3 - Prices in neighborhoods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project 1 - Analysing Seattle and Boston Airbnb datasets\n",
    "#### In this project we will try to answer this 4 folowing questions:\n",
    "1) When is there more (or less) availability in Boston and Seattle? Is there a difference between the two cities?<br>\n",
    "2) When is it cheaper to stay in Boston or Seattle? Is there a difference between the two cities?<br>\n",
    "3) Do neighborhoods influence prices? And the score rating?<br>\n",
    "4) What features do the top rated (score rating +90) properties have in common? What is different from the other properties?\n",
    "\n",
    "#### In this notebook, we will answer 3) questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import data\n",
    "df_boston_listings = pd.read_csv('./airbnb-boston/listings.csv')\n",
    "df_seattle_listings = pd.read_csv('./airbnb-seattle/listings.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyzing Boston data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12147973</td>\n",
       "      <td>https://www.airbnb.com/rooms/12147973</td>\n",
       "      <td>20160906204935</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>Sunny Bungalow in the City</td>\n",
       "      <td>Cozy, sunny, family home.  Master bedroom high...</td>\n",
       "      <td>The house has an open and cozy feel at the sam...</td>\n",
       "      <td>Cozy, sunny, family home.  Master bedroom high...</td>\n",
       "      <td>none</td>\n",
       "      <td>Roslindale is quiet, convenient and friendly. ...</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                            listing_url       scrape_id  \\\n",
       "0  12147973  https://www.airbnb.com/rooms/12147973  20160906204935   \n",
       "\n",
       "  last_scraped                        name  \\\n",
       "0   2016-09-07  Sunny Bungalow in the City   \n",
       "\n",
       "                                             summary  \\\n",
       "0  Cozy, sunny, family home.  Master bedroom high...   \n",
       "\n",
       "                                               space  \\\n",
       "0  The house has an open and cozy feel at the sam...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Cozy, sunny, family home.  Master bedroom high...                none   \n",
       "\n",
       "                               neighborhood_overview  ... review_scores_value  \\\n",
       "0  Roslindale is quiet, convenient and friendly. ...  ...                 NaN   \n",
       "\n",
       "  requires_license license jurisdiction_names instant_bookable  \\\n",
       "0                f     NaN                NaN                f   \n",
       "\n",
       "  cancellation_policy require_guest_profile_picture  \\\n",
       "0            moderate                             f   \n",
       "\n",
       "  require_guest_phone_verification calculated_host_listings_count  \\\n",
       "0                                f                              1   \n",
       "\n",
       "   reviews_per_month  \n",
       "0                NaN  \n",
