{ "cells": [ { "cell_type": "markdown", "id": "6c0589ab-924f-4706-bef7-65500f0c4dd5", "metadata": {}, "source": [ "# Exploratory study of variables : targets, campaign and link stats" ] }, { "cell_type": "markdown", "id": "83319f84-427f-43aa-af26-06797244e89c", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "## First steps : package importations, set up working environment and import data" ] }, { "cell_type": "code", "execution_count": 253, "id": "a26f3f09-3961-43fe-b4d9-1abe3b906a2c", "metadata": {}, "outputs": [], "source": [ "# importations\n", "\n", "import os \n", "import s3fs\n", "import pandas as pd\n", "import re\n", "from datetime import datetime, timezone, timedelta\n", "import math\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 188, "id": "78478dbf-bd91-45e0-9f2b-2d9e6b0f648c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['bdc2324-data/1',\n", " 'bdc2324-data/10',\n", " 'bdc2324-data/101',\n", " 'bdc2324-data/11',\n", " 'bdc2324-data/12',\n", " 'bdc2324-data/13',\n", " 'bdc2324-data/14',\n", " 'bdc2324-data/2',\n", " 'bdc2324-data/3',\n", " 'bdc2324-data/4',\n", " 'bdc2324-data/5',\n", " 'bdc2324-data/6',\n", " 'bdc2324-data/7',\n", " 'bdc2324-data/8',\n", " 'bdc2324-data/9']" ] }, "execution_count": 188, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# bucket for accessing the data\n", "\n", "S3_ENDPOINT_URL = \"https://\" + os.environ[\"AWS_S3_ENDPOINT\"]\n", "\n", "fs = s3fs.S3FileSystem(client_kwargs = {\"endpoint_url\" : S3_ENDPOINT_URL})\n", "BUCKET = \"bdc2324-data\"\n", "fs.ls(BUCKET)" ] }, { "cell_type": "code", "execution_count": 3, "id": "a7e1b277-4381-45c0-b1ec-4050af54a3b6", "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": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "FILE_PATH_S3 = fs.ls(BUCKET)[0] # focus on the company number 1\n", "files_path = fs.ls(FILE_PATH_S3)\n", "files_path" ] }, { "cell_type": "code", "execution_count": 4, "id": "b26f7d2b-391f-4326-a60b-5b379186b4e8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_624/107044352.py:9: 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 related to company 1\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": "5cb3e9dc-ba6e-408c-b1a6-a2c5a2215f71", "metadata": {}, "source": [ "## Target, target types and customer target mapping" ] }, { "cell_type": "code", "execution_count": 5, "id": "c6dbd777-b6da-485f-a650-b0a12f3d90c4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "is_import bool\n", "name object\n", "created_at object\n", "updated_at object\n", "identifier object\n", "dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. target types\n", "df1_target_types.dtypes" ] }, { "cell_type": "code", "execution_count": 6, "id": "04d625e8-b077-450f-a654-1a3b05fc1325", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df1_target_types[\"created_at\"][0])" ] }, { "cell_type": "code", "execution_count": 7, "id": "607441b9-33a8-41a7-a089-120dfe266de0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idis_importnamecreated_atupdated_atidentifier
069Falsemanual_dynamic_filter2020-11-30 09:46:18.881030+01:002020-11-30 09:46:18.881030+01:00e0f4b8693184850fefd6d2a38f10584e
148Truemanual_structure2020-11-04 17:16:19.548275+01:002020-11-04 17:16:19.548275+01:00382bca214204a2d3462f5ec2728d5d1e
21Truemanual_import2020-10-14 18:37:40.521623+02:002020-10-14 18:37:40.521623+02:0012213df2ce68a624e4c0070521437bac
356Falsemanual_static_filter2020-11-04 18:08:37.233486+01:002020-11-04 18:08:37.233486+01:00fb27e81baa4debc6a4e1a8639c20e808
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" ], "text/plain": [ " id is_import name created_at \\\n", "0 69 False manual_dynamic_filter 2020-11-30 09:46:18.881030+01:00 \n", "1 48 True manual_structure 2020-11-04 17:16:19.548275+01:00 \n", "2 1 True manual_import 2020-10-14 18:37:40.521623+02:00 \n", "3 56 False manual_static_filter 2020-11-04 18:08:37.233486+01:00 \n", "\n", " updated_at identifier \n", "0 2020-11-30 09:46:18.881030+01:00 e0f4b8693184850fefd6d2a38f10584e \n", "1 2020-11-04 17:16:19.548275+01:00 382bca214204a2d3462f5ec2728d5d1e \n", "2 2020-10-14 18:37:40.521623+02:00 12213df2ce68a624e4c0070521437bac \n", "3 2020-11-04 18:08:37.233486+01:00 fb27e81baa4debc6a4e1a8639c20e808 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_target_types" ] }, { "cell_type": "code", "execution_count": 8, "id": "6c036742-3069-438d-82af-62acc89aa000", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtarget_type_idnamecreated_atupdated_at
021756DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00
170156consentement optin scolaires2021-12-21 16:03:59.840785+01:002022-02-18 17:23:44.761388+01:00
213456DDCP Newsletter jeune public2020-11-10 09:43:19.667471+01:002021-03-02 18:38:19.052304+01:00
370056consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00
496456DDCP achat billet nbr dep 190520212022-04-14 10:58:17.142834+02:002022-04-14 10:58:23.677264+02:00
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" ], "text/plain": [ " id target_type_id name \\\n", "0 217 56 DDCP PROMO Art contemporain - salle de chauffe... \n", "1 701 56 consentement optin scolaires \n", "2 134 56 DDCP Newsletter jeune public \n", "3 700 56 consentement optout scolaires \n", "4 964 56 DDCP achat billet nbr dep 19052021 \n", "\n", " created_at updated_at \n", "0 2021-01-04 15:00:05.401899+01:00 2021-03-02 18:38:19.025969+01:00 \n", "1 2021-12-21 16:03:59.840785+01:00 2022-02-18 17:23:44.761388+01:00 \n", "2 2020-11-10 09:43:19.667471+01:00 2021-03-02 18:38:19.052304+01:00 \n", "3 2021-12-21 16:01:57.524946+01:00 2022-02-18 17:23:44.807776+01:00 \n", "4 2022-04-14 10:58:17.142834+02:00 2022-04-14 10:58:23.677264+02:00 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. targets\n", "\n", "df1_targets.head()" ] }, { "cell_type": "code", "execution_count": 30, "id": "6bcde543-3eea-4584-82a2-903a1007c4ee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "target_type_id int64\n", "name object\n", "created_at object\n", "updated_at object\n", "dtype: object" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_targets.dtypes" ] }, { "cell_type": "code", "execution_count": 33, "id": "bc9acebd-a030-4a40-bd1f-2ff0ab3f59d2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df1_targets[\"created_at\"][0])" ] }, { "cell_type": "code", "execution_count": 35, "id": "9e1b38d3-220c-4a20-a60b-a8f87dfd5bff", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "target_type_id 0\n", "name 0\n", "created_at 0\n", "updated_at 0\n", "dtype: int64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# valeurs manquantes\n", "\n", "df1_targets.isna().sum()" ] }, { "cell_type": "code", "execution_count": 36, "id": "bf660284-974f-40aa-a914-100d45fceafc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "287" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_targets[\"name\"].nunique()" ] }, { "cell_type": "code", "execution_count": 37, "id": "6589e11c-9c7a-4bd8-8953-3c5a23fa0ba2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtarget_type_idnamecreated_atupdated_at
021756DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00
170156consentement optin scolaires2021-12-21 16:03:59.840785+01:002022-02-18 17:23:44.761388+01:00
213456DDCP Newsletter jeune public2020-11-10 09:43:19.667471+01:002021-03-02 18:38:19.052304+01:00
370056consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00
496456DDCP achat billet nbr dep 190520212022-04-14 10:58:17.142834+02:002022-04-14 10:58:23.677264+02:00
..................
28218111ddcp_promo_ribambelle_2022_mapado_naikko_opt in2022-11-30 15:57:05.681956+01:002022-11-30 16:00:32.649210+01:00
28320061cp 14 mars2023-03-03 18:07:00.223750+01:002023-03-03 18:15:01.390970+01:00
28421931ddcp fichier musique 22023-04-14 14:33:53.628142+02:002023-04-14 15:00:35.608210+02:00
28524291import_mucem2023-06-26 18:32:40.146757+02:002023-06-26 18:45:02.614668+02:00
28624851po_au salon_2e envoi2023-07-03 13:09:48.598072+02:002023-07-03 13:15:03.634600+02:00
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287 rows × 5 columns

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" ], "text/plain": [ " id target_type_id name \\\n", "0 217 56 DDCP PROMO Art contemporain - salle de chauffe... \n", "1 701 56 consentement optin scolaires \n", "2 134 56 DDCP Newsletter jeune public \n", "3 700 56 consentement optout scolaires \n", "4 964 56 DDCP achat billet nbr dep 19052021 \n", ".. ... ... ... \n", "282 1811 1 ddcp_promo_ribambelle_2022_mapado_naikko_opt in \n", "283 2006 1 cp 14 mars \n", "284 2193 1 ddcp fichier musique 2 \n", "285 2429 1 import_mucem \n", "286 2485 1 po_au salon_2e envoi \n", "\n", " created_at updated_at \n", "0 2021-01-04 15:00:05.401899+01:00 2021-03-02 18:38:19.025969+01:00 \n", "1 2021-12-21 16:03:59.840785+01:00 2022-02-18 17:23:44.761388+01:00 \n", "2 2020-11-10 09:43:19.667471+01:00 2021-03-02 18:38:19.052304+01:00 \n", "3 2021-12-21 16:01:57.524946+01:00 2022-02-18 17:23:44.807776+01:00 \n", "4 2022-04-14 10:58:17.142834+02:00 2022-04-14 10:58:23.677264+02:00 \n", ".. ... ... \n", "282 2022-11-30 15:57:05.681956+01:00 2022-11-30 16:00:32.649210+01:00 \n", "283 2023-03-03 18:07:00.223750+01:00 2023-03-03 18:15:01.390970+01:00 \n", "284 2023-04-14 14:33:53.628142+02:00 2023-04-14 15:00:35.608210+02:00 \n", "285 2023-06-26 18:32:40.146757+02:00 2023-06-26 18:45:02.614668+02:00 \n", "286 2023-07-03 13:09:48.598072+02:00 2023-07-03 13:15:03.634600+02:00 \n", "\n", "[287 rows x 5 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_targets" ] }, { "cell_type": "code", "execution_count": 13, "id": "ef56e8ec-0429-475e-9c28-07983654c37b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcustomer_idtarget_idcreated_atupdated_atnameextra_field
011848246454001302021-09-23 09:35:47.617275+02:002021-09-23 09:35:47.617275+02:00NaNNaN
111848256454003452021-09-23 09:35:47.668846+02:002021-09-23 09:35:47.668846+02:00NaNNaN
211848286454021262021-09-23 12:02:51.253269+02:002021-09-23 12:02:51.253269+02:00NaNNaN
311848296454031262021-09-23 12:20:47.394480+02:002021-09-23 12:20:47.394480+02:00NaNNaN
412957706473013462021-09-28 16:02:29.372608+02:002021-09-28 16:02:29.372608+02:00NaNNaN
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" ], "text/plain": [ " id customer_id target_id created_at \\\n", "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", "\n", " updated_at name extra_field \n", "0 2021-09-23 09:35:47.617275+02:00 NaN NaN \n", "1 2021-09-23 09:35:47.668846+02:00 NaN NaN \n", "2 2021-09-23 12:02:51.253269+02:00 NaN NaN \n", "3 2021-09-23 12:20:47.394480+02:00 NaN NaN \n", "4 2021-09-28 16:02:29.372608+02:00 NaN NaN " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 3. customer target mapping\n", "\n", "df1_customer_target_mappings.head()" ] }, { "cell_type": "code", "execution_count": 19, "id": "5244543f-1948-4769-be1f-691ad13174a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0.000000\n", "customer_id 0.000000\n", "target_id 0.000000\n", "created_at 0.000022\n", "updated_at 0.000022\n", "name 1.000000\n", "extra_field 1.000000\n", "dtype: float64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_customer_target_mappings.isna().sum()/df1_customer_target_mappings.shape[0]" ] }, { "cell_type": "code", "execution_count": 17, "id": "1c59e2ae-ee24-4195-bfea-ae55b92368ec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "768024" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_customer_target_mappings[\"id\"].nunique()\n", "# df1_customer_target_mappings.shape[0]" ] }, { "cell_type": "code", "execution_count": 28, "id": "4ed49f39-e6d3-4785-ba7d-bce918d423ee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# les couples customer_id / target_id sont-ils uniques ?\n", "df1_customer_target_mappings.duplicated(subset = [\"customer_id\", \"target_id\"]).sum() # aucun doublon" ] }, { "cell_type": "code", "execution_count": 30, "id": "f8cb1740-2cb0-4b3a-bfb0-d35423dc2cc7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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target_type_idtarget_type_is_importtarget_type_nametarget_type_identifier
069Falsemanual_dynamic_filtere0f4b8693184850fefd6d2a38f10584e
148Truemanual_structure382bca214204a2d3462f5ec2728d5d1e
21Truemanual_import12213df2ce68a624e4c0070521437bac
356Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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" ], "text/plain": [ " target_type_id target_type_is_import target_type_name \\\n", "0 69 False manual_dynamic_filter \n", "1 48 True manual_structure \n", "2 1 True manual_import \n", "3 56 False manual_static_filter \n", "\n", " target_type_identifier \n", "0 e0f4b8693184850fefd6d2a38f10584e \n", "1 382bca214204a2d3462f5ec2728d5d1e \n", "2 12213df2ce68a624e4c0070521437bac \n", "3 fb27e81baa4debc6a4e1a8639c20e808 " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 4.1. merge target with target type\n", "\n", "df1_target_types[[\"id\",\"is_import\",\"name\",\"identifier\"]].add_prefix(\"target_type_\")" ] }, { "cell_type": "code", "execution_count": 94, "id": "ebabdebd-3d75-4048-b65d-4cbd69bee390", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtarget_type_idnamecreated_atupdated_attarget_type_is_importtarget_type_nametarget_type_identifier
021756DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
170156consentement optin scolaires2021-12-21 16:03:59.840785+01:002022-02-18 17:23:44.761388+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
213456DDCP Newsletter jeune public2020-11-10 09:43:19.667471+01:002021-03-02 18:38:19.052304+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
370056consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
496456DDCP achat billet nbr dep 190520212022-04-14 10:58:17.142834+02:002022-04-14 10:58:23.677264+02:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
...........................
