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{
"cells": [
{
"cell_type": "markdown",
"id": "5bf5c226",
"metadata": {},
"source": [
"# Business Data Challenge - Team 1"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b1a5b9d3",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import s3fs\n",
"import re\n",
"import warnings"
]
},
{
"cell_type": "markdown",
"id": "ecfa2219",
"metadata": {},
"source": [
"Configuration de l'accès aux données"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1a094277",
"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": "0294ce71-840e-458b-8ffa-cadabbc6da21",
"metadata": {},
"source": [
"# Debut Travail 25/02"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "30d77451-2df6-4c07-8b15-66e0e990ff03",
"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})\n",
"\n",
"\n",
"# Import cleaning and merge functions\n",
"exec(open('0_KPI_functions.py').read())\n",
"\n",
"# Ignore warning\n",
"warnings.filterwarnings('ignore')\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f1b44d3e-76bb-4860-b9db-a2840db7cf39",
"metadata": {},
"outputs": [],
"source": [
"def load_dataset_2(directory_path, file_name):\n",
" \"\"\"\n",
" This function loads csv file\n",
" \"\"\"\n",
" file_path = \"bdc2324-data\" + \"/\" + directory_path + \"/\" + directory_path + file_name + \".csv\"\n",
" with fs.open(file_path, mode=\"rb\") as file_in:\n",
" df = pd.read_csv(file_in, sep=\",\")\n",
"\n",
" # drop na :\n",
" #df = df.dropna(axis=1, thresh=len(df))\n",
" # if identifier in table : delete it\n",
" if 'identifier' in df.columns:\n",
" df = df.drop(columns = 'identifier')\n",
" return df"
]
},
{
"cell_type": "markdown",
"id": "ca2c8b6a-4965-422e-ba7c-66423a464fc1",
"metadata": {},
"source": [
"## Base communes au types Musée"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f8f988fb-5aab-4b57-80d1-e242f7e5b384",
"metadata": {},
"outputs": [],
"source": [
"companies = {'musee' : ['1', '2', '3', '4', '101'],\n",
" 'sport': ['5', '6', '7', '8', '9'],\n",
" 'musique' : ['10', '11', '12', '13', '14']}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dbce1124-9a22-4502-a47a-fc3d0e2db70b",
"metadata": {},
"outputs": [],
"source": [
"companies['musee']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5080f66e-f779-410a-876d-b4fe2795e17e",
"metadata": {},
"outputs": [],
"source": [
"for i in companies['musique']:\n",
" BUCKET = \"bdc2324-data/\"+i\n",
" liste_base = []\n",
" for base in fs.ls(BUCKET):\n",
" match = re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', base)\n",
" if match:\n",
" nom_base = match.group(3)\n",
" liste_base.append(nom_base)\n",
" globals()['base_'+i] = liste_base\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abd477e1-7479-4c88-a5aa-f987af3f5b79",
"metadata": {},
"outputs": [],
"source": [
"# Trouver l'intersection entre les cinq listes\n",
"intersection = set(base_1).intersection(base_2, base_3, base_4, base_101)\n",
"\n",
"# Convertir le résultat en liste si nécessaire\n",
"intersection_liste = list(intersection)\n",
"\n",
"print(intersection_liste)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8d93888f-a511-4ee5-8bc3-d5173a7f119e",
"metadata": {},
"outputs": [],
"source": [
"# Trouver l'intersection entre les cinq listes\n",
"intersection = set(base_10).intersection(base_12, base_13, base_14, base_11)\n",
"\n",
"# Convertir le résultat en liste si nécessaire\n",
"intersection_liste = list(intersection)\n",
"\n",
"print(intersection_liste)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10e89669-42bb-4652-a4bc-1a3d1caf4d1a",
"metadata": {},
"outputs": [],
"source": [
"len(intersection_liste)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7d058b21-a538-4f59-aefb-ef7966f73fdc",
"metadata": {},
"outputs": [],
"source": [
"df1_tags = load_dataset_2(\"1\", \"tags\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aa441f99-733c-4675-8676-bed4682d3324",
"metadata": {},
"outputs": [],
"source": [
"df1_structure_tag_mappings = load_dataset_2(\"1\", 'structure_tag_mappings')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6767a750-14a4-4c05-903e-d2f07170825b",
"metadata": {},
"outputs": [],
"source": [
"df1_customersplus = load_dataset_2(\"1\", \"customersplus\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "125e9145-a815-46fd-bdf4-07589508b259",
"metadata": {},
"outputs": [],
"source": [
"df1_customersplus.groupby('structure_id')['id'].count().reset_index().sort_values('id', ascending=False).head(20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c17a6976-792f-474d-bcff-c89396eddb3f",
"metadata": {},
"outputs": [],
"source": [
"df1_customersplus['structure_id'].isna().sum() / len(df1_customersplus['structure_id'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecfc155a-cb42-46ec-8da5-33fdcd087355",
"metadata": {},
"outputs": [],
"source": [
"len(df1_structure_tag_mappings)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "071410b8-950d-4fcc-b2b9-57415253c286",
"metadata": {},
"outputs": [],
"source": [
"df1_structure_tag_mappings.groupby('tag_id')['structure_id'].count().reset_index().sort_values('structure_id', ascending=False).head(20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f48d27a9-14e4-4bb9-a60a-73e9438b58fc",
"metadata": {},
"outputs": [],
"source": [
"?np.sort_values()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "14eaa0ea-02cc-430b-ab9b-38e6637810c3",
"metadata": {},
"outputs": [],
"source": [
"def info_colonnes_dataframe(df):\n",
" # Créer une liste pour stocker les informations sur chaque colonne\n",
" infos_colonnes = []\n",
"\n",
" # Parcourir les colonnes du DataFrame\n",
" for nom_colonne, serie in df.items(): # Utiliser items() au lieu de iteritems()\n",
" # Calculer le taux de valeurs manquantes\n",
" taux_na = serie.isna().mean() * 100\n",
"\n",
" # Ajouter les informations à la liste\n",
" infos_colonnes.append({\n",
" 'Nom_colonne': nom_colonne,\n",
" 'Type_colonne': str(serie.dtype),\n",
" 'Taux_NA': taux_na\n",
" })\n",
"\n",
" # Créer une nouvelle DataFrame à partir de la liste d'informations\n",
" df_infos_colonnes = pd.DataFrame(infos_colonnes)\n",
"\n",
" return df_infos_colonnes"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b031c32-d4c8-42a5-9a71-a7810f9bf8d8",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"info_colonnes_dataframe(df1_tags)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1a87f27-c4d4-4832-ac20-0c3c54aa4980",
"metadata": {},
"outputs": [],
"source": [
"info_colonnes_dataframe(df1_structure_tag_mappings)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fa5c65a8-2f74-4f3f-85fc-9ac91e0bb361",
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_colwidth', None)\n",
"\n",
