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{
"cells": [
{
"cell_type": "markdown",
"id": "56b3d44e-1e3f-4726-9916-0f9af107860e",
"metadata": {},
"source": [
"# Business Data Challenge - Team 1"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "15103481-8d74-404c-aa09-7601fe7730da",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import s3fs\n",
"import re"
]
},
{
"cell_type": "markdown",
"id": "c3bb0d13-34b2-4e1c-9985-468cd87c5a0e",
"metadata": {},
"source": [
"Configuration de l'accès aux données"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5d83bb1a-d341-446e-91f6-1c428607f6d4",
"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": "f99da24f-0d93-4618-92bc-3ba81dc0445c",
"metadata": {},
"source": [
"# Exemple sur Company 1"
]
},
{
"cell_type": "markdown",
"id": "9d74b68f-ba07-4a15-9a27-dae931762d70",
"metadata": {},
"source": [
"## Chargement données"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "699664b9-eee4-4f8d-a207-e524526560c5",
"metadata": {},
"outputs": [],
"source": [
"BUCKET = \"bdc2324-data/1\"\n",
"liste_database = fs.ls(BUCKET)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "aaf64d60-bf92-470c-8210-d09abd6a653e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['bdc2324-data/1/1campaign_stats.csv',\n",
" 'bdc2324-data/1/1campaigns.csv',\n",
" 'bdc2324-data/1/1categories.csv',\n",
" 'bdc2324-data/1/1countries.csv',\n",
" 'bdc2324-data/1/1currencies.csv',\n",
" 'bdc2324-data/1/1customer_target_mappings.csv',\n",
" 'bdc2324-data/1/1customersplus.csv',\n",
" 'bdc2324-data/1/1event_types.csv',\n",
" 'bdc2324-data/1/1events.csv',\n",
" 'bdc2324-data/1/1facilities.csv',\n",
" 'bdc2324-data/1/1link_stats.csv',\n",
" 'bdc2324-data/1/1pricing_formulas.csv',\n",
" 'bdc2324-data/1/1product_packs.csv',\n",
" 'bdc2324-data/1/1products.csv',\n",
" 'bdc2324-data/1/1products_groups.csv',\n",
" 'bdc2324-data/1/1purchases.csv',\n",
" 'bdc2324-data/1/1representation_category_capacities.csv',\n",
" 'bdc2324-data/1/1representations.csv',\n",
" 'bdc2324-data/1/1seasons.csv',\n",
" 'bdc2324-data/1/1structure_tag_mappings.csv',\n",
" 'bdc2324-data/1/1suppliers.csv',\n",
" 'bdc2324-data/1/1tags.csv',\n",
" 'bdc2324-data/1/1target_types.csv',\n",
" 'bdc2324-data/1/1targets.csv',\n",
" 'bdc2324-data/1/1tickets.csv',\n",
" 'bdc2324-data/1/1type_of_categories.csv',\n",
" 'bdc2324-data/1/1type_of_pricing_formulas.csv',\n",
" 'bdc2324-data/1/1type_ofs.csv']"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"liste_database"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0cb92854-903b-4efd-ac1b-197e29f044b4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['bdc2324-data/1/1purchases.csv', 'bdc2324-data/1/1suppliers.csv', 'bdc2324-data/1/1tickets.csv', 'bdc2324-data/1/1type_ofs.csv']\n"
]
}
],
"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": 5,
"id": "dd6a3518-b752-4a1e-b77b-9e03e853c3ed",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_9792/4081512283.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"
]
}
],
"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": "e908f516-2a74-45d6-8492-7dcdc3afbe1f",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## tickets.csv"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "14f4158e-c9c0-4beb-826a-5e0f949434a4",
"metadata": {},
"outputs": [
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" id number created_at \\\n",
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"\n",
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},
"execution_count": 6,
"metadata": {},
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],
"source": [
"df1_tickets"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f3c35394-b586-4ae4-b5ab-b03bb01bb618",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1826672 entries, 0 to 1826671\n",
"Data columns (total 11 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 id int64 \n",
" 1 number object \n",
" 2 created_at object \n",
" 3 updated_at object \n",
" 4 purchase_id int64 \n",
" 5 product_id int64 \n",
" 6 is_from_subscription bool \n",
" 7 type_of int64 \n",
" 8 supplier_id int64 \n",
" 9 barcode float64\n",
" 10 identifier object \n",
"dtypes: bool(1), float64(1), int64(5), object(4)\n",
"memory usage: 141.1+ MB\n"
]
}
],
"source": [
"df1_tickets.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c1b42769-03c7-4785-92ce-5e1e6b41908d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"id 0.0\n",
"number 0.0\n",
"created_at 0.0\n",
"updated_at 0.0\n",
"purchase_id 0.0\n",
"product_id 0.0\n",
"is_from_subscription 0.0\n",
"type_of 0.0\n",
"supplier_id 0.0\n",
"barcode 100.0\n",
"identifier 0.0\n",
"dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_tickets.isna().sum()/len(df1_tickets)*100"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "42896791-2d93-4725-a50b-6c7cbe535ec7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_619/232847087.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_tickets_clean.rename(columns = {'id' : 'ticket_id'}, inplace = True)\n"
]
}
],
"source": [
"# Selection des variables\n",
"df1_tickets_clean = df1_tickets[['id', 'purchase_id', 'product_id', 'is_from_subscription', 'type_of', 'supplier_id']]\n",
"df1_tickets_clean.rename(columns = {'id' : 'ticket_id'}, inplace = True)"
]
},
{
"cell_type": "markdown",
"id": "78453f3c-4f89-44ed-a6c6-2a7443b72b52",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## suppliers.csv"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2e0dada0-9457-484c-aa55-77e44613ecca",
"metadata": {},
"outputs": [
{
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" <td>NaN</td>\n",
" <td>2021-07-29 09:21:37.325772+02:00</td>\n",
" <td>2021-07-29 09:21:37.325772+02:00</td>\n",
" <td>NaN</td>\n",
" <td>5958b2a060ac3e31678b438892a1bd2e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>8</td>\n",
" <td>non défini</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-09-03 13:16:35.329062+02:00</td>\n",
" <td>2020-09-03 13:16:35.329062+02:00</td>\n",
" <td>NaN</td>\n",
" <td>52ff3466787b4d538407372e5f7afe0f</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>vad</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-09-03 13:11:23.896992+02:00</td>\n",
" <td>2020-09-03 13:11:23.896992+02:00</td>\n",
" <td>NaN</td>\n",
" <td>1225483c97b36018cab2bea14ab78ea6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>fort saint jean</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-09-03 13:11:23.833073+02:00</td>\n",
" <td>2020-09-03 13:11:23.833073+02:00</td>\n",
" <td>NaN</td>\n",
" <td>001b9b4a524fe407150b8235b304d4ec</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>j4</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-09-03 13:11:23.888993+02:00</td>\n",
" <td>2020-09-03 13:11:23.888993+02:00</td>\n",
" <td>NaN</td>\n",
" <td>6a0cf6edf20060344b465706b61719aa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>revendeur</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-09-03 13:11:23.900987+02:00</td>\n",
" <td>2020-09-03 13:11:23.900987+02:00</td>\n",
" <td>NaN</td>\n",
" <td>931239d4acb6214d7e5c98edecfb4916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3</td>\n",
" <td>vente en ligne</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-09-03 13:11:23.893097+02:00</td>\n",
" <td>2020-09-03 13:11:23.893097+02:00</td>\n",
" <td>NaN</td>\n",
" <td>bde8f2ccff510df8572d3214d86b837d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>6</td>\n",
" <td>ccr</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-09-03 13:11:23.904974+02:00</td>\n",
" <td>2020-09-03 13:11:23.904974+02:00</td>\n",
" <td>NaN</td>\n",
" <td>b48ec279411f7dbbb68393c61a9724d9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7</td>\n",
" <td>dab</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-09-03 13:11:23.908970+02:00</td>\n",
" <td>2020-09-03 13:11:23.908970+02:00</td>\n",
" <td>NaN</td>\n",
" <td>11c6d471fa4e354e62e684d293694202</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id name manually_added label itr \\\n",
"0 1617 j4 administration False NaN NaN \n",
"1 8 non défini False NaN NaN \n",
"2 4 vad False NaN NaN \n",
"3 1 fort saint jean False NaN NaN \n",
"4 2 j4 False NaN NaN \n",
"5 5 revendeur False NaN NaN \n",
"6 3 vente en ligne False NaN NaN \n",
"7 6 ccr False NaN NaN \n",
"8 7 dab False NaN NaN \n",
"\n",
" updated_at created_at \\\n",
"0 2021-07-29 09:21:37.325772+02:00 2021-07-29 09:21:37.325772+02:00 \n",
"1 2020-09-03 13:16:35.329062+02:00 2020-09-03 13:16:35.329062+02:00 \n",
"2 2020-09-03 13:11:23.896992+02:00 2020-09-03 13:11:23.896992+02:00 \n",
"3 2020-09-03 13:11:23.833073+02:00 2020-09-03 13:11:23.833073+02:00 \n",
"4 2020-09-03 13:11:23.888993+02:00 2020-09-03 13:11:23.888993+02:00 \n",
"5 2020-09-03 13:11:23.900987+02:00 2020-09-03 13:11:23.900987+02:00 \n",
"6 2020-09-03 13:11:23.893097+02:00 2020-09-03 13:11:23.893097+02:00 \n",
"7 2020-09-03 13:11:23.904974+02:00 2020-09-03 13:11:23.904974+02:00 \n",
"8 2020-09-03 13:11:23.908970+02:00 2020-09-03 13:11:23.908970+02:00 \n",
"\n",
" commission identifier \n",
"0 NaN 5958b2a060ac3e31678b438892a1bd2e \n",
"1 NaN 52ff3466787b4d538407372e5f7afe0f \n",
"2 NaN 1225483c97b36018cab2bea14ab78ea6 \n",
"3 NaN 001b9b4a524fe407150b8235b304d4ec \n",
"4 NaN 6a0cf6edf20060344b465706b61719aa \n",
"5 NaN 931239d4acb6214d7e5c98edecfb4916 \n",
"6 NaN bde8f2ccff510df8572d3214d86b837d \n",
"7 NaN b48ec279411f7dbbb68393c61a9724d9 \n",
"8 NaN 11c6d471fa4e354e62e684d293694202 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_suppliers"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b583be02-ab60-4e14-9325-0204f203a1af",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 9 entries, 0 to 8\n",
"Data columns (total 9 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 id 9 non-null int64 \n",
" 1 name 9 non-null object \n",
" 2 manually_added 9 non-null bool \n",
" 3 label 0 non-null float64\n",
" 4 itr 0 non-null float64\n",
" 5 updated_at 9 non-null object \n",
" 6 created_at 9 non-null object \n",
" 7 commission 0 non-null float64\n",
" 8 identifier 9 non-null object \n",
"dtypes: bool(1), float64(3), int64(1), object(4)\n",
"memory usage: 713.0+ bytes\n"
]
}
],
"source": [
"df1_suppliers.info()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "6d7f338e-e4d3-422b-9cdc-dec967c0b28e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"id 0.0\n",
"name 0.0\n",
"manually_added 0.0\n",
"label 100.0\n",
"itr 100.0\n",
"updated_at 0.0\n",
"created_at 0.0\n",
"commission 100.0\n",
"identifier 0.0\n",
"dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_suppliers.isna().sum()/len(df1_suppliers)*100"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3c645ab7-16bf-4054-9ae2-15a8c32e29c6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_619/302783287.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_suppliers_clean.rename(columns = {'name' : 'supplier_name'}, inplace = True)\n"
]
}
],
"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": "4de7e2e2-6da4-4618-8444-b524399c5493",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>supplier_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1617</td>\n",
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" <td>8</td>\n",
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" <th>2</th>\n",
" <td>4</td>\n",
" <td>vad</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>fort saint jean</td>\n",
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" <th>4</th>\n",
" <td>2</td>\n",
" <td>j4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>revendeur</td>\n",
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" <th>6</th>\n",
" <td>3</td>\n",
" <td>vente en ligne</td>\n",
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" <th>7</th>\n",
" <td>6</td>\n",
" <td>ccr</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7</td>\n",
" <td>dab</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id supplier_name\n",
"0 1617 j4 administration\n",
"1 8 non défini\n",
"2 4 vad\n",
"3 1 fort saint jean\n",
"4 2 j4\n",
"5 5 revendeur\n",
"6 3 vente en ligne\n",
"7 6 ccr\n",
"8 7 dab"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_suppliers_clean"
]
},
{
"cell_type": "markdown",
"id": "0a6df975-c7fc-45bc-92af-a0bdab17d795",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## type_ofs.csv"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "a02f6594-3e91-4e87-bbb6-649c28d4f7e9",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>children</th>\n",
" <th>created_at</th>\n",
" <th>updated_at</th>\n",
" <th>identifier</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Atelier</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-01-05 11:55:51.188106+01:00</td>\n",
" <td>2021-01-05 11:55:51.188106+01:00</td>\n",
" <td>623ec4067827558b28972cf39fe81ee7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Billet en nombre</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-01-11 12:13:19.286301+01:00</td>\n",
" <td>2021-01-11 12:13:19.286301+01:00</td>\n",
" <td>a53d313a97296ee37caa066dbfe7a45c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Groupe</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-01-11 12:19:22.842917+01:00</td>\n",
" <td>2021-01-11 12:19:22.842917+01:00</td>\n",
" <td>1ab143efc3b85acbbc752fe8eb2b0b86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Revendeur</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-01-12 12:34:20.481236+01:00</td>\n",
" <td>2021-01-12 12:34:20.481236+01:00</td>\n",
" <td>8b332723366a07e1eef5f1c92f9ae067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Cinéma scolaire</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-01-25 19:16:05.141719+01:00</td>\n",
" <td>2021-01-25 19:16:05.141719+01:00</td>\n",
" <td>a12e62cb4c4f47e7406bd8fbff2bfe30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>Musée famille</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-01-25 19:23:06.692627+01:00</td>\n",
" <td>2021-01-25 19:23:06.692627+01:00</td>\n",
" <td>1ec6c19283111ccb3ed67f52d414470e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>Spectacle famille</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-01-25 19:28:21.390016+01:00</td>\n",
" <td>2021-01-25 19:28:21.390016+01:00</td>\n",
" <td>05e2104f1b74ced229c06847d6e91938</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>Masterclass</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-01-25 19:31:05.076904+01:00</td>\n",
" <td>2021-01-25 19:31:05.076904+01:00</td>\n",
" <td>9cc946edfb25e11b4282f58db16e6ae9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>Spectacle</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-01-25 19:38:41.260535+01:00</td>\n",
" <td>2021-01-25 19:38:41.260535+01:00</td>\n",
" <td>d88321c347f0e0ab101184cdf25c94bf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>Cinema</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-02-05 11:12:31.932576+01:00</td>\n",
" <td>2021-02-05 11:12:31.932576+01:00</td>\n",
" <td>0870fef2bfcd5b30a12e4f5c7f4aaba7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>Musee</td>\n",
" <td>pricing_formula</td>\n",
" <td>2021-02-05 11:52:05.468207+01:00</td>\n",
" <td>2021-02-05 11:52:05.468207+01:00</td>\n",
" <td>8ba8934454cc62c7cdb3eb6e1b39df0c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>Tarifs plein</td>\n",
" <td>category</td>\n",
" <td>2023-03-13 11:31:50.528331+01:00</td>\n",
" <td>2023-03-13 11:31:50.528331+01:00</td>\n",
" <td>a6969df76efc15d157be48e87a7bcf9a</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id name children created_at \\\n",
"0 1 Atelier pricing_formula 2021-01-05 11:55:51.188106+01:00 \n",
"1 2 Billet en nombre pricing_formula 2021-01-11 12:13:19.286301+01:00 \n",
"2 3 Groupe pricing_formula 2021-01-11 12:19:22.842917+01:00 \n",
"3 4 Revendeur pricing_formula 2021-01-12 12:34:20.481236+01:00 \n",
"4 5 Cinéma scolaire pricing_formula 2021-01-25 19:16:05.141719+01:00 \n",
"5 6 Musée famille pricing_formula 2021-01-25 19:23:06.692627+01:00 \n",
"6 7 Spectacle famille pricing_formula 2021-01-25 19:28:21.390016+01:00 \n",
"7 8 Masterclass pricing_formula 2021-01-25 19:31:05.076904+01:00 \n",
"8 9 Spectacle pricing_formula 2021-01-25 19:38:41.260535+01:00 \n",
"9 10 Cinema pricing_formula 2021-02-05 11:12:31.932576+01:00 \n",
"10 11 Musee pricing_formula 2021-02-05 11:52:05.468207+01:00 \n",
"11 12 Tarifs plein category 2023-03-13 11:31:50.528331+01:00 \n",
"\n",
" updated_at identifier \n",
"0 2021-01-05 11:55:51.188106+01:00 623ec4067827558b28972cf39fe81ee7 \n",
"1 2021-01-11 12:13:19.286301+01:00 a53d313a97296ee37caa066dbfe7a45c \n",
"2 2021-01-11 12:19:22.842917+01:00 1ab143efc3b85acbbc752fe8eb2b0b86 \n",
"3 2021-01-12 12:34:20.481236+01:00 8b332723366a07e1eef5f1c92f9ae067 \n",
"4 2021-01-25 19:16:05.141719+01:00 a12e62cb4c4f47e7406bd8fbff2bfe30 \n",
"5 2021-01-25 19:23:06.692627+01:00 1ec6c19283111ccb3ed67f52d414470e \n",
"6 2021-01-25 19:28:21.390016+01:00 05e2104f1b74ced229c06847d6e91938 \n",
"7 2021-01-25 19:31:05.076904+01:00 9cc946edfb25e11b4282f58db16e6ae9 \n",
"8 2021-01-25 19:38:41.260535+01:00 d88321c347f0e0ab101184cdf25c94bf \n",
"9 2021-02-05 11:12:31.932576+01:00 0870fef2bfcd5b30a12e4f5c7f4aaba7 \n",
"10 2021-02-05 11:52:05.468207+01:00 8ba8934454cc62c7cdb3eb6e1b39df0c \n",
"11 2023-03-13 11:31:50.528331+01:00 a6969df76efc15d157be48e87a7bcf9a "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_type_ofs"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e9c8d32b-22f4-4581-8af7-31cc1c31fa0e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 12 entries, 0 to 11\n",
"Data columns (total 6 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 id 12 non-null int64 \n",
" 1 name 12 non-null object\n",
" 2 children 12 non-null object\n",
" 3 created_at 12 non-null object\n",
" 4 updated_at 12 non-null object\n",
" 5 identifier 12 non-null object\n",
"dtypes: int64(1), object(5)\n",
"memory usage: 704.0+ bytes\n"
]
}
],
"source": [
"df1_type_ofs.info()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "cbb5e614-1fe5-4da0-bca0-8a242e0885da",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_619/81842251.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_type_ofs_clean.rename(columns = {'name' : 'type_of_ticket_name'}, inplace = True)\n"
]
}
],
"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": "676a9869-9a8b-4cd2-8b1c-0644b5229c72",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## purchases.csv"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f8d36b72-f8e7-45e5-b4fa-e0803493fd3c",
"metadata": {
"scrolled": true
},
"outputs": [
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"text/plain": [
" id purchase_date customer_id \\\n",
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},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_purchases"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "3f266a9d-6eee-4b27-b6cc-d401bc2fa0b8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 742250 entries, 0 to 742249\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 id 742250 non-null int64 \n",
" 1 purchase_date 742250 non-null object\n",
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"memory usage: 39.6+ MB\n"
]
}
],
"source": [
"df1_purchases.info()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "8b24ccbc-ccf0-4722-8cd9-8ee8aa90d1fd",
"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": "27d18584-228f-4698-85d6-4d23151ea5ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 742250 entries, 0 to 742249\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 id 742250 non-null int64 \n",
" 1 purchase_date 742250 non-null datetime64[ns, UTC]\n",
" 2 customer_id 742250 non-null int64 \n",
" 3 created_at 742250 non-null object \n",
" 4 updated_at 742250 non-null object \n",
" 5 number 742250 non-null object \n",
" 6 identifier 742250 non-null object \n",
"dtypes: datetime64[ns, UTC](1), int64(2), object(4)\n",
"memory usage: 39.6+ MB\n"
]
}
],
"source": [
"df1_purchases.info()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "ea22e3a2-2b25-481d-8ebc-194e11a06cd9",
"metadata": {},
"outputs": [],
"source": [
"# Selection des variables\n",
"df1_purchases_clean = df1_purchases[['id', 'purchase_date', 'customer_id']]"
]
},
{
"cell_type": "markdown",
"id": "53227600-c1c5-48aa-9f5d-db5a23a8a22a",
"metadata": {},
"source": [
"## Fusion de l'ensemble des données billétiques"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "e0b8b47a-b321-4a79-823c-36a131a78ac7",
"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": "7572e6e7-f28d-43ba-b045-b9fa09e68e1d",
"metadata": {
"scrolled": true
},
"outputs": [
{
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],
"text/plain": [
" ticket_id product_id is_from_subscription supplier_name \\\n",
"0 13070859 225251 False vente en ligne \n",
"1 13070860 224914 False vente en ligne \n",
"2 13070861 224914 False vente en ligne \n",
"3 13070862 224914 False vente en ligne \n",
"4 13070863 224914 False vente en ligne \n",
"... ... ... ... ... \n",
"1826667 20662815 405689 False vente en ligne \n",
"1826668 20662816 403658 False vente en ligne \n",
"1826669 20662817 403658 False vente en ligne \n",
"1826670 20662818 403658 False vente en ligne \n",
"1826671 20662819 403658 False vente en ligne \n",
"\n",
" type_of_ticket_name children purchase_date \\\n",
"0 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
"1 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
"2 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
"3 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
"4 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
"... ... ... ... \n",
"1826667 Atelier pricing_formula 2023-11-08 17:23:54+00:00 \n",
"1826668 Atelier pricing_formula 2023-11-08 18:32:18+00:00 \n",
"1826669 Atelier pricing_formula 2023-11-08 18:32:18+00:00 \n",
"1826670 Atelier pricing_formula 2023-11-08 19:30:28+00:00 \n",
"1826671 Atelier pricing_formula 2023-11-08 19:30:28+00:00 \n",
"\n",
" customer_id \n",
"0 48187 \n",
"1 48187 \n",
"2 48187 \n",
"3 48187 \n",
"4 48187 \n",
"... ... \n",
"1826667 1256135 \n",
"1826668 1256136 \n",
"1826669 1256136 \n",
"1826670 1256137 \n",
"1826671 1256137 \n",
"\n",
"[1826672 rows x 8 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_ticket_information"
]
},
{
"cell_type": "markdown",
"id": "ad2d0059-76d3-44b9-b0eb-0b0ca4d4ba75",
"metadata": {},
"source": [
"# Utilisation de fonctions"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "d237be96-8c86-4a91-b7a1-487e87a16c3d",
"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": 51,
"id": "c1afe322-ff41-4760-819e-0195fed5b27d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 20 entries, 0 to 19\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 opened_at 8 non-null object \n",
" 1 opened_at_clean 8 non-null datetime64[ns, UTC]\n",
"dtypes: datetime64[ns, UTC](1), object(1)\n",
"memory usage: 448.0+ bytes\n"
]
}
],
"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": "27ecf058-23eb-4018-abbd-68c4ebe7c786",
"metadata": {},
"source": [
"## Nettoyage, selection et fusion"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d887898c-6a21-41ed-901d-4d6fdbca5372",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" ticket_id product_id is_from_subscription type_of supplier_name \\\n",
"0 13070859 225251 False 1 vente en ligne \n",
"1 13070860 224914 False 1 vente en ligne \n",
"2 13070861 224914 False 1 vente en ligne \n",
"3 13070862 224914 False 1 vente en ligne \n",
"4 13070863 224914 False 1 vente en ligne \n",
"... ... ... ... ... ... \n",
"1826667 20662815 405689 False 1 vente en ligne \n",
"1826668 20662816 403658 False 1 vente en ligne \n",
"1826669 20662817 403658 False 1 vente en ligne \n",
"1826670 20662818 403658 False 1 vente en ligne \n",
"1826671 20662819 403658 False 1 vente en ligne \n",
"\n",
" purchase_date customer_id \n",
"0 2018-12-28 14:47:50+00:00 48187 \n",
"1 2018-12-28 14:47:50+00:00 48187 \n",
"2 2018-12-28 14:47:50+00:00 48187 \n",
"3 2018-12-28 14:47:50+00:00 48187 \n",
"4 2018-12-28 14:47:50+00:00 48187 \n",
"... ... ... \n",
"1826667 2023-11-08 17:23:54+00:00 1256135 \n",
"1826668 2023-11-08 18:32:18+00:00 1256136 \n",
"1826669 2023-11-08 18:32:18+00:00 1256136 \n",
"1826670 2023-11-08 19:30:28+00:00 1256137 \n",
"1826671 2023-11-08 19:30:28+00:00 1256137 \n",
"\n",
"[1826672 rows x 7 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_ticket_information"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ac9a6373-c1c6-46b5-873b-dc22f17bcbdb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1826672 entries, 0 to 1826671\n",
"Data columns (total 7 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 ticket_id int64 \n",
" 1 product_id int64 \n",
" 2 is_from_subscription bool \n",
" 3 type_of int64 \n",
" 4 supplier_name object \n",
" 5 purchase_date datetime64[ns, UTC]\n",
" 6 customer_id int64 \n",
"dtypes: bool(1), datetime64[ns, UTC](1), int64(4), object(1)\n",
"memory usage: 85.4+ MB\n"
]
}
],
"source": [
"df1_ticket_information.info()"
]
},
{
"cell_type": "markdown",
"id": "b1719943-89eb-4ba0-a107-2f96d5d01ec9",
"metadata": {},
"source": [
"# Customer information"
]
},
{
"cell_type": "markdown",
"id": "a2132ee2-3f22-45fd-b65b-72689c8b672c",
"metadata": {},
"source": [
"## Target area"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "da5d4708-7147-4cc8-8686-52d4bcba5a7a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_619/2625134041.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": "b4fa5fe3-ce8e-4b0a-af94-fb468d241bad",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"id 5.080902\n",
"dtype: float64"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"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": 57,
"id": "8072bbb7-1360-4882-bb2b-2f43b6beea0d",
"metadata": {
"scrolled": true
},
"outputs": [
{
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" <td>manual_static_filter</td>\n",
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" <tr>\n",
" <th>449620</th>\n",
" <td>4411897</td>\n",
" <td>1</td>\n",
" <td>FORMATION _ acheteurs optin last year</td>\n",
" <td>False</td>\n",
" <td>manual_dynamic_filter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>503809</th>\n",
" <td>4734591</td>\n",
" <td>1</td>\n",
" <td>consentement optin mediation specialisee</td>\n",
" <td>False</td>\n",
" <td>manual_static_filter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>651222</th>\n",
" <td>3554426</td>\n",
" <td>1</td>\n",
" <td>consentement optin b2c</td>\n",
" <td>False</td>\n",
" <td>manual_static_filter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>654246</th>\n",
" <td>5182212</td>\n",
" <td>1</td>\n",
" <td>DDCP spectateurs Festival de Marseille 2023</td>\n",
" <td>False</td>\n",
" <td>manual_static_filter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>654395</th>\n",
" <td>5182456</td>\n",
" <td>1</td>\n",
" <td>rencontres_echelle_spectateurs_2021_2023</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 \\\n",
"8793 4584599 1 consentement optin jeune public \n",
"13249 4567465 1 DDCP rentrée culturelle 2023 \n",
"21424 4544805 1 spectateurs cine dimanche_cine concert_2122 \n",
"21665 4544911 1 DDCP Cine 2023 \n",
"22811 4545766 1 DDCP OLBJ! 2023 \n",
"57305 4457909 1 ddcp_promo_visiteurs occasionnels_musee_8mois \n",
"58843 3688872 1 DDCP promo livemag \n",
"66813 4313646 1 DDCP spectateurs Classique mais pas que 2022 \n",
"68367 4547662 1 ddcp_promo_musee_au moins 3 achats_dps8mois \n",
"77320 4285520 1 DDCP spectateurs Iminente \n",
"84350 4037805 1 DDCP spectateurs Marseille Jazz 18-19-21 \n",
"85383 4569504 1 DDCP rendez-vous de septembre offre spéciale \n",
"92868 4433064 1 ddcp_promo_plein air_ateliers_jardins \n",
"99670 3858684 1 Acid Arab \n",
"105477 4321810 1 Arenametrix_bascule tel vers sib \n",
"169513 3697992 1 ddcp_achats billets nb dps 19052021 \n",
"214421 2925324 1 consentement optout scolaires \n",
"234546 4575957 1 Portrait de Leila shahid \n",
"259808 3722259 1 consentement optin b2b \n",
"274380 4510423 1 DDCP_marseille_jazz_2023 \n",
"307511 5174466 1 ddcp actoral 21-22 \n",
"357509 4442526 1 ddcp musique barvalo \n",
"392920 4390642 1 ddcp_md_promo_spectateurs theatre contempo \n",
"449620 4411897 1 FORMATION _ acheteurs optin last year \n",
"503809 4734591 1 consentement optin mediation specialisee \n",
"651222 3554426 1 consentement optin b2c \n",
"654246 5182212 1 DDCP spectateurs Festival de Marseille 2023 \n",
"654395 5182456 1 rencontres_echelle_spectateurs_2021_2023 \n",
"\n",
" target_type_is_import target_type_name \n",
"8793 False manual_static_filter \n",
"13249 False manual_static_filter \n",
"21424 False manual_static_filter \n",
"21665 False manual_static_filter \n",
"22811 False manual_static_filter \n",
"57305 False manual_dynamic_filter \n",
"58843 False manual_static_filter \n",
"66813 False manual_static_filter \n",
"68367 False manual_dynamic_filter \n",
"77320 False manual_static_filter \n",
"84350 False manual_static_filter \n",
"85383 False manual_static_filter \n",
"92868 False manual_static_filter \n",
"99670 False manual_static_filter \n",
"105477 False manual_static_filter \n",
"169513 False manual_static_filter \n",
"214421 False manual_static_filter \n",
"234546 False manual_static_filter \n",
"259808 False manual_static_filter \n",
"274380 False manual_static_filter \n",
"307511 False manual_static_filter \n",
"357509 False manual_static_filter \n",
"392920 False manual_static_filter \n",
"449620 False manual_dynamic_filter \n",
"503809 False manual_static_filter \n",
"651222 False manual_static_filter \n",
"654246 False manual_static_filter \n",
"654395 False manual_static_filter "
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_targets_full[df1_targets_full['customer_id'] == 1]"
]
},
{
"cell_type": "markdown",
"id": "2f665824-a026-4acd-8358-b408a61854b4",
"metadata": {},
"source": [
"## Campaign area"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "5d05203c-ea30-4208-a29f-fef7737c672e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_9792/1967867975.py:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\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",
" df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
"/tmp/ipykernel_9792/1967867975.py:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\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",
" df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
"/tmp/ipykernel_9792/1967867975.py:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\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",
" df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n"
]
}
],
"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": "8ac634cf-2a30-4ccc-a34d-0fd401a49aaa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 6214808 entries, 0 to 6214807\n",
"Data columns (total 8 columns):\n",
" # Column Dtype \n",
"--- ------ ----- \n",
" 0 id int64 \n",
" 1 customer_id int64 \n",
" 2 opened_at datetime64[ns, UTC]\n",
" 3 sent_at datetime64[ns, UTC]\n",
" 4 delivered_at datetime64[ns, UTC]\n",
" 5 campaign_name object \n",
" 6 campaign_service_id int64 \n",
" 7 campaign_sent_at datetime64[ns, UTC]\n",
"dtypes: datetime64[ns, UTC](4), int64(3), object(1)\n",
"memory usage: 379.3+ MB\n"
]
}
],
"source": [
"df1_campaigns_full.info()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "7d22cdd5-2060-4922-8e04-27b613d4ee27",
"metadata": {},
"outputs": [
{
"data": {
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" <td>404</td>\n",
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" <td>2021-03-28 16:09:47+00:00</td>\n",
" <td>Le Mucem chez vous, gardons le lien #22</td>\n",
" <td>404</td>\n",
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" <td>2021-03-28 16:05:03+00:00</td>\n",
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" <td>2023-10-23 09:32:33+00:00</td>\n",
" <td>2023-10-23 09:32:34+00:00</td>\n",
" <td>dre_nov_2023</td>\n",
" <td>1318</td>\n",
" <td>2023-10-23 09:31:17+00:00</td>\n",
" </tr>\n",
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" <td>2023-10-23 09:32:49+00:00</td>\n",
" <td>2023-10-23 09:32:49+00:00</td>\n",
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" <td>2023-10-23 09:31:54+00:00</td>\n",
" <td>dre_nov_2023</td>\n",
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" <td>2023-10-23 09:33:55+00:00</td>\n",
" <td>dre_nov_2023</td>\n",
" <td>1318</td>\n",
" <td>2023-10-23 09:31:17+00:00</td>\n",
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"</div>"
],
"text/plain": [
" id customer_id opened_at \\\n",
"0 19793 112597 NaT \n",
"1 14211 113666 NaT \n",
"2 13150 280561 NaT \n",
"3 7073 101007 2021-03-28 18:11:06+00:00 \n",
"4 5175 103972 NaT \n",
"... ... ... ... \n",
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"6214804 8303307 21355 2023-10-23 09:44:02+00:00 \n",
"6214805 8304346 21849 2023-10-23 09:45:52+00:00 \n",
"6214806 8302037 667789 2023-10-23 09:47:32+00:00 \n",
"6214807 8304939 294154 NaT \n",
"\n",
" sent_at delivered_at \\\n",
"0 2021-03-28 16:01:09+00:00 2021-03-28 16:24:18+00:00 \n",
"1 2021-03-28 16:01:09+00:00 2021-03-28 16:21:02+00:00 \n",
"2 2021-03-28 16:00:59+00:00 2021-03-28 16:08:45+00:00 \n",
"3 2021-03-28 16:00:59+00:00 2021-03-28 16:09:47+00:00 \n",
"4 2021-03-28 16:01:06+00:00 2021-03-28 16:05:03+00:00 \n",
"... ... ... \n",
"6214803 2023-10-23 09:32:33+00:00 2023-10-23 09:32:34+00:00 \n",
"6214804 2023-10-23 09:32:49+00:00 2023-10-23 09:32:49+00:00 \n",
"6214805 2023-10-23 09:33:28+00:00 2023-10-23 09:33:29+00:00 \n",
"6214806 2023-10-23 09:31:53+00:00 2023-10-23 09:31:54+00:00 \n",
"6214807 2023-10-23 09:33:54+00:00 2023-10-23 09:33:55+00:00 \n",
"\n",
" campaign_name campaign_service_id \\\n",
"0 Le Mucem chez vous, gardons le lien #22 404 \n",
"1 Le Mucem chez vous, gardons le lien #22 404 \n",
"2 Le Mucem chez vous, gardons le lien #22 404 \n",
"3 Le Mucem chez vous, gardons le lien #22 404 \n",
"4 Le Mucem chez vous, gardons le lien #22 404 \n",
"... ... ... \n",
"6214803 dre_nov_2023 1318 \n",
"6214804 dre_nov_2023 1318 \n",
"6214805 dre_nov_2023 1318 \n",
"6214806 dre_nov_2023 1318 \n",
"6214807 dre_nov_2023 1318 \n",
"\n",
" campaign_sent_at \n",
"0 2021-03-27 23:00:00+00:00 \n",
"1 2021-03-27 23:00:00+00:00 \n",
"2 2021-03-27 23:00:00+00:00 \n",
"3 2021-03-27 23:00:00+00:00 \n",
"4 2021-03-27 23:00:00+00:00 \n",
"... ... \n",
"6214803 2023-10-23 09:31:17+00:00 \n",
"6214804 2023-10-23 09:31:17+00:00 \n",
"6214805 2023-10-23 09:31:17+00:00 \n",
"6214806 2023-10-23 09:31:17+00:00 \n",
"6214807 2023-10-23 09:31:17+00:00 \n",
"\n",
"[6214808 rows x 8 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_campaigns_information"
]
},
{
"cell_type": "markdown",
"id": "0a5b24f0-4bca-4cde-a6ba-eb130b38cac4",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Link area"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "bc63bc4e-6cc1-4d35-9635-faf55339e186",
"metadata": {},
"outputs": [
{
"data": {
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" <td>2022-02-03 14:17:27.119582+01:00</td>\n",
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" <th>3</th>\n",
" <td>1319283</td>\n",
" <td>Vacances de la Toussaint - centres des loisirs</td>\n",
" <td>590</td>\n",
" <td>2021-09-28 18:01:04.692073+02:00</td>\n",
" <td>2022-02-03 14:17:27.124408+01:00</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1319636</td>\n",
" <td>ddcp_promo_md_livemag</td>\n",
" <td>730</td>\n",
" <td>2022-01-27 18:00:41.053069+01:00</td>\n",
" <td>2022-02-03 14:17:27.127607+01:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>d5cfead94f5350c12c322b5b664544c1</td>\n",
" <td>2022-01-27 00:00:00+01:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>952</th>\n",
" <td>1320072</td>\n",
" <td>dre_gaza0106</td>\n",
" <td>881</td>\n",
" <td>2022-05-26 09:01:35.523639+02:00</td>\n",
" <td>2022-12-02 17:51:22.614046+01:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>7504adad8bb96320eb3afdd4df6e1f60</td>\n",
" <td>2022-05-26 00:00:00+02:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>953</th>\n",
" <td>661398</td>\n",
" <td>DDCP Plan Bis 4 - Marketing direct - MJ5C</td>\n",
" <td>183</td>\n",
" <td>2021-06-18 10:30:01.259578+02:00</td>\n",
" <td>2021-09-24 11:56:09.082785+02:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>cedebb6e872f539bef8c3f919874e9d7</td>\n",
" <td>2020-07-27 00:00:00+02:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>954</th>\n",
" <td>1320487</td>\n",
" <td>Invitation portes ouvertes amitiés</td>\n",
" <td>988</td>\n",
" <td>2022-09-29 18:01:33.834090+02:00</td>\n",
" <td>2022-12-02 17:51:23.258324+01:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>9908279ebbf1f9b250ba689db6a0222b</td>\n",
" <td>2022-09-29 00:00:00+02:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>955</th>\n",
" <td>906903</td>\n",
" <td>DDCP PROMO La méditerranée des philosophes #3 ...</td>\n",
" <td>310</td>\n",
" <td>2021-07-19 14:07:16.177390+02:00</td>\n",
" <td>2021-09-24 11:56:09.086101+02:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>06eb61b839a0cefee4967c67ccb099dc</td>\n",
" <td>2020-12-23 00:00:00+01:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>956</th>\n",
" <td>579313</td>\n",
" <td>ddcp_promo_automation_manuel_pre_visit</td>\n",
" <td>481</td>\n",
" <td>2021-06-08 17:38:54.041310+02:00</td>\n",
" <td>2021-09-24 11:56:09.089394+02:00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>9461cce28ebe3e76fb4b931c35a169b0</td>\n",
" <td>2021-06-08 00:00:00+02:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>957 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" id name service_id \\\n",
"0 1319613 newsletter enseignants janvier 2022 721 \n",
"1 1319586 lsf_janvier_2022 717 \n",
"2 1319282 Invitation à déjeuner au Mucem | Vernissage « ... 591 \n",
"3 1319283 Vacances de la Toussaint - centres des loisirs 590 \n",
"4 1319636 ddcp_promo_md_livemag 730 \n",
".. ... ... ... \n",
"952 1320072 dre_gaza0106 881 \n",
"953 661398 DDCP Plan Bis 4 - Marketing direct - MJ5C 183 \n",
"954 1320487 Invitation portes ouvertes amitiés 988 \n",
"955 906903 DDCP PROMO La méditerranée des philosophes #3 ... 310 \n",
"956 579313 ddcp_promo_automation_manuel_pre_visit 481 \n",
"\n",
" created_at updated_at \\\n",
"0 2022-01-14 16:06:42.586321+01:00 2022-02-03 14:17:27.112963+01:00 \n",
"1 2022-01-07 11:30:35.315895+01:00 2022-02-03 14:17:27.116171+01:00 \n",
"2 2021-09-28 12:50:24.448752+02:00 2022-02-03 14:17:27.119582+01:00 \n",
"3 2021-09-28 18:01:04.692073+02:00 2022-02-03 14:17:27.124408+01:00 \n",
"4 2022-01-27 18:00:41.053069+01:00 2022-02-03 14:17:27.127607+01:00 \n",
".. ... ... \n",
"952 2022-05-26 09:01:35.523639+02:00 2022-12-02 17:51:22.614046+01:00 \n",
"953 2021-06-18 10:30:01.259578+02:00 2021-09-24 11:56:09.082785+02:00 \n",
"954 2022-09-29 18:01:33.834090+02:00 2022-12-02 17:51:23.258324+01:00 \n",
"955 2021-07-19 14:07:16.177390+02:00 2021-09-24 11:56:09.086101+02:00 \n",
"956 2021-06-08 17:38:54.041310+02:00 2021-09-24 11:56:09.089394+02:00 \n",
"\n",
" process_id report_url category to_be_synced \\\n",
"0 NaN NaN 0.0 False \n",
"1 NaN NaN 0.0 False \n",
"2 NaN NaN 0.0 False \n",
"3 NaN NaN 0.0 False \n",
"4 NaN NaN 0.0 False \n",
".. ... ... ... ... \n",
"952 NaN NaN 0.0 False \n",
