Nettoyage
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@ -27,32 +27,6 @@
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"import matplotlib.pyplot as plt"
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"import matplotlib.pyplot as plt"
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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": 2,
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"id": "59ce5096-4e2c-45c1-be78-43e14db4142c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# # modification des variables categorielles\n",
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" \n",
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"# ### variable gender\n",
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"# df1_customer_product[\"gender_label\"] = df1_customer_product[\"gender\"].map({\n",
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"# 0: 'female',\n",
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"# 1: 'male',\n",
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"# 2: 'other'\n",
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"# })\n",
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" \n",
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"# ### variable country -> on indique si le pays est france\n",
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"# df1_customer_product[\"country_fr\"] = df1_customer_product[\"country\"].apply(lambda x : int(x==\"fr\") if pd.notna(x) else np.nan)\n",
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"\n",
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"# # Création des indicatrices de gender\n",
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"# gender_dummies = pd.get_dummies(df1_customer_product[\"gender_label\"], prefix='gender').astype(int)\n",
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" \n",
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"# # Concaténation des indicatrices avec le dataframe d'origine\n",
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"# df1_customer_product = pd.concat([df1_customer_product, gender_dummies], axis=1)"
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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": 3,
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"execution_count": 3,
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