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BDC-team-1/1_Descriptive_Statistics.ipynb

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Ajout notebook pour statistiques descriptives
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{
"cells": [
{
"cell_type": "markdown",
"id": "3f41343f-7205-41d9-89dd-88039e301413",
"metadata": {},
"source": [
"# Statistiques descriptives"
]
},
{
"cell_type": "code",
add new stat
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"execution_count": 63,
Ajout notebook pour statistiques descriptives
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"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",
Ajout graph circulaire
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"import re\n",
"import matplotlib.pyplot as plt"
Ajout notebook pour statistiques descriptives
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]
},
{
"cell_type": "code",
add new stat
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"execution_count": 64,
Ajout notebook pour statistiques descriptives
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"id": "7fb72fa3-7940-496f-ac78-c2837f65eefa",
"metadata": {},
"outputs": [],
"source": [
Modification access key
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"# Create filesystem object\n",
"S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n",
"fs = s3fs.S3FileSystem(client_kwargs={'endpoint_url': S3_ENDPOINT_URL})"
Ajout notebook pour statistiques descriptives
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]
},
{
"cell_type": "markdown",
"id": "45d5261f-4d46-49cb-8582-dd2121122b05",
"metadata": {},
"source": [
"# 1 - Comportement d'achat"
]
},
{
"cell_type": "code",
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"execution_count": 65,
Ajout notebook pour statistiques descriptives
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"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",
add new stat
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"execution_count": 66,
Ajout notebook pour statistiques descriptives
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"id": "1855dcca-cfce-4c54-90ae-55d9a1ab5d45",
"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>customer_id</th>\n",
" <th>event_type_id</th>\n",
" <th>nb_tickets</th>\n",
add new stat
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" <th>nb_purchases</th>\n",
Ajout notebook pour statistiques descriptives
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" <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",
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" <th>name_event_types</th>\n",
" <th>avg_amount</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>384226</td>\n",
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" <td>194790</td>\n",
Ajout notebook pour statistiques descriptives
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" <td>2686540.5</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
add new stat
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" <td>3262.190868</td>\n",
" <td>4.179306</td>\n",
" <td>3258.011562</td>\n",
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" <td>51.0</td>\n",
add new stat
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" <td>offre muséale individuel</td>\n",
" <td>6.150659</td>\n",
Ajout notebook pour statistiques descriptives
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>453242</td>\n",
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" <td>228945</td>\n",
Ajout notebook pour statistiques descriptives
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" <td>3248965.5</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
add new stat
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" <td>3698.198229</td>\n",
" <td>5.221840</td>\n",
" <td>3692.976389</td>\n",
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" <td>2988.0</td>\n",
add new stat
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" <td>spectacle vivant</td>\n",
" <td>7.762474</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>201750</td>\n",
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" <td>107110</td>\n",
Ajout notebook pour statistiques descriptives
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" <td>1459190.0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
add new stat
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" <td>3803.369792</td>\n",
" <td>0.146331</td>\n",
" <td>3803.223461</td>\n",
Ajout notebook pour statistiques descriptives
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" <td>9.0</td>\n",
add new stat
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" <td>offre muséale groupe</td>\n",
" <td>4.452618</td>\n",
Ajout notebook pour statistiques descriptives
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>217356</td>\n",
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" <td>111786</td>\n",
Ajout notebook pour statistiques descriptives
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" <td>1435871.5</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
add new stat
