diff --git a/0_Cleaning_and_merge.ipynb b/0_Cleaning_and_merge.ipynb
index a8dfc0f..b2c2018 100644
--- a/0_Cleaning_and_merge.ipynb
+++ b/0_Cleaning_and_merge.ipynb
@@ -72,54 +72,6 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "aaf64d60-bf92-470c-8210-d09abd6a653e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['bdc2324-data/1/1campaign_stats.csv',\n",
- " 'bdc2324-data/1/1campaigns.csv',\n",
- " 'bdc2324-data/1/1categories.csv',\n",
- " 'bdc2324-data/1/1countries.csv',\n",
- " 'bdc2324-data/1/1currencies.csv',\n",
- " 'bdc2324-data/1/1customer_target_mappings.csv',\n",
- " 'bdc2324-data/1/1customersplus.csv',\n",
- " 'bdc2324-data/1/1event_types.csv',\n",
- " 'bdc2324-data/1/1events.csv',\n",
- " 'bdc2324-data/1/1facilities.csv',\n",
- " 'bdc2324-data/1/1link_stats.csv',\n",
- " 'bdc2324-data/1/1pricing_formulas.csv',\n",
- " 'bdc2324-data/1/1product_packs.csv',\n",
- " 'bdc2324-data/1/1products.csv',\n",
- " 'bdc2324-data/1/1products_groups.csv',\n",
- " 'bdc2324-data/1/1purchases.csv',\n",
- " 'bdc2324-data/1/1representation_category_capacities.csv',\n",
- " 'bdc2324-data/1/1representations.csv',\n",
- " 'bdc2324-data/1/1seasons.csv',\n",
- " 'bdc2324-data/1/1structure_tag_mappings.csv',\n",
- " 'bdc2324-data/1/1suppliers.csv',\n",
- " 'bdc2324-data/1/1tags.csv',\n",
- " 'bdc2324-data/1/1target_types.csv',\n",
- " 'bdc2324-data/1/1targets.csv',\n",
- " 'bdc2324-data/1/1tickets.csv',\n",
- " 'bdc2324-data/1/1type_of_categories.csv',\n",
- " 'bdc2324-data/1/1type_of_pricing_formulas.csv',\n",
- " 'bdc2324-data/1/1type_ofs.csv']"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "liste_database"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
"id": "dd6a3518-b752-4a1e-b77b-9e03e853c3ed",
"metadata": {},
"outputs": [
@@ -127,7 +79,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2240/4081512283.py:10: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+ "/tmp/ipykernel_3658/4081512283.py:10: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" df = pd.read_csv(file_in)\n"
]
}
@@ -158,7 +110,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"id": "d237be96-8c86-4a91-b7a1-487e87a16c3d",
"metadata": {},
"outputs": [],
@@ -199,7 +151,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"id": "7e7b90ce-da54-4f00-bc34-64c543b0858f",
"metadata": {},
"outputs": [],
@@ -221,7 +173,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"id": "03329e32-00a5-42c8-9470-75f7b6216ccd",
"metadata": {},
"outputs": [],
@@ -239,7 +191,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"id": "b95464b1-26bc-4aac-84b4-45da83b92251",
"metadata": {},
"outputs": [],
@@ -282,7 +234,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"id": "3e1d2ba7-ff4f-48eb-93a8-2bb648c70396",
"metadata": {},
"outputs": [
@@ -290,17 +242,17 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2240/1591303091.py:5: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_3658/1591303091.py:5: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" tickets.rename(columns = {'id' : 'ticket_id'}, inplace = True)\n",
- "/tmp/ipykernel_2240/1591303091.py:9: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_3658/1591303091.py:9: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" suppliers.rename(columns = {'name' : 'supplier_name'}, inplace = True)\n",
- "/tmp/ipykernel_2240/1591303091.py:13: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_3658/1591303091.py:13: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