"print(df1_tags['name'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a59bf932-5b54-4600-81f5-c55ac93ae510",
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_rows', None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4ab298e-2cae-4865-9f00-4caff5f75ea1",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"print(df1_tags['name'])"
]
},
{
"cell_type": "markdown",
"id": "76bffba1-5f7e-4308-9224-437ca66148f8",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## KPI sur target_type"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d91d5895",
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_colwidth', None)\n"
]
},
{
"cell_type": "markdown",
"id": "c58b17d3",
"metadata": {},
"source": [
"Raisonnement : on prends les target_type qui représente 90% des clients et on fait des catégories dessus."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d74426b3",
"metadata": {},
"outputs": [],
"source": [
"targets = load_dataset_2(\"3\", \"targets\")\n",
"target_types = load_dataset_2(\"3\", \"target_types\")\n",
"\n",
"# target_all = pd.merge(targets, target_types, left_on= 'target_type_id', right_on= 'id' ,how = 'inner')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6930bff5",
"metadata": {},
"outputs": [],
"source": [
"def print_main_target(tenant_id, nb_print = 40):\n",
" df_target = display_databases(tenant_id, \"target_information\")\n",
"\n",
" print('Nombre de ciblage : ', len(df_target))\n",
" nb_customers = df_target['customer_id'].nunique()\n",
" print('Nombre de client avec étiquette target : ', nb_customers) \n",
"\n",
" nb_custumers_per_target = df_target.groupby(\"target_name\")['customer_id'].count().reset_index().sort_values('customer_id', ascending=False)\n",
" nb_custumers_per_target['cumulative_customers'] = nb_custumers_per_target['customer_id'].cumsum()/len(df_target)\n",
" nb_custumers_per_target['customer_id'] = nb_custumers_per_target['customer_id']/nb_customers\n",
"\n",
" return nb_custumers_per_target.head(nb_print)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1e7ee1a0",
"metadata": {},
"outputs": [],
"source": [
"print_main_target('1')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b57a28ac",
"metadata": {},
"outputs": [],
"source": [
"print_main_target('2', 25)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a65991f",
"metadata": {},
"outputs": [],
"source": [
"print_main_target('3', 40)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c66a4dc1",
"metadata": {},
"outputs": [],
"source": [
"pd.set_option('display.max_rows', None)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5f34b8bf",
"metadata": {},
"outputs": [],
"source": [
"print_main_target('4', 80)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "40fe3676",
"metadata": {},
"outputs": [],
"source": [
"print_main_target('101', 100)"
]
},
{
"cell_type": "markdown",
"id": "605cced5-052f-4a99-ac26-020c5d2ab633",
"metadata": {},
"source": [
"## KPI sur tags"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "916c3e2b-04d3-4877-b894-8f26f10d926e",
"metadata": {},
"outputs": [],
"source": [
"customersplus = load_dataset_2(\"4\", \"customersplus\")[['id', 'structure_id']]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "46847b24-15a4-464e-969f-f16ed3653f1f",
"metadata": {},
"outputs": [],
"source": [
"structure_tag_mappings = load_dataset_2('4', \"structure_tag_mappings\")[['structure_id', 'tag_id']]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "3c10c69d-735f-453e-96bf-750697d965d0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"19427"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"customersplus[customersplus['structure_id'].notna()]['structure_id'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "9b0e77b3-5f16-4484-9564-7d3826583418",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"33645"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(customersplus[customersplus['structure_id'].notna()])"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "dfa27722-37f9-435a-8221-8aa6f9a4a107",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3431"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"structure_tag_mappings['structure_id'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "2daabdd5-31e3-4918-9856-9bbc30cde602",
"metadata": {},
"outputs": [],
"source": [
"def tags_information(tenant_id, first_tags):\n",
"\n",
" customersplus = load_dataset_2(tenant_id, \"customersplus\")[['id', 'structure_id']]\n",
" customersplus.rename(columns = {'id' : 'customer_id'}, inplace = True)\n",
" tags = load_dataset_2(tenant_id, \"tags\")[['id', 'name']]\n",
" tags.rename(columns = {'id' : 'tag_id', 'name' : 'tag_name'}, inplace = True)\n",
" structure_tag_mappings = load_dataset_2(tenant_id, \"structure_tag_mappings\")[['structure_id', 'tag_id']]\n",
" \n",
" customer_tags = pd.merge(customersplus, structure_tag_mappings, on = 'structure_id', how = 'left')\n",
" customer_tags = pd.merge(customer_tags, tags, on = 'tag_id', how = 'inner')\n",
" \n",
" nb_customers_with_tag = customer_tags['customer_id'].nunique()\n",
" \n",
" print('Nombre de client avec tag : ', nb_customers_with_tag)\n",
" print('Proportion de clients avec tags : ', nb_customers_with_tag/len(customersplus))\n",
" print('Moyenne de tags par client : ', len(customer_tags)/nb_customers_with_tag)\n",
" \n",
" info = customer_tags.groupby(['tag_id', 'tag_name'])['customer_id'].count().reset_index().sort_values('customer_id', ascending = False).head(first_tags)\n",
"\n",
" return info"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "0b9f5f71-a927-4cc8-bb0c-9538e28d3553",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nombre de client avec tag : 13320\n",
"Proportion de clients avec tags : 0.0877089012682233\n",
"Moyenne de tags par client : 2.1725975975975977\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>tag_id</th>\n",
" <th>tag_name</th>\n",
" <th>customer_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11029.0</td>\n",
" <td>individuels</td>\n",
" <td>3270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>11047.0</td>\n",
" <td>groupes scolaires</td>\n",
" <td>2417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>11033.0</td>\n",
" <td>association</td>\n",
" <td>2308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>11028.0</td>\n",
" <td>structures culturelles</td>\n",
" <td>2011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>11051.0</td>\n",
" <td>etablissement ens scolaire</td>\n",
" <td>1732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11036.0</td>\n",
" <td>champ social</td>\n",
" <td>1603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>11072.0</td>\n",
" <td>etab d'enseignement</td>\n",
" <td>1036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>11043.0</td>\n",