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" <td>2502.715509</td>\n",
" <td>1408.715532</td>\n",
" <td>1093.999977</td>\n",
Ajout notebook pour statistiques descriptives
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" <td>5.0</td>\n",
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" <td>formule adhésion</td>\n",
" <td>6.439463</td>\n",
Ajout notebook pour statistiques descriptives
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>143</td>\n",
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" <td>143</td>\n",
Ajout notebook pour statistiques descriptives
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" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>2041.274549</td>\n",
" <td>1340.308160</td>\n",
" <td>700.966389</td>\n",
Ajout notebook pour statistiques descriptives
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" <td>0.0</td>\n",
add new stat
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" <td>offre muséale individuel</td>\n",
" <td>6.150659</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
add new stat
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" 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",
Ajout notebook pour statistiques descriptives
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"\n",
add new stat
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" 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",
Ajout notebook pour statistiques descriptives
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"\n",
add new stat
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" 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 "
Ajout notebook pour statistiques descriptives
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]
},
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"execution_count": 66,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tickets_kpi.head()"
]
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},
{
"cell_type": "code",
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"execution_count": 67,
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"id": "0e5d3b2e-1a75-4d46-80e6-c306e9f8de84",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
add new stat
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"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",
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" dtype='object')"
]
},
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"execution_count": 67,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tickets_kpi.columns"
]
},
{
"cell_type": "code",
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"execution_count": 68,
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"id": "7667e8eb-9a1e-4216-96f4-bf987c6e30b5",
"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>customer_id</th>\n",
" <th>event_type_id</th>\n",
" <th>nb_tickets</th>\n",
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" <th>nb_purchases</th>\n",
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" <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",
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" <th>name_event_types</th>\n",
" <th>avg_amount</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>453242</td>\n",
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" <td>228945</td>\n",
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" <td>3248965.5</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
add new stat
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" <td>3698.198229</td>\n",
" <td>5.221840</td>\n",
" <td>3692.976389</td>\n",
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" <td>2988.0</td>\n",
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" <td>spectacle vivant</td>\n",
" <td>7.762474</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>384226</td>\n",
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" <td>194790</td>\n",
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" <td>2686540.5</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
add new stat
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" <td>3262.190868</td>\n",
" <td>4.179306</td>\n",
" <td>3258.011562</td>\n",
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" <td>51.0</td>\n",
add new stat
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" <td>offre muséale individuel</td>\n",
" <td>6.150659</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>217356</td>\n",
add new stat
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" <td>111786</td>\n",
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" <td>1435871.5</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
add new stat
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" <td>2502.715509</td>\n",
" <td>1408.715532</td>\n",
" <td>1093.999977</td>\n",
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" <td>5.0</td>\n",
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" <td>formule adhésion</td>\n",
" <td>6.439463</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>201750</td>\n",
add new stat
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" <td>107110</td>\n",