@@ -314,7 +266,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"id": "4b18edfc-6450-4c6a-9e7b-ee5a5808c8c9",
"metadata": {},
"outputs": [
@@ -405,127 +357,33 @@
" | 2018-12-28 14:47:50+00:00 | \n",
" 48187 | \n",
" \n",
- " \n",
- " | ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " | 1826667 | \n",
- " 18643847 | \n",
- " 350454 | \n",
- " False | \n",
- " vad | \n",
- " Billet en nombre | \n",
- " pricing_formula | \n",
- " 2022-08-02 08:59:17+00:00 | \n",
- " 41 | \n",
- "
\n",
- " \n",
- " | 1826668 | \n",
- " 19853111 | \n",
- " 383564 | \n",
- " False | \n",
- " vad | \n",
- " Billet en nombre | \n",
- " pricing_formula | \n",
- " 2022-11-04 14:25:42+00:00 | \n",
- " 62763 | \n",
- "
\n",
- " \n",
- " | 1826669 | \n",
- " 19860514 | \n",
- " 383751 | \n",
- " False | \n",
- " vad | \n",
- " Billet en nombre | \n",
- " pricing_formula | \n",
- " 2022-11-18 10:47:26+00:00 | \n",
- " 1195566 | \n",
- "
\n",
- " \n",
- " | 1826670 | \n",
- " 19860515 | \n",
- " 383751 | \n",
- " False | \n",
- " vad | \n",
- " Billet en nombre | \n",
- " pricing_formula | \n",
- " 2022-11-18 10:47:26+00:00 | \n",
- " 1195566 | \n",
- "
\n",
- " \n",
- " | 1826671 | \n",
- " 19860516 | \n",
- " 383751 | \n",
- " False | \n",
- " vad | \n",
- " Billet en nombre | \n",
- " pricing_formula | \n",
- " 2022-11-18 10:47:26+00:00 | \n",
- " 1195566 | \n",
- "
\n",
" \n",
"\n",
- "1826672 rows × 8 columns
\n",
""
],
"text/plain": [
- " ticket_id product_id is_from_subscription supplier_name \\\n",
- "0 13070859 225251 False vente en ligne \n",
- "1 13070860 224914 False vente en ligne \n",
- "2 13070861 224914 False vente en ligne \n",
- "3 13070862 224914 False vente en ligne \n",
- "4 13070863 224914 False vente en ligne \n",
- "... ... ... ... ... \n",
- "1826667 18643847 350454 False vad \n",
- "1826668 19853111 383564 False vad \n",
- "1826669 19860514 383751 False vad \n",
- "1826670 19860515 383751 False vad \n",
- "1826671 19860516 383751 False vad \n",
+ " ticket_id product_id is_from_subscription supplier_name \\\n",
+ "0 13070859 225251 False vente en ligne \n",
+ "1 13070860 224914 False vente en ligne \n",
+ "2 13070861 224914 False vente en ligne \n",
+ "3 13070862 224914 False vente en ligne \n",
+ "4 13070863 224914 False vente en ligne \n",
"\n",
- " type_of_ticket_name children purchase_date \\\n",
- "0 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
- "1 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
- "2 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
- "3 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
- "4 Atelier pricing_formula 2018-12-28 14:47:50+00:00 \n",
- "... ... ... ... \n",
- "1826667 Billet en nombre pricing_formula 2022-08-02 08:59:17+00:00 \n",
- "1826668 Billet en nombre pricing_formula 2022-11-04 14:25:42+00:00 \n",
- "1826669 Billet en nombre pricing_formula 2022-11-18 10:47:26+00:00 \n",
- "1826670 Billet en nombre pricing_formula 2022-11-18 10:47:26+00:00 \n",
- "1826671 Billet en nombre pricing_formula 2022-11-18 10:47:26+00:00 \n",
- "\n",
- " customer_id \n",
- "0 48187 \n",
- "1 48187 \n",
- "2 48187 \n",
- "3 48187 \n",
- "4 48187 \n",
- "... ... \n",
- "1826667 41 \n",
- "1826668 62763 \n",
- "1826669 1195566 \n",
- "1826670 1195566 \n",
- "1826671 1195566 \n",
- "\n",
- "[1826672 rows x 8 columns]"
+ " type_of_ticket_name children purchase_date customer_id \n",