       "\n",
       "[1 rows x 95 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking into data\n",
    "df_boston_listings.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.585000e+03</td>\n",
       "      <td>3585</td>\n",
       "      <td>2772.000000</td>\n",
       "      <td>3585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Jamaica Plain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.440875e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.916667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.500787e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.531686</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.353000e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.679319e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.577620e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.278953e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.250000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.493346e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id neighbourhood_cleansed  review_scores_rating    price\n",
       "count   3.585000e+03                   3585           2772.000000     3585\n",
       "unique           NaN                     25                   NaN      324\n",
       "top              NaN          Jamaica Plain                   NaN  $150.00\n",
       "freq             NaN                    343                   NaN      144\n",
       "mean    8.440875e+06                    NaN             91.916667      NaN\n",
       "std     4.500787e+06                    NaN              9.531686      NaN\n",
       "min     3.353000e+03                    NaN             20.000000      NaN\n",
       "25%     4.679319e+06                    NaN             89.000000      NaN\n",
       "50%     8.577620e+06                    NaN             94.000000      NaN\n",
       "75%     1.278953e+07                    NaN             98.250000      NaN\n",
       "max     1.493346e+07                    NaN            100.000000      NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_boston_listings[['id','neighbourhood_cleansed','review_scores_rating','price']].describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter dataframe keeping the columns that are interesting \n",
    "df_boston_values = df_boston_listings[['review_scores_rating','neighbourhood_cleansed','price']]\n",
    "df_boston_values = df_boston_values.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_scores_rating      float64\n",
       "neighbourhood_cleansed     object\n",
       "price                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert price in float\n",
    "df_boston_values['price'] = df_boston_values['price'].str.replace(',','').str.replace('$','').astype('float')\n",
    "df_boston_values.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Allston</th>\n",
       "      <td>112.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Back Bay</th>\n",
       "      <td>240.950331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bay Village</th>\n",