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" <td>1459190.0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
add new stat
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" <td>3803.369792</td>\n",
" <td>0.146331</td>\n",
" <td>3803.223461</td>\n",
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" <td>9.0</td>\n",
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" <td>offre muséale groupe</td>\n",
" <td>4.452618</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5032</th>\n",
" <td>6733</td>\n",
" <td>6</td>\n",
" <td>14208</td>\n",
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" <td>114</td>\n",
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" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
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" <td>2492.187199</td>\n",
" <td>1442.405116</td>\n",
" <td>1049.782083</td>\n",
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" <td>13497.0</td>\n",
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" <td>formule adhésion</td>\n",
" <td>6.439463</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5029</th>\n",
" <td>6733</td>\n",
" <td>2</td>\n",
" <td>11656</td>\n",
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" <td>158</td>\n",
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" <td>471.0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
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" <td>2982.237384</td>\n",
" <td>489.495324</td>\n",
" <td>2492.742060</td>\n",
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" <td>9815.0</td>\n",
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" <td>offre muséale individuel</td>\n",
" <td>6.150659</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5030</th>\n",
" <td>6733</td>\n",
" <td>4</td>\n",
" <td>7440</td>\n",
add new stat
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" <td>162</td>\n",
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" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
add new stat
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" <td>1036.392674</td>\n",
" <td>426.201944</td>\n",
" <td>610.190729</td>\n",
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" <td>7419.0</td>\n",
add new stat
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" <td>spectacle vivant</td>\n",
" <td>7.762474</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>41</td>\n",
" <td>6</td>\n",
" <td>6583</td>\n",
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" <td>634</td>\n",
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" <td>12546.5</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
add new stat
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" <td>2501.337905</td>\n",
" <td>1409.370521</td>\n",
" <td>1091.967384</td>\n",
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" <td>6391.0</td>\n",
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" <td>formule adhésion</td>\n",
" <td>6.439463</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>41</td>\n",
" <td>2</td>\n",
" <td>6514</td>\n",
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" <td>812</td>\n",
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" <td>22423.0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
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" <td>3576.106609</td>\n",
" <td>247.232697</td>\n",
" <td>3328.873912</td>\n",
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" <td>5321.0</td>\n",
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" <td>offre muséale individuel</td>\n",
" <td>6.150659</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>36376</th>\n",
" <td>63488</td>\n",
" <td>4</td>\n",
" <td>5750</td>\n",
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" <td>9</td>\n",
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" <td>63250.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <td>887.298484</td>\n",
" <td>440.265162</td>\n",
" <td>447.033322</td>\n",
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" <td>5750.0</td>\n",
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" <td>spectacle vivant</td>\n",
" <td>7.762474</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" 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",
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"\n",
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" 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",
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"\n",
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" 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 "
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]
},
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"execution_count": 68,
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"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",
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"execution_count": 69,
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"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",
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"execution_count": 70,