+ "0 Atelier pricing_formula 2018-12-28 14:47:50+00:00 48187 \n",
+ "1 Atelier pricing_formula 2018-12-28 14:47:50+00:00 48187 \n",
+ "2 Atelier pricing_formula 2018-12-28 14:47:50+00:00 48187 \n",
+ "3 Atelier pricing_formula 2018-12-28 14:47:50+00:00 48187 \n",
+ "4 Atelier pricing_formula 2018-12-28 14:47:50+00:00 48187 "
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df1_ticket_information"
+ "df1_ticket_information.head()"
]
},
{
@@ -538,7 +396,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"id": "043303fe-e90f-4689-a2a9-5d690555a045",
"metadata": {},
"outputs": [],
@@ -567,7 +425,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"id": "5882234a-1ed5-4269-87a6-0d75613476e3",
"metadata": {},
"outputs": [],
@@ -577,7 +435,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 13,
"id": "a7a452a6-cd5e-4c8b-b250-8a7d26e48fad",
"metadata": {},
"outputs": [
@@ -662,121 +520,33 @@
" 2017-03-13 16:07:27+00:00 | \n",
" 0 days 00:00:00 | \n",
" \n",
- " \n",
- " | ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " | 128360 | \n",
- " 406026 | \n",
- " 1 | \n",
- " 11 | \n",
- " 2 | \n",
- " 2023-11-08 12:53:31+00:00 | \n",
- " 2023-11-08 09:30:28+00:00 | \n",
- " 0 days 03:23:03 | \n",
- "
\n",
- " \n",
- " | 128361 | \n",
- " 406027 | \n",
- " 1 | \n",
- " 31 | \n",
- " 2 | \n",
- " 2023-11-08 15:59:11+00:00 | \n",
- " 2023-11-08 09:15:36+00:00 | \n",
- " 0 days 06:43:35 | \n",
- "
\n",
- " \n",
- " | 128362 | \n",
- " 406028 | \n",
- " 1 | \n",
- " 2 | \n",
- " 1 | \n",
- " 2023-11-08 14:56:08+00:00 | \n",
- " 2023-11-08 11:18:37+00:00 | \n",
- " 0 days 03:37:31 | \n",
- "
\n",
- " \n",
- " | 128363 | \n",
- " 406029 | \n",
- " 1256130 | \n",
- " 2 | \n",
- " 1 | \n",
- " 2023-11-08 10:35:43+00:00 | \n",
- " 2023-11-08 10:35:43+00:00 | \n",
- " 0 days 00:00:00 | \n",
- "
\n",
- " \n",
- " | 128364 | \n",
- " 406029 | \n",
- " 1256133 | \n",
- " 3 | \n",
- " 1 | \n",
- " 2023-11-08 16:51:19+00:00 | \n",
- " 2023-11-08 16:51:19+00:00 | \n",
- " 0 days 00:00:00 | \n",
- "
\n",
" \n",
"\n",
- "128365 rows × 7 columns
\n",
""
],
"text/plain": [
- " product_id customer_id nb_tickets nb_suppliers \\\n",
- "0 107310 2805 4 2 \n",
- "1 110089 54355 1 1 \n",
- "2 110089 54356 1 1 \n",
- "3 110089 54357 1 1 \n",
- "4 110089 54358 1 1 \n",
- "... ... ... ... ... \n",
- "128360 406026 1 11 2 \n",
- "128361 406027 1 31 2 \n",
- "128362 406028 1 2 1 \n",
- "128363 406029 1256130 2 1 \n",
- "128364 406029 1256133 3 1 \n",
+ " product_id customer_id nb_tickets nb_suppliers \\\n",
+ "0 107310 2805 4 2 \n",
+ "1 110089 54355 1 1 \n",
+ "2 110089 54356 1 1 \n",
+ "3 110089 54357 1 1 \n",
+ "4 110089 54358 1 1 \n",
"\n",
- " purchase_date_max purchase_date_min \\\n",
- "0 2019-06-05 14:37:13+00:00 2019-06-05 14:18:38+00:00 \n",
- "1 2017-02-17 13:32:51+00:00 2017-02-17 13:32:51+00:00 \n",
- "2 2017-03-02 14:36:16+00:00 2017-03-02 14:36:16+00:00 \n",
- "3 2017-03-06 15:16:41+00:00 2017-03-06 15:16:41+00:00 \n",
- "4 2017-03-13 16:07:27+00:00 2017-03-13 16:07:27+00:00 \n",
- "... ... ... \n",
- "128360 2023-11-08 12:53:31+00:00 2023-11-08 09:30:28+00:00 \n",
- "128361 2023-11-08 15:59:11+00:00 2023-11-08 09:15:36+00:00 \n",
- "128362 2023-11-08 14:56:08+00:00 2023-11-08 11:18:37+00:00 \n",
- "128363 2023-11-08 10:35:43+00:00 2023-11-08 10:35:43+00:00 \n",
- "128364 2023-11-08 16:51:19+00:00 2023-11-08 16:51:19+00:00 \n",