" <td>etablissement public</td>\n",
" <td>935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>11035.0</td>\n",
" <td>organisme de tourisme</td>\n",
" <td>892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>11045.0</td>\n",
" <td>centre de loisirs</td>\n",
" <td>864</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>11073.0</td>\n",
" <td>musée, site &amp; fondation</td>\n",
" <td>786</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>11053.0</td>\n",
" <td>groupes etudiants</td>\n",
" <td>758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11032.0</td>\n",
" <td>entreprise</td>\n",
" <td>750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11039.0</td>\n",
" <td>etablissement d'enseignement</td>\n",
" <td>741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>11034.0</td>\n",
" <td>asso. culturelle</td>\n",
" <td>692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>11044.0</td>\n",
" <td>administration et collectivité</td>\n",
" <td>676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>11046.0</td>\n",
" <td>tour opérateur</td>\n",
" <td>642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>11048.0</td>\n",
" <td>entreprises</td>\n",
" <td>515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>11619.0</td>\n",
" <td>structures culturelles;musée, site &amp; fondation</td>\n",
" <td>427</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>11037.0</td>\n",
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" <td>426</td>\n",
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],
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" tag_id tag_name customer_id\n",
"1 11029.0 individuels 3270\n",
"18 11047.0 groupes scolaires 2417\n",
"4 11033.0 association 2308\n",
"0 11028.0 structures culturelles 2011\n",
"22 11051.0 etablissement ens scolaire 1732\n",
"7 11036.0 champ social 1603\n",
"43 11072.0 etab d'enseignement 1036\n",
"14 11043.0 etablissement public 935\n",
"6 11035.0 organisme de tourisme 892\n",
"16 11045.0 centre de loisirs 864\n",
"44 11073.0 musée, site & fondation 786\n",
"24 11053.0 groupes etudiants 758\n",
"3 11032.0 entreprise 750\n",
"10 11039.0 etablissement d'enseignement 741\n",
"5 11034.0 asso. culturelle 692\n",
"15 11044.0 administration et collectivité 676\n",
"17 11046.0 tour opérateur 642\n",
"19 11048.0 entreprises 515\n",
"72 11619.0 structures culturelles;musée, site & fondation 427\n",
"8 11037.0 handicap 426"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tags_information(\"1\", 20)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "bd5bef41-1774-4601-86b5-b7c1aea8f1d2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nombre de client avec tag : 5953\n",
"Proportion de clients avec tags : 0.021598421025897787\n",
"Moyenne de tags par client : 1.0\n"
]
},
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"cell_type": "code",
"execution_count": 42,
"id": "c7b2c670-7122-4f67-b1aa-8c80a10f16d8",
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Nombre de client avec tag : 23659\n",
"Proportion de clients avec tags : 0.09207484608139978\n",
"Moyenne de tags par client : 3.0620482691576143\n"
]
},
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" <td>43667.0</td>\n",
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" <th>78</th>\n",
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],
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" tag_id tag_name customer_id\n",
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"16 7028.0 lycee 1577\n",
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"15 7024.0 centre de loisirs 1080\n",
"153 44515.0 entreprise 998\n",
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"152 44514.0 particulier 838\n",
"36 43663.0 président 816\n",
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]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tags_information(\"3\", 20)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "76639995-252d-4a58-83d8-c0c00900c3a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nombre de client avec tag : 10495\n",
"Proportion de clients avec tags : 0.03271416949025744\n",
"Moyenne de tags par client : 5.298427822772749\n"
]
},
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" <td>130.0</td>\n",
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" <th>3</th>\n",
" <td>51.0</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>64.0</td>\n",
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],
"text/plain": [
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]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tags_information(\"4\", 20)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "07e91791-d4d4-42b1-ac18-22d3b0b9f7bd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nombre de client avec tag : 532342\n",
"Proportion de clients avec tags : 0.18660686931118298\n",
"Moyenne de tags par client : 24.114082676174338\n"
]
},
{
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" <th>31</th>\n",
" <td>379.0</td>\n",
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" <tr>\n",
" <th>30</th>\n",
" <td>364.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>6.0</td>\n",
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" <th>18</th>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>13.0</td>\n",
" <td>enseignement superieur</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>359.0</td>\n",
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" <td>492078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>7186.0</td>\n",
" <td>individuel</td>\n",
" <td>491913</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7.0</td>\n",
" <td>groupe amis / famille</td>\n",
" <td>491900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.0</td>\n",
" <td>client internet</td>\n",
" <td>491896</td>\n",
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"</table>\n",
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],
"text/plain": [
" tag_id tag_name customer_id\n",
"20 349.0 clients internet 517491\n",
"24 356.0 associations / clubs 495520\n",
"5 10.0 agence de voyages 493774\n",
"32 410.0 guides conférenciers 493378\n",
"26 360.0 groupe amis ou famille 493021\n",
"23 354.0 ce / entreprises 493016\n",
"8 17.0 association/club 493008\n",
"1 3.0 c.e. / entreprise 492656\n",
"6 11.0 college 492552\n",
"13 69.0 tour operator 492549\n",
"4 9.0 ecole primaire 492540\n",
"31 379.0 parent goûter anniversaire 492468\n",
"30 364.0 institutions 492364\n",
"2 6.0 institution 492321\n",
"18 186.0 autocaristes 492153\n",
"7 13.0 enseignement superieur 492131\n",
"25 359.0 hotels / campings 492078\n",
"42 7186.0 individuel 491913\n",
"3 7.0 groupe amis / famille 491900\n",
