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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 25,
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"id": "afd044b8-ac83-4a35-b959-700cae0b3b41",
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"metadata": {},
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"outputs": [
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@ -421,7 +421,7 @@
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}
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],
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"source": [
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"#creation de KPI aggreger par thématique\n",
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"#creation des base de KPI aggreger pour les 5 entreprises\n",
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"\n",
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"nb_compagnie=['10','11','12','13','14']\n",
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"for directory_path in nb_compagnie:\n",
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@ -454,181 +454,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 38,
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"id": "05b9a396-dcd7-4d3d-8b39-5ca48beba4b0",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>customer_id</th>\n",
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" <th>nb_campaigns</th>\n",
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" <th>nb_campaigns_opened</th>\n",
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" <th>time_to_open</th>\n",
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" <th>Number_compagnie</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>29</td>\n",
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" <td>4</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>37</td>\n",
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" <td>3</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>39</td>\n",
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" <td>4</td>\n",
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" <td>1.0</td>\n",
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" <td>0 days 05:16:38</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>41</td>\n",
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" <td>4</td>\n",
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" <td>1.0</td>\n",
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" <td>0 days 01:12:29</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>44</td>\n",
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" <td>4</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>10</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>254699</th>\n",
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" <td>6837769</td>\n",
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" <td>1</td>\n",
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" <td>1.0</td>\n",
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" <td>0 days 23:42:15</td>\n",
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" <td>14</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>254700</th>\n",
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" <td>6875038</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>14</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>254701</th>\n",
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" <td>6875066</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>14</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>254702</th>\n",
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" <td>6875099</td>\n",
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" <td>1</td>\n",
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" <td>0.0</td>\n",
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" <td>NaT</td>\n",
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" <td>14</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>254703</th>\n",
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" <td>6875143</td>\n",
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" <td>1</td>\n",
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" <td>1.0</td>\n",
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" <td>0 days 01:17:01</td>\n",
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" <td>14</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>688953 rows × 5 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" customer_id nb_campaigns nb_campaigns_opened time_to_open \\\n",
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"0 29 4 0.0 NaT \n",
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"1 37 3 0.0 NaT \n",
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"2 39 4 1.0 0 days 05:16:38 \n",
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"3 41 4 1.0 0 days 01:12:29 \n",
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"4 44 4 0.0 NaT \n",
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"... ... ... ... ... \n",
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"254699 6837769 1 1.0 0 days 23:42:15 \n",
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"254700 6875038 1 0.0 NaT \n",
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"254701 6875066 1 0.0 NaT \n",
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"254702 6875099 1 0.0 NaT \n",
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"254703 6875143 1 1.0 0 days 01:17:01 \n",
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"\n",
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" Number_compagnie \n",
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"0 10 \n",
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"1 10 \n",
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"2 10 \n",
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"3 10 \n",
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"4 10 \n",
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"... ... \n",
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"254699 14 \n",
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"254700 14 \n",
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"254701 14 \n",
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"254702 14 \n",
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"254703 14 \n",
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"\n",
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"[688953 rows x 5 columns]"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"#customerplus_clean_spectacle\n",
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"campaigns_information_spectacle\n",
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"#products_purchased_reduced_spectacle\n",
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"#target_information_spectacle"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f2db28cf-c162-4c59-a278-1a30060bdfc0",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"#customerplus_clean_spectacle.isna().sum()\n",
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"#campaigns_information_spectacle.isna().sum()\n",
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"#products_purchased_reduced_spectacle.isna().sum()\n",
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"#target_information_spectacle.isna().sum()"
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]
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}
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],
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"metadata": {
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