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"id": "36141803-8865-4210-bd39-0a980301fd0c",
"metadata": {},
"outputs": [
{
"data": {
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"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",
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"execution_count": 71,
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"id": "94cf1a25-9ded-48f2-b1b2-75225bdaf49d",
"metadata": {},
"outputs": [],
"source": [
"tickets_kpi_filtered = tickets_kpi[tickets_kpi['customer_id'] != 1]"
]
},
add stat on customer
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{
"cell_type": "markdown",
"id": "b8a90eaa-c383-4f73-9fd6-6fbbe8eeefb8",
"metadata": {},
"source": [
"# 2 - Comportement d'achat bis (Alexis)"
]
},
{
"cell_type": "code",
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"execution_count": 72,
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"id": "dc45c1cd-2a78-48a6-aa2b-6a501254b6f2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"(156289, 41)\n"
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]
},
{
"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>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",
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" <th>nb_categories</th>\n",
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" <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",
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" <td>3262.190868</td>\n",
" <td>4.179306</td>\n",
" <td>3258.011562</td>\n",
add stat on customer
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" <td>51.0</td>\n",
" <td>offre muséale individuel</td>\n",
" <td>6.150659</td>\n",
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" <td>14.0</td>\n",
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" <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",
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" <td>2502.715509</td>\n",
" <td>1408.715532</td>\n",
" <td>1093.999977</td>\n",
add stat on customer
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" <td>5.0</td>\n",
" <td>formule adhésion</td>\n",
" <td>6.439463</td>\n",
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" <td>9.0</td>\n",
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" <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",
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" <td>3698.198229</td>\n",
" <td>5.221840</td>\n",
" <td>3692.976389</td>\n",
add stat on customer
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" <td>2988.0</td>\n",
" <td>spectacle vivant</td>\n",
" <td>7.762474</td>\n",
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" <td>9.0</td>\n",
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" <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",
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" <td>3803.369792</td>\n",
" <td>0.146331</td>\n",
" <td>3803.223461</td>\n",
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" <td>9.0</td>\n",
" <td>offre muséale groupe</td>\n",
" <td>4.452618</td>\n",
add new stat
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" <td>5.0</td>\n",
add stat on customer
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" <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",
add new stat
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" <td>1705.261192</td>\n",
" <td>1456.333715</td>\n",
" <td>248.927477</td>\n",
add stat on customer
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" <td>0.0</td>\n",
" <td>formule adhésion</td>\n",
" <td>6.439463</td>\n",
add new stat
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" <td>1.0</td>\n",
add stat on customer
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" <td>4.0</td>\n",
add new stat
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" <td>NaN</td>\n",
add stat on customer
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" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
add new stat
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"<p>5 rows × 41 columns</p>\n",
add stat on customer
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"</div>"
],
"text/plain": [
add new stat
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" 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",
add stat on customer
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"\n",
add new stat
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" opt_in structure_id profession language ... purchase_date_min \\\n",
"0 False NaN NaN NaN ... 3262.190868 \n",
"1 False NaN NaN NaN ... 2502.715509 \n",
"2 False NaN NaN NaN ... 3698.198229 \n",
"3 False NaN NaN NaN ... 3803.369792 \n",
"4 True NaN NaN NaN ... 1705.261192 \n",
add stat on customer
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"\n",
add new stat
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" purchase_date_max time_between_purchase nb_tickets_internet \\\n",
"0 4.179306 3258.011562 51.0 \n",
"1 1408.715532 1093.999977 5.0 \n",
"2 5.221840 3692.976389 2988.0 \n",
"3 0.146331 3803.223461 9.0 \n",
