Complétion brouillon
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@ -10,7 +10,7 @@
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 22,
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"id": "b1a5b9d3",
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"id": "b1a5b9d3",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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@ -20,7 +20,9 @@
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"import os\n",
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"import os\n",
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"import s3fs\n",
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"import s3fs\n",
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"import re\n",
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"import re\n",
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"import warnings"
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"import warnings\n",
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"import io\n",
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"import matplotlib.pyplot as plt\n"
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]
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]
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},
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},
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{
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{
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@ -97,6 +99,24 @@
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" df.to_csv(file_out, index = False)"
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" df.to_csv(file_out, index = False)"
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]
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "108fc5ef-c56a-4f03-a867-943d9d6492fd",
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"metadata": {},
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"outputs": [],
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"source": [
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"def save_file_s3(File_name, type_of_activity):\n",
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" image_buffer = io.BytesIO()\n",
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" plt.savefig(image_buffer, format='png')\n",
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" image_buffer.seek(0)\n",
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" FILE_PATH = f\"projet-bdc2324-team1/stat_desc/{type_of_activity}/\"\n",
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" FILE_PATH_OUT_S3 = FILE_PATH + File_name + type_of_activity + '.png'\n",
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" with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:\n",
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" s3_file.write(image_buffer.read())\n",
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" plt.close()"
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]
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},
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"id": "ccf597b0-b459-4ea5-baf0-5ba8c90915e4",
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"id": "ccf597b0-b459-4ea5-baf0-5ba8c90915e4",
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@ -599,7 +619,7 @@
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" 'sport': ['5', '6', '7', '8', '9'],\n",
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" 'sport': ['5', '6', '7', '8', '9'],\n",
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" 'musique' : ['10', '11', '12', '13', '14']}\n",
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" 'musique' : ['10', '11', '12', '13', '14']}\n",
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"\n",
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"\n",
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"nb_compagnie = companies['sport']\n",
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"nb_compagnie = companies['mu']\n",
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"\n",
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"\n",
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"def load_files(nb_compagnie):\n",
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"def load_files(nb_compagnie):\n",
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" targets = pd.DataFrame()\n",
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" targets = pd.DataFrame()\n",
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@ -632,13 +652,13 @@
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 27,
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"id": "3c911274-0ebd-49af-9487-26524ba20e74",
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"id": "3c911274-0ebd-49af-9487-26524ba20e74",
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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"\n",
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"\n",
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"def target_description(targets):\n",
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"def target_description(targets, type_of_activity):\n",
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"\n",
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"\n",
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" describe_target = targets.groupby('number_company').agg(\n",
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" describe_target = targets.groupby('number_company').agg(\n",
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" prop_target_jeune=('target_jeune', lambda x: (x.sum() / x.count())*100),\n",
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" prop_target_jeune=('target_jeune', lambda x: (x.sum() / x.count())*100),\n",
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@ -665,12 +685,13 @@
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" # Adding a legend\n",
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" # Adding a legend\n",
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" plot.legend([\"Youth\", \"School\", \"Enterprise\", \"Family\", \"Optin\", \"Optout\", \"Newsletter\", \"Subscriber\"], title=\"Target Category\")\n",
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" plot.legend([\"Youth\", \"School\", \"Enterprise\", \"Family\", \"Optin\", \"Optout\", \"Newsletter\", \"Subscriber\"], title=\"Target Category\")\n",
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"\n",
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"\n",
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" # save_file_s3(\"target_category_proportion_\", type_of_activity)\n",
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" return plot"
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" return plot"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 28,
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"id": "af62ecef-9120-4107-af3e-512588a96800",
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"id": "af62ecef-9120-4107-af3e-512588a96800",
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@ -680,7 +701,7 @@
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"<Axes: title={'center': 'Distribution of Targets by Category'}, xlabel='Company Number', ylabel='Target Proportion'>"
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"<Axes: title={'center': 'Distribution of Targets by Category'}, xlabel='Company Number', ylabel='Target Proportion'>"
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]
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]
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},
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},
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"execution_count": 22,
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"execution_count": 28,
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"metadata": {},
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"metadata": {},
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"output_type": "execute_result"
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"output_type": "execute_result"
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},
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},
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@ -696,7 +717,7 @@
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}
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}
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],
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],
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"source": [
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"source": [
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"target_description(targets)"
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"target_description(targets, 'sport')"
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]
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]
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},
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},
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{
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{
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