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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the hotel reviews from CSV (you can )\n",
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We want to find the most useful tags to keep\n",
    "# Remove opening and closing brackets\n",
    "df.Tags = df.Tags.str.strip(\"[']\")\n",
    "# remove all quotes too\n",
    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
    "# Now split the strings into a list\n",
    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove leading and trailing spaces\n",
    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the 6 columns into one with melt\n",
    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The shape of the tags with no filtering: (2514684, 2)\n",
      "                        index   count\n",
      "0                Leisure trip  338423\n",
      "1                      Couple  205305\n",
      "2               Solo traveler   89779\n",
      "3               Business trip   68176\n",
      "4                       Group   51593\n",
      "5  Family with young children   49318\n",
      "6  Family with older children   21509\n",
      "7      Travelers with friends    1610\n",
      "8                  With a pet    1078\n"
     ]
    }
   ],
   "source": [
    "# Get the value counts\n",
    "tag_vc = df_tags.value.value_counts()\n",
    "# print(tag_vc)\n",
    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
    "print(tag_vc[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。\n"
   ]
  }
 ]
}