28218111ddcp_promo_ribambelle_2022_mapado_naikko_opt in2022-11-30 15:57:05.681956+01:002022-11-30 16:00:32.649210+01:00Truemanual_import12213df2ce68a624e4c0070521437bac
28320061cp 14 mars2023-03-03 18:07:00.223750+01:002023-03-03 18:15:01.390970+01:00Truemanual_import12213df2ce68a624e4c0070521437bac
28421931ddcp fichier musique 22023-04-14 14:33:53.628142+02:002023-04-14 15:00:35.608210+02:00Truemanual_import12213df2ce68a624e4c0070521437bac
28524291import_mucem2023-06-26 18:32:40.146757+02:002023-06-26 18:45:02.614668+02:00Truemanual_import12213df2ce68a624e4c0070521437bac
28624851po_au salon_2e envoi2023-07-03 13:09:48.598072+02:002023-07-03 13:15:03.634600+02:00Truemanual_import12213df2ce68a624e4c0070521437bac
\n", "

287 rows × 8 columns

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" ], "text/plain": [ " id target_type_id name \\\n", "0 217 56 DDCP PROMO Art contemporain - salle de chauffe... \n", "1 701 56 consentement optin scolaires \n", "2 134 56 DDCP Newsletter jeune public \n", "3 700 56 consentement optout scolaires \n", "4 964 56 DDCP achat billet nbr dep 19052021 \n", ".. ... ... ... \n", "282 1811 1 ddcp_promo_ribambelle_2022_mapado_naikko_opt in \n", "283 2006 1 cp 14 mars \n", "284 2193 1 ddcp fichier musique 2 \n", "285 2429 1 import_mucem \n", "286 2485 1 po_au salon_2e envoi \n", "\n", " created_at updated_at \\\n", "0 2021-01-04 15:00:05.401899+01:00 2021-03-02 18:38:19.025969+01:00 \n", "1 2021-12-21 16:03:59.840785+01:00 2022-02-18 17:23:44.761388+01:00 \n", "2 2020-11-10 09:43:19.667471+01:00 2021-03-02 18:38:19.052304+01:00 \n", "3 2021-12-21 16:01:57.524946+01:00 2022-02-18 17:23:44.807776+01:00 \n", "4 2022-04-14 10:58:17.142834+02:00 2022-04-14 10:58:23.677264+02:00 \n", ".. ... ... \n", "282 2022-11-30 15:57:05.681956+01:00 2022-11-30 16:00:32.649210+01:00 \n", "283 2023-03-03 18:07:00.223750+01:00 2023-03-03 18:15:01.390970+01:00 \n", "284 2023-04-14 14:33:53.628142+02:00 2023-04-14 15:00:35.608210+02:00 \n", "285 2023-06-26 18:32:40.146757+02:00 2023-06-26 18:45:02.614668+02:00 \n", "286 2023-07-03 13:09:48.598072+02:00 2023-07-03 13:15:03.634600+02:00 \n", "\n", " target_type_is_import target_type_name \\\n", "0 False manual_static_filter \n", "1 False manual_static_filter \n", "2 False manual_static_filter \n", "3 False manual_static_filter \n", "4 False manual_static_filter \n", ".. ... ... \n", "282 True manual_import \n", "283 True manual_import \n", "284 True manual_import \n", "285 True manual_import \n", "286 True manual_import \n", "\n", " target_type_identifier \n", "0 fb27e81baa4debc6a4e1a8639c20e808 \n", "1 fb27e81baa4debc6a4e1a8639c20e808 \n", "2 fb27e81baa4debc6a4e1a8639c20e808 \n", "3 fb27e81baa4debc6a4e1a8639c20e808 \n", "4 fb27e81baa4debc6a4e1a8639c20e808 \n", ".. ... \n", "282 12213df2ce68a624e4c0070521437bac \n", "283 12213df2ce68a624e4c0070521437bac \n", "284 12213df2ce68a624e4c0070521437bac \n", "285 12213df2ce68a624e4c0070521437bac \n", "286 12213df2ce68a624e4c0070521437bac \n", "\n", "[287 rows x 8 columns]" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# merge\n", "\n", "df1_targets_full = pd.merge(df1_targets, df1_target_types[[\"id\",\"is_import\",\"name\",\"identifier\"]].add_prefix(\"target_type_\"), left_on='target_type_id', right_on='target_type_id', how='left')\n", "df1_targets_full" ] }, { "cell_type": "code", "execution_count": 32, "id": "f0b03a5d-b622-496a-bc71-ef92e91f9e51", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcustomer_idtarget_idcreated_atupdated_atnameextra_field
011848246454001302021-09-23 09:35:47.617275+02:002021-09-23 09:35:47.617275+02:00NaNNaN
111848256454003452021-09-23 09:35:47.668846+02:002021-09-23 09:35:47.668846+02:00NaNNaN
211848286454021262021-09-23 12:02:51.253269+02:002021-09-23 12:02:51.253269+02:00NaNNaN
311848296454031262021-09-23 12:20:47.394480+02:002021-09-23 12:20:47.394480+02:00NaNNaN
412957706473013462021-09-28 16:02:29.372608+02:002021-09-28 16:02:29.372608+02:00NaNNaN
\n", "
" ], "text/plain": [ " id customer_id target_id created_at \\\n", "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", "\n", " updated_at name extra_field \n", "0 2021-09-23 09:35:47.617275+02:00 NaN NaN \n", "1 2021-09-23 09:35:47.668846+02:00 NaN NaN \n", "2 2021-09-23 12:02:51.253269+02:00 NaN NaN \n", "3 2021-09-23 12:20:47.394480+02:00 NaN NaN \n", "4 2021-09-28 16:02:29.372608+02:00 NaN NaN " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 4.2. merge df1_customer_target_mappings with df1_targets_full\n", "\n", "df1_customer_target_mappings.head()" ] }, { "cell_type": "code", "execution_count": 42, "id": "906e01fd-23b3-4da7-bc5e-6618599fbb05", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Q : les dates de création et de mise à jour de la table customer target mapping sont elles égales ??\n", "\n", "# 17 observations for which creation date != update date, ms ce sont que des Nan, OK !\n", "(df1_customer_target_mappings[\"created_at\"] != df1_customer_target_mappings[\"updated_at\"]).sum() " ] }, { "cell_type": "code", "execution_count": 43, "id": "c9265d2f-b636-415e-bc2d-99b932b89424", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcustomer_idtarget_idcreated_atupdated_atnameextra_field
6054841691570661701264NaNNaNNaNNaN
6545491832071651594264NaNNaNNaNNaN
6545501832072663061264NaNNaNNaNNaN
6545511832073663114264NaNNaNNaNNaN
6551621949466663865264NaNNaNNaNNaN
7540382154438664300264NaNNaNNaNNaN
7609292282079665557264NaNNaNNaNNaN
7609302282080665563264NaNNaNNaNNaN
7617872675293661492264NaNNaNNaNNaN
7617982721237665931264NaNNaNNaNNaN
7617992721238665932264NaNNaNNaNNaN
7618002721239665938264NaNNaNNaNNaN
7618012721240665956264NaNNaNNaNNaN
7679182736960666466264NaNNaNNaNNaN
7679192736961666468264NaNNaNNaNNaN
7679682737357666824264NaNNaNNaNNaN
7679842737489107743264NaNNaNNaNNaN
\n", "
" ], "text/plain": [ " id customer_id target_id created_at updated_at name \\\n", "605484 1691570 661701 264 NaN NaN NaN \n", "654549 1832071 651594 264 NaN NaN NaN \n", "654550 1832072 663061 264 NaN NaN NaN \n", "654551 1832073 663114 264 NaN NaN NaN \n", "655162 1949466 663865 264 NaN NaN NaN \n", "754038 2154438 664300 264 NaN NaN NaN \n", "760929 2282079 665557 264 NaN NaN NaN \n", "760930 2282080 665563 264 NaN NaN NaN \n", "761787 2675293 661492 264 NaN NaN NaN \n", "761798 2721237 665931 264 NaN NaN NaN \n", "761799 2721238 665932 264 NaN NaN NaN \n", "761800 2721239 665938 264 NaN NaN NaN \n", "761801 2721240 665956 264 NaN NaN NaN \n", "767918 2736960 666466 264 NaN NaN NaN \n", "767919 2736961 666468 264 NaN NaN NaN \n", "767968 2737357 666824 264 NaN NaN NaN \n", "767984 2737489 107743 264 NaN NaN NaN \n", "\n", " extra_field \n", "605484 NaN \n", "654549 NaN \n", "654550 NaN \n", "654551 NaN \n", "655162 NaN \n", "754038 NaN \n", "760929 NaN \n", "760930 NaN \n", "761787 NaN \n", "761798 NaN \n", "761799 NaN \n", "761800 NaN \n", "761801 NaN \n", "767918 NaN \n", "767919 NaN \n", "767968 NaN \n", "767984 NaN " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_customer_target_mappings[df1_customer_target_mappings[\"created_at\"] != df1_customer_target_mappings[\"updated_at\"]]" ] }, { "cell_type": "code", "execution_count": 44, "id": "63e4ce23-ce13-46fc-82c5-9065a774b4b5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcustomer_idtarget_idcreated_atupdated_atnameextra_field
140341626517512642022-01-28 20:00:16.448920+01:002022-01-28 20:00:16.448920+01:00NaNNaN
149341627422132642022-01-28 20:30:17.323634+01:002022-01-28 20:30:17.323634+01:00NaNNaN
1120429205411560592642022-09-29 07:00:43.003440+02:002022-09-29 07:00:43.003440+02:00NaNNaN
1121429205511560632642022-09-29 07:00:43.003440+02:002022-09-29 07:00:43.003440+02:00NaNNaN
40064428048349162642023-03-14 07:01:27.868349+01:002023-03-14 07:01:27.868349+01:00NaNNaN
........................
7618012721240665956264NaNNaNNaNNaN
7679182736960666466264NaNNaNNaNNaN
7679192736961666468264NaNNaNNaNNaN
7679682737357666824264NaNNaNNaNNaN
7679842737489107743264NaNNaNNaNNaN
\n", "

1954 rows × 7 columns

\n", "
" ], "text/plain": [ " id customer_id target_id created_at \\\n", "140 3416265 1751 264 2022-01-28 20:00:16.448920+01:00 \n", "149 3416274 2213 264 2022-01-28 20:30:17.323634+01:00 \n", "1120 4292054 1156059 264 2022-09-29 07:00:43.003440+02:00 \n", "1121 4292055 1156063 264 2022-09-29 07:00:43.003440+02:00 \n", "4006 4428048 34916 264 2023-03-14 07:01:27.868349+01:00 \n", "... ... ... ... ... \n", "761801 2721240 665956 264 NaN \n", "767918 2736960 666466 264 NaN \n", "767919 2736961 666468 264 NaN \n", "767968 2737357 666824 264 NaN \n", "767984 2737489 107743 264 NaN \n", "\n", " updated_at name extra_field \n", "140 2022-01-28 20:00:16.448920+01:00 NaN NaN \n", "149 2022-01-28 20:30:17.323634+01:00 NaN NaN \n", "1120 2022-09-29 07:00:43.003440+02:00 NaN NaN \n", "1121 2022-09-29 07:00:43.003440+02:00 NaN NaN \n", "4006 2023-03-14 07:01:27.868349+01:00 NaN NaN \n", "... ... ... ... \n", "761801 NaN NaN NaN \n", "767918 NaN NaN NaN \n", "767919 NaN NaN NaN \n", "767968 NaN NaN NaN \n", "767984 NaN NaN NaN \n", "\n", "[1954 rows x 7 columns]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ces données manquantes concernent le target avec id 264, mais les autres valeurs pr ce même target sont bien renseignées\n", "df1_customer_target_mappings[df1_customer_target_mappings[\"target_id\"]==264]" ] }, { "cell_type": "code", "execution_count": 71, "id": "0681b3e6-71bb-4132-b11a-646382f78de6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2021-10-28 11:30:42.717180+02:00'" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Q : les dates de creation / update sont elles-uniques selon le client ou selon la target ?\n", "\n", "df1_customer_target_mappings[df1_customer_target_mappings[\"target_id\"]==217][\"updated_at\"].max()" ] }, { "cell_type": "code", "execution_count": 65, "id": "93e4a125-08dd-42ba-baa6-0dc5996a76af", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idtarget_type_idnamecreated_atupdated_attarget_type_is_importtarget_type_nametarget_type_identifier
021756DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
\n", "
" ], "text/plain": [ " id target_type_id name \\\n", "0 217 56 DDCP PROMO Art contemporain - salle de chauffe... \n", "\n", " created_at updated_at \\\n", "0 2021-01-04 15:00:05.401899+01:00 2021-03-02 18:38:19.025969+01:00 \n", "\n", " target_type_is_import target_type_name \\\n", "0 False manual_static_filter \n", "\n", " target_type_identifier \n", "0 fb27e81baa4debc6a4e1a8639c20e808 " ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_targets_full[df1_targets_full[\"id\"]==217]" ] }, { "cell_type": "code", "execution_count": 69, "id": "88eac1a6-74b1-4ce1-91a1-c1c69e7a9264", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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370056consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00Falsemanual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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target_idtarget_nametarget_created_attarget_updated_attarget_type_is_importtarget_type_idtarget_type_nametarget_type_identifier
0217DDCP PROMO Art contemporain - salle de chauffe...2021-01-04 15:00:05.401899+01:002021-03-02 18:38:19.025969+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
1701consentement optin scolaires2021-12-21 16:03:59.840785+01:002022-02-18 17:23:44.761388+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
2134DDCP Newsletter jeune public2020-11-10 09:43:19.667471+01:002021-03-02 18:38:19.052304+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
3700consentement optout scolaires2021-12-21 16:01:57.524946+01:002022-02-18 17:23:44.807776+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
4964DDCP achat billet nbr dep 190520212022-04-14 10:58:17.142834+02:002022-04-14 10:58:23.677264+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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" ], "text/plain": [ " target_id target_name \\\n", "0 217 DDCP PROMO Art contemporain - salle de chauffe... \n", "1 701 consentement optin scolaires \n", "2 134 DDCP Newsletter jeune public \n", "3 700 consentement optout scolaires \n", "4 964 DDCP achat billet nbr dep 19052021 \n", "\n", " target_created_at target_updated_at \\\n", "0 2021-01-04 15:00:05.401899+01:00 2021-03-02 18:38:19.025969+01:00 \n", "1 2021-12-21 16:03:59.840785+01:00 2022-02-18 17:23:44.761388+01:00 \n", "2 2020-11-10 09:43:19.667471+01:00 2021-03-02 18:38:19.052304+01:00 \n", "3 2021-12-21 16:01:57.524946+01:00 2022-02-18 17:23:44.807776+01:00 \n", "4 2022-04-14 10:58:17.142834+02:00 2022-04-14 10:58:23.677264+02:00 \n", "\n", " target_type_is_import target_type_id target_type_name \\\n", "0 False 56 manual_static_filter \n", "1 False 56 manual_static_filter \n", "2 False 56 manual_static_filter \n", "3 False 56 manual_static_filter \n", "4 False 56 manual_static_filter \n", "\n", " target_type_identifier \n", "0 fb27e81baa4debc6a4e1a8639c20e808 \n", "1 fb27e81baa4debc6a4e1a8639c20e808 \n", "2 fb27e81baa4debc6a4e1a8639c20e808 \n", "3 fb27e81baa4debc6a4e1a8639c20e808 \n", "4 fb27e81baa4debc6a4e1a8639c20e808 " ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_targets_full = df1_targets_full.rename(columns=lambda x: 'target_' + x if not x.startswith('target_') else x)\n", "df1_targets_full.head()" ] }, { "cell_type": "code", "execution_count": 110, "id": "cda50294-e9f3-4c0e-9172-85fde93efa70", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " id customer_id target_id created_at\n", "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00\n", "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00\n", "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00\n", "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00\n", "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_customer_target_mappings[[\"id\", \"customer_id\", \"target_id\", \"created_at\"]].head()" ] }, { "cell_type": "code", "execution_count": 111, "id": "1aaac887-5ea9-4651-8628-920c7d80f120", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcustomer_idtarget_idcreated_attarget_nametarget_created_attarget_updated_attarget_type_is_importtarget_type_idtarget_type_nametarget_type_identifier
011848246454001302021-09-23 09:35:47.617275+02:00DDCP PROMO Réseau livres2020-11-04 18:40:49.500866+01:002021-03-02 18:38:19.084287+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
111848256454003452021-09-23 09:35:47.668846+02:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
211848286454021262021-09-23 12:02:51.253269+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
311848296454031262021-09-23 12:20:47.394480+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
412957706473013462021-09-28 16:02:29.372608+02:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
....................................
76801927375456669833452021-12-14 14:48:05.456842+01:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802027375466669833462021-12-14 14:48:05.465830+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802127375756669863462021-12-14 23:15:42.757832+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802227375766669873452021-12-15 00:14:59.018215+01:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802327375776669873462021-12-15 00:14:59.029434+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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768024 rows × 11 columns

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" ], "text/plain": [ " id customer_id target_id created_at \\\n", "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", "... ... ... ... ... \n", "768019 2737545 666983 345 2021-12-14 14:48:05.456842+01:00 \n", "768020 2737546 666983 346 2021-12-14 14:48:05.465830+01:00 \n", "768021 2737575 666986 346 2021-12-14 23:15:42.757832+01:00 \n", "768022 2737576 666987 345 2021-12-15 00:14:59.018215+01:00 \n", "768023 2737577 666987 346 2021-12-15 00:14:59.029434+01:00 \n", "\n", " target_name target_created_at \\\n", "0 DDCP PROMO Réseau livres 2020-11-04 18:40:49.500866+01:00 \n", "1 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", "2 DDCP PROMO Art contemporain 2020-11-04 18:38:53.016572+01:00 \n", "3 DDCP PROMO Art contemporain 2020-11-04 18:38:53.016572+01:00 \n", "4 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", "... ... ... \n", "768019 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", "768020 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", "768021 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", "768022 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", "768023 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", "\n", " target_updated_at target_type_is_import \\\n", "0 2021-03-02 18:38:19.084287+01:00 False \n", "1 2021-04-16 17:17:26.069199+02:00 False \n", "2 2021-04-16 17:17:25.850107+02:00 False \n", "3 2021-04-16 17:17:25.850107+02:00 False \n", "4 2021-04-16 17:17:26.080378+02:00 False \n", "... ... ... \n", "768019 2021-04-16 17:17:26.069199+02:00 False \n", "768020 2021-04-16 17:17:26.080378+02:00 False \n", "768021 2021-04-16 17:17:26.080378+02:00 False \n", "768022 2021-04-16 17:17:26.069199+02:00 False \n", "768023 2021-04-16 17:17:26.080378+02:00 False \n", "\n", " target_type_id target_type_name target_type_identifier \n", "0 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "1 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "2 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "3 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "4 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "... ... ... ... \n", "768019 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "768020 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "768021 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "768022 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "768023 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "\n", "[768024 rows x 11 columns]" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# finally, merge\n", "\n", "# pour df1_customer_target_mappings on enlève les colonnes name, extra_field, et updated_at (valeur égale à created_at)\n", "# note : by making a left join on df1_customer_target_mappings, we suppress 2 targets that have no customer associated\n", "\n", "df1_customer_targets = pd.merge(df1_customer_target_mappings[[\"id\", \"customer_id\", \"target_id\", \"created_at\"]], \n", " df1_targets_full, left_on='target_id', right_on='target_id', how='left')\n", "df1_customer_targets" ] }, { "cell_type": "code", "execution_count": 138, "id": "95657bda-d060-48ca-8217-3e3f119028c1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcustomer_idtarget_idcreated_attarget_nametarget_created_attarget_updated_attarget_type_is_importtarget_type_idtarget_type_nametarget_type_identifier
011848246454001302021-09-23 09:35:47.617275+02:00DDCP PROMO Réseau livres2020-11-04 18:40:49.500866+01:002021-03-02 18:38:19.084287+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
111848256454003452021-09-23 09:35:47.668846+02:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
211848286454021262021-09-23 12:02:51.253269+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
311848296454031262021-09-23 12:20:47.394480+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
412957706473013462021-09-28 16:02:29.372608+02:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
....................................