       "      <td>266.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beacon Hill</th>\n",
       "      <td>224.443299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brighton</th>\n",
       "      <td>118.767568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             price\n",
       "neighbourhood_cleansed            \n",
       "Allston                 112.307692\n",
       "Back Bay                240.950331\n",
       "Bay Village             266.833333\n",
       "Beacon Hill             224.443299\n",
       "Brighton                118.767568"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the average price of properties by neighbourhood\n",
    "df_neighbour_boston = df_boston_values[['neighbourhood_cleansed','price']].groupby('neighbourhood_cleansed').mean()\n",
    "df_neighbour_boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column with the average price of dataset for comparison\n",
    "df_neighbour_boston['mean'] = df_boston_values['price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_neighbour_boston=df_neighbour_boston.sort_values(by='price', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2520x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = df_neighbour_boston[['price']].plot(figsize=(35,10), kind='bar')\n",
    "plt.xticks(rotation=0)\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(ax.get_xticks(), df_neighbour_boston[['mean']], color='orange')\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></th>\n",
       "      <th>price</th>\n",
       "      <th>mean</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South Boston Waterfront</th>\n",
       "      <td>306.060241</td>\n",
       "      <td>173.925802</td>\n",
       "      <td>0.759717</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              price        mean   percent\n",
       "neighbourhood_cleansed                                   \n",
       "South Boston Waterfront  306.060241  173.925802  0.759717"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a column with the percentage difference between the average price by neighbourhood and the average price of dataset\n",
    "df_neighbour_boston['percent'] = (df_neighbour_boston['price']/df_neighbour_boston['mean'])-1\n",
    "# look at the highest positive difference\n",
    "df_neighbour_boston.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>price</th>\n",
       "      <th>mean</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mattapan</th>\n",
       "      <td>75.125</td>\n",
       "      <td>173.925802</td>\n",
       "      <td>-0.568063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         price        mean   percent\n",
       "neighbourhood_cleansed                              \n",
       "Mattapan                75.125  173.925802 -0.568063"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the highest negative difference\n",
    "df_neighbour_boston.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that the average prices vary according to the neighborhood\n",
    "- We have 13 neighborhoods with an average price above the Boston average and 12 neighborhoods with an average price below the Boston average\n",