"4 1456.333715 248.927477 0.0 \n",
add stat on customer
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"\n",
add new stat
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" name_event_types avg_amount nb_categories nb_campaigns \\\n",
"0 offre muséale individuel 6.150659 14.0 NaN \n",
"1 formule adhésion 6.439463 9.0 NaN \n",
"2 spectacle vivant 7.762474 9.0 NaN \n",
"3 offre muséale groupe 4.452618 5.0 NaN \n",
"4 formule adhésion 6.439463 1.0 4.0 \n",
add stat on customer
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"\n",
add new stat
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" 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",
add stat on customer
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"\n",
add new stat
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"[5 rows x 41 columns]"
add stat on customer
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]
},
add new stat
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"execution_count": 72,
add stat on customer
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"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",
add new stat
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"execution_count": 73,
"id": "fd0d3a3b-dd5f-444f-a130-ba84c9b05347",
"metadata": {},
"outputs": [],
"source": [
"# Remove customer 1 as outlier\n",
"\n",
"customer = customer[customer['customer_id']!=1]"
]
},
{
"cell_type": "code",
"execution_count": 74,
add stat on customer
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"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",
add new stat
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" '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_categories', 'nb_campaigns',\n",
" 'nb_campaigns_opened', 'time_to_open'],\n",
add stat on customer
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" dtype='object')"
]
},
add new stat
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"execution_count": 74,
add stat on customer
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customer.columns"
]
},
{
"cell_type": "code",
add new stat
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"execution_count": 88,
add stat on customer
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"id": "d7b2356a-d5fc-4547-b3ff-fded0e304fb6",
"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>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>4</th>\n",
" <td>2</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
add new stat
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" <td>1.0</td>\n",
" <td>307</td>\n",
add stat on customer
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" <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",
add new stat
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" <td>2.0</td>\n",
" <td>3</td>\n",
add stat on customer
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" <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>2.0</td>\n",
add new stat
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" <td>2</td>\n",
add stat on customer
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" <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",
add new stat
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" <td>2.0</td>\n",
" <td>1</td>\n",
add stat on customer
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" <td>19.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
add new stat
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" <tr>\n",
" <th>10</th>\n",
" <td>6</td>\n",
" <td>15.000000</td>\n",
" <td>1.000000</td>\n",
" <td>30.000000</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>30.0</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
add stat on customer
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
add new stat
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" customer_id average_price average_purchase_delay average_price_basket \\\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",
"10 6 15.000000 1.000000 30.000000 \n",
add stat on customer
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"\n",
add new stat
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" average_ticket_basket purchase_count total_price nb_campaigns \\\n",
"4 1.0 307 0.0 4.0 \n",
"6 2.0 3 110.0 222.0 \n",
"7 2.0 2 41.0 7.0 \n",
"9 2.0 1 19.0 4.0 \n",
"10 2.0 1 30.0 20.0 \n",
add stat on customer
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"\n",
add new stat
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" nb_campaigns_opened \n",
"4 0.0 \n",
"6 124.0 \n",
"7 7.0 \n",
"9 0.0 \n",
"10 0.0 "
add stat on customer
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]
},
add new stat
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"execution_count": 88,
add stat on customer
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"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",
add new stat
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"execution_count": 89,
add stat on customer
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"id": "5559748f-1745-4651-a9f6-94702c7ee66f",