"0 2.0 client internet 491896"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tags_information(\"101\", 20)"
]
},
{
"cell_type": "markdown",
"id": "87d131cd-ead0-4ef4-a8ee-b09022d08ffa",
"metadata": {},
"source": [
"## KPI product"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "26582be9-cfd1-48ea-a0a7-31101fdeb9d1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"File path : projet-bdc2324-team1/0_Input/Company_1/products_purchased_reduced.csv\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\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>ticket_id</th>\n",
" <th>customer_id</th>\n",
" <th>purchase_id</th>\n",
" <th>event_type_id</th>\n",
" <th>supplier_name</th>\n",
" <th>purchase_date</th>\n",
" <th>amount</th>\n",
" <th>is_full_price</th>\n",
" <th>name_event_types</th>\n",
" <th>name_facilities</th>\n",
" <th>name_categories</th>\n",
" <th>name_events</th>\n",
" <th>name_seasons</th>\n",
" <th>start_date_time</th>\n",
" <th>end_date_time</th>\n",
" <th>open</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>13070859</td>\n",
" <td>48187</td>\n",
" <td>5107462</td>\n",
" <td>4</td>\n",
" <td>vente en ligne</td>\n",
" <td>2018-12-28 14:47:50+00:00</td>\n",
" <td>8.0</td>\n",
" <td>False</td>\n",
" <td>spectacle vivant</td>\n",
" <td>mucem</td>\n",
" <td>indiv prog enfant</td>\n",
" <td>l'école des magiciens</td>\n",
" <td>2018</td>\n",
" <td>2018-12-31 14:15:00+01:00</td>\n",
" <td>1901-01-01 00:09:21+00:09</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>13070860</td>\n",
" <td>48187</td>\n",
" <td>5107462</td>\n",
" <td>4</td>\n",
" <td>vente en ligne</td>\n",
" <td>2018-12-28 14:47:50+00:00</td>\n",
" <td>4.0</td>\n",
" <td>False</td>\n",
" <td>spectacle vivant</td>\n",
" <td>mucem</td>\n",
" <td>indiv prog enfant</td>\n",
" <td>l'école des magiciens</td>\n",
" <td>2018</td>\n",
" <td>2018-12-31 14:15:00+01:00</td>\n",
" <td>1901-01-01 00:09:21+00:09</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>13070861</td>\n",
" <td>48187</td>\n",
" <td>5107462</td>\n",
" <td>4</td>\n",
" <td>vente en ligne</td>\n",
" <td>2018-12-28 14:47:50+00:00</td>\n",
" <td>4.0</td>\n",
" <td>False</td>\n",
" <td>spectacle vivant</td>\n",
" <td>mucem</td>\n",
" <td>indiv prog enfant</td>\n",
" <td>l'école des magiciens</td>\n",
" <td>2018</td>\n",
" <td>2018-12-31 14:15:00+01:00</td>\n",
" <td>1901-01-01 00:09:21+00:09</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>13070862</td>\n",
" <td>48187</td>\n",
" <td>5107462</td>\n",
" <td>4</td>\n",
" <td>vente en ligne</td>\n",
" <td>2018-12-28 14:47:50+00:00</td>\n",
" <td>4.0</td>\n",
" <td>False</td>\n",
" <td>spectacle vivant</td>\n",
" <td>mucem</td>\n",
" <td>indiv prog enfant</td>\n",
" <td>l'école des magiciens</td>\n",
" <td>2018</td>\n",
" <td>2018-12-31 14:15:00+01:00</td>\n",
" <td>1901-01-01 00:09:21+00:09</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>13070863</td>\n",
" <td>48187</td>\n",
" <td>5107462</td>\n",
" <td>4</td>\n",
" <td>vente en ligne</td>\n",
" <td>2018-12-28 14:47:50+00:00</td>\n",
" <td>4.0</td>\n",
" <td>False</td>\n",
" <td>spectacle vivant</td>\n",
" <td>mucem</td>\n",
" <td>indiv prog enfant</td>\n",
" <td>l'école des magiciens</td>\n",
" <td>2018</td>\n",
" <td>2018-12-31 14:15:00+01:00</td>\n",
" <td>1901-01-01 00:09:21+00:09</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ticket_id customer_id purchase_id event_type_id supplier_name \\\n",
"0 13070859 48187 5107462 4 vente en ligne \n",
"1 13070860 48187 5107462 4 vente en ligne \n",
"2 13070861 48187 5107462 4 vente en ligne \n",
"3 13070862 48187 5107462 4 vente en ligne \n",
"4 13070863 48187 5107462 4 vente en ligne \n",
"\n",
" purchase_date amount is_full_price name_event_types \\\n",
"0 2018-12-28 14:47:50+00:00 8.0 False spectacle vivant \n",
"1 2018-12-28 14:47:50+00:00 4.0 False spectacle vivant \n",
"2 2018-12-28 14:47:50+00:00 4.0 False spectacle vivant \n",
"3 2018-12-28 14:47:50+00:00 4.0 False spectacle vivant \n",
"4 2018-12-28 14:47:50+00:00 4.0 False spectacle vivant \n",
"\n",
" name_facilities name_categories name_events name_seasons \\\n",
"0 mucem indiv prog enfant l'école des magiciens 2018 \n",
"1 mucem indiv prog enfant l'école des magiciens 2018 \n",
"2 mucem indiv prog enfant l'école des magiciens 2018 \n",
"3 mucem indiv prog enfant l'école des magiciens 2018 \n",
"4 mucem indiv prog enfant l'école des magiciens 2018 \n",
"\n",
" start_date_time end_date_time open \n",
"0 2018-12-31 14:15:00+01:00 1901-01-01 00:09:21+00:09 True \n",
"1 2018-12-31 14:15:00+01:00 1901-01-01 00:09:21+00:09 True \n",
"2 2018-12-31 14:15:00+01:00 1901-01-01 00:09:21+00:09 True \n",
"3 2018-12-31 14:15:00+01:00 1901-01-01 00:09:21+00:09 True \n",
"4 2018-12-31 14:15:00+01:00 1901-01-01 00:09:21+00:09 True "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tenant_id = \"1\"\n",
"\n",
"df_product = display_databases(tenant_id, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])\n",
"\n",
"df_product.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "533bf499-dd56-4d29-b261-ca1e4928c9c7",
"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>name_event_types</th>\n",
" <th>name_events</th>\n",
" <th>ticket_id</th>\n",
" <th>prop_tickets</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>offre muséale groupe</td>\n",
" <td>visite générale du mucem (1h30)</td>\n",
" <td>43814</td>\n",
" <td>0.024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>212</th>\n",
" <td>offre muséale individuel</td>\n",
" <td>visite autonome scolaires (2h00)</td>\n",
" <td>34423</td>\n",
" <td>0.019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>offre muséale groupe</td>\n",
" <td>visite autonome exposition (1h30)</td>\n",
" <td>26489</td>\n",
" <td>0.015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>210</th>\n",
" <td>offre muséale individuel</td>\n",
" <td>visite autonome adultes (2h00)</td>\n",
" <td>22065</td>\n",
" <td>0.012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>offre muséale groupe</td>\n",
" <td>visites des exterieurs scolaires</td>\n",
" <td>15595</td>\n",
" <td>0.009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>364</th>\n",
" <td>spectacle vivant</td>\n",
" <td>kay ! lettres à un poète disparu</td>\n",
" <td>1</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>443</th>\n",
" <td>spectacle vivant</td>\n",
" <td>mauvais genre</td>\n",
" <td>1</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\n",
" <td>spectacle vivant</td>\n",
" <td>la madre que parió a la música</td>\n",
" <td>1</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>260</th>\n",
" <td>spectacle vivant</td>\n",
" <td>ali a les yeux bleus (dès 12 ans)</td>\n",