76801927375456669833452021-12-14 14:48:05.456842+01:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802027375466669833462021-12-14 14:48:05.465830+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802127375756669863462021-12-14 23:15:42.757832+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802227375766669873452021-12-15 00:14:59.018215+01:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
76802327375776669873462021-12-15 00:14:59.029434+01:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808
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768024 rows × 11 columns

\n", "
" ], "text/plain": [ " id customer_id target_id created_at \\\n", "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", "... ... ... ... ... \n", "768019 2737545 666983 345 2021-12-14 14:48:05.456842+01:00 \n", "768020 2737546 666983 346 2021-12-14 14:48:05.465830+01:00 \n", "768021 2737575 666986 346 2021-12-14 23:15:42.757832+01:00 \n", "768022 2737576 666987 345 2021-12-15 00:14:59.018215+01:00 \n", "768023 2737577 666987 346 2021-12-15 00:14:59.029434+01:00 \n", "\n", " target_name target_created_at \\\n", "0 DDCP PROMO Réseau livres 2020-11-04 18:40:49.500866+01:00 \n", "1 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", "2 DDCP PROMO Art contemporain 2020-11-04 18:38:53.016572+01:00 \n", "3 DDCP PROMO Art contemporain 2020-11-04 18:38:53.016572+01:00 \n", "4 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", "... ... ... \n", "768019 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", "768020 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", "768021 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", "768022 Inscrits NL générale site web 2021-04-16 17:17:26.069199+02:00 \n", "768023 Votre première liste 2021-04-16 17:17:26.080378+02:00 \n", "\n", " target_updated_at target_type_is_import \\\n", "0 2021-03-02 18:38:19.084287+01:00 False \n", "1 2021-04-16 17:17:26.069199+02:00 False \n", "2 2021-04-16 17:17:25.850107+02:00 False \n", "3 2021-04-16 17:17:25.850107+02:00 False \n", "4 2021-04-16 17:17:26.080378+02:00 False \n", "... ... ... \n", "768019 2021-04-16 17:17:26.069199+02:00 False \n", "768020 2021-04-16 17:17:26.080378+02:00 False \n", "768021 2021-04-16 17:17:26.080378+02:00 False \n", "768022 2021-04-16 17:17:26.069199+02:00 False \n", "768023 2021-04-16 17:17:26.080378+02:00 False \n", "\n", " target_type_id target_type_name target_type_identifier \n", "0 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "1 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "2 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "3 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "4 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "... ... ... ... \n", "768019 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "768020 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "768021 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "768022 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "768023 56 manual_static_filter fb27e81baa4debc6a4e1a8639c20e808 \n", "\n", "[768024 rows x 11 columns]" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# rq : on dirait que la date de création des targets est à peine inférieure à la date minimum de création des targets des customers \n", "# idée : les targets sont créées puis envoyées aux clients, d'où un léger délai \n", "# mais question substiste : pourquoi les clients ne reçoivent-ils pas la target en même temps ? \n", "\n", "# vérifions que la date de création de la target est tjrs inférieure à la date de création minimum pour tous les clients ayant reçu la target\n", "\n", "# first step : convert strings into dates\n", "\n", "df1_customer_targets[\"created_at\"] = df1_customer_targets[\"created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", "df1_customer_targets[\"target_created_at\"] = df1_customer_targets[\"target_created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", "df1_customer_targets[\"target_updated_at\"] = df1_customer_targets[\"target_updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", "\n" ] }, { "cell_type": "code", "execution_count": 144, "id": "58b22fab-d13d-456a-8250-1da035572fe9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "target_id\n", "116 0 days 00:00:00.949028\n", "117 0 days 00:00:00.037337\n", "119 0 days 00:00:00.024423\n", "120 0 days 00:00:00.058732\n", "122 0 days 00:00:00.027283\n", " ... \n", "2779 0 days 00:00:19.087958\n", "2788 0 days 00:01:36.372927\n", "2825 0 days 00:00:00.028771\n", "2830 0 days 00:00:01.587058\n", "2833 0 days 00:00:00.031071\n", "Name: creation_delay, Length: 283, dtype: object" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# second step : compute delay and minimum by target\n", "\n", "df1_customer_targets[\"creation_delay\"] = df1_customer_targets[\"created_at\"] -df1_customer_targets[\"target_created_at\"]\n", "\n", "\n", "df1_customer_targets.groupby(\"target_id\")[\"creation_delay\"].min()" ] }, { "cell_type": "code", "execution_count": 148, "id": "4b5c8f3e-9227-466c-a4c0-2280864a5036", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 days 00:00:00.009293\n", "686 days 23:14:10.435866\n" ] } ], "source": [ "print(df1_customer_targets.groupby(\"target_id\")[\"creation_delay\"].min().min())\n", "print((df1_customer_targets.groupby(\"target_id\")[\"creation_delay\"].min()).max())" ] }, { "cell_type": "code", "execution_count": 153, "id": "41e4040c-45a0-41ac-be91-4c86ef5ab1a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "target_id\n", "335 285 days 22:56:30.356536\n", "339 86 days 21:34:19.282253\n", "469 7 days 07:24:03.446563\n", "490 3 days 16:28:38.068677\n", "502 7 days 20:15:19.326651\n", "515 1 days 22:49:33.761856\n", "517 76 days 00:41:25.366394\n", "528 26 days 06:17:44.689111\n", "529 6 days 02:41:29.617761\n", "530 1 days 04:34:33.843116\n", "642 219 days 16:50:10.816034\n", "695 668 days 03:31:22.896313\n", "697 58 days 20:26:26.744823\n", "699 686 days 23:14:10.435866\n", "786 625 days 14:47:48.797084\n", "1747 14 days 04:08:24.295840\n", "2094 239 days 15:13:18.681637\n", "2321 167 days 21:19:37.490219\n", "Name: creation_delay, dtype: object" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# glt, le délai création de la target - création pour le premier client est très court, envoi quasi instantanné\n", "# mais parfois, le délai est très long, plus d'une année pour les cas extrêmes\n", "\n", "min_target_delay = df1_customer_targets.groupby(\"target_id\")[\"creation_delay\"].min()\n", "min_target_delay[min_target_delay > timedelta(days=1)]" ] }, { "cell_type": "code", "execution_count": 155, "id": "ffb2d1be-b1cb-4285-9584-d96ffeee146e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "target_type_id\n", "1 0 days 00:00:06.490151\n", "56 0 days 00:00:00.009293\n", "69 0 days 00:00:00.032269\n", "Name: creation_delay, dtype: object" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_customer_targets.groupby(\"target_type_id\")[\"creation_delay\"].min() # les target de type 1 ont un plus grd délai" ] }, { "cell_type": "code", "execution_count": 159, "id": "44d5a1f5-0691-43de-bb9f-9915830bbb77", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[56 69 1]\n", "[56 69 1]\n" ] } ], "source": [ "print(df1_customer_targets[\"target_type_id\"].unique())\n", "print(df1_targets[\"target_type_id\"].unique()) # rq : slt 3 types de target sur les 4 sont dans la table" ] }, { "cell_type": "code", "execution_count": 165, "id": "3a21df0d-0199-45d7-9019-e69dab67c9a8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcustomer_idtarget_idcreated_attarget_nametarget_created_attarget_updated_attarget_type_is_importtarget_type_idtarget_type_nametarget_type_identifiercreation_delay
011848246454001302021-09-23 09:35:47.617275+02:00DDCP PROMO Réseau livres2020-11-04 18:40:49.500866+01:002021-03-02 18:38:19.084287+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808322 days, 13:54:58.116409
111848256454003452021-09-23 09:35:47.668846+02:00Inscrits NL générale site web2021-04-16 17:17:26.069199+02:002021-04-16 17:17:26.069199+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808159 days, 16:18:21.599647
211848286454021262021-09-23 12:02:51.253269+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808322 days, 16:23:58.236697
311848296454031262021-09-23 12:20:47.394480+02:00DDCP PROMO Art contemporain2020-11-04 18:38:53.016572+01:002021-04-16 17:17:25.850107+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808322 days, 16:41:54.377908
412957706473013462021-09-28 16:02:29.372608+02:00Votre première liste2021-04-16 17:17:26.080378+02:002021-04-16 17:17:26.080378+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808164 days, 22:45:03.292230
511848336456273982021-09-24 18:16:33.432760+02:00DDCP PROMO MD participants ateliers yoga2021-05-26 10:54:12.232999+02:002021-05-26 10:54:22.378253+02:00False69manual_dynamic_filtere0f4b8693184850fefd6d2a38f10584e121 days, 7:22:21.199761
6445281812087366312023-05-06 03:29:43.875970+02:00consentement optin b2b2021-11-30 10:03:37.430645+01:002022-02-18 17:21:30.653027+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808521 days, 16:26:06.445325
7429170211558455022022-09-28 12:55:36.843316+02:00Automation_parrainage_newsletter_générale2021-08-10 15:25:56.142538+02:002021-08-10 15:26:06.275964+02:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808413 days, 21:29:40.700778
8409640611216514692022-07-31 11:45:19.694236+02:00RI Newsletter Alexandrie (inscriptions formula...2021-07-08 11:31:10.246495+02:002022-01-26 12:14:17.941253+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808388 days, 0:14:09.447741
9445282412087426312023-05-06 03:29:43.901323+02:00consentement optin b2b2021-11-30 10:03:37.430645+01:002022-02-18 17:21:30.653027+01:00False56manual_static_filterfb27e81baa4debc6a4e1a8639c20e808521 days, 16:26:06.470678
\n", "
" ], "text/plain": [ " id customer_id target_id created_at \\\n", "0 1184824 645400 130 2021-09-23 09:35:47.617275+02:00 \n", "1 1184825 645400 345 2021-09-23 09:35:47.668846+02:00 \n", "2 1184828 645402 126 2021-09-23 12:02:51.253269+02:00 \n", "3 1184829 645403 126 2021-09-23 12:20:47.394480+02:00 \n", "4 1295770 647301 346 2021-09-28 16:02:29.372608+02:00 \n", "5 1184833 645627 398 2021-09-24 18:16:33.432760+02:00 \n", "6 4452818 1208736 631 2023-05-06 03:29:43.875970+02:00 \n", "7 4291702 1155845 502 2022-09-28 12:55:36.843316+02:00 \n", "8 4096406 1121651 469 2022-07-31 11:45:19.694236+02:00 \n", "9 4452824 1208742 631 2023-05-06 03:29:43.901323+02:00 \n", "\n", " target_name \\\n", "0 DDCP PROMO Réseau livres \n", "1 Inscrits NL générale site web \n", "2 DDCP PROMO Art contemporain \n", "3 DDCP PROMO Art contemporain \n", "4 Votre première liste \n", "5 DDCP PROMO MD participants ateliers yoga \n", "6 consentement optin b2b \n", "7 Automation_parrainage_newsletter_générale \n", "8 RI Newsletter Alexandrie (inscriptions formula... \n", "9 consentement optin b2b \n", "\n", " target_created_at target_updated_at \\\n", "0 2020-11-04 18:40:49.500866+01:00 2021-03-02 18:38:19.084287+01:00 \n", "1 2021-04-16 17:17:26.069199+02:00 2021-04-16 17:17:26.069199+02:00 \n", "2 2020-11-04 18:38:53.016572+01:00 2021-04-16 17:17:25.850107+02:00 \n", "3 2020-11-04 18:38:53.016572+01:00 2021-04-16 17:17:25.850107+02:00 \n", "4 2021-04-16 17:17:26.080378+02:00 2021-04-16 17:17:26.080378+02:00 \n", "5 2021-05-26 10:54:12.232999+02:00 2021-05-26 10:54:22.378253+02:00 \n", "6 2021-11-30 10:03:37.430645+01:00 2022-02-18 17:21:30.653027+01:00 \n", "7 2021-08-10 15:25:56.142538+02:00 2021-08-10 15:26:06.275964+02:00 \n", "8 2021-07-08 11:31:10.246495+02:00 2022-01-26 12:14:17.941253+01:00 \n", "9 2021-11-30 10:03:37.430645+01:00 2022-02-18 17:21:30.653027+01:00 \n", "\n", " target_type_is_import target_type_id target_type_name \\\n", "0 False 56 manual_static_filter \n", "1 False 56 manual_static_filter \n", "2 False 56 manual_static_filter \n", "3 False 56 manual_static_filter \n", "4 False 56 manual_static_filter \n", "5 False 69 manual_dynamic_filter \n", "6 False 56 manual_static_filter \n", "7 False 56 manual_static_filter \n", "8 False 56 manual_static_filter \n", "9 False 56 manual_static_filter \n", "\n", " target_type_identifier creation_delay \n", "0 fb27e81baa4debc6a4e1a8639c20e808 322 days, 13:54:58.116409 \n", "1 fb27e81baa4debc6a4e1a8639c20e808 159 days, 16:18:21.599647 \n", "2 fb27e81baa4debc6a4e1a8639c20e808 322 days, 16:23:58.236697 \n", "3 fb27e81baa4debc6a4e1a8639c20e808 322 days, 16:41:54.377908 \n", "4 fb27e81baa4debc6a4e1a8639c20e808 164 days, 22:45:03.292230 \n", "5 e0f4b8693184850fefd6d2a38f10584e 121 days, 7:22:21.199761 \n", "6 fb27e81baa4debc6a4e1a8639c20e808 521 days, 16:26:06.445325 \n", "7 fb27e81baa4debc6a4e1a8639c20e808 413 days, 21:29:40.700778 \n", "8 fb27e81baa4debc6a4e1a8639c20e808 388 days, 0:14:09.447741 \n", "9 fb27e81baa4debc6a4e1a8639c20e808 521 days, 16:26:06.470678 " ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# final visu : nice table for targets\n", "\n", "# pour la suite, on peut supprimer la colonne creation delay, \n", "# était juste utile pour vérifier que la date de création était postérieure à la date de création de la target\n", "\n", "df1_customer_targets.head(10)" ] }, { "cell_type": "markdown", "id": "d762394b-3aee-4284-a472-40a6b6f4308a", "metadata": {}, "source": [ "## Campaign stats, campaigns" ] }, { "cell_type": "code", "execution_count": 189, "id": "9d338a1a-52a5-49c4-a277-37be3f190e81", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
01319613newsletter enseignants janvier 20227212022-01-14 16:06:42.586321+01:002022-02-03 14:17:27.112963+01:00NaNNaN0.0Falseaba3b6fd5d186d28e06ff97135cade7f2022-01-14 00:00:00+01:00
11319586lsf_janvier_20227172022-01-07 11:30:35.315895+01:002022-02-03 14:17:27.116171+01:00NaNNaN0.0False788d986905533aba051261497ecffcbb2022-01-07 00:00:00+01:00
21319282Invitation à déjeuner au Mucem | Vernissage « ...5912021-09-28 12:50:24.448752+02:002022-02-03 14:17:27.119582+01:00NaNNaN0.0False3493894fa4ea036cfc6433c3e2ee63b02021-09-28 00:00:00+02:00
31319283Vacances de la Toussaint - centres des loisirs5902021-09-28 18:01:04.692073+02:002022-02-03 14:17:27.124408+01:00NaNNaN0.0False08b255a5d42b89b0585260b6f2360bdd2021-09-28 00:00:00+02:00
41319636ddcp_promo_md_livemag7302022-01-27 18:00:41.053069+01:002022-02-03 14:17:27.127607+01:00NaNNaN0.0Falsed5cfead94f5350c12c322b5b664544c12022-01-27 00:00:00+01:00
\n", "
" ], "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", " 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", " 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", " 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 " ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. campaigns\n", "\n", "df1_campaigns.head()" ] }, { "cell_type": "code", "execution_count": 171, "id": "fad1a58c-cece-45f9-a44f-ca46884a9a81", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0.000000\n", "name 0.000000\n", "service_id 0.000000\n", "created_at 0.000000\n", "updated_at 0.000000\n", "process_id 1.000000\n", "report_url 1.000000\n", "category 0.002090\n", "to_be_synced 0.000000\n", "identifier 0.000000\n", "sent_at 0.003135\n", "dtype: float64" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# part de Nan pour chaque variable\n", "\n", "df1_campaigns.isna().sum() / df1_campaigns.shape[0]" ] }, { "cell_type": "code", "execution_count": 185, "id": "cdeebf18-a3a4-4131-ad88-d45c39ec5786", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "name object\n", "service_id int64\n", "created_at object\n", "updated_at object\n", "process_id float64\n", "report_url float64\n", "category float64\n", "to_be_synced bool\n", "identifier object\n", "sent_at object\n", "dtype: object" ] }, "execution_count": 185, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns.dtypes" ] }, { "cell_type": "code", "execution_count": 186, "id": "5c9b669a-477b-4f33-86df-b22ff2c21382", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df1_campaigns[\"identifier\"][0])" ] }, { "cell_type": "code", "execution_count": 187, "id": "b5b0af8d-b9a0-4224-a229-d74d90ac2686", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0., nan])" ] }, "execution_count": 187, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# category\n", "\n", "df1_campaigns[\"category\"].isna()" ] }, { "cell_type": "code", "execution_count": 191, "id": "4cc618ae-063f-48fc-bce7-8b72d30ad4ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "957\n", "957\n" ] } ], "source": [ "# identifier\n", "\n", "print(df1_campaigns[\"identifier\"].nunique())\n", "print(df1_campaigns.shape[0]) # identifier is unique" ] }, { "cell_type": "code", "execution_count": 194, "id": "d13c3f21-ebd7-4e9b-baca-1f3a10ac24a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "id 957\n", "name 855\n", "service_id 957\n", "created_at 957\n", "updated_at 957\n", "process_id 0\n", "report_url 0\n", "category 1\n", "to_be_synced 2\n", "identifier 957\n", "sent_at 737\n", "dtype: int64\n" ] } ], "source": [ "# service id\n", "\n", "print(df1_campaigns.nunique()) # on a un identifiant de service par campagne, mais pas un nom unique" ] }, { "cell_type": "code", "execution_count": 211, "id": "aea65b10-8a7f-472e-a7f5-455a90d3cfef", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
7771319239\"L'Orient sonore\" au Mucem à partir du 22 juillet1842021-09-24 11:56:09.277085+02:002021-09-24 11:56:09.277085+02:00NaNNaN0.0False6cdd60ea0045eb7a6ec44c54d29ed4022020-07-15 00:00:00+02:00
7781319240\"L'Orient sonore\" au Mucem à partir du 22 juillet1812021-09-24 11:56:09.284647+02:002021-09-24 11:56:09.284647+02:00NaNNaN0.0Falsefc221309746013ac554571fbd180e1c82020-07-09 00:00:00+02:00
2551320926Alexandrie NL211162023-01-31 11:08:55.915268+01:002023-01-31 11:08:56.286044+01:00NaNNaN0.0Falsedd77279f7d325eec933f05b1672f6a1f2023-01-31 12:08:54+01:00
1611320910Alexandrie NL210772023-01-24 09:01:00.250855+01:002023-01-24 09:01:00.271292+01:00NaNNaN0.0False062ddb6c727310e76b6200b7c71f63b52023-01-24 10:00:58+01:00
2411320574Alexandrie NL27312022-10-11 07:00:50.971513+02:002022-12-02 17:51:21.670983+01:00NaNNaN0.0False59c33016884a62116be975a9bb8257e32022-10-11 00:00:00+02:00
3171320972Centres_loisirs _vacances de février11242023-02-08 12:01:16.732961+01:002023-02-08 12:01:16.808008+01:00NaNNaN0.0Falsec7635bfd99248a2cdef8249ef7bfbef42023-02-08 13:01:15+01:00
1661320954Centres_loisirs _vacances de février11102023-02-01 09:30:41.267232+01:002023-02-01 09:30:41.354117+01:00NaNNaN0.0False2cbca44843a864533ec05b321ae1f9d12023-02-01 10:30:40+01:00
672148Champ social décembre 20202832021-04-03 18:24:42.186026+02:002021-09-24 11:56:08.182818+02:00NaNNaN0.0False0f49c89d1e7298bb9930789c8ed59d482020-12-03 00:00:00+01:00
56972Champ social décembre 20202842021-03-29 15:41:53.631952+02:002021-09-24 11:56:07.748770+02:00NaNNaN0.0False46ba9f2a6976570b0353203ec44742172020-12-04 00:00:00+01:00
1751319881Champ social mars 20228332022-04-25 10:00:26.029871+02:002022-12-02 17:51:22.319899+01:00NaNNaN0.0False013a006f03dbc5392effeb8f18fda7552022-04-25 00:00:00+02:00
3161319760Champ social mars 20227852022-03-11 13:00:28.333251+01:002022-12-02 17:51:21.991906+01:00NaNNaN0.0False4b04a686b0ad13dce35fa99fa4161c652022-03-11 00:00:00+01:00
3261319798DDCP Newsletter Destination Mucem Est 28042022-03-22 10:21:02.122363+01:002022-12-02 17:51:22.119041+01:00NaNNaN0.0Falsedc5689792e08eb2e219dce49e64c885b2022-03-22 00:00:00+01:00
1771319882DDCP Newsletter Destination Mucem Est 28432022-04-26 09:00:44.083713+02:002022-12-02 17:51:22.454684+01:00NaNNaN0.0False3d8e28caf901313a554cebc7d32e67e52022-04-26 00:00:00+02:00
3471319883DDCP Newsletter Destination Mucem Nord 28452022-04-26 09:00:46.020370+02:002022-12-02 17:51:22.463986+01:00NaNNaN0.0Falseb86e8d03fe992d1b0e19656875ee557c2022-04-26 00:00:00+02:00
3191319768DDCP Newsletter Destination Mucem Nord 27892022-03-17 10:20:51.757178+01:002022-12-02 17:51:22.064760+01:00NaNNaN0.0False68053af2923e00204c3ca7c6a3150cf72022-03-17 00:00:00+01:00