"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>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",
add new stat
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" <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",
add stat on customer
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" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
add new stat
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" <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",
add stat on customer
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" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
add new stat
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" <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",
add stat on customer
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" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
add new stat
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" <td>-44863.000000</td>\n",
add stat on customer
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" <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",
add new stat
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" <td>5.000000</td>\n",
add stat on customer
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" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>11.000000</td>\n",
" <td>0.000000</td>\n",
add new stat
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" <td>19.000000</td>\n",
add stat on customer
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" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
add new stat
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" <td>20.000000</td>\n",
" <td>32.000000</td>\n",
" <td>3.000000</td>\n",
add stat on customer
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" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
add new stat
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" <td>320.000000</td>\n",
add stat on customer
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" <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",
add new stat
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"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",
add stat on customer
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"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
add new stat
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"75% 11.000000 0.000000 19.000000 \n",
"max 320.000000 1914.000000 9900.000000 \n",
add stat on customer
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"\n",
" average_ticket_basket purchase_count total_price nb_campaigns \\\n",
add new stat
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"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",
add stat on customer
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"min 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 2.000000 \n",
add new stat
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"50% 0.000000 0.000000 0.000000 5.000000 \n",
"75% 2.000000 1.000000 20.000000 32.000000 \n",
add stat on customer
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"max 900.000000 1508.000000 64350.000000 439.000000 \n",
"\n",
" nb_campaigns_opened \n",
add new stat
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"count 151865.000000 \n",
"mean 7.870681 \n",
"std 23.119061 \n",
add stat on customer
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"min 0.000000 \n",
"25% 0.000000 \n",
"50% 1.000000 \n",
add new stat
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"75% 3.000000 \n",
add stat on customer
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"max 434.000000 "
]
},
add new stat
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"execution_count": 89,
add stat on customer
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
add new stat
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"customer_achat[['average_price', 'average_purchase_delay', 'average_price_basket',\n",
add stat on customer
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" 'average_ticket_basket', 'purchase_count', 'total_price', 'nb_campaigns', 'nb_campaigns_opened']].describe()"
]
},
{
"cell_type": "markdown",
"id": "b49c9e93-f324-42ee-a262-34ffb44a2261",
"metadata": {},
"source": [
"# 3 - Event"
]
},
{
"cell_type": "code",
add new stat
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"execution_count": 77,
add stat on customer
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"id": "4971e35d-a762-4e18-9443-fd9571bd3f1e",
"metadata": {},
"outputs": [
{
"data": {
add new stat
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"image/png": "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
add stat on customer
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"text/plain": [
add new stat
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"<Figure size 800x500 with 1 Axes>"
add stat on customer
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]
},
"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",
add new stat