" <td>1</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>484</th>\n",
" <td>spectacle vivant</td>\n",
" <td>rengaine (dès 12 ans)</td>\n",
" <td>1</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>544 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" name_event_types name_events ticket_id \\\n",
"118 offre muséale groupe visite générale du mucem (1h30) 43814 \n",
"212 offre muséale individuel visite autonome scolaires (2h00) 34423 \n",
"68 offre muséale groupe visite autonome exposition (1h30) 26489 \n",
"210 offre muséale individuel visite autonome adultes (2h00) 22065 \n",
"160 offre muséale groupe visites des exterieurs scolaires 15595 \n",
".. ... ... ... \n",
"364 spectacle vivant kay ! lettres à un poète disparu 1 \n",
"443 spectacle vivant mauvais genre 1 \n",
"375 spectacle vivant la madre que parió a la música 1 \n",
"260 spectacle vivant ali a les yeux bleus (dès 12 ans) 1 \n",
"484 spectacle vivant rengaine (dès 12 ans) 1 \n",
"\n",
" prop_tickets \n",
"118 0.024 \n",
"212 0.019 \n",
"68 0.015 \n",
"210 0.012 \n",
"160 0.009 \n",
".. ... \n",
"364 0.000 \n",
"443 0.000 \n",
"375 0.000 \n",
"260 0.000 \n",
"484 0.000 \n",
"\n",
"[544 rows x 4 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nb_tickets_per_events = df_product.groupby(['name_event_types', 'name_events'])['ticket_id'].count().reset_index().sort_values('ticket_id', ascending = False)\n",
"nb_tickets_per_events['prop_tickets'] = round(nb_tickets_per_events['ticket_id']/len(df_product), 3)\n",
"nb_tickets_per_events"
]
},
{
"cell_type": "markdown",
"id": "1ede9eaa-7f0a-4856-9349-b2747d6a4901",
"metadata": {},
"source": [
"# Fin travail 25/02"
]
},
{
"cell_type": "markdown",
"id": "c437eaec",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"# Exemple sur Company 1"
]
},
{
"cell_type": "markdown",
"id": "a1c1fc39",
"metadata": {},
"source": [
"## Chargement données"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "66f8c17b",
"metadata": {},
"outputs": [],
"source": [
"BUCKET = \"bdc2324-data/1\"\n",
"liste_database = fs.ls(BUCKET)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c08e6798",
"metadata": {},
"outputs": [],
"source": [
"liste_database_select = ['suppliers', 'ticket', 'purchase', 'consumption', 'type_ofs']\n",
"\n",
"# Filtrer la liste pour les éléments contenant au moins un élément de la liste à tester\n",
"liste_database_filtered = [element for element in liste_database if any(element_part in element for element_part in liste_database_select)]\n",
"\n",
"# Afficher le résultat\n",
"print(liste_database_filtered)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "675f518d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<<<<<<< local <modified: >\n",
"/tmp/ipykernel_445/4081512283.py:10: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" df = pd.read_csv(file_in)\n",
"=======\n",
"/tmp/ipykernel_15285/4081512283.py:10: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" df = pd.read_csv(file_in)\n",
">>>>>>> remote <modified: >\n"
]
}
],
"source": [
"# loop to create dataframes from liste\n",
"files_path = liste_database\n",
"\n",
"client_number = files_path[0].split(\"/\")[1]\n",
"df_prefix = \"df\" + str(client_number) + \"_\"\n",
"\n",
"for i in range(len(files_path)) :\n",
" current_path = files_path[i]\n",
" with fs.open(current_path, mode=\"rb\") as file_in:\n",
" df = pd.read_csv(file_in)\n",
" # the pattern of the name is df1xxx\n",
" nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n",
" globals()[nom_dataframe] = df"
]
},
{
"cell_type": "markdown",
"id": "e855f403",
"metadata": {},
"source": [
"## customersplus.csv"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "91a8f8c4",
"metadata": {},
"outputs": [],
"source": [
"a = pd.DataFrame(df1_customersplus.info())"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "2fda171d",
"metadata": {},
"outputs": [],
"source": [
"def info_colonnes_dataframe(df):\n",
" # Créer une liste pour stocker les informations sur chaque colonne\n",
" infos_colonnes = []\n",
"\n",
" # Parcourir les colonnes du DataFrame\n",
" for nom_colonne, serie in df.items(): # Utiliser items() au lieu de iteritems()\n",
" # Calculer le taux de valeurs manquantes\n",
" taux_na = serie.isna().mean() * 100\n",
"\n",
" # Ajouter les informations à la liste\n",
" infos_colonnes.append({\n",
" 'Nom_colonne': nom_colonne,\n",
" 'Type_colonne': str(serie.dtype),\n",
" 'Taux_NA': taux_na\n",
" })\n",
"\n",
" # Créer une nouvelle DataFrame à partir de la liste d'informations\n",
" df_infos_colonnes = pd.DataFrame(infos_colonnes)\n",
"\n",
" return df_infos_colonnes"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "205eeeab",
"metadata": {},
"outputs": [],
"source": [
"def cleaning_date(df, column_name):\n",
" \"\"\"\n",
" Nettoie la colonne spécifiée du DataFrame en convertissant les valeurs en datetime avec le format ISO8601.\n",
"\n",
" Parameters:\n",
" - df: DataFrame\n",
" Le DataFrame contenant la colonne à nettoyer.\n",
" - column_name: str\n",
" Le nom de la colonne à nettoyer.\n",
"\n",
" Returns:\n",
" - DataFrame\n",
" Le DataFrame modifié avec la colonne nettoyée.\n",
" \"\"\"\n",
" df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "634282c5",
"metadata": {},
"outputs": [],
"source": [
"a = info_colonnes_dataframe(df1_customersplus)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "0e8d4133",
"metadata": {},
"outputs": [],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "1268ad5a",
"metadata": {},
"outputs": [],
"source": [
"a = pd.DataFrame(df1_customersplus.isna().sum()/len(df1_customersplus)*100)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "bd41dc80",
"metadata": {},
"outputs": [],
"source": [
"# Selection des variables\n",
"df1_customersplus_clean = df1_customersplus.copy()\n",
"\n",
"cleaning_date(df1_customersplus_clean, 'first_buying_date')\n",
"cleaning_date(df1_customersplus_clean, 'last_visiting_date')\n",
"\n",
"df1_customersplus_clean.drop(['lastname', 'firstname', 'email', 'civility', 'note', 'created_at', 'updated_at', 'deleted_at', 'extra', 'reference', 'extra_field', 'identifier', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'zipcode', 'last_visiting_date'], axis = 1, inplace=True)\n",
"df1_customersplus_clean.rename(columns = {'id' : 'customer_id'}, inplace = True)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "64d0f76b",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## tickets.csv"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7e683711",
"metadata": {},
"outputs": [],
"source": [
"df1_tickets"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e7b9a52e",
"metadata": {},
"outputs": [],
"source": [
"df1_tickets.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "568280e8",