1761319885DDCP Newsletter Destination Mucem Nord Est 28422022-04-26 09:30:57.232149+02:002022-12-02 17:51:22.447304+01:00NaNNaN0.0Falsefc3cf452d3da8402bebb765225ce8c0e2022-04-26 00:00:00+02:00
3241319769DDCP Newsletter Destination Mucem Nord Est 28002022-03-17 10:22:58.736431+01:002022-12-02 17:51:22.107694+01:00NaNNaN0.0False7a53928fa4dd31e82c6ef826f341daec2022-03-17 00:00:00+01:00
2431319884DDCP Newsletter Destination Mucem Sud 28442022-04-26 09:00:46.894528+02:002022-12-02 17:51:22.459272+01:00NaNNaN0.0Falsee97ee2054defb209c35fe4dc945990612022-04-26 00:00:00+02:00
3271319799DDCP Newsletter Destination Mucem Sud 28052022-03-22 10:24:05.787335+01:002022-12-02 17:51:22.123726+01:00NaNNaN0.0False846c260d715e5b854ffad5f70a516c882022-03-22 00:00:00+01:00
6202681DDCP PROMO programmation Orient sonore Pass mu...2262021-04-08 21:10:40.634455+02:002021-09-24 11:56:07.922243+02:00NaNNaN0.0False9cfdf10e8fc047a44b08ed031e1f0ed12020-10-09 00:00:00+02:00
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" ], "text/plain": [ " id name service_id \\\n", "777 1319239 \"L'Orient sonore\" au Mucem à partir du 22 juillet 184 \n", "778 1319240 \"L'Orient sonore\" au Mucem à partir du 22 juillet 181 \n", "255 1320926 Alexandrie NL2 1116 \n", "161 1320910 Alexandrie NL2 1077 \n", "241 1320574 Alexandrie NL2 731 \n", "317 1320972 Centres_loisirs _vacances de février 1124 \n", "166 1320954 Centres_loisirs _vacances de février 1110 \n", "672 148 Champ social décembre 2020 283 \n", "569 72 Champ social décembre 2020 284 \n", "175 1319881 Champ social mars 2022 833 \n", "316 1319760 Champ social mars 2022 785 \n", "326 1319798 DDCP Newsletter Destination Mucem Est 2 804 \n", "177 1319882 DDCP Newsletter Destination Mucem Est 2 843 \n", "347 1319883 DDCP Newsletter Destination Mucem Nord 2 845 \n", "319 1319768 DDCP Newsletter Destination Mucem Nord 2 789 \n", "176 1319885 DDCP Newsletter Destination Mucem Nord Est 2 842 \n", "324 1319769 DDCP Newsletter Destination Mucem Nord Est 2 800 \n", "243 1319884 DDCP Newsletter Destination Mucem Sud 2 844 \n", "327 1319799 DDCP Newsletter Destination Mucem Sud 2 805 \n", "620 2681 DDCP PROMO programmation Orient sonore Pass mu... 226 \n", "\n", " created_at updated_at \\\n", "777 2021-09-24 11:56:09.277085+02:00 2021-09-24 11:56:09.277085+02:00 \n", "778 2021-09-24 11:56:09.284647+02:00 2021-09-24 11:56:09.284647+02:00 \n", "255 2023-01-31 11:08:55.915268+01:00 2023-01-31 11:08:56.286044+01:00 \n", "161 2023-01-24 09:01:00.250855+01:00 2023-01-24 09:01:00.271292+01:00 \n", "241 2022-10-11 07:00:50.971513+02:00 2022-12-02 17:51:21.670983+01:00 \n", "317 2023-02-08 12:01:16.732961+01:00 2023-02-08 12:01:16.808008+01:00 \n", "166 2023-02-01 09:30:41.267232+01:00 2023-02-01 09:30:41.354117+01:00 \n", "672 2021-04-03 18:24:42.186026+02:00 2021-09-24 11:56:08.182818+02:00 \n", "569 2021-03-29 15:41:53.631952+02:00 2021-09-24 11:56:07.748770+02:00 \n", "175 2022-04-25 10:00:26.029871+02:00 2022-12-02 17:51:22.319899+01:00 \n", "316 2022-03-11 13:00:28.333251+01:00 2022-12-02 17:51:21.991906+01:00 \n", "326 2022-03-22 10:21:02.122363+01:00 2022-12-02 17:51:22.119041+01:00 \n", "177 2022-04-26 09:00:44.083713+02:00 2022-12-02 17:51:22.454684+01:00 \n", "347 2022-04-26 09:00:46.020370+02:00 2022-12-02 17:51:22.463986+01:00 \n", "319 2022-03-17 10:20:51.757178+01:00 2022-12-02 17:51:22.064760+01:00 \n", "176 2022-04-26 09:30:57.232149+02:00 2022-12-02 17:51:22.447304+01:00 \n", "324 2022-03-17 10:22:58.736431+01:00 2022-12-02 17:51:22.107694+01:00 \n", "243 2022-04-26 09:00:46.894528+02:00 2022-12-02 17:51:22.459272+01:00 \n", "327 2022-03-22 10:24:05.787335+01:00 2022-12-02 17:51:22.123726+01:00 \n", "620 2021-04-08 21:10:40.634455+02:00 2021-09-24 11:56:07.922243+02:00 \n", "\n", " process_id report_url category to_be_synced \\\n", "777 NaN NaN 0.0 False \n", "778 NaN NaN 0.0 False \n", "255 NaN NaN 0.0 False \n", "161 NaN NaN 0.0 False \n", "241 NaN NaN 0.0 False \n", "317 NaN NaN 0.0 False \n", "166 NaN NaN 0.0 False \n", "672 NaN NaN 0.0 False \n", "569 NaN NaN 0.0 False \n", "175 NaN NaN 0.0 False \n", "316 NaN NaN 0.0 False \n", "326 NaN NaN 0.0 False \n", "177 NaN NaN 0.0 False \n", "347 NaN NaN 0.0 False \n", "319 NaN NaN 0.0 False \n", "176 NaN NaN 0.0 False \n", "324 NaN NaN 0.0 False \n", "243 NaN NaN 0.0 False \n", "327 NaN NaN 0.0 False \n", "620 NaN NaN 0.0 False \n", "\n", " identifier sent_at \n", "777 6cdd60ea0045eb7a6ec44c54d29ed402 2020-07-15 00:00:00+02:00 \n", "778 fc221309746013ac554571fbd180e1c8 2020-07-09 00:00:00+02:00 \n", "255 dd77279f7d325eec933f05b1672f6a1f 2023-01-31 12:08:54+01:00 \n", "161 062ddb6c727310e76b6200b7c71f63b5 2023-01-24 10:00:58+01:00 \n", "241 59c33016884a62116be975a9bb8257e3 2022-10-11 00:00:00+02:00 \n", "317 c7635bfd99248a2cdef8249ef7bfbef4 2023-02-08 13:01:15+01:00 \n", "166 2cbca44843a864533ec05b321ae1f9d1 2023-02-01 10:30:40+01:00 \n", "672 0f49c89d1e7298bb9930789c8ed59d48 2020-12-03 00:00:00+01:00 \n", "569 46ba9f2a6976570b0353203ec4474217 2020-12-04 00:00:00+01:00 \n", "175 013a006f03dbc5392effeb8f18fda755 2022-04-25 00:00:00+02:00 \n", "316 4b04a686b0ad13dce35fa99fa4161c65 2022-03-11 00:00:00+01:00 \n", "326 dc5689792e08eb2e219dce49e64c885b 2022-03-22 00:00:00+01:00 \n", "177 3d8e28caf901313a554cebc7d32e67e5 2022-04-26 00:00:00+02:00 \n", "347 b86e8d03fe992d1b0e19656875ee557c 2022-04-26 00:00:00+02:00 \n", "319 68053af2923e00204c3ca7c6a3150cf7 2022-03-17 00:00:00+01:00 \n", "176 fc3cf452d3da8402bebb765225ce8c0e 2022-04-26 00:00:00+02:00 \n", "324 7a53928fa4dd31e82c6ef826f341daec 2022-03-17 00:00:00+01:00 \n", "243 e97ee2054defb209c35fe4dc94599061 2022-04-26 00:00:00+02:00 \n", "327 846c260d715e5b854ffad5f70a516c88 2022-03-22 00:00:00+01:00 \n", "620 9cfdf10e8fc047a44b08ed031e1f0ed1 2020-10-09 00:00:00+02:00 " ] }, "execution_count": 211, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# name\n", "\n", "df1_campaigns[df1_campaigns.duplicated(subset = [\"name\"], keep=False)].sort_values(\"name\").head(20)" ] }, { "cell_type": "code", "execution_count": 207, "id": "35ea834e-01a3-4841-a9a9-351c25c5af37", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "175 True\n", "316 True\n", "dtype: bool" ] }, "execution_count": 207, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns[df1_campaigns[\"name\"]==\"Champ social mars 2022\"].duplicated(subset=\"name\", keep=False)" ] }, { "cell_type": "code", "execution_count": 226, "id": "5e16bf37-c2e0-48c9-8a90-6713f7c6206c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Share of campaigns to synce : 0.52 % \n" ] } ], "source": [ "# to be synced \n", "\n", "share_campaigns_to_be_synced = round(100 * df1_campaigns[\"to_be_synced\"].mean(),2)\n", "print(f\"Share of campaigns to synce : {share_campaigns_to_be_synced} % \") # 0.5% of campaigns to synce" ] }, { "cell_type": "code", "execution_count": 235, "id": "88a6f9d4-ddd2-4288-9bba-7d9e76c66f51", "metadata": {}, "outputs": [ { "data": { 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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
431320752dre_alors_on_sort0712_tech&cult1212_lesreveill...10192022-11-28 09:30:31.189207+01:002022-12-02 17:51:23.474745+01:00NaNNaN0.0True03e0704b5690a2dee1861dc3ad3316c92022-11-28 00:00:00+01:00
791320755News hebdo du 5 au 4 décembre 202210602022-12-04 18:01:29.971417+01:002022-12-04 18:01:30.037656+01:00NaNNaN0.0True299a23a2291e2126b91d54f3601ec1622022-12-04 19:01:27+01:00
4641320749dre_le_sel_24112210542022-11-24 09:01:37.467710+01:002022-12-02 17:51:23.622812+01:00NaNNaN0.0Truedb576a7d2453575f29eab4bac787b9192022-11-24 00:00:00+01:00
4651320751News hebdo du 28 novembre au 4 décembre10572022-11-27 18:01:44.546081+01:002022-12-02 17:51:23.627178+01:00NaNNaN0.0Trued8700cbd38cc9f30cecb34f0c195b1372022-11-27 00:00:00+01:00
8881319474ddcp_promo_temps fort salammbo6702021-11-25 13:19:41.547780+01:002022-02-03 14:17:27.728648+01:00NaNNaN0.0True17c276c8e723eb46aef576537e9d56d02021-11-25 00:00:00+01:00
\n", "
" ], "text/plain": [ " id name service_id \\\n", "43 1320752 dre_alors_on_sort0712_tech&cult1212_lesreveill... 1019 \n", "79 1320755 News hebdo du 5 au 4 décembre 2022 1060 \n", "464 1320749 dre_le_sel_241122 1054 \n", "465 1320751 News hebdo du 28 novembre au 4 décembre 1057 \n", "888 1319474 ddcp_promo_temps fort salammbo 670 \n", "\n", " created_at updated_at \\\n", "43 2022-11-28 09:30:31.189207+01:00 2022-12-02 17:51:23.474745+01:00 \n", "79 2022-12-04 18:01:29.971417+01:00 2022-12-04 18:01:30.037656+01:00 \n", "464 2022-11-24 09:01:37.467710+01:00 2022-12-02 17:51:23.622812+01:00 \n", "465 2022-11-27 18:01:44.546081+01:00 2022-12-02 17:51:23.627178+01:00 \n", "888 2021-11-25 13:19:41.547780+01:00 2022-02-03 14:17:27.728648+01:00 \n", "\n", " process_id report_url category to_be_synced \\\n", "43 NaN NaN 0.0 True \n", "79 NaN NaN 0.0 True \n", "464 NaN NaN 0.0 True \n", "465 NaN NaN 0.0 True \n", "888 NaN NaN 0.0 True \n", "\n", " identifier sent_at \n", "43 03e0704b5690a2dee1861dc3ad3316c9 2022-11-28 00:00:00+01:00 \n", "79 299a23a2291e2126b91d54f3601ec162 2022-12-04 19:01:27+01:00 \n", "464 db576a7d2453575f29eab4bac787b919 2022-11-24 00:00:00+01:00 \n", "465 d8700cbd38cc9f30cecb34f0c195b137 2022-11-27 00:00:00+01:00 \n", "888 17c276c8e723eb46aef576537e9d56d0 2021-11-25 00:00:00+01:00 " ] }, "execution_count": 235, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# focus : campaigns to synce - 5 cases\n", "# la date d'envoie semble cohérente. Pas d'observation particulière sur ces cas ...\n", "\n", "df1_campaigns[df1_campaigns[\"to_be_synced\"]]" ] }, { "cell_type": "code", "execution_count": 234, "id": "cf9dedd6-2554-4f9e-a09b-f1465718a18d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
431320752dre_alors_on_sort0712_tech&cult1212_lesreveill...10192022-11-28 09:30:31.189207+01:002022-12-02 17:51:23.474745+01:00NaNNaN0.0True03e0704b5690a2dee1861dc3ad3316c92022-11-28 00:00:00+01:00
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4641320749dre_le_sel_24112210542022-11-24 09:01:37.467710+01:002022-12-02 17:51:23.622812+01:00NaNNaN0.0Truedb576a7d2453575f29eab4bac787b9192022-11-24 00:00:00+01:00
4651320751News hebdo du 28 novembre au 4 décembre10572022-11-27 18:01:44.546081+01:002022-12-02 17:51:23.627178+01:00NaNNaN0.0Trued8700cbd38cc9f30cecb34f0c195b1372022-11-27 00:00:00+01:00
8881319474ddcp_promo_temps fort salammbo6702021-11-25 13:19:41.547780+01:002022-02-03 14:17:27.728648+01:00NaNNaN0.0True17c276c8e723eb46aef576537e9d56d02021-11-25 00:00:00+01:00
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" ], "text/plain": [ " id name service_id \\\n", "43 1320752 dre_alors_on_sort0712_tech&cult1212_lesreveill... 1019 \n", "79 1320755 News hebdo du 5 au 4 décembre 2022 1060 \n", "464 1320749 dre_le_sel_241122 1054 \n", "465 1320751 News hebdo du 28 novembre au 4 décembre 1057 \n", "888 1319474 ddcp_promo_temps fort salammbo 670 \n", "\n", " created_at updated_at \\\n", "43 2022-11-28 09:30:31.189207+01:00 2022-12-02 17:51:23.474745+01:00 \n", "79 2022-12-04 18:01:29.971417+01:00 2022-12-04 18:01:30.037656+01:00 \n", "464 2022-11-24 09:01:37.467710+01:00 2022-12-02 17:51:23.622812+01:00 \n", "465 2022-11-27 18:01:44.546081+01:00 2022-12-02 17:51:23.627178+01:00 \n", "888 2021-11-25 13:19:41.547780+01:00 2022-02-03 14:17:27.728648+01:00 \n", "\n", " process_id report_url category to_be_synced \\\n", "43 NaN NaN 0.0 True \n", "79 NaN NaN 0.0 True \n", "464 NaN NaN 0.0 True \n", "465 NaN NaN 0.0 True \n", "888 NaN NaN 0.0 True \n", "\n", " identifier sent_at \n", "43 03e0704b5690a2dee1861dc3ad3316c9 2022-11-28 00:00:00+01:00 \n", "79 299a23a2291e2126b91d54f3601ec162 2022-12-04 19:01:27+01:00 \n", "464 db576a7d2453575f29eab4bac787b919 2022-11-24 00:00:00+01:00 \n", "465 d8700cbd38cc9f30cecb34f0c195b137 2022-11-27 00:00:00+01:00 \n", "888 17c276c8e723eb46aef576537e9d56d0 2021-11-25 00:00:00+01:00 " ] }, "execution_count": 234, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns[df1_campaigns[\"name\"].isin(df1_campaigns[df1_campaigns[\"to_be_synced\"]][\"name\"].unique()) ]" ] }, { "cell_type": "code", "execution_count": 237, "id": "ba2f188f-be49-4e19-9cb3-0ec54e58d0c1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
01319613newsletter enseignants janvier 20227212022-01-14 16:06:42.586321+01:002022-02-03 14:17:27.112963+01:00NaNNaN0.0Falseaba3b6fd5d186d28e06ff97135cade7f2022-01-14 00:00:00+01:00
11319586lsf_janvier_20227172022-01-07 11:30:35.315895+01:002022-02-03 14:17:27.116171+01:00NaNNaN0.0False788d986905533aba051261497ecffcbb2022-01-07 00:00:00+01:00
21319282Invitation à déjeuner au Mucem | Vernissage « ...5912021-09-28 12:50:24.448752+02:002022-02-03 14:17:27.119582+01:00NaNNaN0.0False3493894fa4ea036cfc6433c3e2ee63b02021-09-28 00:00:00+02:00
31319283Vacances de la Toussaint - centres des loisirs5902021-09-28 18:01:04.692073+02:002022-02-03 14:17:27.124408+01:00NaNNaN0.0False08b255a5d42b89b0585260b6f2360bdd2021-09-28 00:00:00+02:00
41319636ddcp_promo_md_livemag7302022-01-27 18:00:41.053069+01:002022-02-03 14:17:27.127607+01:00NaNNaN0.0Falsed5cfead94f5350c12c322b5b664544c12022-01-27 00:00:00+01:00
51319614News hebdo du 17 janv au 23 janv 20227122022-01-16 18:01:28.974157+01:002022-02-03 14:17:27.130944+01:00NaNNaN0.0False19bc916108fc6938f52cb96f7e0879412022-01-16 00:00:00+01:00
61319263ddcp_promo_automne_littérature_relance_nn_ouverts5862021-09-24 15:00:04.174247+02:002021-09-24 16:13:10.505400+02:00NaNNaN0.0False605ff764c617d3cd28dbbdd72be8f9a22021-09-24 00:00:00+02:00
71319284Invitation au vernissage de l'exposition \"La C...5932021-09-30 14:47:18.135394+02:002022-02-03 14:17:27.134073+01:00NaNNaN0.0Falseacc3e0404646c57502b480dc052c4fe12021-09-30 00:00:00+02:00
81319625dre_mobilisations_artistiques_et_politiques7042022-01-27 10:01:16.716706+01:002022-02-03 14:17:27.172039+01:00NaNNaN0.0Falsef64eac11f2cd8f0efa196f8ad173178e2022-01-27 00:00:00+01:00
91319285ddcp_promo_soyinka_taubira_infos_pratiques5942021-10-01 12:16:57.031796+02:002022-02-03 14:17:27.137444+01:00NaNNaN0.0False076a0c97d09cf1a0ec3e19c7f2529f2b2021-10-01 00:00:00+02:00
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" ], "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", "5 1319614 News hebdo du 17 janv au 23 janv 2022 712 \n", "6 1319263 ddcp_promo_automne_littérature_relance_nn_ouverts 586 \n", "7 1319284 Invitation au vernissage de l'exposition \"La C... 593 \n", "8 1319625 dre_mobilisations_artistiques_et_politiques 704 \n", "9 1319285 ddcp_promo_soyinka_taubira_infos_pratiques 594 \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", "5 2022-01-16 18:01:28.974157+01:00 2022-02-03 14:17:27.130944+01:00 \n", "6 2021-09-24 15:00:04.174247+02:00 2021-09-24 16:13:10.505400+02:00 \n", "7 2021-09-30 14:47:18.135394+02:00 2022-02-03 14:17:27.134073+01:00 \n", "8 2022-01-27 10:01:16.716706+01:00 2022-02-03 14:17:27.172039+01:00 \n", "9 2021-10-01 12:16:57.031796+02:00 2022-02-03 14:17:27.137444+01:00 \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", "5 NaN NaN 0.0 False \n", "6 NaN NaN 0.0 False \n", "7 NaN NaN 0.0 False \n", "8 NaN NaN 0.0 False \n", "9 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", "5 19bc916108fc6938f52cb96f7e087941 2022-01-16 00:00:00+01:00 \n", "6 605ff764c617d3cd28dbbdd72be8f9a2 2021-09-24 00:00:00+02:00 \n", "7 acc3e0404646c57502b480dc052c4fe1 2021-09-30 00:00:00+02:00 \n", "8 f64eac11f2cd8f0efa196f8ad173178e 2022-01-27 00:00:00+01:00 \n", "9 076a0c97d09cf1a0ec3e19c7f2529f2b 2021-10-01 00:00:00+02:00 " ] }, "execution_count": 237, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns[~df1_campaigns[\"to_be_synced\"]].head(10)" ] }, { "cell_type": "code", "execution_count": 240, "id": "4bf2cbdd-6236-43b8-9a13-74f2803a6ac5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_at
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37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00
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71871458341732021-03-29 05:31:37+02:002021-03-28 18:00:58+02:002021-03-28 18:31:02+02:002021-03-28 18:31:07.619032+02:002022-04-15 22:52:04.397693+02:00
8171195834992NaN2021-03-28 18:00:58+02:002021-03-28 18:28:00+02:002021-03-28 18:28:03.574600+02:002022-04-15 22:52:04.397693+02:00
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" ], "text/plain": [ " id campaign_id customer_id opened_at \\\n", "0 19793 58 112597 NaN \n", "1 14211 58 113666 NaN \n", "2 13150 58 280561 NaN \n", "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", "4 5175 58 103972 NaN \n", "5 4809 58 104599 2021-03-28 18:12:12+02:00 \n", "6 11605 58 280579 2021-03-28 18:16:14+02:00 \n", "7 18714 58 34173 2021-03-29 05:31:37+02:00 \n", "8 17119 58 34992 NaN \n", "9 14001 58 35343 NaN \n", "\n", " sent_at delivered_at \\\n", "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", "5 2021-03-28 18:01:06+02:00 2021-03-28 18:04:18+02:00 \n", "6 2021-03-28 18:00:59+02:00 2021-03-28 18:16:09+02:00 \n", "7 2021-03-28 18:00:58+02:00 2021-03-28 18:31:02+02:00 \n", "8 2021-03-28 18:00:58+02:00 2021-03-28 18:28:00+02:00 \n", "9 2021-03-28 18:00:58+02:00 2021-03-28 18:20:48+02:00 \n", "\n", " created_at updated_at \n", "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 \n", "5 2021-03-28 18:04:19.662496+02:00 2022-04-15 22:52:04.397693+02:00 \n", "6 2021-03-28 18:16:10.974208+02:00 2022-04-15 22:52:04.397693+02:00 \n", "7 2021-03-28 18:31:07.619032+02:00 2022-04-15 22:52:04.397693+02:00 \n", "8 2021-03-28 18:28:03.574600+02:00 2022-04-15 22:52:04.397693+02:00 \n", "9 2021-03-28 18:20:49.258826+02:00 2022-04-15 22:52:04.397693+02:00 " ] }, "execution_count": 240, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2. campaigns stats\n", "\n", "df1_campaign_stats.head(10)" ] }, { "cell_type": "code", "execution_count": 242, "id": "0bf6f3d8-40f3-4268-a89d-fc962acd6c4a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0.000000\n", "campaign_id 0.000000\n", "customer_id 0.000000\n", "opened_at 0.807672\n", "sent_at 0.000969\n", "delivered_at 0.021495\n", "created_at 0.000000\n", "updated_at 0.000000\n", "dtype: float64" ] }, "execution_count": 242, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaign_stats.isna().sum() / df1_campaign_stats.shape[0]" ] }, { "cell_type": "code", "execution_count": 243, "id": "2d3140db-fa86-41dd-81c9-2c6ca1e9402e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "campaign_id int64\n", "customer_id int64\n", "opened_at object\n", "sent_at object\n", "delivered_at object\n", "created_at object\n", "updated_at object\n", "dtype: object" ] }, "execution_count": 243, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaign_stats.dtypes" ] }, { "cell_type": "code", "execution_count": 244, "id": "e4cc1b7c-5956-41c3-ad59-2738c5f2778c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 6214808\n", "campaign_id 949\n", "customer_id 130472\n", "opened_at 1102699\n", "sent_at 152184\n", "delivered_at 380248\n", "created_at 4295988\n", "updated_at 2176478\n", "dtype: int64" ] }, "execution_count": 244, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(df1_campaign_stats.shape[0])\n", "print(df1_campaign_stats.nunique())" ] }, { "cell_type": "code", "execution_count": 262, "id": "8735c5dd-1d02-4dae-804e-70ee1be08df8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_at
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00