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"event_counts.plot(kind='bar', figsize=(8, 5))\n",
add stat on customer
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"plt.xlabel(\"Type d'évènement\")\n",
"plt.ylabel('Nombre de consommateurs uniques')\n",
"plt.title(\"Nombre de consommateurs uniques par type d'évènement\")\n",
add new stat
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"plt.xticks(rotation=45)\n",
add stat on customer
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"plt.show()"
]
},
{
"cell_type": "code",
add new stat
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"execution_count": 78,
add stat on customer
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"id": "bc65a711-d172-4839-b487-3047280fc3a6",
"metadata": {},
"outputs": [
{
"data": {
add new stat
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"image/png": "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
add stat on customer
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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",
add new stat
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"execution_count": 79,
add stat on customer
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"id": "c95cc35c-abfc-47c7-9b8a-ac69bfd60dd8",
"metadata": {},
"outputs": [
{
"data": {
add new stat
2024-02-12 11:49:24 +01:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAwsAAAJgCAYAAADF4v+XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACNv0lEQVR4nOzdd1hT1/8H8HfYQ0BQQERkSB0IuFAL1j1x1lE3DrBWrXVbVxW31lpni6OKOCnuvbc4vgoiOMA6UKyCG3GBQs7vD3+kxiQICrmA79fz8Dzk3JvknZvkJp+cc8+VCSEEiIiIiIiIPqAjdQAiIiIiIsqfWCwQEREREZFaLBaIiIiIiEgtFgtERERERKQWiwUiIiIiIlKLxQIREREREanFYoGIiIiIiNRisUBERERERGqxWCAiIiIiIrVYLBARFWB3796FjY0Nhg8fLnUUIqJc061bN5QrVw6PHj365Nvw9/dHmTJlkJycnHvBvkA5KhZCQkIgk8lgZGSE27dvqyyvV68e3N3dcy1cTvTq1QtFihSR5L4/RiaTYeLEibl6m2lpafjjjz/wzTffwNLSEgYGBrC3t0fHjh1x7NixXL2vgqpevXqoV6/eZ9/O27dvYWtri6+//lrjOnK5HKVLl4anp+dn3586QUFBCAkJyZPb/hgnJyfIZDKN23LVqlWQyWSQyWQ4evSoVrNJ6dWrV5g4caLax5y5r7x165airVevXnByclJaz8nJCb169frkDOnp6ejcuTMaNGiA2bNnf/LtaNvnPu5Pxf2mdmX1HqHsk3L/nxuOHj2q8vnQq1evLD+fFy9ejEOHDmHv3r0oXrz4J9/30qVLUbZsWUn2N/nN9OnTsXXr1k+67if1LKSlpeGXX375pDukz/fo0SPUqlULw4YNg7u7O0JCQnDo0CH8/vvv0NXVRcOGDREdHS11zEJDX18ffn5++N///ocrV66oXefgwYO4c+cOAgIC8iSD1B8WZmZmOH78OG7cuKGyLDg4GObm5hKkktarV68wadIktV+EWrRogdOnT8POzi7L29iyZQvGjx//yRnGjBkDPT09RcFGmnG/qX1ZvUco+6Te/2vb+fPnMX78eOzevRvOzs6fdVt6enrYsGED7ty5g99++y2XEhZMn1Ms6H3KlZo1a4Z169ZhxIgRqFSp0ifdcX4ihEBqaiqMjY2ljpItPXr0QHR0NPbt24cGDRooLevcuTOGDRsGS0tLidIVTgEBAfj9998RHBys9hfc4OBgGBgYoHv37hKky3vffPMNLl68iODgYEybNk3RfuPGDRw/fhx9+vTBX3/9JWHC/MXa2hrW1tYfXa9KlSqfdT/59cMvIyMD6enpMDQ0lDqKAvebX6ZXr17BxMRE6hhZKmjfQfJa1apV8fDhw1y7vSJFiiAyMjLXbu9L9Ek9Cz///DOKFSuGUaNGfXTd1NRUjBkzBs7Ozoou3x9//FFl/JiTkxNatmyJnTt3okqVKjA2NkaFChWwc+dOAO+69StUqABTU1PUqFEDERERau/v8uXLaNiwIUxNTWFtbY2BAwfi1atXSuvIZDIMHDgQixcvRoUKFWBoaIiVK1cCAK5du4auXbvCxsYGhoaGqFChAv78889sbZeUlBR8//33KFasGIoUKYJmzZrhn3/+Ubvup95PZGQk9uzZg4CAAJUPvEzVq1dH6dKlAQAPHz7EgAED4ObmhiJFisDGxgYNGjTAiRMnlK5z69YtyGQyzJ49G3PmzIGzszOKFCkCb29vnDlzRmndiIgIdO7cGU5OTjA2NoaTkxO6dOmiMjRt4sSJan/t/HCIRnh4OPT19TFixAi16y1fvjzLbSKEwKxZs+Do6AgjIyNUrVoVe/bsUbtuSkoKRowYofR6HDJkCF6+fJnlfVSoUAHe3t5YvXo10tPTlZYlJydj27ZtaNOmDYoVK6bYRq1bt4aVlRWMjIxQpUoVrF+/Xu3jO3LkCPr374/ixYujWLFiaNeuHe7du6dYz8nJCZcvX8axY8cUw33eH86S3ce0YcMG1KxZExYWFjAxMYGLiwv8/f2zfNyZdHR00KNHD6xcuRJyuVzRHhwcDAcHBzRq1Ejt9bZv3w5vb2+YmJjAzMwMjRs3xunTpxXLT5w4AZlMhtDQUJXrZv5afu7cOUVbbm5XTTQNX3t/GNGtW7cUxcCkSZMUz0tmV7e6YUjqfDgcRy6XY+rUqShXrhyMjY1RtGhReHp6Yv78+UrX+5z9VOb+b8mSJShbtiwMDQ3h5uaGv//+W2m9nO47Zs2ahalTp8LZ2RmGhoY4cuRItvJkysvXcU73mwBw6dIltGnTBpaWljAyMkLlypUVnxOZModXhIaGYty4cShZsiTMzc3RqFEjXL16VWndqKgotGzZUvGclSxZEi1atMC///6rWEdbn5eZw3bj4uLQtGlTmJqaws7ODjNnzgQAnDlzBt988w1MTU1RtmxZlcedndfGx94jQPZex5reS+qGtmQOhT5+/Dh8fHxgYmKieG0cPnwY9erVQ7FixWBsbIzSpUujffv2Kt8PPpS5rbds2QJPT08YGRnBxcUFCxYsUFovNTUVw4cPR+XKlWFhYQErKyt4e3tj27ZtKreZ1XcQdfevbv//4sULFC1aFD/88IPKdW7dugVdXV3FjwmZ2/DAgQPo3bs3rKysYGpqilatWuHmzZsq1z948CAaNmwIc3NzmJiYoFatWjh06FCW2ylTXFwcmjVrBhMTExQvXhz9+vXD8+fPs3XdN2/eYOrUqShfvjwMDQ1hbW2N3r17KxUO3377LRwdHZU+hzLVrFkTVatWVVwWQiAoKAiVK1dW7E/btWuH69evK10v83Vz7tw51K5dW7FfmTlzpsr9ZHc/lfkcr1ixQrE/9/LywpkzZyCEwG+//ab4ntWgQQOVTED2nofM71mXL19Gly5dYGFhAVtbW/j7++PZs2dKeV6+fImVK1cqXkc5GqYtcmDFihUCgDh37pyYP3++ACAOHTqkWF63bl1RsWJFxWW5XC6aNm0q9PT0xPjx48X+/fvF7NmzhampqahSpYpITU1VrOvo6ChKlSol3N3dRWhoqNi9e7eoWbOm0NfXFxMmTBC1atUSmzdvFlu2bBFly5YVtra24tWrV4rr9+zZUxgYGIjSpUuLadOmif3794uJEycKPT090bJlS6XHAUDY29sLT09PsW7dOnH48GFx6dIlcfnyZWFhYSE8PDzEqlWrxP79+8Xw4cOFjo6OmDhxYpbbRi6Xi/r16wtDQ0PF/QcGBgoXFxcBQAQGBirW/Zz7mT59ugAg9uzZk+V6meLi4kT//v3F33//LY4ePSp27twpAgIChI6Ojjhy5Ihivfj4eAFAODk5iWbNmomtW7eKrVu3Cg8PD2FpaSmSk5MV627YsEFMmDBBbNmyRRw7dkz8/fffom7dusLa2lo8fPhQsV5gYKBQ9xLLfB3Fx8cr2mbOnCkAiG3btgkhhLh06ZIwMTER3bt3/+hjzLyfgIAAsWfPHrF06VJhb28vSpQoIerWratY7+XLl6Jy5cqiePHiYs6cOeLgwYNi/vz5wsLCQjRo0EDI5fIs72fZsmUCgNi6datS+59//ikAiL179wohhDh8+LAwMDAQtWvXFmFhYWLv3r2iV69eAoBYsWKFynZwcXERP/30k9i3b59YtmyZsLS0FPXr11esd/78eeHi4iKqVKkiTp8+LU6fPi3Onz+fo8d06tQpIZPJROfOncXu3bvF4cOHxYoVK4Sfn99Ht6+jo6No0aKFuH79upDJZGL37t1CCCHS09OFvb29mDBhgtiwYYMAoPSaWrt2rQAgmjRpIrZu3SrCwsJEtWrVhIGBgThx4oRivSpVqohatWqp3G/16tVF9erVFZdze7tqUrduXaXXTaaePXsKR0dHIYQQqampYu/evYrXXebzcv36daUM77/G37/++9u2Z8+eisszZswQurq6IjAwUBw6dEjs3btXzJs3T2m/8Dn7DyHe7f8cHByEm5ubCA0NFdu3bxfNmjUTAMSGDRsU6+V032Fvby/q168vNm7cKPbv36/02D/04ePO69fxp+w
