ML-For-Beginners/translations/pcm/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "orig_nbformat": 4,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.0 64-bit ('3.7')"
  },
  "interpreter": {
   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
  },
  "coopTranslator": {
   "original_hash": "033cb89c85500224b3c63fd04f49b4aa",
   "translation_date": "2025-11-18T19:12:58+00:00",
   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
   "language_code": "pcm"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_address(row):\n",
    "    if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
    "        return \"Amsterdam, Netherlands\"\n",
    "    elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
    "        return \"Barcelona, Spain\"\n",
    "    elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
    "        return \"London, United Kingdom\"\n",
    "    elif \"Milan\" in row[\"Hotel_Address\"]:        \n",
    "        return \"Milan, Italy\"\n",
    "    elif \"France\" in row[\"Hotel_Address\"]:\n",
    "        return \"Paris, France\"\n",
    "    elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
    "        return \"Vienna, Austria\" \n",
    "    else:\n",
    "        return row.Hotel_Address\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the hotel reviews from CSV\n",
    "start = time.time()\n",
    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping columns we will not use:\n",
    "df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace all the addresses with a shortened, more useful form\n",
    "df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop `Additional_Number_of_Scoring`\n",
    "df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
    "# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
    "df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
    "df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Process the Tags into new columns\n",
    "# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
    "# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
    "# Family with young children, Family with older children, With a pet\n",
    "df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
    "df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
    "df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
    "df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
    "df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
    "df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
    "df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
    "df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No longer need any of these columns\n",
    "df.drop([\"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Saving results to Hotel_Reviews_Filtered.csv\n",
      "Filtering took 23.74 seconds\n"
     ]
    }
   ],
   "source": [
    "# Saving new data file with calculated columns\n",
    "print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
    "df.to_csv(r'../../data/Hotel_Reviews_Filtered.csv', index = False)\n",
    "end = time.time()\n",
    "print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Disclaimer**:  \nDis dokyument don use AI transle-shon service [Co-op Translator](https://github.com/Azure/co-op-translator) do di transle-shon. Even as we dey try make sure say e correct, abeg make you sabi say transle-shon wey machine do fit get mistake or no dey accurate well. Di original dokyument for di language wey dem write am first na di one wey you go take as di correct one. For any important mata, e good make you use professional human transle-shon. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because you use dis transle-shon.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
   ]
  }
 ]
}