- "\n",
- " time_between_purchase \n",
- "0 0 days 00:18:35 \n",
- "1 0 days 00:00:00 \n",
- "2 0 days 00:00:00 \n",
- "3 0 days 00:00:00 \n",
- "4 0 days 00:00:00 \n",
- "... ... \n",
- "128360 0 days 03:23:03 \n",
- "128361 0 days 06:43:35 \n",
- "128362 0 days 03:37:31 \n",
- "128363 0 days 00:00:00 \n",
- "128364 0 days 00:00:00 \n",
- "\n",
- "[128365 rows x 7 columns]"
+ " purchase_date_max purchase_date_min time_between_purchase \n",
+ "0 2019-06-05 14:37:13+00:00 2019-06-05 14:18:38+00:00 0 days 00:18:35 \n",
+ "1 2017-02-17 13:32:51+00:00 2017-02-17 13:32:51+00:00 0 days 00:00:00 \n",
+ "2 2017-03-02 14:36:16+00:00 2017-03-02 14:36:16+00:00 0 days 00:00:00 \n",
+ "3 2017-03-06 15:16:41+00:00 2017-03-06 15:16:41+00:00 0 days 00:00:00 \n",
+ "4 2017-03-13 16:07:27+00:00 2017-03-13 16:07:27+00:00 0 days 00:00:00 "
]
},
- "execution_count": 32,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "df1_tickets_kpi"
+ "df1_tickets_kpi.head()"
]
},
{
@@ -826,7 +596,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2240/3848597476.py:4: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_3658/3848597476.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
@@ -893,51 +663,18 @@
" consentement optout b2c | \n",
" 34523 | \n",
" \n",
- " \n",
- " | ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " | Automation_parrainage_newsletter_handicap_visuel | \n",
- " 1 | \n",
- "
\n",
- " \n",
- " | consentement optout mediation specialisee | \n",
- " 1 | \n",
- "
\n",
- " \n",
- " | Inscrits NL LSF formulaire | \n",
- " 1 | \n",
- "
\n",
- " \n",
- " | Market auto - contacts inactifs post-scénario | \n",
- " 1 | \n",
- "
\n",
- " \n",
- " | Inactifs - fin du scénario | \n",
- " 1 | \n",
- "
\n",
" \n",
"\n",
- "283 rows × 1 columns
\n",
""
],
"text/plain": [
- " customer_id\n",
- "target_name \n",
- "consentement optin mediation specialisee 150000\n",
- "consentement optin jeune public 149979\n",
- "consentement optin b2c 108909\n",
- "Arenametrix_bascule tel vers sib 35216\n",
- "consentement optout b2c 34523\n",
- "... ...\n",
- "Automation_parrainage_newsletter_handicap_visuel 1\n",
- "consentement optout mediation specialisee 1\n",
- "Inscrits NL LSF formulaire 1\n",
- "Market auto - contacts inactifs post-scénario 1\n",
- "Inactifs - fin du scénario 1\n",
- "\n",
- "[283 rows x 1 columns]"
+ " customer_id\n",
+ "target_name \n",
+ "consentement optin mediation specialisee 150000\n",
+ "consentement optin jeune public 149979\n",
+ "consentement optin b2c 108909\n",
+ "Arenametrix_bascule tel vers sib 35216\n",
+ "consentement optout b2c 34523"
]
},
"execution_count": 16,
@@ -946,7 +683,7 @@
}
],
"source": [
- "df1_target_information[['target_name', 'customer_id']].groupby('target_name').count().sort_values(by='customer_id', ascending=False)"
+ "df1_target_information[['target_name', 'customer_id']].groupby('target_name').count().sort_values(by='customer_id', ascending=False).head()"
]
},
{
@@ -1006,218 +743,18 @@
" DDCP MD Procès du Siècle | \n",
" 1684 | \n",
" \n",
- " \n",
- " | DDCP Newsletter centres de loisirs | \n",
- " 1032 | \n",
- "
\n",
- " \n",
- " | DDCP Newsletter enseignants | \n",
- " 4510 | \n",
- "
\n",
- " \n",
- " | DDCP Newsletter jeune public | \n",
- " 3862 | \n",
- "
\n",
- " \n",
- " | DDCP Newsletter relais champ social | \n",
- " 2270 | \n",
- "
\n",
- " \n",
- " | DDCP PROMO Participants ateliers (adultes et enfants) | \n",
