16. URL Parsing Challenge.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Records:\t 77677\n"
     ]
    }
   ],
   "source": [
    "with open('./data/url_list.txt') as f:\n",
    "    lines = f.readlines()\n",
    "    \n",
    "print('Total Records:\\t', len(lines))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def parse_search(lines):\n",
    "    \"\"\" function to parse user's search history \"\"\"\n",
    "    length = len(lines)\n",
    "    names = ['checkin', 'checkout', 'customMinimumPriceFilter', \n",
    "             'customMaximumPriceFilter', 'freeCancellation', 'stars_5', \n",
    "             'stars_4', 'stars_3', 'stars_2', 'stars_1', 'max_score', \n",
    "             'min_score', 'couponCode', 'adults', 'city', 'children', \n",
    "             'amenities', 'search_page']\n",
    "    \n",
    "    maps = {}\n",
    "    for name in names:\n",
    "        maps[name] = [np.nan] * length\n",
    "      \n",
    "    for i in range(length):\n",
    "        line = lines[i]\n",
    "        items = line[50:].strip().split('&')\n",
    "        visited = set()\n",
    "        for item in items:\n",
    "            key, value = item.strip().split('=')\n",
    "            key = key.strip().split('.')[1]\n",
    "            if key == 'city':\n",
    "                value = value.strip().replace('+', ' ')\n",
    "            if key not in visited:\n",
    "                maps[key][i] = value\n",
    "                visited.add(key)\n",
    "            else:\n",
    "                maps[key][i] = maps[key][i] + ', ' + value\n",
    "            \n",
    "    # transform into DataFrame\n",
    "    df = pd.DataFrame(maps, columns=names)\n",
    "    df['checkin'] = pd.to_datetime(df['checkin'])\n",
    "    df['checkout'] = pd.to_datetime(df['checkout'])\n",
    "    df = df.rename(columns={'customMinimumPriceFilter': 'MinPrice', \n",
    "                            'customMaximumPriceFilter': 'MaxPrice'})\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkin</th>\n",
       "      <th>checkout</th>\n",
       "      <th>MinPrice</th>\n",
       "      <th>MaxPrice</th>\n",
       "      <th>freeCancellation</th>\n",
       "      <th>stars_5</th>\n",
       "      <th>stars_4</th>\n",
       "      <th>stars_3</th>\n",
       "      <th>stars_2</th>\n",
       "      <th>stars_1</th>\n",
       "      <th>max_score</th>\n",
       "      <th>min_score</th>\n",
       "      <th>couponCode</th>\n",
       "      <th>adults</th>\n",
       "      <th>city</th>\n",
       "      <th>children</th>\n",
       "      <th>amenities</th>\n",
       "      <th>search_page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-09-19</td>\n",
       "      <td>2015-09-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>New York, NY, United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-14</td>\n",
       "      <td>2015-09-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>London, United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-26</td>\n",
       "      <td>2015-09-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>New York, NY, United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>2015-09-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Hong Kong, Hong Kong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-09-20</td>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>London, United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     checkin   checkout MinPrice MaxPrice freeCancellation stars_5 stars_4  \\\n",
       "0 2015-09-19 2015-09-20      NaN      NaN              NaN     NaN     yes   \n",
       "1 2015-09-14 2015-09-15      NaN      NaN              NaN     NaN     NaN   \n",
       "2 2015-09-26 2015-09-27      NaN      175              NaN     NaN     yes   \n",
       "3 2015-09-02 2015-09-03      NaN      NaN              NaN     yes     yes   \n",
       "4 2015-09-20 2015-09-29      NaN      275              NaN     NaN     NaN   \n",
       "\n",
       "  stars_3 stars_2 stars_1 max_score min_score couponCode adults  \\\n",
       "0     NaN     NaN     NaN       NaN         4        NaN      3   \n",
       "1     yes     NaN     NaN       NaN         4        NaN      3   \n",
       "2     NaN     NaN     NaN       NaN         5        NaN      2   \n",
       "3     NaN     NaN     NaN       NaN         4        NaN      1   \n",
       "4     NaN     NaN     NaN       NaN         5        NaN      3   \n",
       "\n",
       "                          city children amenities search_page  \n",
       "0  New York, NY, United States      NaN       NaN           1  \n",
       "1       London, United Kingdom      NaN       NaN           1  \n",
       "2  New York, NY, United States      NaN       NaN           1  \n",
       "3         Hong Kong, Hong Kong      NaN       NaN           1  \n",
       "4       London, United Kingdom      NaN       NaN           1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parse all the data\n",
    "data = parse_search(lines)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 77677 entries, 0 to 77676\n",
      "Data columns (total 18 columns):\n",
      "checkin             77677 non-null datetime64[ns]\n",
      "checkout            77677 non-null datetime64[ns]\n",
      "MinPrice            1270 non-null object\n",
      "MaxPrice            49288 non-null object\n",