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" ], "text/plain": [ " id campaign_id customer_id opened_at \\\n", "0 19793 58 112597 NaN \n", "1 14211 58 113666 NaN \n", "2 13150 58 280561 NaN \n", "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", "4 5175 58 103972 NaN \n", "\n", " sent_at delivered_at \\\n", "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", "\n", " created_at updated_at \n", "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 " ] }, "execution_count": 262, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 3. merge campaigns and campaigns stats\n", "\n", "df1_campaign_stats.head()" ] }, { "cell_type": "code", "execution_count": 273, "id": "1e88efca-96b1-4977-b633-25d13830633e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1319243, 1319245, 1319247, 1319248, 1319250, 1319259, 1319260,\n", " 1319262])" ] }, "execution_count": 273, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# étape préalable au merge : les identifiants de campagne des deux tabes sont - ils égaux ?\n", "\n", "id_campaigns = np.sort(df1_campaigns[\"id\"].unique())\n", "id_campaigns_stats = np.sort(df1_campaign_stats[\"campaign_id\"].unique())\n", "np.setdiff1d(id_campaigns, id_campaigns_stats)" ] }, { "cell_type": "code", "execution_count": 275, "id": "43440e38-b141-43f1-9e0c-fa8559218e76", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnameservice_idcreated_atupdated_atprocess_idreport_urlcategoryto_be_syncedidentifiersent_at
7891319243DRE Exposer le récit 13 mars1112021-09-24 11:56:09.307905+02:002021-09-24 11:56:09.307905+02:00NaNNaN0.0False698d51a19d8a121ce581499d7b7016682020-03-03 00:00:00+01:00
7911319245SDR Relance invit petit dej voyage voyages1092021-09-24 11:56:09.323919+02:002021-09-24 11:56:09.323919+02:00NaNNaN0.0False2723d092b63885e0d7c260cc007e8b9d2020-02-24 00:00:00+01:00
7931319247Au Mucem en 2020972021-09-24 11:56:09.339127+02:002021-09-24 11:56:09.339127+02:00NaNNaN0.0Falsee2ef524fbf3d9fe611d5a8e90fefdc9c2020-01-31 00:00:00+01:00
7941319248DRE Giono922021-09-24 11:56:09.346887+02:002021-09-24 11:56:09.346887+02:00NaNNaN0.0False92cc227532d17e56e07902b254dfad102020-01-29 00:00:00+01:00
7961319250Portes ouvertes \"Voyage, voyages\" au Mucem | M...772021-09-24 11:56:09.362114+02:002021-09-24 11:56:09.362114+02:00NaNNaN0.0False28dd2c7955ce926456240b2ff0100bde2020-01-13 00:00:00+01:00
8051319259Save the date | Vernissage \"Voyage, voyages\" a...382021-09-24 11:56:09.432720+02:002021-09-24 11:56:09.432720+02:00NaNNaN0.0Falsea5771bce93e200c36f7cd9dfd0e5deaa2019-11-20 00:00:00+01:00
8061319260Portes ouvertes \"Massilia Toy\" au Mucem | Merc...372021-09-24 11:56:09.440465+02:002021-09-24 11:56:09.440465+02:00NaNNaN0.0Falsea5bfc9e07964f8dddeb95fc584cd965d2019-11-20 00:00:00+01:00
8081319262TENK S-1 Corse172021-09-24 11:56:09.456460+02:002021-09-24 11:56:09.456460+02:00NaNNaN0.0False70efdf2ec9b086079795c442636b55fb2019-11-07 00:00:00+01:00
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" ], "text/plain": [ " id name service_id \\\n", "789 1319243 DRE Exposer le récit 13 mars 111 \n", "791 1319245 SDR Relance invit petit dej voyage voyages 109 \n", "793 1319247 Au Mucem en 2020 97 \n", "794 1319248 DRE Giono 92 \n", "796 1319250 Portes ouvertes \"Voyage, voyages\" au Mucem | M... 77 \n", "805 1319259 Save the date | Vernissage \"Voyage, voyages\" a... 38 \n", "806 1319260 Portes ouvertes \"Massilia Toy\" au Mucem | Merc... 37 \n", "808 1319262 TENK S-1 Corse 17 \n", "\n", " created_at updated_at \\\n", "789 2021-09-24 11:56:09.307905+02:00 2021-09-24 11:56:09.307905+02:00 \n", "791 2021-09-24 11:56:09.323919+02:00 2021-09-24 11:56:09.323919+02:00 \n", "793 2021-09-24 11:56:09.339127+02:00 2021-09-24 11:56:09.339127+02:00 \n", "794 2021-09-24 11:56:09.346887+02:00 2021-09-24 11:56:09.346887+02:00 \n", "796 2021-09-24 11:56:09.362114+02:00 2021-09-24 11:56:09.362114+02:00 \n", "805 2021-09-24 11:56:09.432720+02:00 2021-09-24 11:56:09.432720+02:00 \n", "806 2021-09-24 11:56:09.440465+02:00 2021-09-24 11:56:09.440465+02:00 \n", "808 2021-09-24 11:56:09.456460+02:00 2021-09-24 11:56:09.456460+02:00 \n", "\n", " process_id report_url category to_be_synced \\\n", "789 NaN NaN 0.0 False \n", "791 NaN NaN 0.0 False \n", "793 NaN NaN 0.0 False \n", "794 NaN NaN 0.0 False \n", "796 NaN NaN 0.0 False \n", "805 NaN NaN 0.0 False \n", "806 NaN NaN 0.0 False \n", "808 NaN NaN 0.0 False \n", "\n", " identifier sent_at \n", "789 698d51a19d8a121ce581499d7b701668 2020-03-03 00:00:00+01:00 \n", "791 2723d092b63885e0d7c260cc007e8b9d 2020-02-24 00:00:00+01:00 \n", "793 e2ef524fbf3d9fe611d5a8e90fefdc9c 2020-01-31 00:00:00+01:00 \n", "794 92cc227532d17e56e07902b254dfad10 2020-01-29 00:00:00+01:00 \n", "796 28dd2c7955ce926456240b2ff0100bde 2020-01-13 00:00:00+01:00 \n", "805 a5771bce93e200c36f7cd9dfd0e5deaa 2019-11-20 00:00:00+01:00 \n", "806 a5bfc9e07964f8dddeb95fc584cd965d 2019-11-20 00:00:00+01:00 \n", "808 70efdf2ec9b086079795c442636b55fb 2019-11-07 00:00:00+01:00 " ] }, "execution_count": 275, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# ci-dessous des campagnes sans customer associé dans la table\n", "# elles seront retirées lors du merge car pas utiles à notre étude\n", "# on fera un merge à gauche en se basant sur campaign_stats \n", "\n", "df1_campaigns[df1_campaigns[\"id\"].isin([1319243, 1319245, 1319247, 1319248, 1319250, 1319259, 1319260,\n", " 1319262])]" ] }, { "cell_type": "code", "execution_count": 338, "id": "6cbcd261-a6ba-497c-929b-29a714e1812d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
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" ], "text/plain": [ " id campaign_id customer_id opened_at \\\n", "0 19793 58 112597 NaN \n", "1 14211 58 113666 NaN \n", "2 13150 58 280561 NaN \n", "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", "4 5175 58 103972 NaN \n", "\n", " sent_at delivered_at \\\n", "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", "\n", " created_at updated_at \\\n", "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 \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", " campaign_created_at campaign_updated_at \\\n", "0 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "1 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "2 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "3 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "4 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "\n", " campaign_sent_at campaign_identifier \n", "0 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "1 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "2 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "3 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "4 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a " ] }, "execution_count": 338, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# merge \n", "\n", "# de campaigns on supprile les var valant tjrs NaN et to_be_synced qui semble pas très informatif\n", "\n", "df1_campaigns_full = pd.merge(df1_campaign_stats, \n", " df1_campaigns[[\"id\", \"name\", \"service_id\", \"created_at\", \"updated_at\", \"sent_at\", \"identifier\"]].add_prefix(\"campaign_\"),\n", " on = \"campaign_id\", how = \"left\")\n", "df1_campaigns_full.head()" ] }, { "cell_type": "code", "execution_count": 328, "id": "81e549e9-d165-439a-a824-17f053a33983", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "campaign_id 0\n", "customer_id 0\n", "opened_at 5019527\n", "sent_at 6023\n", "delivered_at 133590\n", "created_at 0\n", "updated_at 0\n", "campaign_name 0\n", "campaign_service_id 0\n", "campaign_created_at 0\n", "campaign_updated_at 0\n", "campaign_sent_at 6\n", "campaign_identifier 0\n", "dtype: int64" ] }, "execution_count": 328, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns_full.isna().sum()" ] }, { "cell_type": "code", "execution_count": 297, "id": "aa249cdc-e0ac-41ec-b6f8-b9459f31eca3", "metadata": {}, "outputs": [], "source": [ "# lien entre sent at et campaign sent at ? \n", "# à quoi correspond la date de la campagne, est-ce le premier envoi à un client ?\n", "\n", "# first step : transform dates to have the good format\n", "# VERY time-consuming bc the df has 6M lines !!!!\n", "\n", "from dateutil import parser\n", "\n", "def convert_to_datetime(column):\n", " return column.apply(lambda x: parser.parse(str(x)) if pd.notna(x) else pd.NaT)\n", "\n", "# Liste des colonnes à convertir\n", "columns_to_convert = [\"sent_at\", \"delivered_at\", \"created_at\", \"updated_at\", \n", " \"campaign_sent_at\", \"campaign_created_at\", \"campaign_updated_at\"]\n", "\n", "# Appliquer la fonction à chaque colonne spécifiée\n", "df1_campaigns_full[columns_to_convert] = df1_campaigns_full[columns_to_convert].apply(convert_to_datetime)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 329, "id": "f2b05227-e8d8-4ca8-8359-dc3471841763", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "UTC: 2021-03-28 16:01:09+00:00\n", "Local: 2021-03-28 18:01:09+02:00\n" ] } ], "source": [ "# Exemple d'élément\n", "date_string = '2021-03-28 18:01:09+02:00'\n", "\n", "# Convertir en datetime en utilisant pd.to_datetime avec utc=True\n", "datetime_object_utc = pd.to_datetime(date_string, utc=True)\n", "print(\"UTC:\", datetime_object_utc)\n", "\n", "# Convertir en datetime en utilisant pd.to_datetime avec utc=False (ou sans spécifier utc)\n", "datetime_object_local = pd.to_datetime(date_string, utc=False)\n", "print(\"Local:\", datetime_object_local)" ] }, { "cell_type": "code", "execution_count": 332, "id": "63fa4af8-0c28-4b20-97e2-560da4d4b77e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "UTC: 2021-03-28 16:00:00+00:00\n", "Différence en heures: 1.5\n" ] } ], "source": [ "import pandas as pd\n", "\n", "# Exemple d'élément\n", "date_string = '2021-03-28 18:00:00+02:00'\n", "\n", "# Convertir en datetime en utilisant pd.to_datetime avec utc=True\n", "datetime_object_utc = pd.to_datetime(date_string, utc=True)\n", "\n", "# Afficher l'objet datetime en UTC\n", "print(\"UTC:\", datetime_object_utc)\n", "\n", "# Effectuer un calcul de différence entre deux dates en UTC\n", "other_date_string = '2021-03-28 20:30:00+03:00'\n", "other_datetime_object_utc = pd.to_datetime(other_date_string, utc=True)\n", "\n", "# Calculer la différence entre les dates\n", "time_difference = other_datetime_object_utc - datetime_object_utc\n", "\n", "# Afficher la différence\n", "print(\"Différence en heures:\", time_difference.total_seconds() / 3600)\n" ] }, { "cell_type": "code", "execution_count": 321, "id": "9388c008-e2a5-463d-95d2-8f5fea0d6a5a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
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" ], "text/plain": [ " id campaign_id customer_id opened_at \\\n", "0 19793 58 112597 NaN \n", "1 14211 58 113666 NaN \n", "2 13150 58 280561 NaN \n", "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", "4 5175 58 103972 NaN \n", "\n", " sent_at delivered_at \\\n", "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", "\n", " created_at updated_at \\\n", "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 \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", " campaign_created_at campaign_updated_at \\\n", "0 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "1 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "2 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "3 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "4 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "\n", " campaign_sent_at campaign_identifier \n", "0 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "1 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "2 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "3 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "4 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a " ] }, "execution_count": 321, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# etape supp pour s'assurer que les dates non convertibles sont bien des Nan\n", "\n", "df1_campaigns_full[columns_to_convert] = df1_campaigns_full[columns_to_convert].apply(pd.to_datetime, errors='coerce')\n", "df1_campaigns_full.head()" ] }, { "cell_type": "code", "execution_count": 333, "id": "edb2f622-bf19-4c51-8213-1b8a3dacf72e", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_624/1309539541.py:3: FutureWarning: In a future version of pandas, parsing datetimes with mixed time zones will raise an error unless `utc=True`. Please specify `utc=True` to opt in to the new behaviour and silence this warning. To create a `Series` with mixed offsets and `object` dtype, please use `apply` and `datetime.datetime.strptime`\n", " df1_campaigns_full[\"sent_at\"] = pd.to_datetime(df1_campaigns_full[\"sent_at\"] , utc=False).astype('datetime64[ns]')\n" ] }, { "ename": "ValueError", "evalue": "Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True, at position 18", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[333], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# autre methode\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m df1_campaigns_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msent_at\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf1_campaigns_full\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msent_at\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdatetime64[ns]\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/generic.py:6534\u001b[0m, in \u001b[0;36mNDFrame.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 6530\u001b[0m results \u001b[38;5;241m=\u001b[39m [ser\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy) \u001b[38;5;28;01mfor\u001b[39;00m _, ser \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems()]\n\u001b[1;32m 6532\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6533\u001b[0m \u001b[38;5;66;03m# else, only a single dtype is given\u001b[39;00m\n\u001b[0;32m-> 6534\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6535\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor_from_mgr(new_data, axes\u001b[38;5;241m=\u001b[39mnew_data\u001b[38;5;241m.\u001b[39maxes)\n\u001b[1;32m 6536\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mastype\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/internals/managers.py:414\u001b[0m, in \u001b[0;36mBaseBlockManager.astype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m using_copy_on_write():\n\u001b[1;32m 412\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 414\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mastype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43musing_cow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43musing_copy_on_write\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/internals/managers.py:354\u001b[0m, in \u001b[0;36mBaseBlockManager.apply\u001b[0;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[1;32m 352\u001b[0m applied \u001b[38;5;241m=\u001b[39m b\u001b[38;5;241m.\u001b[39mapply(f, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 353\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 354\u001b[0m applied \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 355\u001b[0m result_blocks \u001b[38;5;241m=\u001b[39m extend_blocks(applied, result_blocks)\n\u001b[1;32m 357\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mfrom_blocks(result_blocks, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/internals/blocks.py:616\u001b[0m, in \u001b[0;36mBlock.astype\u001b[0;34m(self, dtype, copy, errors, using_cow)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 597\u001b[0m \u001b[38;5;124;03mCoerce to the new dtype.\u001b[39;00m\n\u001b[1;32m 598\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 612\u001b[0m \u001b[38;5;124;03mBlock\u001b[39;00m\n\u001b[1;32m 613\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 614\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m--> 616\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array_safe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 618\u001b[0m new_values \u001b[38;5;241m=\u001b[39m maybe_coerce_values(new_values)\n\u001b[1;32m 620\u001b[0m refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:238\u001b[0m, in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m 235\u001b[0m dtype \u001b[38;5;241m=\u001b[39m dtype\u001b[38;5;241m.\u001b[39mnumpy_dtype\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 238\u001b[0m new_values \u001b[38;5;241m=\u001b[39m \u001b[43mastype_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 239\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n\u001b[1;32m 240\u001b[0m \u001b[38;5;66;03m# e.g. _astype_nansafe can fail on object-dtype of strings\u001b[39;00m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;66;03m# trying to convert to float\u001b[39;00m\n\u001b[1;32m 242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:183\u001b[0m, in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m 180\u001b[0m values \u001b[38;5;241m=\u001b[39m values\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39mcopy)\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 183\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43m_astype_nansafe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[38;5;66;03m# in pandas we don't store numpy str dtypes, so convert to object\u001b[39;00m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(dtype, np\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(values\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mtype, \u001b[38;5;28mstr\u001b[39m):\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/dtypes/astype.py:110\u001b[0m, in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mis_np_dtype(dtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m to_datetime\n\u001b[0;32m--> 110\u001b[0m dti \u001b[38;5;241m=\u001b[39m \u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mravel\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 111\u001b[0m dta \u001b[38;5;241m=\u001b[39m dti\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39mreshape(arr\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dta\u001b[38;5;241m.\u001b[39mastype(dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39m_ndarray\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:1131\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1123\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1124\u001b[0m \u001b[38;5;66;03m# error: Argument 1 to \"_maybe_cache\" has incompatible type\u001b[39;00m\n\u001b[1;32m 1125\u001b[0m \u001b[38;5;66;03m# \"Union[float, str, datetime, List[Any], Tuple[Any, ...], ExtensionArray,\u001b[39;00m\n\u001b[1;32m 1126\u001b[0m \u001b[38;5;66;03m# ndarray[Any, Any], Series]\"; expected \"Union[List[Any], Tuple[Any, ...],\u001b[39;00m\n\u001b[1;32m 1127\u001b[0m \u001b[38;5;66;03m# Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series], Series]\"\u001b[39;00m\n\u001b[1;32m 1128\u001b[0m argc \u001b[38;5;241m=\u001b[39m cast(\n\u001b[1;32m 1129\u001b[0m Union[\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, ExtensionArray, np\u001b[38;5;241m.\u001b[39mndarray, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSeries\u001b[39m\u001b[38;5;124m\"\u001b[39m, Index], arg\n\u001b[1;32m 1130\u001b[0m )\n\u001b[0;32m-> 1131\u001b[0m cache_array \u001b[38;5;241m=\u001b[39m \u001b[43m_maybe_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43margc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert_listlike\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1132\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m OutOfBoundsDatetime:\n\u001b[1;32m 1133\u001b[0m \u001b[38;5;66;03m# caching attempts to create a DatetimeIndex, which may raise\u001b[39;00m\n\u001b[1;32m 1134\u001b[0m \u001b[38;5;66;03m# an OOB. If that's the desired behavior, then just reraise...