stat_des
This commit is contained in:
parent
3d4e661be9
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"id": "17949e81-c30b-4fdf-9872-d7dc2b22ba9e",
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"metadata": {},
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"outputs": [],
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@ -29,7 +29,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"id": "9c1737a2-bad8-4266-8dec-452085d8cfe7",
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"metadata": {},
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"outputs": [
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@ -42,7 +42,7 @@
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" 'projet-bdc2324-team1/0_Input/Company_10/target_information.csv']"
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]
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},
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"execution_count": 4,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -58,7 +58,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"id": "a35dc2f6-2017-4b21-abd2-2c4c112c96b2",
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"metadata": {},
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"outputs": [],
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@ -72,7 +72,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 5,
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"id": "40b705eb-fd18-436b-b150-61611a3c6a84",
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"metadata": {},
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"outputs": [],
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@ -89,13 +89,300 @@
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" return 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": "e22eb500-80da-4dd9-8b20-9e4deec64831",
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"metadata": {},
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"source": [
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"nb_compagnie=['10','11','12','13','14']\n",
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"for directory_path in nb_compagnie:\n",
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" df_customerplus_clean = display_databases(directory_path, file_name = \"customerplus_cleaned\")\n",
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" df_campaigns_information = display_databases(directory_path, file_name = \"campaigns_information\", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])\n",
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" df_products_purchased_reduced = display_databases(directory_path, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])\n",
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" df_customerplus_clean[\"Number_compagnie\"]=int(directory_path)\n",
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" df_campaigns_information[\"Number_compagnie\"]=int(directory_path)\n",
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" df_products_purchased_reduced[\"Number_compagnie\"]=int(directory_path)\n",
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"\n",
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" if nb_compagnie.index(directory_path)>=1:\n",
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" customerplus_clean_spectacle=pd.concat([customerplus_clean_spectacle,df_customerplus_clean],axis=0)\n",
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" campaigns_information_spectacle=pd.concat([campaigns_information_spectacle,df_campaigns_information],axis=0)\n",
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" products_purchased_reduced_spectacle=pd.concat([products_purchased_reduced_spectacle,df_products_purchased_reduced],axis=0)\n",
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" else:\n",
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" customerplus_clean_spectacle=df_customerplus_clean\n",
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" campaigns_information_spectacle=df_campaigns_information\n",
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" products_purchased_reduced_spectacle=df_products_purchased_reduced\n",
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" "
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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": "e56aa16f-a167-4bff-9f8b-f764d1f28ebd",
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"execution_count": 6,
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"id": "1c4a07ec-cf8e-420e-88f0-5eb4b83e2bc1",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"directory_path='10'"
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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": 7,
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"id": "4249cdcd-ef59-4dd6-a345-4218bb90e526",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"File path : projet-bdc2324-team1/0_Input/Company_10/customerplus_cleaned.csv\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_1282/2987234667.py:8: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
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" df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"File path : projet-bdc2324-team1/0_Input/Company_10/campaigns_information.csv\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_1282/2987234667.py:8: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
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" df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"File path : projet-bdc2324-team1/0_Input/Company_10/products_purchased_reduced.csv\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_1282/2987234667.py:8: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.\n",
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" df = pd.read_csv(file_in, sep=\",\", parse_dates = datetime_col, date_parser=custom_date_parser)\n"
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]
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}
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],
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"source": [
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" # Import customerplus\n",
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"df_customerplus_clean_0 = display_databases(directory_path, file_name = \"customerplus_cleaned\")\n",
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"df_campaigns_information = display_databases(directory_path, file_name = \"campaigns_information\", datetime_col = ['opened_at', 'sent_at', 'campaign_sent_at'])\n",
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"df_products_purchased_reduced = display_databases(directory_path, file_name = \"products_purchased_reduced\", datetime_col = ['purchase_date'])\n"
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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": 9,
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"id": "e56aa16f-a167-4bff-9f8b-f764d1f28ebd",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<string>:27: SettingWithCopyWarning: \n",
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"A value is trying to be set on a copy of a slice from a DataFrame\n",
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"\n",
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"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
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]
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}
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],
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"source": [
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" # Creation des KPI\n",
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" # KPI sur les campagnes publicitaires\n",
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"df_campaigns_kpi = campaigns_kpi_function(campaigns_information = df_campaigns_information) \n",
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"\n",
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" # KPI sur le comportement d'achat\n",
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"df_tickets_kpi = tickets_kpi_function(tickets_information = df_products_purchased_reduced)\n",
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"\n",
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" # KPI sur les données socio-démographiques\n",
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"df_customerplus_clean = customerplus_kpi_function(customerplus_clean = df_customerplus_clean_0)\n",
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"\n",
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"#creation de la colonne Number compagnie\n",
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"df_tickets_kpi[\"Number_compagnie\"]=int(directory_path)\n",
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"df_campaigns_kpi[\"Number_compagnie\"]=int(directory_path)\n",
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"df_customerplus_clean[\"Number_compagnie\"]=int(directory_path)"
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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": 11,
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"id": "838722c3-4c78-4ffa-a6b6-01ac60f4bdbd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>customer_id</th>\n",
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" <th>nb_campaigns</th>\n",
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" <th>nb_campaigns_opened</th>\n",
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" <th>time_to_open</th>\n",
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" <th>Number_compagnie</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>29</td>\n",
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" <td>4</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>37</td>\n",
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" <td>3</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>39</td>\n",
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" <td>4</td>\n",
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" <td>1.0</td>\n",
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" <td>0 days 05:16:38</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>41</td>\n",
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" <td>4</td>\n",
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" <td>1.0</td>\n",
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" <td>0 days 01:12:29</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>44</td>\n",
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" <td>4</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>57138</th>\n",
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" <td>827940</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>57139</th>\n",
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" <td>827941</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>57140</th>\n",
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" <td>827942</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>57141</th>\n",
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" <td>827943</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>57142</th>\n",
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" <td>827944</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>57143 rows × 5 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" customer_id nb_campaigns nb_campaigns_opened time_to_open \\\n",
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"0 29 4 0.0 NaT \n",
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"1 37 3 0.0 NaT \n",
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"2 39 4 1.0 0 days 05:16:38 \n",
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"3 41 4 1.0 0 days 01:12:29 \n",
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"4 44 4 0.0 NaT \n",
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"... ... ... ... ... \n",
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"57138 827940 1 0.0 NaT \n",
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"57139 827941 1 0.0 NaT \n",
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"57140 827942 1 0.0 NaT \n",
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"57141 827943 1 0.0 NaT \n",
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"57142 827944 1 0.0 NaT \n",
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"\n",
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" Number_compagnie \n",
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"0 10 \n",
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"1 10 \n",
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"2 10 \n",
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"3 10 \n",
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"4 10 \n",
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"... ... \n",
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"57138 10 \n",
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"57139 10 \n",
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"57140 10 \n",
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"57141 10 \n",
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"57142 10 \n",
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"\n",
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"[57143 rows x 5 columns]"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df_campaigns_kpi"
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]
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}
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],
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"metadata": {
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