",
       "min         0.000000           -44863.000000              0.000000   \n",
       "25%         0.000000                0.000000              0.000000   \n",
       "50%         0.000000                0.000000              0.000000   \n",
       "75%        11.000000                0.000000             19.000000   \n",
       "max       320.000000             1914.000000           9900.000000   \n",
       "\n",
       "       average_ticket_basket  purchase_count    total_price   nb_campaigns  \\\n",
       "count          151865.000000   151865.000000  151865.000000  151865.000000   \n",
       "mean                1.723372        0.655148      16.994064      40.923241   \n",
       "std                 7.045950        5.694038     313.099102      70.445724   \n",
       "min                 0.000000        0.000000       0.000000       0.000000   \n",
       "25%                 0.000000        0.000000       0.000000       2.000000   \n",
       "50%                 0.000000        0.000000       0.000000       5.000000   \n",
       "75%                 2.000000        1.000000      20.000000      32.000000   \n",
       "max               900.000000     1508.000000   64350.000000     439.000000   \n",
       "\n",
       "       nb_campaigns_opened  \n",
       "count        151865.000000  \n",
       "mean              7.870681  \n",
       "std              23.119061  \n",
       "min               0.000000  \n",
       "25%               0.000000  \n",
       "50%               1.000000  \n",
       "75%               3.000000  \n",
       "max             434.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_wto_outlier = customer_achat[customer_achat['customer_id']!=1]\n",
    "\n",
    "customer_wto_outlier[['average_price', 'average_purchase_delay', 'average_price_basket',\n",
    "          'average_ticket_basket', 'purchase_count', 'total_price', 'nb_campaigns', 'nb_campaigns_opened']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b49c9e93-f324-42ee-a262-34ffb44a2261",
   "metadata": {},
   "source": [
    "# Event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4971e35d-a762-4e18-9443-fd9571bd3f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nombre de consommateurs uniques par type d'évènement\n",
    "\n",
    "event_counts = customer.groupby('name_event_types')['customer_id'].nunique()\n",
    "\n",
    "event_counts.plot(kind='bar')\n",
    "plt.xlabel(\"Type d'évènement\")\n",
    "plt.ylabel('Nombre de consommateurs uniques')\n",
    "plt.title(\"Nombre de consommateurs uniques par type d'évènement\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc65a711-d172-4839-b487-3047280fc3a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nombre Total de tickets achetés par Type d'évènements\n",
    "\n",
    "total_tickets_by_event = customer.groupby('name_event_types')['nb_tickets'].sum()\n",
    "\n",
    "total_tickets_by_event.plot(kind='bar', figsize=(8, 5))\n",
    "plt.xlabel(\"Type d'évènements\")\n",
    "plt.ylabel('Nombre Total de tickets achetés')\n",
    "plt.title(\"Nombre Total de tickets achetés par Type d'évènements\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c95cc35c-abfc-47c7-9b8a-ac69bfd60dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nombre de Canaux de Ventes Moyen utilisé par les Consommateurs par type d'évènement\n",
    "\n",
    "avg_supp_event = customer.groupby('name_event_types')['nb_suppliers'].mean()\n",
    "avg_supp_event.plot(kind='bar')\n",
    "plt.xlabel(\"Type d'évènement\")\n",
    "plt.ylabel('Nombre de Canaux de Ventes Moyen')\n",
    "plt.title(\"Nombre de Canaux de Ventes Moyen utilisé par les Consommateurs par type d'évènement\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "49d5fd2d-9bc1-43ac-9270-1efd73759854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Nombre Total de tickets achetés sur Internet par Type d'évènements\n",
    "\n",
    "nb_tickets_internet = customer.groupby('name_event_types')['nb_tickets_internet'].sum()\n",
    "nb_tickets_internet.plot(kind='bar', figsize=(8, 5))\n",
    "plt.xlabel(\"Type d'évènements\")\n",
    "plt.ylabel('Nombre Total de tickets achetés sur Internet')\n",
    "plt.title(\"Nombre Total de tickets achetés sur Internet par Type d'évènements\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc071992-cf4d-4b9f-9c3b-3f0e98e20eff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4f9561a9-6a94-434e-b8e7-9b708f5b5529",
   "metadata": {},
   "source": [
    "# 3 - Caractéristiques Démographiques (peu exploitable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e50e2583-4b8f-478e-87ac-591dde200af8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_id', 'birthdate', 'street_id', 'is_partner', 'gender',\n",
       "       'is_email_true', 'opt_in', 'structure_id', 'profession', 'language',\n",
       "       'mcp_contact_id', 'last_buying_date', 'max_price', 'ticket_sum',\n",
       "       'average_price', 'fidelity', 'average_purchase_delay',\n",
       "       'average_price_basket', 'average_ticket_basket', 'total_price',\n",
       "       'purchase_count', 'first_buying_date', 'country', 'age', 'tenant_id',\n",
       "       'event_type_id', 'nb_tickets', 'nb_purchases', 'total_amount',\n",
       "       'nb_suppliers', 'vente_internet_max', 'purchase_date_min',\n",
       "       'purchase_date_max', 'time_between_purchase', 'nb_tickets_internet',\n",
       "       'name_event_types', 'avg_amount', 'nb_campaigns', 'nb_campaigns_opened',\n",
       "       'time_to_open'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c724a315-9fe8-4874-be8f-a8115b17b5e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_of_na(df, column):\n",
    "    na_percentage = df[column].isna().mean() * 100\n",
    "    non_na_percentage = 100 - na_percentage\n",
    "    \n",
    "    labels = ['Valeurs Manquantes', 'Non-Valeurs Manquantes']\n",
    "    sizes = [na_percentage, non_na_percentage]\n",
    "    colors = ['#ff9999','#66b3ff']\n",
    "    explode = (0.1, 0)\n",
    "\n",
    "    plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', startangle=140)\n",
    "    plt.axis('equal') \n",
    "    plt.title('Pourcentage de Valeurs Manquantes : {}'.format(column))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "58af5dcb-673e-4f4d-ad5c-f66ce1e8a22c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percent_of_na(customer, 'profession')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc3437f7-8b36-4398-9da6-ff15e8e4c8d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "percent_of_na(customer, 'language')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c34164d2-5ab2-4923-a165-30dc5c070233",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}