\u001b[39;00m\n\u001b[1;32m 1135\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:254\u001b[0m, in \u001b[0;36m_maybe_cache\u001b[0;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[1;32m 252\u001b[0m unique_dates \u001b[38;5;241m=\u001b[39m unique(arg)\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(unique_dates) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mlen\u001b[39m(arg):\n\u001b[0;32m--> 254\u001b[0m cache_dates \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43munique_dates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;66;03m# GH#45319\u001b[39;00m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:490\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _array_strptime_with_fallback(arg, name, utc, \u001b[38;5;28mformat\u001b[39m, exact, errors)\n\u001b[0;32m--> 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mobjects_to_datetime64ns\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 491\u001b[0m \u001b[43m \u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 492\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 493\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 497\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 502\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result, dtype\u001b[38;5;241m=\u001b[39mtz_to_dtype(tz_parsed))\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/arrays/datetimes.py:2346\u001b[0m, in \u001b[0;36mobjects_to_datetime64ns\u001b[0;34m(data, dayfirst, yearfirst, utc, errors, allow_object)\u001b[0m\n\u001b[1;32m 2343\u001b[0m \u001b[38;5;66;03m# if str-dtype, convert\u001b[39;00m\n\u001b[1;32m 2344\u001b[0m data \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(data, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mobject_)\n\u001b[0;32m-> 2346\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m \u001b[43mtslib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray_to_datetime\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2347\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2348\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2349\u001b[0m \u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2350\u001b[0m \u001b[43m \u001b[49m\u001b[43mdayfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdayfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2351\u001b[0m \u001b[43m \u001b[49m\u001b[43myearfirst\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43myearfirst\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2352\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2355\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 2356\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;66;03m# Return i8 values to denote unix timestamps\u001b[39;00m\n\u001b[1;32m 2358\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\u001b[38;5;241m.\u001b[39mview(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mi8\u001b[39m\u001b[38;5;124m\"\u001b[39m), tz_parsed\n", "File \u001b[0;32mtslib.pyx:403\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32mtslib.pyx:552\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32mtslib.pyx:480\u001b[0m, in \u001b[0;36mpandas._libs.tslib.array_to_datetime\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32mconversion.pyx:716\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.conversion.convert_timezone\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Tz-aware datetime.datetime cannot be converted to datetime64 unless utc=True, at position 18" ] } ], "source": [ "# autre methode\n", "\n", "df1_campaigns_full[\"sent_at\"] = pd.to_datetime(df1_campaigns_full[\"sent_at\"] , utc=False).astype('datetime64[ns]')" ] }, { "cell_type": "code", "execution_count": 334, "id": "92bbdf80-e34b-4146-864a-b0dd4e04c5e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " sent_at\n", "0 2022-01-01 10:34:56+00:00\n", "1 2022-02-01 13:45:30+00:00\n", "2 2022-03-01 16:30:00+00:00\n" ] } ], "source": [ "import pandas as pd\n", "\n", "# Exemple de DataFrame avec une colonne 'sent_at' contenant des dates en format string\n", "df1_campaigns_full = pd.DataFrame({\n", " 'sent_at': ['2022-01-01 12:34:56+02:00', '2022-02-01 15:45:30+02:00', '2022-03-01 18:30:00+02:00']\n", "})\n", "\n", "# Convertir la colonne 'sent_at' en datetime en conservant l'information sur le fuseau horaire (datetime64[ns])\n", "df1_campaigns_full['sent_at'] = pd.to_datetime(df1_campaigns_full['sent_at'], utc=True)\n", "\n", "# Afficher le DataFrame résultant\n", "print(df1_campaigns_full)\n" ] }, { "cell_type": "code", "execution_count": 324, "id": "a8ad41ed-433c-4f7e-9f67-888dcb54d24e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "campaign_id\n", "1 2021-03-24 00:00:00+01:00\n", "2 2021-03-14 00:00:00+01:00\n", "3 2021-03-15 00:00:00+01:00\n", "4 2021-03-21 00:00:00+01:00\n", "5 2021-03-10 00:00:00+01:00\n", " ... \n", "1321501 2023-11-06 13:30:12+01:00\n", "1321503 2023-11-07 17:31:16+01:00\n", "1321505 2023-11-08 11:15:52+01:00\n", "1321506 2023-11-08 19:00:25+01:00\n", "1321507 2023-11-08 19:00:37+01:00\n", "Name: campaign_sent_at, Length: 949, dtype: datetime64[ns, tzoffset(None, 3600)]\n" ] }, { "ename": "TypeError", "evalue": "'bool' object is not callable", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[324], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# comparison \u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(df1_campaigns_full\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_id\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_sent_at\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mfirst()) \u001b[38;5;66;03m# envoi des campagnes\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdf1_campaigns_full\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcampaign_id\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msent_at\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mmin())\n", "\u001b[0;31mTypeError\u001b[0m: 'bool' object is not callable" ] } ], "source": [ "# comparison \n", "\n", "print(df1_campaigns_full.groupby(\"campaign_id\")[\"campaign_sent_at\"].first()) # envoi des campagnes\n", "print(df1_campaigns_full.groupby(\"campaign_id\")[\"sent_at\"].dropna().min())" ] }, { "cell_type": "code", "execution_count": 325, "id": "1771adeb-bbc9-40ef-afb6-49a6b3ff2e79", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "campaign_id 0\n", "customer_id 0\n", "opened_at 5019527\n", "sent_at 2741358\n", "delivered_at 2807002\n", "created_at 1547090\n", "updated_at 766803\n", "campaign_name 0\n", "campaign_service_id 0\n", "campaign_created_at 2216183\n", "campaign_updated_at 2561268\n", "campaign_sent_at 3504140\n", "campaign_identifier 0\n", "dtype: int64" ] }, "execution_count": 325, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns_full.isna().sum()" ] }, { "cell_type": "code", "execution_count": 326, "id": "1a5a1d98-a076-4988-aaf3-e753c117e518", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "name 0\n", "service_id 0\n", "created_at 0\n", "updated_at 0\n", "process_id 957\n", "report_url 957\n", "category 2\n", "to_be_synced 0\n", "identifier 0\n", "sent_at 3\n", "dtype: int64" ] }, "execution_count": 326, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns.isna().sum()" ] }, { "cell_type": "code", "execution_count": 320, "id": "749df9f0-8a18-49f0-a820-05cc674a5fce", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-06-02 10:24:08+02:00\n", "2020-06-02 10:24:08+02:00\n" ] } ], "source": [ "# df1_campaigns_full[\"sent_at\"] = \n", "print(pd.to_datetime(df1_campaigns_full[\"sent_at\"], errors='coerce').min())\n", "print(df1_campaigns_full[\"sent_at\"].dropna().min())" ] }, { "cell_type": "code", "execution_count": 313, "id": "f46000b8-4b7b-4121-b0af-8e8a388ce33c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6214808" ] }, "execution_count": 313, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns_full[\"sent_at\"].apply(lambda x : isinstance(x, datetime)).sum()\n", "# df1_campaigns_full[\"sent_at\"].tail(30)" ] }, { "cell_type": "code", "execution_count": 314, "id": "0ae4aeca-6edc-44e8-bc72-74f19b62a8f3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6214808" ] }, "execution_count": 314, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns_full.shape[0]" ] }, { "cell_type": "code", "execution_count": 340, "id": "4ef4d3d5-5f0a-4798-86d1-1b56641fcce4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "campaign_id int64\n", "customer_id int64\n", "opened_at object\n", "sent_at object\n", "delivered_at object\n", "created_at object\n", "updated_at object\n", "campaign_name object\n", "campaign_service_id int64\n", "campaign_created_at object\n", "campaign_updated_at object\n", "campaign_sent_at object\n", "campaign_identifier object\n", "dtype: object" ] }, "execution_count": 340, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns_full.dtypes" ] }, { "cell_type": "code", "execution_count": 341, "id": "8de270ac-c205-4686-8d53-6cd52d8239d0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
\n", "
" ], "text/plain": [ " id campaign_id customer_id opened_at \\\n", "0 19793 58 112597 NaN \n", "1 14211 58 113666 NaN \n", "2 13150 58 280561 NaN \n", "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", "4 5175 58 103972 NaN \n", "\n", " sent_at delivered_at \\\n", "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", "\n", " created_at updated_at \\\n", "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 \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", " campaign_created_at campaign_updated_at \\\n", "0 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "1 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "2 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "3 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "4 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "\n", " campaign_sent_at campaign_identifier \n", "0 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "1 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "2 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "3 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "4 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a " ] }, "execution_count": 341, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns_full.head()" ] }, { "cell_type": "code", "execution_count": 342, "id": "e2d81bd1-9fd6-40c7-96f9-998771a4fd77", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "time data '2022-05-06 12:00:23+02:00' does not match format '%Y-%m-%d %H:%M:%S.%f%z'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[342], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# conversion colonne par colonne\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# precision a la Ns\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m df1_campaigns_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mdf1_campaigns_full\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcreated_at\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatetime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrptime\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mY-\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mm-\u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mH:\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mM:\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mS.\u001b[39;49m\u001b[38;5;132;43;01m%f\u001b[39;49;00m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnotna\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mNaT\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"updated_at\"] = df1_campaigns_full[\"updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_created_at\"] = df1_campaigns_full[\"campaign_created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_updated_at\"] = df1_campaigns_full[\"campaign_updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"delivered_at\"] = df1_campaigns_full[\"delivered_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_sent_at\"] = df1_campaigns_full[\"campaign_sent_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/series.py:4764\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m 4629\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\n\u001b[1;32m 4630\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4631\u001b[0m func: AggFuncType,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4636\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 4637\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m 4638\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4639\u001b[0m \u001b[38;5;124;03m Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m 4640\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4755\u001b[0m \u001b[38;5;124;03m dtype: float64\u001b[39;00m\n\u001b[1;32m 4756\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 4757\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4758\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4759\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4760\u001b[0m \u001b[43m \u001b[49m\u001b[43mconvert_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4761\u001b[0m \u001b[43m \u001b[49m\u001b[43mby_row\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby_row\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4762\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4763\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m-> 4764\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/apply.py:1209\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_compat()\n\u001b[1;32m 1208\u001b[0m \u001b[38;5;66;03m# self.func is Callable\u001b[39;00m\n\u001b[0;32m-> 1209\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/apply.py:1289\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1283\u001b[0m \u001b[38;5;66;03m# row-wise access\u001b[39;00m\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[1;32m 1285\u001b[0m \u001b[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[1;32m 1286\u001b[0m \u001b[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[1;32m 1287\u001b[0m \u001b[38;5;66;03m# Categorical (GH51645).\u001b[39;00m\n\u001b[1;32m 1288\u001b[0m action \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, CategoricalDtype) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1289\u001b[0m mapped \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_values\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1290\u001b[0m \u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurried\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\n\u001b[1;32m 1291\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[38;5;241m0\u001b[39m], ABCSeries):\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m 1295\u001b[0m \u001b[38;5;66;03m# See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex)\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/base.py:921\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[0;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mmap(mapper, na_action\u001b[38;5;241m=\u001b[39mna_action)\n\u001b[0;32m--> 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/algorithms.py:1814\u001b[0m, in \u001b[0;36mmap_array\u001b[0;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m 1812\u001b[0m values \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 1813\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1814\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1815\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1816\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer_mask(\n\u001b[1;32m 1817\u001b[0m values, mapper, mask\u001b[38;5;241m=\u001b[39misna(values)\u001b[38;5;241m.\u001b[39mview(np\u001b[38;5;241m.\u001b[39muint8), convert\u001b[38;5;241m=\u001b[39mconvert\n\u001b[1;32m 1818\u001b[0m )\n", "File \u001b[0;32mlib.pyx:2926\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n", "Cell \u001b[0;32mIn[342], line 4\u001b[0m, in \u001b[0;36m\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# conversion colonne par colonne\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# precision a la Ns\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m df1_campaigns_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df1_campaigns_full[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x : \u001b[43mdatetime\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrptime\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mY-\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mm-\u001b[39;49m\u001b[38;5;132;43;01m%d\u001b[39;49;00m\u001b[38;5;124;43m \u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mH:\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mM:\u001b[39;49m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mS.\u001b[39;49m\u001b[38;5;132;43;01m%f\u001b[39;49;00m\u001b[38;5;124;43m%\u001b[39;49m\u001b[38;5;124;43mz\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mnotna(x) \u001b[38;5;28;01melse\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mNaT)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"updated_at\"] = df1_campaigns_full[\"updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_created_at\"] = df1_campaigns_full[\"campaign_created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_updated_at\"] = df1_campaigns_full[\"campaign_updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"delivered_at\"] = df1_campaigns_full[\"delivered_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# df1_campaigns_full[\"campaign_sent_at\"] = df1_campaigns_full[\"campaign_sent_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\u001b[39;00m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/_strptime.py:568\u001b[0m, in \u001b[0;36m_strptime_datetime\u001b[0;34m(cls, data_string, format)\u001b[0m\n\u001b[1;32m 565\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_strptime_datetime\u001b[39m(\u001b[38;5;28mcls\u001b[39m, data_string, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%a\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mb \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mH:\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mM:\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mS \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 566\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a class cls instance based on the input string and the\u001b[39;00m\n\u001b[1;32m 567\u001b[0m \u001b[38;5;124;03m format string.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 568\u001b[0m tt, fraction, gmtoff_fraction \u001b[38;5;241m=\u001b[39m \u001b[43m_strptime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_string\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 569\u001b[0m tzname, gmtoff \u001b[38;5;241m=\u001b[39m tt[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m:]\n\u001b[1;32m 570\u001b[0m args \u001b[38;5;241m=\u001b[39m tt[:\u001b[38;5;241m6\u001b[39m] \u001b[38;5;241m+\u001b[39m (fraction,)\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/_strptime.py:349\u001b[0m, in \u001b[0;36m_strptime\u001b[0;34m(data_string, format)\u001b[0m\n\u001b[1;32m 347\u001b[0m found \u001b[38;5;241m=\u001b[39m format_regex\u001b[38;5;241m.\u001b[39mmatch(data_string)\n\u001b[1;32m 348\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m found:\n\u001b[0;32m--> 349\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime data \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m does not match format \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m 350\u001b[0m (data_string, \u001b[38;5;28mformat\u001b[39m))\n\u001b[1;32m 351\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data_string) \u001b[38;5;241m!=\u001b[39m found\u001b[38;5;241m.\u001b[39mend():\n\u001b[1;32m 352\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munconverted data remains: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m 353\u001b[0m data_string[found\u001b[38;5;241m.\u001b[39mend():])\n", "\u001b[0;31mValueError\u001b[0m: time data '2022-05-06 12:00:23+02:00' does not match format '%Y-%m-%d %H:%M:%S.%f%z'" ] } ], "source": [ "# conversion colonne par colonne\n", "\n", "# precision a la Ns\n", "df1_campaigns_full[\"created_at\"] = df1_campaigns_full[\"created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", "# df1_campaigns_full[\"updated_at\"] = df1_campaigns_full[\"updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", "# df1_campaigns_full[\"campaign_created_at\"] = df1_campaigns_full[\"campaign_created_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", "# df1_campaigns_full[\"campaign_updated_at\"] = df1_campaigns_full[\"campaign_updated_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n", "\n", "# precision a la sec\n", "# df1_campaigns_full[\"opened_at\"] = df1_campaigns_full[\"opened_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\n", "# df1_campaigns_full[\"sent_at\"] = df1_campaigns_full[\"sent_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\n", "# df1_campaigns_full[\"delivered_at\"] = df1_campaigns_full[\"delivered_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S%z\") if pd.notna(x) else pd.NaT)\n", "# df1_campaigns_full[\"campaign_sent_at\"] = df1_campaigns_full[\"campaign_sent_at\"].apply(lambda x : datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S.