add stat on customer
2024-02-11 11:47:58 +01:00
"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",
add new stat
2024-02-12 11:49:24 +01:00
"execution_count": 80,
add stat on customer
2024-02-11 11:47:58 +01:00
"id": "49d5fd2d-9bc1-43ac-9270-1efd73759854",
"metadata": {},
"outputs": [
{
"data": {
add new stat
2024-02-12 11:49:24 +01:00
"image/png": "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
add stat on customer
2024-02-11 11:47:58 +01:00
"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",
add new stat
2024-02-12 11:49:24 +01:00
"execution_count": 81,
add stat on customer
2024-02-11 11:47:58 +01:00
"id": "dc071992-cf4d-4b9f-9c3b-3f0e98e20eff",
"metadata": {},
add new stat
2024-02-12 11:49:24 +01:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Nombre moyen de participation par consommateur par type d'événement\n",
"\n",
"participation = customer.groupby('name_event_types')['nb_purchases'].mean()\n",
"participation.plot(kind='bar', figsize=(8, 5))\n",
"plt.xlabel(\"Type d'évènements\")\n",
"plt.ylabel('Nombre Moyen de Participation par Consommateur')\n",
"plt.title(\"Nombre Moyen de Participation par Consommateur par type d'événement\")\n",
"plt.xticks(rotation=45)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "fc1229bc-6e4d-45ab-8474-2618d749d873",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Nombre Moyen de Catégories de Produits Achetés par les Consommateurs par Type d'Evénement\n",
"\n",
"cat = customer.groupby('name_event_types')['nb_categories'].mean()\n",
"cat.plot(kind='bar', figsize=(8, 5))\n",
"plt.xlabel(\"Type d'évènements\")\n",
"plt.ylabel('Nombre Moyen de Catégories par les Consommateurs')\n",
"plt.title(\"Nombre Moyen de Catégories de Produits Achetés par les Consommateurs par Type d'Evénement\")\n",
"plt.xticks(rotation=45)\n",
"plt.show()"
]
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},
{
"cell_type": "markdown",
"id": "4f9561a9-6a94-434e-b8e7-9b708f5b5529",
"metadata": {},
"source": [
"# 3 - Caractéristiques Démographiques (peu exploitable)"
]
},
{
"cell_type": "code",
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"execution_count": 83,
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"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",
add new stat
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" '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_categories', 'nb_campaigns',\n",
" 'nb_campaigns_opened', 'time_to_open'],\n",
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" dtype='object')"
]
},
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"execution_count": 83,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customer.columns"
]
},
{
"cell_type": "code",
add new stat
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"execution_count": 84,
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"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",
add new stat
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"execution_count": 85,
add stat on customer
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"id": "58af5dcb-673e-4f4d-ad5c-f66ce1e8a22c",
"metadata": {},
"outputs": [
{
"data": {
add new stat
2024-02-12 11:49:24 +01:00
"image/png": "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
add stat on customer
2024-02-11 11:47:58 +01:00
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"percent_of_na(customer, 'profession')"
]
},
{
"cell_type": "code",
add new stat
2024-02-12 11:49:24 +01:00
"execution_count": 86,
add stat on customer
2024-02-11 11:47:58 +01:00
"id": "cc3437f7-8b36-4398-9da6-ff15e8e4c8d7",
"metadata": {},
"outputs": [
{
"data": {
add new stat
2024-02-12 11:49:24 +01:00
"image/png": "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
add stat on customer
2024-02-11 11:47:58 +01:00
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"percent_of_na(customer, 'language')"
]
},
Ajout graph circulaire
2024-02-10 22:05:09 +01:00
{
"cell_type": "code",
add new stat
2024-02-12 11:49:24 +01:00
"execution_count": 87,
add stat on customer
2024-02-11 11:47:58 +01:00
"id": "c34164d2-5ab2-4923-a165-30dc5c070233",
Ajout graph circulaire
2024-02-10 22:05:09 +01:00
"metadata": {},
add new stat
2024-02-12 11:49:24 +01:00
"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, 'birthdate')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07a7c145-8efa-42d3-a934-8f5038277fd7",
"metadata": {},
Ajout graph circulaire
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"outputs": [],
"source": []
Ajout notebook pour statistiques descriptives
2024-02-10 19:37:38 +01:00
}
],
"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",
add new stat
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"version": "3.11.6"
Ajout notebook pour statistiques descriptives
2024-02-10 19:37:38 +01:00
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink