Ajout graph pour targets
This commit is contained in:
parent
c620f23507
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e1f6f1ba68
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@ -100,7 +100,9 @@
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{
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"cell_type": "markdown",
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"id": "ccf597b0-b459-4ea5-baf0-5ba8c90915e4",
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"metadata": {},
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"# Cleaning target area and tags"
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]
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@ -551,16 +553,6 @@
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"## Base communes au types Musée"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dbce1124-9a22-4502-a47a-fc3d0e2db70b",
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"metadata": {},
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"outputs": [],
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"source": [
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"companies['musee']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -789,7 +781,9 @@
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{
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"cell_type": "markdown",
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"id": "76bffba1-5f7e-4308-9224-437ca66148f8",
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"metadata": {},
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## KPI sur target_type"
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]
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@ -1230,74 +1224,17 @@
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{
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"cell_type": "markdown",
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"id": "c437eaec",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"metadata": {},
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"source": [
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"# Exemple sur Company 1"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a1c1fc39",
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"metadata": {},
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"source": [
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"## Chargement données"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "66f8c17b",
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"metadata": {},
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"outputs": [],
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"source": [
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"BUCKET = \"bdc2324-data/1\"\n",
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"liste_database = fs.ls(BUCKET)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c08e6798",
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"metadata": {},
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"outputs": [],
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"source": [
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"liste_database_select = ['suppliers', 'ticket', 'purchase', 'consumption', 'type_ofs']\n",
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"\n",
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"# Filtrer la liste pour les éléments contenant au moins un élément de la liste à tester\n",
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"liste_database_filtered = [element for element in liste_database if any(element_part in element for element_part in liste_database_select)]\n",
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"\n",
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"# Afficher le résultat\n",
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"print(liste_database_filtered)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "675f518d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# loop to create dataframes from liste\n",
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"files_path = liste_database\n",
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"\n",
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"client_number = files_path[0].split(\"/\")[1]\n",
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"df_prefix = \"df\" + str(client_number) + \"_\"\n",
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"\n",
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"for i in range(len(files_path)) :\n",
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" current_path = files_path[i]\n",
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" with fs.open(current_path, mode=\"rb\") as file_in:\n",
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" df = pd.read_csv(file_in)\n",
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" # the pattern of the name is df1xxx\n",
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" nom_dataframe = df_prefix + re.search(r'\\/(\\d+)\\/(\\d+)([a-zA-Z_]+)\\.csv$', current_path).group(3)\n",
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" globals()[nom_dataframe] = df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e855f403",
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"metadata": {},
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## customersplus.csv"
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]
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@ -1414,172 +1351,6 @@
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "64d0f76b",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## tickets.csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7e683711",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_tickets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e7b9a52e",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_tickets.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "568280e8",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_tickets.isna().sum()/len(df1_tickets)*100"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "29ecec90",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Selection des variables\n",
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"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",
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"df1_tickets_clean.rename(columns = {'id' : 'customer_id'}, inplace = True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "22bb5de4",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## suppliers.csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6a9a91f4",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_suppliers"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bab4758a",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_suppliers.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b5fff251",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_suppliers.isna().sum()/len(df1_suppliers)*100"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b09e2a3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Selection des variables\n",
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"df1_suppliers_clean = df1_suppliers[['id', 'name']]\n",
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"df1_suppliers_clean.rename(columns = {'name' : 'supplier_name'}, inplace = True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ecee7cdc",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_suppliers_clean"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c8e6e69b",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## type_ofs.csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1a6cff1f",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_type_ofs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "93630b41",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_type_ofs.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4f94481a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Selection des variables\n",
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"df1_type_ofs_clean = df1_type_ofs[['id', 'name', 'children']]\n",
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"df1_type_ofs_clean.rename(columns = {'name' : 'type_of_ticket_name'}, inplace = True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b2811e2",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## purchases.csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -1635,105 +1406,6 @@
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"df1_purchases_clean = df1_purchases[['id', 'purchase_date', 'customer_id']]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f210e730",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## Fusion de l'ensemble des données billétiques"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1f8b3aa7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Fusion avec fournisseurs\n",
