From 383057bf6c1f8d55facb5f01ad829f4a6ceee480 Mon Sep 17 00:00:00 2001 From: rigel-B Date: Tue, 7 Sep 2021 11:02:42 +0200 Subject: [PATCH] translated columns --- .../Connect Python_MySQL-D-checkpoint.ipynb | 65 +- Connect Python_MySQL-D.ipynb | 65 +- translated_product_df.csv | 32329 ++++++++++++++++ 3 files changed, 32359 insertions(+), 100 deletions(-) create mode 100644 translated_product_df.csv diff --git a/.ipynb_checkpoints/Connect Python_MySQL-D-checkpoint.ipynb b/.ipynb_checkpoints/Connect Python_MySQL-D-checkpoint.ipynb index 3fee21a..3413415 100644 --- a/.ipynb_checkpoints/Connect Python_MySQL-D-checkpoint.ipynb +++ b/.ipynb_checkpoints/Connect Python_MySQL-D-checkpoint.ipynb @@ -101177,6 +101177,7 @@ } ], "source": [ + "#adding another query for translating columns\n", "query9 = \"SELECT * FROM olist.product_category_name_translation\"\n", "product_category_translation_df = pd.read_sql_query(query9, db_connection)\n", "product_category_translation_df.head()" @@ -101592,7 +101593,7 @@ { "cell_type": "code", "execution_count": null, - "id": "51130353", + "id": "69d2214c", "metadata": {}, "outputs": [], "source": [ @@ -101608,14 +101609,14 @@ { "cell_type": "code", "execution_count": null, - "id": "70624fa0", + "id": "f8232b38", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "f0b5527b", + "id": "899bb2c6", "metadata": {}, "source": [ "----" @@ -101656,7 +101657,7 @@ { "cell_type": "code", "execution_count": 73, - "id": "44b5ef0e", + "id": "7999c226", "metadata": {}, "outputs": [], "source": [ @@ -101681,7 +101682,7 @@ { "cell_type": "code", "execution_count": 55, - "id": "5840033c", + "id": "e5c67064", "metadata": {}, "outputs": [], "source": [ @@ -101736,34 +101737,6 @@ "clean_df[\"product_weight_g\"].isnull().values.sum()" ] }, - { - "cell_type": "code", - "execution_count": 74, - "id": "f0e8afbd", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"['product_name_length' 'product_description_length' 'product_photos_qty'] not found in axis\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdrop_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproducts_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m\u001b[0m in \u001b[0;36mdrop_columns\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdrop_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mproducts_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'product_name_length'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'product_description_length'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'product_photos_qty'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\DataEnv\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 309\u001b[0m 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\u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6017\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6018\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6019\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6020\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: \"['product_name_length' 'product_description_length' 'product_photos_qty'] not found in axis\"" - ] - } - ], - "source": [ - "df1 = drop_columns(products_df)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -101776,7 +101749,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 80, "id": "cda6924f", "metadata": {}, "outputs": [ @@ -101875,26 +101848,18 @@ "4 13.0 15.0 perfumery\\r " ] }, - "execution_count": 75, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "translated_product_df = pd.merge(products_df, product_category_translation_df, on ='product_category_name')\n", - "translated_product_df.rename(columns={'product_category_name_english': 'product_category'}, inplace=True)\n", - "translated_product_df.drop('product_category_name', axis=1, inplace=True) #along the columns\n", - "translated_product_df.head()" + "trans_product_df = pd.merge(products_df, product_category_translation_df, on ='product_category_name')\n", + "trans_product_df.rename(columns={'product_category_name_english': 'product_category'}, inplace=True)\n", + "trans_product_df.drop('product_category_name', axis=1, inplace=True) #along the columns\n", + "trans_product_df.head()" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "861c5570", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "c55bb1ab", @@ -101907,12 +101872,12 @@ }, { "cell_type": "code", - "execution_count": 45, - "id": "4dc5090e", + "execution_count": 76, + "id": "d73b9e17", "metadata": {}, "outputs": [], "source": [ - "products_df.to_csv('products_df.csv')" + "translated_product_df.to_csv('translated_product_df.csv')" ] }, { diff --git a/Connect Python_MySQL-D.ipynb b/Connect Python_MySQL-D.ipynb index 3fee21a..3413415 100644 --- a/Connect Python_MySQL-D.ipynb +++ b/Connect Python_MySQL-D.ipynb @@ -101177,6 +101177,7 @@ } ], "source": [ + "#adding another query for translating columns\n", "query9 = \"SELECT * FROM olist.product_category_name_translation\"\n", "product_category_translation_df = pd.read_sql_query(query9, db_connection)\n", "product_category_translation_df.head()" @@ -101592,7 +101593,7 @@ { "cell_type": "code", "execution_count": null, - "id": "51130353", + "id": "69d2214c", "metadata": {}, "outputs": [], "source": [ @@ -101608,14 +101609,14 @@ { "cell_type": "code", "execution_count": null, - "id": "70624fa0", + "id": "f8232b38", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "f0b5527b", + "id": "899bb2c6", "metadata": {}, "source": [ "----" @@ -101656,7 +101657,7 @@ { "cell_type": "code", "execution_count": 73, - "id": "44b5ef0e", + "id": "7999c226", "metadata": {}, "outputs": [], "source": [ @@ -101681,7 +101682,7 @@ { "cell_type": "code", "execution_count": 55, - "id": "5840033c", + "id": "e5c67064", "metadata": {}, "outputs": [], "source": [ @@ -101736,34 +101737,6 @@ "clean_df[\"product_weight_g\"].isnull().values.sum()" ] }, - { - "cell_type": "code", - "execution_count": 74, - "id": "f0e8afbd", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "\"['product_name_length' 'product_description_length' 'product_photos_qty'] not found in axis\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdrop_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproducts_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m\u001b[0m in \u001b[0;36mdrop_columns\u001b[1;34m(df)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdrop_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mproducts_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'product_name_length'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'product_description_length'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'product_photos_qty'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\DataEnv\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 309\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 310\u001b[0m )\n\u001b[1;32m--> 311\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 313\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\DataEnv\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m 4899\u001b[0m \u001b[0mweight\u001b[0m \u001b[1;36m1.0\u001b[0m \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4900\u001b[0m \"\"\"\n\u001b[1;32m-> 4901\u001b[1;33m return super().drop(\n\u001b[0m\u001b[0;32m 4902\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4903\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\DataEnv\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m 4145\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4146\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4147\u001b[1;33m \u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4149\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\DataEnv\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[1;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[0;32m 4180\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4181\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4182\u001b[1;33m \u001b[0mnew_axis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4183\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4184\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Local\\conda\\conda\\envs\\DataEnv\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mdrop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m 6016\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6017\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6018\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{labels[mask]} not found in axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6019\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6020\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: \"['product_name_length' 'product_description_length' 'product_photos_qty'] not found in axis\"" - ] - } - ], - "source": [ - "df1 = drop_columns(products_df)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -101776,7 +101749,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 80, "id": "cda6924f", "metadata": {}, "outputs": [ @@ -101875,26 +101848,18 @@ "4 13.0 15.0 perfumery\\r " ] }, - "execution_count": 75, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "translated_product_df = pd.merge(products_df, product_category_translation_df, on ='product_category_name')\n", - "translated_product_df.rename(columns={'product_category_name_english': 'product_category'}, inplace=True)\n", - "translated_product_df.drop('product_category_name', axis=1, inplace=True) #along the columns\n", - "translated_product_df.head()" + "trans_product_df = pd.merge(products_df, product_category_translation_df, on ='product_category_name')\n", + "trans_product_df.rename(columns={'product_category_name_english': 'product_category'}, inplace=True)\n", + "trans_product_df.drop('product_category_name', axis=1, inplace=True) #along the columns\n", + "trans_product_df.head()" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "861c5570", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "c55bb1ab", @@ -101907,12 +101872,12 @@ }, { "cell_type": "code", - "execution_count": 45, - "id": "4dc5090e", + "execution_count": 76, + "id": "d73b9e17", "metadata": {}, "outputs": [], "source": [ - "products_df.to_csv('products_df.csv')" + "translated_product_df.to_csv('translated_product_df.csv')" ] }, { diff --git a/translated_product_df.csv b/translated_product_df.csv new file mode 100644 index 0000000..499b050 --- /dev/null +++ b/translated_product_df.csv @@ -0,0 +1,32329 @@ +,product_id,product_weight_g,product_length_cm,product_height_cm,product_width_cm,product_category +0,1e9e8ef04dbcff4541ed26657ea517e5,225.0,16.0,10.0,14.0,"perfumery " +1,6a2fb4dd53d2cdb88e0432f1284a004c,400.0,27.0,5.0,20.0,"perfumery " +2,0d009643171aee696f4733340bc2fdd0,422.0,21.0,16.0,18.0,"perfumery " +3,b1eae565a61935e0011ee7682fef9dc9,267.0,17.0,13.0,17.0,"perfumery " +4,8da90b37f0fb171b4877c124f965b1f6,377.0,18.0,13.0,15.0,"perfumery " 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