ML-For-Beginners/translations/hr/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",
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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**Odricanje od odgovornosti**:  \nOvaj dokument je preveden pomoću AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati autoritativnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane ljudskog prevoditelja. Ne preuzimamo odgovornost za bilo kakve nesporazume ili pogrešne interpretacije koje proizlaze iz korištenja ovog prijevoda.\n"
   ]
  }
 ]
}