      "freeCancellation    17949 non-null object\n",
      "stars_5             4303 non-null object\n",
      "stars_4             12704 non-null object\n",
      "stars_3             14862 non-null object\n",
      "stars_2             9931 non-null object\n",
      "stars_1             9323 non-null object\n",
      "max_score           5517 non-null object\n",
      "min_score           60826 non-null object\n",
      "couponCode          2221 non-null object\n",
      "adults              77677 non-null object\n",
      "city                77677 non-null object\n",
      "children            260 non-null object\n",
      "amenities           704 non-null object\n",
      "search_page         77677 non-null object\n",
      "dtypes: datetime64[ns](2), object(16)\n",
      "memory usage: 10.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# check basic information\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>missing</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MinPrice</td>\n",
       "      <td>76407</td>\n",
       "      <td>0.983650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MaxPrice</td>\n",
       "      <td>28389</td>\n",
       "      <td>0.365475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>freeCancellation</td>\n",
       "      <td>59728</td>\n",
       "      <td>0.768928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>stars_5</td>\n",
       "      <td>73374</td>\n",
       "      <td>0.944604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>stars_4</td>\n",
       "      <td>64973</td>\n",
       "      <td>0.836451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>stars_3</td>\n",
       "      <td>62815</td>\n",
       "      <td>0.808669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>stars_2</td>\n",
       "      <td>67746</td>\n",
       "      <td>0.872150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>stars_1</td>\n",
       "      <td>68354</td>\n",
       "      <td>0.879977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>max_score</td>\n",
       "      <td>72160</td>\n",
       "      <td>0.928975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>min_score</td>\n",
       "      <td>16851</td>\n",
       "      <td>0.216937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>couponCode</td>\n",
       "      <td>75456</td>\n",
       "      <td>0.971407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>children</td>\n",
       "      <td>77417</td>\n",
       "      <td>0.996653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>amenities</td>\n",
       "      <td>76973</td>\n",
       "      <td>0.990937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name  missing     ratio\n",
       "2           MinPrice    76407  0.983650\n",
       "3           MaxPrice    28389  0.365475\n",
       "4   freeCancellation    59728  0.768928\n",
       "5            stars_5    73374  0.944604\n",
       "6            stars_4    64973  0.836451\n",
       "7            stars_3    62815  0.808669\n",
       "8            stars_2    67746  0.872150\n",
       "9            stars_1    68354  0.879977\n",
       "10         max_score    72160  0.928975\n",
       "11         min_score    16851  0.216937\n",
       "12        couponCode    75456  0.971407\n",
       "15          children    77417  0.996653\n",
       "16         amenities    76973  0.990937"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing values\n",
    "missing = data.isnull().sum().reset_index()\n",
    "missing = missing.rename(columns={'index': 'name', 0: 'missing'})\n",
    "missing['ratio'] = missing['missing'] / len(data)\n",
    "missing[missing['missing'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "Create a clean data set where each column is a field in the URL, each row is a given search and the cells are the corresponding URL values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checkin</th>\n",
       "      <th>checkout</th>\n",
       "      <th>MinPrice</th>\n",
       "      <th>MaxPrice</th>\n",
       "      <th>freeCancellation</th>\n",
       "      <th>stars_5</th>\n",
       "      <th>stars_4</th>\n",
       "      <th>stars_3</th>\n",
       "      <th>stars_2</th>\n",
       "      <th>stars_1</th>\n",
       "      <th>max_score</th>\n",
       "      <th>min_score</th>\n",
       "      <th>couponCode</th>\n",
       "      <th>adults</th>\n",
       "      <th>city</th>\n",
       "      <th>children</th>\n",
       "      <th>amenities</th>\n",
       "      <th>search_page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-09-19</td>\n",
       "      <td>2015-09-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>New York, NY, United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-09-14</td>\n",
       "      <td>2015-09-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>London, United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-26</td>\n",
       "      <td>2015-09-27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>New York, NY, United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-09-02</td>\n",
       "      <td>2015-09-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Hong Kong, Hong Kong</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-09-20</td>\n",