%f%z\") if pd.notna(x) else pd.NaT)\n" ] }, { "cell_type": "code", "execution_count": 346, "id": "5a1fe408-ae4c-4957-a39b-50a4d5423319", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6214778 2023-10-23 09:31:50.168545+02:00\n", "6214779 2023-10-23 09:31:28.570386+02:00\n", "6214780 2023-10-23 09:02:26.494195+02:00\n", "6214781 2023-10-23 09:32:34.454957+02:00\n", "6214782 2023-10-23 09:31:29.139217+02:00\n", "6214783 2023-10-23 09:32:06.223901+02:00\n", "6214784 2023-10-23 09:31:52.702258+02:00\n", "6214785 2023-10-23 09:31:45.051321+02:00\n", "6214786 2023-10-23 09:32:55.350092+02:00\n", "6214787 2023-10-23 09:33:14.007405+02:00\n", "6214788 2023-10-23 09:32:44.645432+02:00\n", "6214789 2023-10-23 09:02:27.578671+02:00\n", "6214790 2023-10-23 09:34:24.879045+02:00\n", "6214791 2023-10-23 09:34:02.075066+02:00\n", "6214792 2023-10-23 09:33:20.349918+02:00\n", "6214793 2023-10-23 09:34:25.631234+02:00\n", "6214794 2023-10-23 09:34:27.581150+02:00\n", "6214795 2023-10-23 09:31:45.192200+02:00\n", "6214796 2023-10-23 09:32:52.018890+02:00\n", "6214797 2023-10-23 09:02:01.558573+02:00\n", "6214798 2023-10-23 09:34:48.543213+02:00\n", "6214799 2023-10-23 09:32:15.109097+02:00\n", "6214800 2023-10-23 09:34:26.590416+02:00\n", "6214801 2023-10-23 09:32:02.729363+02:00\n", "6214802 2023-10-23 09:31:41.055337+02:00\n", "6214803 2023-10-23 09:32:36.564696+02:00\n", "6214804 2023-10-23 09:32:50.829641+02:00\n", "6214805 2023-10-23 09:33:31.102500+02:00\n", "6214806 2023-10-23 09:31:55.768547+02:00\n", "6214807 2023-10-23 09:33:57.477892+02:00\n", "Name: created_at, dtype: object" ] }, "execution_count": 346, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns_full[\"created_at\"].tail(30)" ] }, { "cell_type": "code", "execution_count": 349, "id": "feb3fc34-51f2-45d5-8f34-9940a14e9060", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "time data \"2023-10-23 09:31:50.168545+02:00\" doesn't match format \"%Y-%m-%d %H:%M:%S%z\", at position 1. You might want to try:\n - passing `format` if your strings have a consistent format;\n - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[349], line 9\u001b[0m\n\u001b[1;32m 4\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate_str\u001b[39m\u001b[38;5;124m'\u001b[39m: [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2022-05-06 12:00:23+02:00\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2023-10-23 09:31:50.168545+02:00\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 6\u001b[0m })\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Convertir la colonne 'date_str' en datetime en conservant l'information sur le fuseau horaire (datetime64[ns])\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdate_str\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Afficher le DataFrame résultant\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28mprint\u001b[39m(df)\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:1112\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1110\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39mmap(cache_array)\n\u001b[1;32m 1111\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1112\u001b[0m values \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1113\u001b[0m result \u001b[38;5;241m=\u001b[39m arg\u001b[38;5;241m.\u001b[39m_constructor(values, index\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mindex, name\u001b[38;5;241m=\u001b[39marg\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 1114\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, (ABCDataFrame, abc\u001b[38;5;241m.\u001b[39mMutableMapping)):\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:488\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m 486\u001b[0m \u001b[38;5;66;03m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[39;00m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_array_strptime_with_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m objects_to_datetime64ns(\n\u001b[1;32m 491\u001b[0m arg,\n\u001b[1;32m 492\u001b[0m dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 496\u001b[0m allow_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 497\u001b[0m )\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:519\u001b[0m, in \u001b[0;36m_array_strptime_with_fallback\u001b[0;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[1;32m 508\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_array_strptime_with_fallback\u001b[39m(\n\u001b[1;32m 509\u001b[0m arg,\n\u001b[1;32m 510\u001b[0m name,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 514\u001b[0m errors: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m 515\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Index:\n\u001b[1;32m 516\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 517\u001b[0m \u001b[38;5;124;03m Call array_strptime, with fallback behavior depending on 'errors'.\u001b[39;00m\n\u001b[1;32m 518\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 519\u001b[0m result, timezones \u001b[38;5;241m=\u001b[39m \u001b[43marray_strptime\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mutc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 520\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(tz \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m tz \u001b[38;5;129;01min\u001b[39;00m timezones):\n\u001b[1;32m 521\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _return_parsed_timezone_results(result, timezones, utc, name)\n", "File \u001b[0;32mstrptime.pyx:534\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n", "File \u001b[0;32mstrptime.pyx:355\u001b[0m, in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: time data \"2023-10-23 09:31:50.168545+02:00\" doesn't match format \"%Y-%m-%d %H:%M:%S%z\", at position 1. You might want to try:\n - passing `format` if your strings have a consistent format;\n - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this." ] } ], "source": [ "import pandas as pd\n", "\n", "# Exemple de DataFrame avec une colonne 'date_str' contenant des dates en formats différents\n", "df = pd.DataFrame({\n", " 'date_str': ['2022-05-06 12:00:23+02:00', '2023-10-23 09:31:50.168545+02:00']\n", "})\n", "\n", "# Convertir la colonne 'date_str' en datetime en conservant l'information sur le fuseau horaire (datetime64[ns])\n", "df['date'] = pd.to_datetime(df['date_str'], utc=True)\n", "\n", "# Afficher le DataFrame résultant\n", "print(df)\n" ] }, { "cell_type": "code", "execution_count": 350, "id": "da01f2d8-3c1e-4d43-92ef-6236a24963d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " date_str date\n", "0 2022-05-06 12:00:23+02:00 2022-05-06 10:00:23+00:00\n", "1 023-10-23 09:31:50.168545+02:00 023-10-23 09:31:50.168545+02:00\n" ] } ], "source": [ "\n", "# Exemple de DataFrame avec une colonne 'date_str' contenant des dates en formats différents\n", "df = pd.DataFrame({\n", " 'date_str': ['2022-05-06 12:00:23+02:00', '023-10-23 09:31:50.168545+02:00']\n", "})\n", "\n", "# Fonction lambda pour convertir la colonne 'date_str' en datetime avec précision\n", "def convert_to_datetime_with_precision(x):\n", " if pd.notna(x):\n", " # Format avec nanosecondes\n", " try:\n", " return pd.to_datetime(x, utc=True)\n", " except ValueError:\n", " pass\n", "\n", " # Format sans nanosecondes\n", " try:\n", " return pd.to_datetime(x, utc=True, format=\"%Y-%m-%d %H:%M:%S%z\")\n", " except ValueError:\n", " pass\n", "\n", " return x\n", "\n", "# Appliquer la fonction lambda à la colonne 'date_str'\n", "df['date'] = df['date_str'].apply(convert_to_datetime_with_precision)\n", "\n", "# Afficher le DataFrame résultant\n", "print(df)\n", "\n" ] }, { "cell_type": "code", "execution_count": 351, "id": "e6ca12c8-be66-4537-b759-036123b74b7b", "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[351], line 7\u001b[0m\n\u001b[1;32m 3\u001b[0m columns_to_convert \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msent_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelivered_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupdated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_sent_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_created_at\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcampaign_updated_at\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m column \u001b[38;5;129;01min\u001b[39;00m columns_to_convert :\n\u001b[0;32m----> 7\u001b[0m df1_campaigns_full[column] \u001b[38;5;241m=\u001b[39m \u001b[43mdf1_campaigns_full\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconvert_to_datetime_with_precision\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/series.py:4764\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m 4629\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\n\u001b[1;32m 4630\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 4631\u001b[0m func: AggFuncType,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4636\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 4637\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m 4638\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 4639\u001b[0m \u001b[38;5;124;03m Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m 4640\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 4755\u001b[0m \u001b[38;5;124;03m dtype: float64\u001b[39;00m\n\u001b[1;32m 4756\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 4757\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4758\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4759\u001b[0m \u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4760\u001b[0m \u001b[43m \u001b[49m\u001b[43mconvert_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4761\u001b[0m \u001b[43m \u001b[49m\u001b[43mby_row\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby_row\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4762\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4763\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m-> 4764\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/apply.py:1209\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_compat()\n\u001b[1;32m 1208\u001b[0m \u001b[38;5;66;03m# self.func is Callable\u001b[39;00m\n\u001b[0;32m-> 1209\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/apply.py:1289\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1283\u001b[0m \u001b[38;5;66;03m# row-wise access\u001b[39;00m\n\u001b[1;32m 1284\u001b[0m \u001b[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[1;32m 1285\u001b[0m \u001b[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[1;32m 1286\u001b[0m \u001b[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[1;32m 1287\u001b[0m \u001b[38;5;66;03m# Categorical (GH51645).\u001b[39;00m\n\u001b[1;32m 1288\u001b[0m action \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, CategoricalDtype) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1289\u001b[0m mapped \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_values\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1290\u001b[0m \u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurried\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\n\u001b[1;32m 1291\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[38;5;241m0\u001b[39m], ABCSeries):\n\u001b[1;32m 1294\u001b[0m \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m 1295\u001b[0m \u001b[38;5;66;03m# See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m 1296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex)\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/base.py:921\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[0;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mmap(mapper, na_action\u001b[38;5;241m=\u001b[39mna_action)\n\u001b[0;32m--> 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/algorithms.py:1814\u001b[0m, in \u001b[0;36mmap_array\u001b[0;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m 1812\u001b[0m values \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 1813\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1814\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1815\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1816\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer_mask(\n\u001b[1;32m 1817\u001b[0m values, mapper, mask\u001b[38;5;241m=\u001b[39misna(values)\u001b[38;5;241m.\u001b[39mview(np\u001b[38;5;241m.\u001b[39muint8), convert\u001b[38;5;241m=\u001b[39mconvert\n\u001b[1;32m 1818\u001b[0m )\n", "File \u001b[0;32mlib.pyx:2926\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n", "Cell \u001b[0;32mIn[350], line 11\u001b[0m, in \u001b[0;36mconvert_to_datetime_with_precision\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pd\u001b[38;5;241m.\u001b[39mnotna(x):\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Format avec nanosecondes\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_datetime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:1146\u001b[0m, in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m 1144\u001b[0m result \u001b[38;5;241m=\u001b[39m convert_listlike(argc, \u001b[38;5;28mformat\u001b[39m)\n\u001b[1;32m 1145\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1146\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mconvert_listlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43marg\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 1147\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arg, \u001b[38;5;28mbool\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, np\u001b[38;5;241m.\u001b[39mbool_):\n\u001b[1;32m 1148\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mbool\u001b[39m(result) \u001b[38;5;66;03m# TODO: avoid this kludge.\u001b[39;00m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:488\u001b[0m, in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m 486\u001b[0m \u001b[38;5;66;03m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[39;00m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mformat\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmixed\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 488\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_array_strptime_with_fallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexact\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 490\u001b[0m result, tz_parsed \u001b[38;5;241m=\u001b[39m objects_to_datetime64ns(\n\u001b[1;32m 491\u001b[0m arg,\n\u001b[1;32m 492\u001b[0m dayfirst\u001b[38;5;241m=\u001b[39mdayfirst,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 496\u001b[0m allow_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 497\u001b[0m )\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tz_parsed \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 500\u001b[0m \u001b[38;5;66;03m# We can take a shortcut since the datetime64 numpy array\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[38;5;66;03m# is in UTC\u001b[39;00m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:521\u001b[0m, in \u001b[0;36m_array_strptime_with_fallback\u001b[0;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[1;32m 519\u001b[0m result, timezones \u001b[38;5;241m=\u001b[39m array_strptime(arg, fmt, exact\u001b[38;5;241m=\u001b[39mexact, errors\u001b[38;5;241m=\u001b[39merrors, utc\u001b[38;5;241m=\u001b[39mutc)\n\u001b[1;32m 520\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(tz \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m tz \u001b[38;5;129;01min\u001b[39;00m timezones):\n\u001b[0;32m--> 521\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_return_parsed_timezone_results\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimezones\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mutc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _box_as_indexlike(result, utc\u001b[38;5;241m=\u001b[39mutc, name\u001b[38;5;241m=\u001b[39mname)\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/tools/datetimes.py:344\u001b[0m, in \u001b[0;36m_return_parsed_timezone_results\u001b[0;34m(result, timezones, utc, name)\u001b[0m\n\u001b[1;32m 342\u001b[0m tz_results \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(\u001b[38;5;28mlen\u001b[39m(result), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mobject\u001b[39m)\n\u001b[1;32m 343\u001b[0m non_na_timezones \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n\u001b[0;32m--> 344\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m zone \u001b[38;5;129;01min\u001b[39;00m \u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimezones\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 345\u001b[0m mask \u001b[38;5;241m=\u001b[39m timezones \u001b[38;5;241m==\u001b[39m zone\n\u001b[1;32m 346\u001b[0m dta \u001b[38;5;241m=\u001b[39m DatetimeArray(result[mask])\u001b[38;5;241m.\u001b[39mtz_localize(zone)\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/algorithms.py:401\u001b[0m, in \u001b[0;36munique\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 307\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munique\u001b[39m(values):\n\u001b[1;32m 308\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;124;03m Return unique values based on a hash table.\u001b[39;00m\n\u001b[1;32m 310\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;124;03m array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)\u001b[39;00m\n\u001b[1;32m 400\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 401\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munique_with_mask\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m/opt/mamba/lib/python3.10/site-packages/pandas/core/algorithms.py:440\u001b[0m, in \u001b[0;36munique_with_mask\u001b[0;34m(values, mask)\u001b[0m\n\u001b[1;32m 438\u001b[0m table \u001b[38;5;241m=\u001b[39m hashtable(\u001b[38;5;28mlen\u001b[39m(values))\n\u001b[1;32m 439\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 440\u001b[0m uniques \u001b[38;5;241m=\u001b[39m \u001b[43mtable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munique\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 441\u001b[0m uniques \u001b[38;5;241m=\u001b[39m _reconstruct_data(uniques, original\u001b[38;5;241m.\u001b[39mdtype, original)\n\u001b[1;32m 442\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m uniques\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "# loop over all dates to convert \n", "\n", "columns_to_convert = [\"sent_at\", \"delivered_at\", \"created_at\", \"updated_at\", \n", " \"campaign_sent_at\", \"campaign_created_at\", \"campaign_updated_at\"]\n", "\n", "for column in columns_to_convert :\n", " df1_campaigns_full[column] = df1_campaigns_full[column].apply(convert_to_datetime_with_precision)" ] }, { "cell_type": "code", "execution_count": 356, "id": "61e1f604-23ce-4cb2-8ad3-523c62e80e68", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
408100223728588268NaN2021-03-28 18:00:57+02:002021-03-28 18:43:38+02:002021-03-28 18:43:42.928685+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
40761394552581472NaN2021-03-28 18:00:57+02:002021-03-28 18:03:26+02:002021-03-28 18:03:28.229670+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4081572140705879782021-03-29 08:38:06+02:002021-03-28 18:00:57+02:002021-03-28 18:20:45+02:002021-03-28 18:20:49.431860+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
409483369695832211NaN2021-03-28 18:00:57+02:002021-03-28 18:09:18+02:002021-03-28 18:09:20.571462+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4094827966258309802021-04-04 17:54:51+02:002021-03-28 18:00:57+02:002021-03-28 18:03:29+02:002021-03-28 18:13:33.153720+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
.............................................