- " 1954 | \n",
- "
\n",
- " \n",
- " | DDCP billets famille | \n",
- " 3609 | \n",
- "
\n",
- " \n",
- " | DDCP promo MD pass musées dps oct 2018 | \n",
- " 1785 | \n",
- "
\n",
- " \n",
- " | DDCP promo Plan B 2019 (concerts) | \n",
- " 1948 | \n",
- "
\n",
- " \n",
- " | DDCP promo spectateurs prog 21-22 (spectacles, ciné, ateliers) | \n",
- " 1293 | \n",
- "
\n",
- " \n",
- " | DDCP rentrée culturelle 2023 | \n",
- " 1757 | \n",
- "
\n",
- " \n",
- " | DDCP_marseille_jazz_2023 | \n",
- " 1043 | \n",
- "
\n",
- " \n",
- " | DRE Festival Jean Rouch | \n",
- " 1502 | \n",
- "
\n",
- " \n",
- " | DRE MucemLab | \n",
- " 2302 | \n",
- "
\n",
- " \n",
- " | DRE chercheurs | \n",
- " 1557 | \n",
- "
\n",
- " \n",
- " | DRE institutionnels | \n",
- " 2229 | \n",
- "
\n",
- " \n",
- " | FORMATION _ acheteurs optin last year | \n",
- " 10485 | \n",
- "
\n",
- " \n",
- " | Inscrits NL générale (export_291019 + operation_videomaton) | \n",
- " 14086 | \n",
- "
\n",
- " \n",
- " | Inscrits NL générale site web | \n",
- " 3732 | \n",
- "
\n",
- " \n",
- " | Inscrits NL jeune public site web | \n",
- " 1249 | \n",
- "
\n",
- " \n",
- " | Votre première liste | \n",
- " 3715 | \n",
- "
\n",
- " \n",
- " | consentement optin b2b | \n",
- " 12735 | \n",
- "
\n",
- " \n",
- " | consentement optin b2c | \n",
- " 108909 | \n",
- "
\n",
- " \n",
- " | consentement optin dre | \n",
- " 4527 | \n",
- "
\n",
- " \n",
- " | consentement optin jeune public | \n",
- " 149979 | \n",
- "
\n",
- " \n",
- " | consentement optin mediation specialisee | \n",
- " 150000 | \n",
- "
\n",
- " \n",
- " | consentement optin newsletter generale | \n",
- " 22095 | \n",
- "
\n",
- " \n",
- " | consentement optin scolaires | \n",
- " 4849 | \n",
- "
\n",
- " \n",
- " | consentement optout b2b | \n",
- " 14219 | \n",
- "
\n",
- " \n",
- " | consentement optout b2c | \n",
- " 34523 | \n",
- "
\n",
- " \n",
- " | consentement optout dre | \n",
- " 14328 | \n",
- "
\n",
- " \n",
- " | consentement optout newsletter generale | \n",
- " 18855 | \n",
- "
\n",
- " \n",
- " | consentement optout scolaires | \n",
- " 15744 | \n",
- "
\n",
- " \n",
- " | ddcp_md_scene_ouverte_au_talent | \n",
- " 1577 | \n",
- "
\n",
- " \n",
- " | ddcp_promo_MD_billet_musée_oct_2019_agarder2 | \n",
- " 5482 | \n",
- "
\n",
- " \n",
- " | ddcp_promo_md_musée_dps 011019 | \n",
- " 6010 | \n",
- "
\n",
- " \n",
- " | ddcp_promo_visiteurs occasionnels_musee_8mois | \n",
- " 6640 | \n",
- "
\n",
- " \n",
- " | ddcp_visiteurs dps 010622 | \n",
- " 12355 | \n",
- "
\n",
- " \n",
- " | festival_jean_rouch | \n",
- " 1502 | \n",
- "
\n",
- " \n",
- " | rappel po barvalo | \n",
- " 1248 | \n",
- "
\n",
- " \n",
- " | structures_etiquette champ social | \n",
- " 1488 | \n",
- "
\n",
" \n",
"\n",
""
],
"text/plain": [
- " customer_id\n",
- "target_name \n",
- "Arenametrix_bascule tel vers sib 35216\n",
- "Autres_interet_exposition 1021\n",
- "COM Inscrits NL générale (historique) 23005\n",
- "Contacts_prenomsdoubles 11643\n",
- "DDCP MD Procès du Siècle 1684\n",
- "DDCP Newsletter centres de loisirs 1032\n",
- "DDCP Newsletter enseignants 4510\n",
- "DDCP Newsletter jeune public 3862\n",
- "DDCP Newsletter relais champ social 2270\n",
- "DDCP PROMO Participants ateliers (adultes et en... 1954\n",
- "DDCP billets famille 3609\n",
- "DDCP promo MD pass musées dps oct 2018 1785\n",
- "DDCP promo Plan B 2019 (concerts) 1948\n",