       "      <td>2015-09-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>London, United Kingdom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     checkin   checkout MinPrice MaxPrice freeCancellation stars_5 stars_4  \\\n",
       "0 2015-09-19 2015-09-20      NaN      NaN              NaN     NaN     yes   \n",
       "1 2015-09-14 2015-09-15      NaN      NaN              NaN     NaN     NaN   \n",
       "2 2015-09-26 2015-09-27      NaN      175              NaN     NaN     yes   \n",
       "3 2015-09-02 2015-09-03      NaN      NaN              NaN     yes     yes   \n",
       "4 2015-09-20 2015-09-29      NaN      275              NaN     NaN     NaN   \n",
       "\n",
       "  stars_3 stars_2 stars_1 max_score min_score couponCode adults  \\\n",
       "0     NaN     NaN     NaN       NaN         4        NaN      3   \n",
       "1     yes     NaN     NaN       NaN         4        NaN      3   \n",
       "2     NaN     NaN     NaN       NaN         5        NaN      2   \n",
       "3     NaN     NaN     NaN       NaN         4        NaN      1   \n",
       "4     NaN     NaN     NaN       NaN         5        NaN      3   \n",
       "\n",
       "                          city children amenities search_page  \n",
       "0  New York, NY, United States      NaN       NaN           1  \n",
       "1       London, United Kingdom      NaN       NaN           1  \n",
       "2  New York, NY, United States      NaN       NaN           1  \n",
       "3         Hong Kong, Hong Kong      NaN       NaN           1  \n",
       "4       London, United Kingdom      NaN       NaN           1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "For each search query, how many amenities were selected?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "internet                272\n",
       "yes_smoking             170\n",
       "shuttle                 111\n",
       "yes_pet                  85\n",
       "breakfast                39\n",
       "lounge                   22\n",
       "yes_smoking, yes_pet      4\n",
       "breakfast, yes_pet        1\n",
       "Name: amenities, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts\n",
    "data['amenities'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    76973\n",
       "1      699\n",
       "2        5\n",
       "Name: amenities_count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check number of \n",
    "data['amenities_count'] = data['amenities'].apply(lambda x: 0 if pd.isnull(x) \n",
    "                                                  else len(x.split(', ')))\n",
    "data['amenities_count'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "Often, to measure the quality of a search algorithm, data scientists use some metric based on how often users click on the second page, third page, and so on. The idea here is that a great search algorithm should return all interesting results on the first page and never force users to visit the other pages (how often do you click on the second page results when you search on Google? Almost never, right?).\n",
    "* Create a metric based on the above idea and find the city with the worst search algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['page'] = data['search_page'].apply(lambda x: 'First Page' if x == '1' else 'Other Pages')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "sns.countplot(x='city', hue='page', data=data, ax=ax)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>page</th>\n",
       "      <th>First Page</th>\n",
       "      <th>Other Pages</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hong Kong, Hong Kong</th>\n",
       "      <td>10735</td>\n",
       "      <td>1051</td>\n",
       "      <td>0.089174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London, United Kingdom</th>\n",
       "      <td>14775</td>\n",
       "      <td>13283</td>\n",
       "      <td>0.473412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York, NY, United States</th>\n",
       "      <td>16385</td>\n",
       "      <td>12999</td>\n",
       "      <td>0.442384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco, California, United States</th>\n",
       "      <td>8105</td>\n",
       "      <td>344</td>\n",
       "      <td>0.040715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "page                                      First Page  Other Pages     ratio\n",
       "city                                                                       \n",
       "Hong Kong, Hong Kong                           10735         1051  0.089174\n",
       "London, United Kingdom                         14775        13283  0.473412\n",
       "New York, NY, United States                    16385        12999  0.442384\n",
       "San Francisco, California, United States        8105          344  0.040715"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = data.groupby(['city', 'page'])['checkin'].count().unstack()\n",
    "grouped['ratio'] = grouped['Other Pages'] / (grouped['First Page'] + grouped['Other Pages'])\n",
    "\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "sns.barplot(x=grouped.index, y='ratio', data=grouped, ax=ax)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we use the ratio of `other pages` versus `total visits`, we can see that `London` and `New York` have pretty high ratio. It's clear that `London` and `New York` have pretty bad search algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}