"metadata": {},
"outputs": [],
"source": [
"df1_tickets.isna().sum()/len(df1_tickets)*100"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "29ecec90",
"metadata": {},
"outputs": [],
"source": [
"# Selection des variables\n",
"df1_tickets_clean = df1_tickets.drop(['lastname', 'firstname', 'email', 'created_at', 'updated_at', 'extra', 'reference', 'extra_field', 'identifier', 'need_reload', 'preferred_category', 'preferred_supplier', 'preferred_formula', 'zipcode'], axis = 1, inplace=True)\n",
"df1_tickets_clean.rename(columns = {'id' : 'customer_id'}, inplace = True)"
]
},
{
"cell_type": "markdown",
"id": "22bb5de4",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## suppliers.csv"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "6a9a91f4",
"metadata": {},
"outputs": [],
"source": [
"df1_suppliers"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "bab4758a",
"metadata": {},
"outputs": [],
"source": [
"df1_suppliers.info()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b5fff251",
"metadata": {},
"outputs": [],
"source": [
"df1_suppliers.isna().sum()/len(df1_suppliers)*100"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8b09e2a3",
"metadata": {},
"outputs": [],
"source": [
"# Selection des variables\n",
"df1_suppliers_clean = df1_suppliers[['id', 'name']]\n",
"df1_suppliers_clean.rename(columns = {'name' : 'supplier_name'}, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ecee7cdc",
"metadata": {},
"outputs": [],
"source": [
"df1_suppliers_clean"
]
},
{
"cell_type": "markdown",
"id": "c8e6e69b",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## type_ofs.csv"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "1a6cff1f",
"metadata": {},
"outputs": [],
"source": [
"df1_type_ofs"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "93630b41",
"metadata": {},
"outputs": [],
"source": [
"df1_type_ofs.info()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "4f94481a",
"metadata": {},
"outputs": [],
"source": [
"# Selection des variables\n",
"df1_type_ofs_clean = df1_type_ofs[['id', 'name', 'children']]\n",
"df1_type_ofs_clean.rename(columns = {'name' : 'type_of_ticket_name'}, inplace = True)"
]
},
{
"cell_type": "markdown",
"id": "1b2811e2",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## purchases.csv"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "2455d2e1",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df1_purchases"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "5f9a159d",
"metadata": {},
"outputs": [],
"source": [
"df1_purchases.info()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "db201bf7",
"metadata": {},
"outputs": [],
"source": [
"# Nettoyage purchase_date\n",
"df1_purchases['purchase_date'] = pd.to_datetime(df1_purchases['purchase_date'], utc = True)\n",
"df1_purchases['purchase_date'] = pd.to_datetime(df1_purchases['purchase_date'], format = 'ISO8601')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "bd436fca",
"metadata": {},
"outputs": [],
"source": [
"df1_purchases.info()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "83435862",
"metadata": {},
"outputs": [],
"source": [
"# Selection des variables\n",
"df1_purchases_clean = df1_purchases[['id', 'purchase_date', 'customer_id']]"
]
},
{
"cell_type": "markdown",
"id": "f210e730",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Fusion de l'ensemble des données billétiques"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "1f8b3aa7",
"metadata": {},
"outputs": [],
"source": [
"# Fusion avec fournisseurs\n",
"df1_ticket_information = pd.merge(df1_tickets_clean, df1_suppliers_clean, left_on = 'supplier_id', right_on = 'id', how = 'inner')\n",
"df1_ticket_information.drop(['supplier_id', 'id'], axis = 1, inplace=True)\n",
"\n",
"# Fusion avec type de tickets\n",
"df1_ticket_information = pd.merge(df1_ticket_information, df1_type_ofs_clean, left_on = 'type_of', right_on = 'id', how = 'inner')\n",
"df1_ticket_information.drop(['type_of', 'id'], axis = 1, inplace=True)\n",
"\n",
"# Fusion avec achats\n",
"df1_ticket_information = pd.merge(df1_ticket_information, df1_purchases_clean, left_on = 'purchase_id', right_on = 'id', how = 'inner')\n",
"df1_ticket_information.drop(['purchase_id', 'id'], axis = 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "83a4d021",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df1_ticket_information"
]
},
{
"cell_type": "markdown",
"id": "56e6ebd1",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"# Utilisation de fonctions"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "88fcde4b",
"metadata": {},
"outputs": [],
"source": [
"# Créer un DataFrame exemple\n",
"df_not_clean = df1_campaign_stats[['opened_at']].head(20)\n",
"\n",
"# Appliquer la fonction pour nettoyer la colonne 'purchase_date' de manière vectorisée\n",
"df_clean = cleaning_date(df_not_clean, 'opened_at')\n",
"df_clean.rename(columns = {'opened_at' : 'opened_at_clean'}, inplace = True)\n",
"\n",
"test = pd.concat([df1_campaign_stats[['opened_at']].head(20), df_clean], axis=1)\n",
"\n",
"test.info()"
]
},
{
"cell_type": "markdown",
"id": "818f69db",
"metadata": {},
"source": [
"## Nettoyage, selection et fusion"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "c9654eda",
"metadata": {},
"outputs": [],
"source": [
"df1_ticket_information"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7f2b620c",
"metadata": {},
"outputs": [],
"source": [
"df1_ticket_information.info()"
]
},
{
"cell_type": "markdown",
"id": "637bdb72",
"metadata": {},
"source": [
"# Customer information"
]
},
{
"cell_type": "markdown",
"id": "14c52894",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Target area"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d83abfbf",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_15285/2625134041.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df1_targets_clean.rename(columns = {'id' : 'target_id' , 'name' : 'target_name'}, inplace = True)\n"
]
}
],
"source": [
"# Target.csv cleaning\n",
"df1_targets_clean = df1_targets[[\"id\", \"target_type_id\", \"name\"]]\n",
"df1_targets_clean.rename(columns = {'id' : 'target_id' , 'name' : 'target_name'}, inplace = True)\n",
"\n",
"# target_type cleaning\n",
"df1_target_types_clean = df1_target_types[[\"id\",\"is_import\",\"name\"]].add_prefix(\"target_type_\")\n",
"\n",
"#customer_target_mappings cleaning\n",
"df1_customer_target_mappings_clean = df1_customer_target_mappings[[\"id\", \"customer_id\", \"target_id\"]]\n",
"\n",
"# Merge target et target_type\n",
"df1_targets_full = pd.merge(df1_targets_clean, df1_target_types_clean, left_on='target_type_id', right_on='target_type_id', how='inner')\n",
"df1_targets_full.drop(['target_type_id'], axis = 1, inplace=True)\n",