- "DDCP promo spectateurs prog 21-22 (spectacles, ... 1293\n",
- "DDCP rentrée culturelle 2023 1757\n",
- "DDCP_marseille_jazz_2023 1043\n",
- "DRE Festival Jean Rouch 1502\n",
- "DRE MucemLab 2302\n",
- "DRE chercheurs 1557\n",
- "DRE institutionnels 2229\n",
- "FORMATION _ acheteurs optin last year 10485\n",
- "Inscrits NL générale (export_291019 + operation... 14086\n",
- "Inscrits NL générale site web 3732\n",
- "Inscrits NL jeune public site web 1249\n",
- "Votre première liste 3715\n",
- "consentement optin b2b 12735\n",
- "consentement optin b2c 108909\n",
- "consentement optin dre 4527\n",
- "consentement optin jeune public 149979\n",
- "consentement optin mediation specialisee 150000\n",
- "consentement optin newsletter generale 22095\n",
- "consentement optin scolaires 4849\n",
- "consentement optout b2b 14219\n",
- "consentement optout b2c 34523\n",
- "consentement optout dre 14328\n",
- "consentement optout newsletter generale 18855\n",
- "consentement optout scolaires 15744\n",
- "ddcp_md_scene_ouverte_au_talent 1577\n",
- "ddcp_promo_MD_billet_musée_oct_2019_agarder2 5482\n",
- "ddcp_promo_md_musée_dps 011019 6010\n",
- "ddcp_promo_visiteurs occasionnels_musee_8mois 6640\n",
- "ddcp_visiteurs dps 010622 12355\n",
- "festival_jean_rouch 1502\n",
- "rappel po barvalo 1248\n",
- "structures_etiquette champ social 1488"
+ " customer_id\n",
+ "target_name \n",
+ "Arenametrix_bascule tel vers sib 35216\n",
+ "Autres_interet_exposition 1021\n",
+ "COM Inscrits NL générale (historique) 23005\n",
+ "Contacts_prenomsdoubles 11643\n",
+ "DDCP MD Procès du Siècle 1684"
]
},
"execution_count": 17,
@@ -1227,7 +764,7 @@
],
"source": [
"df1_target_information_reduced = df1_target_information[['target_name', 'customer_id']].groupby('target_name').count()\n",
- "df1_target_information_reduced[df1_target_information_reduced['customer_id'] >= 1000]"
+ "df1_target_information_reduced[df1_target_information_reduced['customer_id'] >= 1000].head()"
]
},
{
@@ -1273,19 +810,19 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2240/1967867975.py:15: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_3658/1967867975.py:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
- "/tmp/ipykernel_2240/1967867975.py:15: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_3658/1967867975.py:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" df[column_name] = pd.to_datetime(df[column_name], utc = True, format = 'ISO8601')\n",
- "/tmp/ipykernel_2240/1967867975.py:15: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_3658/1967867975.py:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
@@ -1393,131 +930,31 @@
" 404 | \n",
" 2021-03-27 23:00:00+00:00 | \n",
" \n",
- " \n",
- " | ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " | 6214803 | \n",
- " 8302994 | \n",
- " 266155 | \n",
- " 2023-10-23 09:43:25+00:00 | \n",
- " 2023-10-23 09:32:33+00:00 | \n",
- " 2023-10-23 09:32:34+00:00 | \n",
- " dre_nov_2023 | \n",
- " 1318 | \n",
- " 2023-10-23 09:31:17+00:00 | \n",
- "
\n",
- " \n",
- " | 6214804 | \n",
- " 8303307 | \n",
- " 21355 | \n",
- " 2023-10-23 09:44:02+00:00 | \n",
- " 2023-10-23 09:32:49+00:00 | \n",
- " 2023-10-23 09:32:49+00:00 | \n",
- " dre_nov_2023 | \n",
- " 1318 | \n",
- " 2023-10-23 09:31:17+00:00 | \n",
- "
\n",
- " \n",
- " | 6214805 | \n",
- " 8304346 | \n",
- " 21849 | \n",
- " 2023-10-23 09:45:52+00:00 | \n",