89611241758334002021-03-28 21:27:57+02:002021-03-28 18:17:35+02:002021-03-28 18:17:36+02:002021-03-28 18:17:36.735495+02:002021-03-28 19:27:57.503961+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
203801820558106495NaN2021-03-28 18:30:08+02:002021-03-28 18:30:11+02:002021-03-28 18:30:11.453742+02:002021-03-28 18:30:11.474019+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
272982210758104781NaN2021-03-28 18:39:55+02:002021-03-28 18:39:56+02:002021-03-28 18:39:56.430679+02:002021-03-28 18:39:56.435656+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
291072238958111570NaN2021-03-28 18:40:38+02:002021-03-28 18:40:40+02:002021-03-28 18:40:40.975334+02:002021-03-28 18:40:40.979852+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
2796229258581194962021-03-29 21:03:52+02:002021-03-28 20:52:26+02:002021-03-28 20:52:30+02:002021-03-28 20:52:30.261271+02:002021-03-29 19:03:52.527753+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
\n", "

26464 rows × 14 columns

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" ], "text/plain": [ " id campaign_id customer_id opened_at \\\n", "4081002 23728 58 8268 NaN \n", "4076139 4552 58 1472 NaN \n", "4081572 14070 58 7978 2021-03-29 08:38:06+02:00 \n", "4094833 6969 58 32211 NaN \n", "4094827 9662 58 30980 2021-04-04 17:54:51+02:00 \n", "... ... ... ... ... \n", "8961 12417 58 33400 2021-03-28 21:27:57+02:00 \n", "20380 18205 58 106495 NaN \n", "27298 22107 58 104781 NaN \n", "29107 22389 58 111570 NaN \n", "27962 29258 58 119496 2021-03-29 21:03:52+02:00 \n", "\n", " sent_at delivered_at \\\n", "4081002 2021-03-28 18:00:57+02:00 2021-03-28 18:43:38+02:00 \n", "4076139 2021-03-28 18:00:57+02:00 2021-03-28 18:03:26+02:00 \n", "4081572 2021-03-28 18:00:57+02:00 2021-03-28 18:20:45+02:00 \n", "4094833 2021-03-28 18:00:57+02:00 2021-03-28 18:09:18+02:00 \n", "4094827 2021-03-28 18:00:57+02:00 2021-03-28 18:03:29+02:00 \n", "... ... ... \n", "8961 2021-03-28 18:17:35+02:00 2021-03-28 18:17:36+02:00 \n", "20380 2021-03-28 18:30:08+02:00 2021-03-28 18:30:11+02:00 \n", "27298 2021-03-28 18:39:55+02:00 2021-03-28 18:39:56+02:00 \n", "29107 2021-03-28 18:40:38+02:00 2021-03-28 18:40:40+02:00 \n", "27962 2021-03-28 20:52:26+02:00 2021-03-28 20:52:30+02:00 \n", "\n", " created_at updated_at \\\n", "4081002 2021-03-28 18:43:42.928685+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4076139 2021-03-28 18:03:28.229670+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4081572 2021-03-28 18:20:49.431860+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4094833 2021-03-28 18:09:20.571462+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4094827 2021-03-28 18:13:33.153720+02:00 2022-04-15 22:52:04.397693+02:00 \n", "... ... ... \n", "8961 2021-03-28 18:17:36.735495+02:00 2021-03-28 19:27:57.503961+02:00 \n", "20380 2021-03-28 18:30:11.453742+02:00 2021-03-28 18:30:11.474019+02:00 \n", "27298 2021-03-28 18:39:56.430679+02:00 2021-03-28 18:39:56.435656+02:00 \n", "29107 2021-03-28 18:40:40.975334+02:00 2021-03-28 18:40:40.979852+02:00 \n", "27962 2021-03-28 20:52:30.261271+02:00 2021-03-29 19:03:52.527753+02:00 \n", "\n", " campaign_name campaign_service_id \\\n", "4081002 Le Mucem chez vous, gardons le lien #22 404 \n", "4076139 Le Mucem chez vous, gardons le lien #22 404 \n", "4081572 Le Mucem chez vous, gardons le lien #22 404 \n", "4094833 Le Mucem chez vous, gardons le lien #22 404 \n", "4094827 Le Mucem chez vous, gardons le lien #22 404 \n", "... ... ... \n", "8961 Le Mucem chez vous, gardons le lien #22 404 \n", "20380 Le Mucem chez vous, gardons le lien #22 404 \n", "27298 Le Mucem chez vous, gardons le lien #22 404 \n", "29107 Le Mucem chez vous, gardons le lien #22 404 \n", "27962 Le Mucem chez vous, gardons le lien #22 404 \n", "\n", " campaign_created_at campaign_updated_at \\\n", "4081002 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "4076139 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "4081572 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "4094833 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "4094827 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "... ... ... \n", "8961 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "20380 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "27298 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "29107 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "27962 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "\n", " campaign_sent_at campaign_identifier \n", "4081002 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "4076139 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "4081572 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "4094833 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "4094827 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "... ... ... \n", "8961 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "20380 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "27298 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "29107 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "27962 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "\n", "[26464 rows x 14 columns]" ] }, "execution_count": 356, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# tests\n", "\n", "df1_campaigns_full[df1_campaigns_full[\"campaign_id\"]==58].sort_values(\"sent_at\")" ] }, { "cell_type": "code", "execution_count": 364, "id": "0c07c533-0e24-4e53-96d5-c51db97425a6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
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idclicked_atlink_idcustomer_idcreated_atupdated_at
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122021-03-26 17:16:34+01:0021197682021-03-26 16:16:34.950871+01:002021-03-26 16:16:34.950871+01:00
22722021-03-28 20:03:32+02:00421131052021-03-28 18:03:32.736394+02:002021-03-28 18:03:32.736394+02:00
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151051 rows × 6 columns

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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", " created_at updated_at \n", "0 2021-03-26 15:30:37.050161+01:00 2021-03-26 15:30:37.050161+01:00 \n", "1 2021-03-26 16:16:34.950871+01:00 2021-03-26 16:16:34.950871+01:00 \n", "2 2021-03-28 18:03:32.736394+02:00 2021-03-28 18:03:32.736394+02:00 \n", "3 2021-03-26 16:43:19.338321+01:00 2021-03-26 16:43:19.338321+01:00 \n", "4 2021-03-26 16:46:00.502945+01:00 2021-03-26 16:46:00.502945+01:00 \n", "... ... ... \n", "151046 2023-11-09 15:34:29.425425+01:00 2023-11-09 15:34:29.425425+01:00 \n", "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", "\n", "[151051 rows x 6 columns]" ] }, "execution_count": 238, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_link_stats" ] }, { "cell_type": "code", "execution_count": 365, "id": "e4e4b17c-3338-4b43-8d96-5af3cb304ff9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0.0\n", "clicked_at 0.0\n", "link_id 0.0\n", "customer_id 0.0\n", "created_at 0.0\n", "updated_at 0.0\n", "dtype: float64" ] }, "execution_count": 365, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# share of Nan for every variable\n", "\n", "df1_link_stats.isna().sum() / df1_link_stats.shape[0]" ] }, { "cell_type": "code", "execution_count": 366, "id": "846f24d8-8a34-4774-aab7-957a71f73a2c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id int64\n", "clicked_at object\n", "link_id int64\n", "customer_id int64\n", "created_at object\n", "updated_at object\n", "dtype: object" ] }, "execution_count": 366, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# types of the variables \n", "\n", "df1_link_stats.dtypes" ] }, { "cell_type": "code", "execution_count": 367, "id": "6ee886ee-9ddf-4a78-aee8-002e57d63183", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 151051\n", "clicked_at 137121\n", "link_id 10788\n", "customer_id 26075\n", "created_at 96565\n", "updated_at 96565\n", "dtype: int64" ] }, "execution_count": 367, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# number of unique modalities\n", "\n", "df1_link_stats.nunique()" ] }, { "cell_type": "code", "execution_count": 378, "id": "79d02627-2c31-4843-a3da-4f5419b6fe9d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10788" ] }, "execution_count": 378, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(sorted(df1_link_stats[\"link_id\"].unique()))" ] }, { "cell_type": "code", "execution_count": 379, "id": "7651374c-3e69-4012-badf-c3d1bc6a477a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "949" ] }, "execution_count": 379, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(sorted(df1_campaigns_full[\"campaign_id\"].unique()))" ] }, { "cell_type": "code", "execution_count": 380, "id": "c3a11b25-65bc-44b6-b49c-6192f04b1d36", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idcampaign_idcustomer_idopened_atsent_atdelivered_atcreated_atupdated_atcampaign_namecampaign_service_idcampaign_created_atcampaign_updated_atcampaign_sent_atcampaign_identifier
01979358112597NaN2021-03-28 18:01:09+02:002021-03-28 18:24:18+02:002021-03-28 18:34:20.616136+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
11421158113666NaN2021-03-28 18:01:09+02:002021-03-28 18:21:02+02:002021-03-28 18:21:04.297213+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
21315058280561NaN2021-03-28 18:00:59+02:002021-03-28 18:08:45+02:002021-03-28 18:18:49.991042+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
37073581010072021-03-28 20:11:06+02:002021-03-28 18:00:59+02:002021-03-28 18:09:47+02:002021-03-28 18:09:50.915354+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
4517558103972NaN2021-03-28 18:01:06+02:002021-03-28 18:05:03+02:002021-03-28 18:05:08.507398+02:002022-04-15 22:52:04.397693+02:00Le Mucem chez vous, gardons le lien #224042021-03-28 18:01:45.448313+02:002021-09-24 11:56:07.723413+02:002021-03-28 00:00:00+01:004f4adcbf8c6f66dcfc8a3282ac2bf10a
.............................................
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621480483033071321483213552023-10-23 11:44:02+02:002023-10-23 11:32:49+02:002023-10-23 11:32:49+02:002023-10-23 09:32:50.829641+02:002023-10-23 09:44:04.119578+02:00dre_nov_202313182023-10-23 09:31:19.927528+02:002023-10-23 09:31:20.033243+02:002023-10-23 11:31:17+02:0076cf99d3614e23eabab16fb27e944bf9
621480583043461321483218492023-10-23 11:45:52+02:002023-10-23 11:33:28+02:002023-10-23 11:33:29+02:002023-10-23 09:33:31.102500+02:002023-10-23 09:45:55.927652+02:00dre_nov_202313182023-10-23 09:31:19.927528+02:002023-10-23 09:31:20.033243+02:002023-10-23 11:31:17+02:0076cf99d3614e23eabab16fb27e944bf9
6214806830203713214836677892023-10-23 11:47:32+02:002023-10-23 11:31:53+02:002023-10-23 11:31:54+02:002023-10-23 09:31:55.768547+02:002023-10-23 09:47:33.915460+02:00dre_nov_202313182023-10-23 09:31:19.927528+02:002023-10-23 09:31:20.033243+02:002023-10-23 11:31:17+02:0076cf99d3614e23eabab16fb27e944bf9
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6214808 rows × 14 columns

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" ], "text/plain": [ " id campaign_id customer_id opened_at \\\n", "0 19793 58 112597 NaN \n", "1 14211 58 113666 NaN \n", "2 13150 58 280561 NaN \n", "3 7073 58 101007 2021-03-28 20:11:06+02:00 \n", "4 5175 58 103972 NaN \n", "... ... ... ... ... \n", "6214803 8302994 1321483 266155 2023-10-23 11:43:25+02:00 \n", "6214804 8303307 1321483 21355 2023-10-23 11:44:02+02:00 \n", "6214805 8304346 1321483 21849 2023-10-23 11:45:52+02:00 \n", "6214806 8302037 1321483 667789 2023-10-23 11:47:32+02:00 \n", "6214807 8304939 1321483 294154 NaN \n", "\n", " sent_at delivered_at \\\n", "0 2021-03-28 18:01:09+02:00 2021-03-28 18:24:18+02:00 \n", "1 2021-03-28 18:01:09+02:00 2021-03-28 18:21:02+02:00 \n", "2 2021-03-28 18:00:59+02:00 2021-03-28 18:08:45+02:00 \n", "3 2021-03-28 18:00:59+02:00 2021-03-28 18:09:47+02:00 \n", "4 2021-03-28 18:01:06+02:00 2021-03-28 18:05:03+02:00 \n", "... ... ... \n", "6214803 2023-10-23 11:32:33+02:00 2023-10-23 11:32:34+02:00 \n", "6214804 2023-10-23 11:32:49+02:00 2023-10-23 11:32:49+02:00 \n", "6214805 2023-10-23 11:33:28+02:00 2023-10-23 11:33:29+02:00 \n", "6214806 2023-10-23 11:31:53+02:00 2023-10-23 11:31:54+02:00 \n", "6214807 2023-10-23 11:33:54+02:00 2023-10-23 11:33:55+02:00 \n", "\n", " created_at updated_at \\\n", "0 2021-03-28 18:34:20.616136+02:00 2022-04-15 22:52:04.397693+02:00 \n", "1 2021-03-28 18:21:04.297213+02:00 2022-04-15 22:52:04.397693+02:00 \n", "2 2021-03-28 18:18:49.991042+02:00 2022-04-15 22:52:04.397693+02:00 \n", "3 2021-03-28 18:09:50.915354+02:00 2022-04-15 22:52:04.397693+02:00 \n", "4 2021-03-28 18:05:08.507398+02:00 2022-04-15 22:52:04.397693+02:00 \n", "... ... ... \n", "6214803 2023-10-23 09:32:36.564696+02:00 2023-10-23 09:43:28.038259+02:00 \n", "6214804 2023-10-23 09:32:50.829641+02:00 2023-10-23 09:44:04.119578+02:00 \n", "6214805 2023-10-23 09:33:31.102500+02:00 2023-10-23 09:45:55.927652+02:00 \n", "6214806 2023-10-23 09:31:55.768547+02:00 2023-10-23 09:47:33.915460+02:00 \n", "6214807 2023-10-23 09:33:57.477892+02:00 2023-10-23 09:33:57.842331+02:00 \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_created_at campaign_updated_at \\\n", "0 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "1 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "2 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "3 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "4 2021-03-28 18:01:45.448313+02:00 2021-09-24 11:56:07.723413+02:00 \n", "... ... ... \n", "6214803 2023-10-23 09:31:19.927528+02:00 2023-10-23 09:31:20.033243+02:00 \n", "6214804 2023-10-23 09:31:19.927528+02:00 2023-10-23 09:31:20.033243+02:00 \n", "6214805 2023-10-23 09:31:19.927528+02:00 2023-10-23 09:31:20.033243+02:00 \n", "6214806 2023-10-23 09:31:19.927528+02:00 2023-10-23 09:31:20.033243+02:00 \n", "6214807 2023-10-23 09:31:19.927528+02:00 2023-10-23 09:31:20.033243+02:00 \n", "\n", " campaign_sent_at campaign_identifier \n", "0 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "1 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "2 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "3 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "4 2021-03-28 00:00:00+01:00 4f4adcbf8c6f66dcfc8a3282ac2bf10a \n", "... ... ... \n", "6214803 2023-10-23 11:31:17+02:00 76cf99d3614e23eabab16fb27e944bf9 \n", "6214804 2023-10-23 11:31:17+02:00 76cf99d3614e23eabab16fb27e944bf9 \n", "6214805 2023-10-23 11:31:17+02:00 76cf99d3614e23eabab16fb27e944bf9 \n", "6214806 2023-10-23 11:31:17+02:00 76cf99d3614e23eabab16fb27e944bf9 \n", "6214807 2023-10-23 11:31:17+02:00 76cf99d3614e23eabab16fb27e944bf9 \n", "\n", "[6214808 rows x 14 columns]" ] }, "execution_count": 380, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1_campaigns_full" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 5 }