ML-For-Beginners/translations/da/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**Ansvarsfraskrivelse**:  \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os intet ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.\n"
   ]
  }
 ]
}