- " 2023-10-23 09:33:28+00:00 | \n",
- " 2023-10-23 09:33:29+00:00 | \n",
- " dre_nov_2023 | \n",
- " 1318 | \n",
- " 2023-10-23 09:31:17+00:00 | \n",
- "
\n",
- " \n",
- " | 6214806 | \n",
- " 8302037 | \n",
- " 667789 | \n",
- " 2023-10-23 09:47:32+00:00 | \n",
- " 2023-10-23 09:31:53+00:00 | \n",
- " 2023-10-23 09:31:54+00:00 | \n",
- " dre_nov_2023 | \n",
- " 1318 | \n",
- " 2023-10-23 09:31:17+00:00 | \n",
- "
\n",
- " \n",
- " | 6214807 | \n",
- " 8304939 | \n",
- " 294154 | \n",
- " NaT | \n",
- " 2023-10-23 09:33:54+00:00 | \n",
- " 2023-10-23 09:33:55+00:00 | \n",
- " dre_nov_2023 | \n",
- " 1318 | \n",
- " 2023-10-23 09:31:17+00:00 | \n",
- "
\n",
" \n",
"\n",
- "6214808 rows × 8 columns
\n",
""
],
"text/plain": [
- " id customer_id opened_at \\\n",
- "0 19793 112597 NaT \n",
- "1 14211 113666 NaT \n",
- "2 13150 280561 NaT \n",
- "3 7073 101007 2021-03-28 18:11:06+00:00 \n",
- "4 5175 103972 NaT \n",
- "... ... ... ... \n",
- "6214803 8302994 266155 2023-10-23 09:43:25+00:00 \n",
- "6214804 8303307 21355 2023-10-23 09:44:02+00:00 \n",
- "6214805 8304346 21849 2023-10-23 09:45:52+00:00 \n",
- "6214806 8302037 667789 2023-10-23 09:47:32+00:00 \n",
- "6214807 8304939 294154 NaT \n",
+ " id customer_id opened_at sent_at \\\n",
+ "0 19793 112597 NaT 2021-03-28 16:01:09+00:00 \n",
+ "1 14211 113666 NaT 2021-03-28 16:01:09+00:00 \n",
+ "2 13150 280561 NaT 2021-03-28 16:00:59+00:00 \n",
+ "3 7073 101007 2021-03-28 18:11:06+00:00 2021-03-28 16:00:59+00:00 \n",
+ "4 5175 103972 NaT 2021-03-28 16:01:06+00:00 \n",
"\n",
- " sent_at delivered_at \\\n",
- "0 2021-03-28 16:01:09+00:00 2021-03-28 16:24:18+00:00 \n",
- "1 2021-03-28 16:01:09+00:00 2021-03-28 16:21:02+00:00 \n",
- "2 2021-03-28 16:00:59+00:00 2021-03-28 16:08:45+00:00 \n",
- "3 2021-03-28 16:00:59+00:00 2021-03-28 16:09:47+00:00 \n",
- "4 2021-03-28 16:01:06+00:00 2021-03-28 16:05:03+00:00 \n",
- "... ... ... \n",
- "6214803 2023-10-23 09:32:33+00:00 2023-10-23 09:32:34+00:00 \n",
- "6214804 2023-10-23 09:32:49+00:00 2023-10-23 09:32:49+00:00 \n",
- "6214805 2023-10-23 09:33:28+00:00 2023-10-23 09:33:29+00:00 \n",
- "6214806 2023-10-23 09:31:53+00:00 2023-10-23 09:31:54+00:00 \n",
- "6214807 2023-10-23 09:33:54+00:00 2023-10-23 09:33:55+00:00 \n",
+ " delivered_at campaign_name \\\n",
+ "0 2021-03-28 16:24:18+00:00 Le Mucem chez vous, gardons le lien #22 \n",
+ "1 2021-03-28 16:21:02+00:00 Le Mucem chez vous, gardons le lien #22 \n",
+ "2 2021-03-28 16:08:45+00:00 Le Mucem chez vous, gardons le lien #22 \n",
+ "3 2021-03-28 16:09:47+00:00 Le Mucem chez vous, gardons le lien #22 \n",
+ "4 2021-03-28 16:05:03+00:00 Le Mucem chez vous, gardons le lien #22 \n",
"\n",
- " campaign_name campaign_service_id \\\n",
- "0 Le Mucem chez vous, gardons le lien #22 404 \n",
- "1 Le Mucem chez vous, gardons le lien #22 404 \n",
- "2 Le Mucem chez vous, gardons le lien #22 404 \n",
- "3 Le Mucem chez vous, gardons le lien #22 404 \n",
- "4 Le Mucem chez vous, gardons le lien #22 404 \n",
- "... ... ... \n",
- "6214803 dre_nov_2023 1318 \n",
- "6214804 dre_nov_2023 1318 \n",
- "6214805 dre_nov_2023 1318 \n",
- "6214806 dre_nov_2023 1318 \n",
- "6214807 dre_nov_2023 1318 \n",
- "\n",
- " campaign_sent_at \n",
- "0 2021-03-27 23:00:00+00:00 \n",
- "1 2021-03-27 23:00:00+00:00 \n",
- "2 2021-03-27 23:00:00+00:00 \n",
- "3 2021-03-27 23:00:00+00:00 \n",
- "4 2021-03-27 23:00:00+00:00 \n",
- "... ... \n",