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"df1_ticket_information = pd.merge(df1_tickets_clean, df1_suppliers_clean, left_on = 'supplier_id', right_on = 'id', how = 'inner')\n",
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"df1_ticket_information.drop(['supplier_id', 'id'], axis = 1, inplace=True)\n",
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"\n",
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"# Fusion avec type de tickets\n",
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"df1_ticket_information = pd.merge(df1_ticket_information, df1_type_ofs_clean, left_on = 'type_of', right_on = 'id', how = 'inner')\n",
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"df1_ticket_information.drop(['type_of', 'id'], axis = 1, inplace=True)\n",
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"\n",
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"# Fusion avec achats\n",
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"df1_ticket_information = pd.merge(df1_ticket_information, df1_purchases_clean, left_on = 'purchase_id', right_on = 'id', how = 'inner')\n",
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"df1_ticket_information.drop(['purchase_id', 'id'], axis = 1, inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "83a4d021",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"df1_ticket_information"
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]
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},
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{
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"cell_type": "markdown",
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"id": "56e6ebd1",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"# Utilisation de fonctions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "88fcde4b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Créer un DataFrame exemple\n",
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"df_not_clean = df1_campaign_stats[['opened_at']].head(20)\n",
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"\n",
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"# Appliquer la fonction pour nettoyer la colonne 'purchase_date' de manière vectorisée\n",
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"df_clean = cleaning_date(df_not_clean, 'opened_at')\n",
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"df_clean.rename(columns = {'opened_at' : 'opened_at_clean'}, inplace = True)\n",
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"\n",
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"test = pd.concat([df1_campaign_stats[['opened_at']].head(20), df_clean], axis=1)\n",
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"\n",
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"test.info()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "818f69db",
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"metadata": {},
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"source": [
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"## Nettoyage, selection et fusion"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c9654eda",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_ticket_information"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7f2b620c",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_ticket_information.info()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "637bdb72",
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@ -1745,9 +1417,11 @@
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{
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"cell_type": "markdown",
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"id": "14c52894",
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"metadata": {},
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## Target area"
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"## Target area - NLP"
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]
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},
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{
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@ -1919,254 +1593,6 @@
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"# Afficher le résultat\n",
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"print(df)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "711d3884",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## Campaign area"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c25b5295",
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"metadata": {},
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"outputs": [],
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"source": [
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"# campaign_stats cleaning \n",
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"df1_campaign_stats_clean = df1_campaign_stats[[\"id\", \"campaign_id\", \"customer_id\", \"opened_at\", \"sent_at\", \"delivered_at\"]]\n",
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"cleaning_date(df1_campaign_stats_clean, 'opened_at')\n",
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"cleaning_date(df1_campaign_stats_clean, 'sent_at')\n",
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"cleaning_date(df1_campaign_stats_clean, 'delivered_at')\n",
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"\n",
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"# campaigns cleaning\n",
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"df1_campaigns_clean = df1_campaigns[[\"id\", \"name\", \"service_id\", \"sent_at\"]].add_prefix(\"campaign_\")\n",
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"cleaning_date(df1_campaigns_clean, 'campaign_sent_at')\n",
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"\n",
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"# Merge \n",
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"df1_campaigns_full = pd.merge(df1_campaign_stats_clean, df1_campaigns_clean, on = \"campaign_id\", how = \"left\")\n",
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"df1_campaigns_full.drop(['campaign_id'], axis = 1, inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2a3de6a5",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_campaigns_full.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3fc1f446",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_campaigns_information"
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]
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},
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{
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"cell_type": "markdown",
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"id": "20e69ee3",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"## Link area"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d9cbdbce",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_campaigns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c07459f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1_link_stats"
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]
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},
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{
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"cell_type": "markdown",
|
||||
"id": "80ae4c42",
|
||||
"metadata": {
|
||||
"jp-MarkdownHeadingCollapsed": true
|
||||
},
|
||||
"source": [
|
||||
"## Supplier"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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": null,
|
||||
"id": "7e292935",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df1_suppliers_desc = suppliers_exploration(suppliers = df1_suppliers)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "05b6f2b0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df1_suppliers_desc"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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": null,
|
||||
"id": "10304058",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"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": null,
|
||||
"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": null,
|
||||
"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": null,
|
||||
"id": "1522d8cd",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# df_all[df_all['tenant_id'] == 101]['name'].unique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"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": null,
|
||||
"id": "d299ae91",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_all.groupby('tenant_id')['canal_vente_internet'].max()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user