"\n",
"# Merge\n",
"df1_targets_full = pd.merge(df1_customer_target_mappings_clean, df1_targets_full, left_on='target_id', right_on='target_id', how='inner')\n",
"df1_targets_full.drop(['target_id'], axis = 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "90d71b2c",
"metadata": {},
"outputs": [],
"source": [
"df1_targets_test = df1_targets_full[['id', 'customer_id']].groupby(['customer_id']).count()\n",
"len(df1_targets_test[df1_targets_test['id'] > 1]) / len(df1_targets_test)\n",
"\n",
"# 99,6% des 151 000 client visés sont catégorisés plusieurs fois et en moyenne 5 fois... \n",
"df1_targets_test.mean()\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2301de1e",
"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>id</th>\n",
" <th>customer_id</th>\n",
" <th>target_name</th>\n",
" <th>target_type_is_import</th>\n",
" <th>target_type_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1184824</td>\n",
" <td>645400</td>\n",
" <td>DDCP PROMO Réseau livres</td>\n",
" <td>False</td>\n",
" <td>manual_static_filter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>210571</td>\n",
" <td>2412</td>\n",
" <td>DDCP PROMO Réseau livres</td>\n",
" <td>False</td>\n",
" <td>manual_static_filter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>210572</td>\n",
" <td>4536</td>\n",
" <td>DDCP PROMO Réseau livres</td>\n",
" <td>False</td>\n",
" <td>manual_static_filter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>210573</td>\n",
" <td>6736</td>\n",
" <td>DDCP PROMO Réseau livres</td>\n",
" <td>False</td>\n",
" <td>manual_static_filter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>210574</td>\n",
" <td>38210</td>\n",
" <td>DDCP PROMO Réseau livres</td>\n",
" <td>False</td>\n",
" <td>manual_static_filter</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id customer_id target_name target_type_is_import \\\n",
"0 1184824 645400 DDCP PROMO Réseau livres False \n",
"1 210571 2412 DDCP PROMO Réseau livres False \n",
"2 210572 4536 DDCP PROMO Réseau livres False \n",
"3 210573 6736 DDCP PROMO Réseau livres False \n",
"4 210574 38210 DDCP PROMO Réseau livres False \n",
"\n",
" target_type_name \n",
"0 manual_static_filter \n",
"1 manual_static_filter \n",
"2 manual_static_filter \n",
"3 manual_static_filter \n",
"4 manual_static_filter "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_targets_full.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "75fbc2f7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /home/onyxia/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package stopwords to /home/onyxia/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n",
"[nltk_data] Downloading package wordnet to /home/onyxia/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Catégorisation des target_name\n",
"import pandas as pd\n",
"import nltk\n",
"from nltk.tokenize import word_tokenize\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem import WordNetLemmatizer\n",
"from nltk.probability import FreqDist\n",
"\n",
"# Téléchargement des ressources nécessaires\n",
"nltk.download('punkt')\n",
"nltk.download('stopwords')\n",
"nltk.download('wordnet')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "55cddf92",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mots les plus fréquents:\n",
"consentement: 550777\n",
"optin: 463579\n",
"jeune: 155103\n",
"public: 155103\n",
"mediation: 150001\n"
]
}
],
"source": [
"# Définition des fonctions de tokenisation, suppression des mots vides et lemmatisation\n",
"def preprocess_text(texte):\n",
" # Concaténation des éléments de la liste en une seule chaîne de caractères\n",
" texte_concat = ' '.join(texte)\n",
" \n",
" # Tokenisation des mots\n",
" tokens = word_tokenize(texte_concat.lower())\n",
" \n",
" # Suppression des mots vides (stopwords)\n",
" stop_words = set(stopwords.words('french'))\n",
" filtered_tokens = [word for word in tokens if word not in stop_words]\n",
" \n",
" # Lemmatisation des mots\n",
" lemmatizer = WordNetLemmatizer()\n",
" lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]\n",
" \n",
" return lemmatized_tokens\n",
"\n",
"\n",
"# Appliquer le prétraitement à la colonne de texte\n",
"df1_targets_full['target_name_tokened'] = df1_targets_full['target_name'].apply(preprocess_text)\n",
"\n",
"# Concaténer les listes de mots pour obtenir une liste de tous les mots dans le corpus\n",
"all_words = [word for tokens in df1_targets_full['target_name_tokened'] for word in tokens]\n",
"\n",
"# Calculer la fréquence des mots\n",
"freq_dist = FreqDist(all_words)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "7fd98a85",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mots les plus fréquents:\n",
"consentement: 550777\n",
"optin: 463579\n",
"jeune: 155103\n",
"public: 155103\n",
"mediation: 150001\n",
"specialisee: 150001\n",
"b2c: 143432\n",
"optout: 97683\n",
"newsletter: 56022\n",
"(: 46084\n",
"): 46084\n",
"inscrits: 42296\n",
"nl: 42294\n",
"générale: 41037\n",
"generale: 40950\n"
]
}
],
"source": [
"# Affichage des mots les plus fréquents\n",
"print(\"Mots les plus fréquents:\")\n",
"for mot, freq in freq_dist.most_common(15):\n",
" print(f\"{mot}: {freq}\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "cf94bb1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" texte \\\n",
"0 Le chat noir mange une souris. \n",
"1 Le chien blanc aboie. \n",
"\n",
" texte_preprocessed \n",
"0 [e, h, a, o, i, r, a, g, e, u, e, o, u, r, i, .] \n",
"1 [e, h, i, e, b, a, a, b, o, i, e, .] \n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /home/onyxia/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package stopwords to /home/onyxia/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n",
"[nltk_data] Downloading package wordnet to /home/onyxia/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n"
]
}
],
"source": [
"import pandas as pd\n",
"import nltk\n",
"from nltk.tokenize import word_tokenize\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem import WordNetLemmatizer\n",
"\n",
"# Téléchargement des ressources nécessaires\n",
"nltk.download('punkt')\n",
"nltk.download('stopwords')\n",
"nltk.download('wordnet')\n",
"\n",
"# Création de la DataFrame d'exemple\n",
"data = {'texte': [\"Le chat noir mange une souris.\", \"Le chien blanc aboie.\"]}\n",
"df = pd.DataFrame(data)\n",
"\n",
"# Fonction pour prétraiter le texte\n",
"def preprocess_text(texte):\n",
" # Concaténation des éléments de la liste en une seule chaîne de caractères\n",
" texte_concat = ' '.join(texte)\n",
" \n",
" # Tokenisation des mots\n",
" tokens = word_tokenize(texte_concat.lower())\n",
" \n",
" # Suppression des mots vides (stopwords)\n",
" stop_words = set(stopwords.words('french'))\n",
