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

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   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
   "language_code": "he"
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 "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\n**כתב ויתור**:  \nמסמך זה תורגם באמצעות שירות תרגום מבוסס בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). למרות שאנו שואפים לדיוק, יש לקחת בחשבון שתרגומים אוטומטיים עשויים להכיל שגיאות או אי-דיוקים. המסמך המקורי בשפתו המקורית נחשב למקור הסמכותי. למידע קריטי, מומלץ להשתמש בתרגום מקצועי על ידי בני אדם. איננו נושאים באחריות לכל אי-הבנה או פרשנות שגויה הנובעת משימוש בתרגום זה.  \n"
   ]
  }
 ]
}