"953 NaN NaN 0.0 False \n",
"954 NaN NaN 0.0 False \n",
"955 NaN NaN 0.0 False \n",
"956 NaN NaN 0.0 False \n",
"\n",
" identifier sent_at \n",
"0 aba3b6fd5d186d28e06ff97135cade7f 2022-01-14 00:00:00+01:00 \n",
"1 788d986905533aba051261497ecffcbb 2022-01-07 00:00:00+01:00 \n",
"2 3493894fa4ea036cfc6433c3e2ee63b0 2021-09-28 00:00:00+02:00 \n",
"3 08b255a5d42b89b0585260b6f2360bdd 2021-09-28 00:00:00+02:00 \n",
"4 d5cfead94f5350c12c322b5b664544c1 2022-01-27 00:00:00+01:00 \n",
".. ... ... \n",
"952 7504adad8bb96320eb3afdd4df6e1f60 2022-05-26 00:00:00+02:00 \n",
"953 cedebb6e872f539bef8c3f919874e9d7 2020-07-27 00:00:00+02:00 \n",
"954 9908279ebbf1f9b250ba689db6a0222b 2022-09-29 00:00:00+02:00 \n",
"955 06eb61b839a0cefee4967c67ccb099dc 2020-12-23 00:00:00+01:00 \n",
"956 9461cce28ebe3e76fb4b931c35a169b0 2021-06-08 00:00:00+02:00 \n",
"\n",
"[957 rows x 11 columns]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_campaigns"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "c19b321f-65f9-4d6c-8c1f-edb2eb9d70e7",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <td>2021-03-26 17:16:34+01:00</td>\n",
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" <td>119768</td>\n",
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" <td>2021-03-28 18:03:32.736394+02:00</td>\n",
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" <td>4</td>\n",
" <td>2021-03-26 17:43:19+01:00</td>\n",
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" <td>272280</td>\n",
" <td>2021-03-26 16:43:19.338321+01:00</td>\n",
" <td>2021-03-26 16:43:19.338321+01:00</td>\n",
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" <td>2021-03-26 16:46:00.502945+01:00</td>\n",
" <td>2021-03-26 16:46:00.502945+01:00</td>\n",
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" <td>2023-11-09 15:34:29.425425+01:00</td>\n",
" <td>2023-11-09 15:34:29.425425+01:00</td>\n",
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" <th>151047</th>\n",
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" <td>2023-11-09 16:34:35+01:00</td>\n",
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" <td>2023-11-09 15:34:37.505505+01:00</td>\n",
" <td>2023-11-09 15:34:37.505505+01:00</td>\n",
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" <th>151048</th>\n",
" <td>243559</td>\n",
" <td>2023-11-09 16:51:15+01:00</td>\n",
" <td>14686</td>\n",
" <td>82923</td>\n",
" <td>2023-11-09 15:51:17.439518+01:00</td>\n",
" <td>2023-11-09 15:51:17.439518+01:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151049</th>\n",
" <td>243561</td>\n",
" <td>2023-11-09 16:59:42+01:00</td>\n",
" <td>14677</td>\n",
" <td>82923</td>\n",
" <td>2023-11-09 15:59:44.030922+01:00</td>\n",
" <td>2023-11-09 15:59:44.030922+01:00</td>\n",
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" <th>151050</th>\n",
" <td>243564</td>\n",
" <td>2023-11-09 17:16:41+01:00</td>\n",
" <td>14691</td>\n",
" <td>1254355</td>\n",
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" <td>2023-11-09 16:16:43.012932+01:00</td>\n",
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],
"text/plain": [
" id clicked_at link_id customer_id \\\n",
"0 1 2021-03-26 16:30:36+01:00 1 284033 \n",
"1 2 2021-03-26 17:16:34+01:00 2 119768 \n",
"2 272 2021-03-28 20:03:32+02:00 42 113105 \n",
"3 4 2021-03-26 17:43:19+01:00 3 272280 \n",
"4 5 2021-03-26 17:46:00+01:00 3 105095 \n",
"... ... ... ... ... \n",
"151046 243553 2023-11-09 16:34:27+01:00 14666 998 \n",
"151047 243554 2023-11-09 16:34:35+01:00 14670 998 \n",
"151048 243559 2023-11-09 16:51:15+01:00 14686 82923 \n",
"151049 243561 2023-11-09 16:59:42+01:00 14677 82923 \n",
"151050 243564 2023-11-09 17:16:41+01:00 14691 1254355 \n",
"\n",
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"151047 2023-11-09 15:34:37.505505+01:00 2023-11-09 15:34:37.505505+01:00 \n",
"151048 2023-11-09 15:51:17.439518+01:00 2023-11-09 15:51:17.439518+01:00 \n",
"151049 2023-11-09 15:59:44.030922+01:00 2023-11-09 15:59:44.030922+01:00 \n",
"151050 2023-11-09 16:16:43.012932+01:00 2023-11-09 16:16:43.012932+01:00 \n",
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},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1_link_stats"
]
},
{
"cell_type": "markdown",
"id": "96ea2523-38dc-47ef-a49e-2c2d9ad0b1c6",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Exploration variables"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "aaa41688-ea7e-4dba-851c-1f0b0ec43c71",
"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": 29,
"id": "2fecc2e1-113f-46ed-9065-0b9ee416166e",
"metadata": {},
"outputs": [],
"source": [
"df1_suppliers_desc = suppliers_exploration(suppliers = df1_suppliers)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "55f6170a-36fb-4efb-9810-f982883660cf",
"metadata": {},
"outputs": [
{
"data": {
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" nb_suppliers label_na itr_na commission_na\n",
"0 9 100.0 100.0 100.0"
]
},
"execution_count": 30,
"metadata": {},
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"source": [
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]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "0030fd02-09e3-42f5-9c83-290458a38c29",
"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": 32,
"id": "6b1736d1-8fd7-4fcc-9431-b8bf0c7b4f2b",
"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": 33,
"id": "226b694b-0b00-4167-b69f-3178902254eb",
"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[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\n",
"\n",
" "
]
}
],
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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