" filtered_tokens = [word for word in tokens if word not in stop_words]\n",
" \n",
" # Lemmatisation des mots\n",
" lemmatizer = WordNetLemmatizer()\n",
" lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]\n",
" \n",
" return lemmatized_tokens\n",
"\n",
"# Appliquer la fonction de prétraitement à la colonne de texte\n",
"df['texte_preprocessed'] = df['texte'].apply(preprocess_text)\n",
"\n",
"# Afficher le résultat\n",
"print(df)\n"
]
},
{
"cell_type": "markdown",
"id": "711d3884",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Campaign area"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "c25b5295",
"metadata": {},
"outputs": [],
"source": [
"# campaign_stats cleaning \n",
"df1_campaign_stats_clean = df1_campaign_stats[[\"id\", \"campaign_id\", \"customer_id\", \"opened_at\", \"sent_at\", \"delivered_at\"]]\n",
"cleaning_date(df1_campaign_stats_clean, 'opened_at')\n",
"cleaning_date(df1_campaign_stats_clean, 'sent_at')\n",
"cleaning_date(df1_campaign_stats_clean, 'delivered_at')\n",
"\n",
"# campaigns cleaning\n",
"df1_campaigns_clean = df1_campaigns[[\"id\", \"name\", \"service_id\", \"sent_at\"]].add_prefix(\"campaign_\")\n",
"cleaning_date(df1_campaigns_clean, 'campaign_sent_at')\n",
"\n",
"# Merge \n",
"df1_campaigns_full = pd.merge(df1_campaign_stats_clean, df1_campaigns_clean, on = \"campaign_id\", how = \"left\")\n",
"df1_campaigns_full.drop(['campaign_id'], axis = 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "2a3de6a5",
"metadata": {},
"outputs": [],
"source": [
"df1_campaigns_full.info()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "3fc1f446",
"metadata": {},
"outputs": [],
"source": [
"df1_campaigns_information"
]
},
{
"cell_type": "markdown",
"id": "20e69ee3",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Link area"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "d9cbdbce",
"metadata": {},
"outputs": [],
"source": [
"df1_campaigns"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "c07459f0",
"metadata": {},
"outputs": [],
"source": [
"df1_link_stats"
]
},
{
"cell_type": "markdown",
"id": "80ae4c42",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Supplier"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b50b8f95",
"metadata": {},
"outputs": [],
"source": [
"# Fonction d'exploration pour suppliers.csv = label itr et commission inconnues\n",
"def suppliers_exploration(suppliers = None) : \n",
" \n",
" # Taux de NaN pour ces colonnes\n",
" label_na = suppliers['label'].isna().sum()/len(suppliers)*100\n",
" itr_na = suppliers['itr'].isna().sum()/len(suppliers)*100\n",
" commission_na = suppliers['commission'].isna().sum()/len(suppliers)*100\n",
"\n",
" suppliers_desc = pd.DataFrame({'nb_suppliers' : [suppliers['name'].nunique()],\n",
" 'label_na' : [label_na],\n",
" 'itr_na' : [itr_na],\n",
" 'commission_na' : [commission_na]})\n",
"\n",
" return suppliers_desc"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7e292935",
"metadata": {},
"outputs": [],
"source": [
"df1_suppliers_desc = suppliers_exploration(suppliers = df1_suppliers)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "05b6f2b0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>nb_suppliers</th>\n",
" <th>label_na</th>\n",
" <th>itr_na</th>\n",
" <th>commission_na</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" nb_suppliers label_na itr_na commission_na\n",
"0 9 100.0 100.0 100.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_suppliers_desc"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c9324d80",
"metadata": {},
"outputs": [],
"source": [
"BUCKET = \"bdc2324-data\"\n",
"liste_folders = fs.ls(BUCKET)\n",
"\n",
"liste_files = []\n",
"for company_folder in liste_folders : \n",
" liste_files.extend(fs.ls(company_folder))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "10304058",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['bdc2324-data/1/1suppliers.csv', 'bdc2324-data/10/10suppliers.csv', 'bdc2324-data/101/101suppliers.csv', 'bdc2324-data/11/11suppliers.csv', 'bdc2324-data/12/12suppliers.csv', 'bdc2324-data/13/13suppliers.csv', 'bdc2324-data/14/14suppliers.csv', 'bdc2324-data/2/2suppliers.csv', 'bdc2324-data/3/3suppliers.csv', 'bdc2324-data/4/4suppliers.csv', 'bdc2324-data/5/5suppliers.csv', 'bdc2324-data/6/6suppliers.csv', 'bdc2324-data/7/7suppliers.csv', 'bdc2324-data/8/8suppliers.csv', 'bdc2324-data/9/9suppliers.csv']\n"
]
}
],
"source": [
"liste_database_select = ['suppliers']\n",
"\n",
"# Filtrer la liste pour les éléments contenant au moins un élément de la liste à tester\n",
"liste_suppliers = [element for element in liste_files if any(element_part in element for element_part in liste_database_select)]\n",
"\n",
"# Afficher le résultat\n",
"print(liste_suppliers)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "ffa423e5",
"metadata": {},
"outputs": [],
"source": [
"# loop to create dataframes from file 2\n",
"def database_loading(database_name = None):\n",
" files_path = database_name\n",
" \n",
" client_number = files_path.split(\"/\")[1]\n",
" df_prefix = \"df\" + str(client_number) + \"_\"\n",
" \n",
" current_path = files_path\n",
" with fs.open(current_path, mode=\"rb\") as file_in:\n",
" df = pd.read_csv(file_in)\n",
"\n",
" return df, client_number"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "70bdc88d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 45,
"id": "6a0f567d",
"metadata": {},
"outputs": [],
"source": [
"df_all = pd.DataFrame()\n",
"\n",
"for link in liste_suppliers:\n",
" \n",
" df_supplier, tenant_id = database_loading(link)\n",
" \n",
" df_supplier['tenant_id'] = int(tenant_id)\n",
"\n",
" df_all = pd.concat([df_all, df_supplier], axis = 0)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "1522d8cd",
"metadata": {},
"outputs": [],
"source": [
"# df_all[df_all['tenant_id'] == 101]['name'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "b0e42a61",
"metadata": {},
"outputs": [],
"source": [
"liste_mots = ['en ligne', 'internet', 'web', 'net', 'vad', 'online'] \n",
"# vad = vente à distance\n",
"df_all['name'] = df_all['name'].fillna('')\n",
"\n",
"df_all['canal_vente_internet'] = df_all['name'].str.contains('|'.join(liste_mots), case=False).astype(int)\n"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "d299ae91",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tenant_id\n",
"1 1\n",
"2 1\n",
"3 1\n",
"4 1\n",
"5 1\n",
"6 1\n",
"7 1\n",
"8 1\n",
"9 1\n",
"10 1\n",
"11 1\n",
"12 1\n",
"13 1\n",
"14 1\n",
"101 1\n",
"Name: canal_vente_internet, dtype: int64"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_all.groupby('tenant_id')['canal_vente_internet'].max()"
]
}
],
"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
}
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