- "6214803 2023-10-23 09:31:17+00:00 \n",
- "6214804 2023-10-23 09:31:17+00:00 \n",
- "6214805 2023-10-23 09:31:17+00:00 \n",
- "6214806 2023-10-23 09:31:17+00:00 \n",
- "6214807 2023-10-23 09:31:17+00:00 \n",
- "\n",
- "[6214808 rows x 8 columns]"
+ " campaign_service_id campaign_sent_at \n",
+ "0 404 2021-03-27 23:00:00+00:00 \n",
+ "1 404 2021-03-27 23:00:00+00:00 \n",
+ "2 404 2021-03-27 23:00:00+00:00 \n",
+ "3 404 2021-03-27 23:00:00+00:00 \n",
+ "4 404 2021-03-27 23:00:00+00:00 "
]
},
"execution_count": 20,
@@ -1526,7 +963,7 @@
}
],
"source": [
- "df1_campaigns_information"
+ "df1_campaigns_information.head()"
]
},
{
@@ -1573,7 +1010,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_2240/3700263836.py:11: SettingWithCopyWarning: \n",
+ "/tmp/ipykernel_3658/3700263836.py:11: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
@@ -1654,81 +1091,17 @@
" 0.0 | \n",
" NaT | \n",
" \n",
- " \n",
- " | ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- "
\n",
- " \n",
- " | 130467 | \n",
- " 1256097 | \n",
- " 1 | \n",
- " 1.0 | \n",
- " 0 days 02:11:15 | \n",
- "
\n",
- " \n",
- " | 130468 | \n",
- " 1256098 | \n",
- " 1 | \n",
- " 0.0 | \n",
- " NaT | \n",
- "
\n",
- " \n",
- " | 130469 | \n",
- " 1256099 | \n",
- " 1 | \n",
- " 0.0 | \n",
- " NaT | \n",
- "
\n",
- " \n",
- " | 130470 | \n",
- " 1256100 | \n",
- " 1 | \n",
- " 0.0 | \n",
- " NaT | \n",
- "
\n",
- " \n",
- " | 130471 | \n",
- " 1256101 | \n",
- " 1 | \n",
- " 0.0 | \n",
- " NaT | \n",
- "
\n",
" \n",
"\n",
- "130472 rows × 4 columns
\n",
""
],
"text/plain": [
- " customer_id nb_campaigns nb_campaigns_opened \\\n",
- "0 2 4 0.0 \n",
- "1 3 222 124.0 \n",
- "2 4 7 7.0 \n",
- "3 5 4 0.0 \n",
- "4 6 20 0.0 \n",
- "... ... ... ... \n",
- "130467 1256097 1 1.0 \n",
- "130468 1256098 1 0.0 \n",
- "130469 1256099 1 0.0 \n",
- "130470 1256100 1 0.0 \n",
- "130471 1256101 1 0.0 \n",
- "\n",
- " time_to_open \n",
- "0 NaT \n",
- "1 1 days 00:28:30.169354838 \n",
- "2 1 days 04:31:01.428571428 \n",
- "3 NaT \n",
- "4 NaT \n",
- "... ... \n",
- "130467 0 days 02:11:15 \n",
- "130468 NaT \n",
- "130469 NaT \n",
- "130470 NaT \n",
- "130471 NaT \n",
- "\n",
- "[130472 rows x 4 columns]"
+ " customer_id nb_campaigns nb_campaigns_opened time_to_open\n",
+ "0 2 4 0.0 NaT\n",
+ "1 3 222 124.0 1 days 00:28:30.169354838\n",
+ "2 4 7 7.0 1 days 04:31:01.428571428\n",
+ "3 5 4 0.0 NaT\n",
+ "4 6 20 0.0 NaT"
]
},
"execution_count": 23,
@@ -1737,7 +1110,7 @@
}
],
"source": [
- "df1_campaigns_kpi"
+ "df1_campaigns_kpi.head()"
]
},
{
@@ -2646,19 +2019,19 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 32,
"id": "46de1912-4a66-46e5-8b9e-7768b2d2723b",
"metadata": {},
"outputs": [],
"source": [
"# Fusion liée au product\n",
- "df1_product_purchased = pd.merge(df1_tickets_kpi, products_global, left_on = 'product_id', right_on = 'id_products', how = 'inner')\n",
+ "df1_products_purchased = pd.merge(df1_tickets_kpi, products_global, left_on = 'product_id', right_on = 'id_products', how = 'inner')\n",
"\n",
"# Fusion liée au customer\n",
"df1_customer = pd.merge(df1_customerplus_clean, df1_campaigns_kpi, on = 'customer_id', how = 'left')\n",
"\n",
"# Fusion product et customer\n",
- "df1_customer_product = pd.merge(df1_customer, df1_product_purchased, on = 'customer_id', how = 'left')"
+ "df1_customer_product = pd.merge(df1_customer, df1_products_purchased, on = 'customer_id', how = 'left')"
]
},
{