    "- The neighborhood with the highest average price is South Boston Waterfront costing an average of \\$ 306, which is 76\\% higher than the Boston average\n",
    "- The neighborhood with the lowest average price is Mattapan costing an average of \\$ 75, which is 57\\% less than the Boston average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>review_scores_rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>94.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>99.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_scores_rating  price\n",
       "0                   0.0  250.0\n",
       "1                  94.0   65.0\n",
       "2                  98.0   65.0\n",
       "3                 100.0   75.0\n",
       "4                  99.0   79.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter dataframe keeping the columns that are interesting \n",
    "df_scores_boston = df_boston_values[['review_scores_rating','price']]\n",
    "df_scores_boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='review_scores_rating', ylabel='price'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Am8DFgLbga+n8ax2iOgnnikiboqI+oionzp16pssqpmZdTuqySIidkREZ0R0Ad8CTk8/agZm5SxaB2xL43UZ8bLZvb+NtVv2snt/Wzl3Y2Y2rBQ7RHlJSJoeEdvTt78PdPeUWgV8V9I3gBkkDdlPRkSnpJZ04qUngGXAN8tVvpWNW7nq7nVUV1TQ3tXFVy84mcUL89Z6mZmNGmVLFpK+B7wfmCKpGfg88H5JC0mqkl4A/hQgIjZKWgFsAjqAyyKiM93UpSQ9q2qB+9NHye3e38ZVd6/jQHsXB+gC4Mq717Fo7hQmj68pxy7NzIaNsiWLiPhYRvif+1n+y8CXM+INwDtLWLRMzXtaia6ezSHRFTTvaXWyMLNRz3dwp8aNqaSts2eyaOsMxo2pzLOGmdno4WSR2ravdUBxM7OhppwddI5qA/fQlm8UEY8uYmZDX7k76PjKIrVgxsQ+aUFp3MxsKMvtoNPS1sGB9i6uvHtdSa8wnCxyVFWq3/dmZkNR855Wqit6ns6rKypo3lO6anQni1TznlaS4acOi4iSftlmZuVQN6mW1vaOHrHW9g7qJtWWbB9OFqn2jk46unrGOrqSuJnZUNd79oZSz+bgZJF6YfcbA4qbmQ0VzXtaGVvVs5v/2KpKV0OVw8JZxw0obmY2VNRNqqW9q2fVSHtXl6uhymHutAksO3N2j9iyM2czd9qEQSqRmVlxJo+v4asXnMzY6gom1FQxtrqCr15wcklHn1DvRt2Ror6+PhoaGga8XtOOFhq37GXhrOOcKMxsWNm9v43mPa3UTao94kQhaU1E1PeO+6a8XuZOm+AkYWbD0uTxNWUby87VUGZmVpCThZmZFeRkYWZmBTlZmJlZQWVLFpJukbRT0oac2PGSHpT0fPo8KeezayQ1SXpO0jk58dMkrU8/u16lvi2xl6YdLdzVsIWmHS3l3I2ZWckN1yHKbwX+Abg9J3Y18FBEXCvp6vT9VZLmA0uBBSRzcP9Y0knp1Ko3AsuBx4H7gHMp09Sqn7tnPbc//tKh98vOnM0Xl7yrHLsyMyupYTtEeUQ8ArzaK7wEuC19fRtwfk78johoi4jNQBNwuqTpwMSIWB3JDSG356xTUk07WnokCoDbV7/kKwwzG/JG4hDl0yJiO0D6fEIanwlsyVmuOY3NTF/3jmeStFxSg6SGXbt2DahgjzVlL58vbmY2VIymIcqz2iGin3imiLgpIuojon7q1KkDKsCU8WMHFDczGypG4thQO9KqJdLnnWm8GZiVs1wdsC2N12XES+7Mt03OnCnvzLdNLsfuzMxK5miMDXW0h/tYBVwCXJs+r8yJf1fSN0gauOcBT0ZEp6QWSWcATwDLgG+Wo2CTx9fwf5Yu5IoVjSBBBF+/aGHZbp03MyulxQtnsmjulDc9NlQ+ZUsWkr4HvB+YIqkZ+DxJklgh6RPAS8CFABGxUdIKYBPQAVyW9oQCuJSkZ1UtSS+osvSEgqR+q6JCVKqCzugquLyZ2WjhUWdTu/e3sei6hznQfjhJjK2u4GdXneWrCzMb8krVdTbfqLNDpYF70B2N3gRmZuUwErvODllHozeBmVkp9L5T+2j82PV8Fqnu3gRX9rqMcxWUmQ0lWdVNi+ZOKfuPXSeLHIsXzmT+9ImeKc/MhqTc6qYDJMnhyrvX8bOrzuKi+jpuX314FIqL6utK+mPX1VA5VjZu5bx/eIy//uEmzvuHx1jVuHWwi2Rmdki+6qaN217je09u6RH/3pNb3GZRDkejgcjMbCB6t03ka1t9rbWd9s6ePVvbO4ON2/aVrCxOFin3hjKzfPIN/V3MkOBHOu3BysatnPmVH3PRP67mzK/8mFWNW/PeqT2xtjrPVko3o4PbLFLuDWVmWfLdv9DffQ2797fRvKeV7zzxIisaDo+FWuy0B7v3t3H5HY3pQHjJeenTdzSyaO6UzDu18yWrBTMmvtnDP8RXFqnujF1TVcExYyqpqSr92CpmNrzkq55u2tGSt9p6ZeNWFl33MB+7aXWPRAHFT3uw+j939xkxNdJ4lj2vHxxQ/Ej4yiJHdP83RD+D25rZKNFdPd3d8wiS6unGLXsz4xu37TuURPJp3LK3YE/LV/YfyBvPuqI52JG9v2L2VSxfWaS6f0G0dQRvtHfS1hFu4DYb5fJVTy+cdVxmHNSn7bO3hbOOK7jf35ybPcXCO2ccm3lFM+mY7DaLOZOPKbivYjlZpNzAbTY69ddIna9Bee60CWm1tTimupKaKvHVC05mwYyJfZJIrmVnzj70S7+//c6dNoFlZ87us251VWWf81SF4IFNO/o0ZVcIqqsqi/wWCnM1VKpuUi0tbR09Yi1tHW7gNhvBihl8r3eDMsDaLXtpOdABKOlwFMmpOmskiP959kkcP25Mjxt9i9nvF5e8i2VnzOlxk/Du/W0c6OjssdwbB7v4t15tIwBdAePGOFmU3KO/3Jk3fv67Z2V+ZmbDV767oRfNnXKoh1Fuj6PJ42sOneQrJV4/2POk3b1uoXklCu0319xpE3pciWzc9hpdXcW3p967bhuX/87bB/zdZHGySH3/6ey7tb//9FYnC7M3ofdJd6jsN1/jdfOeVh5reiVz/KX+Gq+71+1OLPmOtb/95lunO0lVIDoH0Pfm6S17il+4gEFJFpJeAFqATqAjIuolHQ/8GzAHeAG4KCL2pMtfA3wiXf7PI+JHpS7TpNrsryJf3MwKK9UcC+XYb77G63FjKjN/+d/08fo+J/ne6xZTbV3onq7eSS73SmSgfucd0wa8Tj6D2cD9gYhYmDPJxtXAQxExD3gofY+k+cBSYAFwLnCDpNJVxHVTnq8iX9zM+jWQIXRyG3uLuSu6FPvN13j9+sHOzM4uEJmN1+NqKhlbXcFfnTef5j2t7N7fRsPm3Xzjgedo2Ly7z/Fk3dPVve53Hn+R9177MB/71uO899qHWdW4NbPzTVKmwt/F7Cnjiv/iChhKP5uXkEzDCnAb8FPgqjR+R0S0AZslNQGnA6tLufMPvH0qK9duz4yb2cAVW92SexXQ2t6BJMZWVR7xlUjznlaiV71+dEVmNU++u6GzfvkvmHFsn8brv/rIfN4581g2bN3Hl+7dRHVFRY+OMtc/3ATAhJqqHseTe09XZ1cXn1+5gZqqyj7tIFfcuZb7//y3MpNU94WGSHo+ZVVPvdba0Td4hAbrZ3MAD0haI2l5GpsWEdsB0ucT0vhMIHc4xeY0VlJPv7R3QHEz618xQ+j0vgro6EoGwHszg3mOG1NJW68zZ1tn5O0ZNHl8DafMOq5Ho/ZfnTe/zxXH5PE1LF44k59ddRbf/uR7+NlVZ/FHZ7yFukm1fOnfNx06hixZd39339PV0QUdXfRJFJB8F9v2tXJRfV3e4w3g3bOPzfxsQ/PevOsN1GBdWSyKiG2STgAelPRsP8tmjYSV2cSTJp7lALNnz85aJK9Hm14ZUNzM+jd5fA0XnVbH7Y/nn2Mh6+ojV6GG3yyvH+ykulI9RmGtruzbe6m33u0c3VcNhRrms65k8sl393d/1jfv5V9z5qnI8osXs0eX3fTywAYv7M+gXFlExLb0eSfwA5JqpR2SpgOkz919WZuB3O5IdcC2PNu9KSLqI6J+6tSBVR9NHTdmQHEz69/u/W2sWNOz//+KhuYeVwpZVx+5jmQwz3FjKjOH6+7vnoOsdo4v/fumPomie9yni29+gkXXJW0KWVcy+Rzo6My8+7s/1z/UdMSDD50wPt9otAN31JOFpHGSJnS/Bj4EbABWAZeki10CrExfrwKWSqqRdCIwD3iy1OV6tTV7wK18cTPrXzGjIvRuZK6qSK4Celf/DMTrBzupqexZIVHTz5XF7v1t/OTZnVSq5zqVEj95duehRvdHfrmLK+/qmVD+4q51PNb0CjVVxZ1KOzuDx5p28T/PPokxlVBTWUGloKqCQ8dfQdIG0a3YRJSl6ZU3jnjd3gajGmoa8AMlf5gq4LsR8R+SfgGskPQJ4CXgQoCI2ChpBbAJ6AAui4j+ryePwIxjj+H5nX2/2BnHlm5sFbPRpNhh/7PukH4z92XUTaqlM3qeYDsjMq9Ququeqir6JpPXD3byhR9u5JofrKerK6iqrKCt14B9bR1dXHv/s33i+XQBX/jhM70icNZJU5g6YSyPPr+TbfsOlmwc0zPmHF+aDTEIySIifgWckhHfDXwwzzpfBr5cznLteyN7lMd8cbPh5mjfHJc19EV3F9Huz7vtef0gz+9oYdyYSuZOmzCg8mUdV/Jj9PAZV+rb9FnM/Qv72w4nkM48CeFATryqImmsHqiHnytP22gphysaSl1nB9W6rfsHFDcbTgbr5rjcq4bc7qW5ZfjcPet7NIIXO0EQZB/XWyaPo7NXg3NnRtfZcgwSeiSJopzuadzGxe89sSTb8h1nqXx/4yH2t7cSebM3fg0nQ2F++X2tB/nivRv7lKFh8+4eiQLyTxDU+2+W77g272qhd+ekroBXWnrWEowbU3lEd0UPJ9v3lS4h+srCRp3B+pU9WJr3tNLe6ydve0fXgLukHolDYxpJtHX0PINXV1TwyPPZ1S+9J+3JdwWRddPfqoybawHu37CdD87/tUPvuxvC30wD8lC3o6V0Pwh8ZWGjylD4lX20tXd09rm7tzOSeDnlftdvZN1w1tXF++ZNyVw3d4KgrL/ZX9y1li2vvsHBzp7bbe/q4kB79nHtaunZs7FuUi2qyLqNa+QYQA/dgpwsbFRVyYzGSa42bHttQPEsR/JvJN+YRsdUVx7qFlt/4uTMSX5yryqyttPWEXzmzrX0zneLT5nBrpbsTinb9r7Ro/yHx2gStUV2fR1uSlnJ5mqoUW60VckU252zWIM1/PZATMlTrnzx3o7030jWd11TVcE/fvw0FsyYeOj7+uKSd/Hb86bywKYdfGj+tB5VRbv3t7Gv9SBvHOw7jMaBjNbkFRmTAHV7ftcbLLru4R53ZicXXKK9c2S3XZSCk8UoNpBJWEaKrO6cR3LjFwyfRPuOX5swoHi3ZLKdfVx511raOmLA/0a6v+u/uGsdlRWisyv42kdP5n0n9RxdIbc31L81NB/qDZV7D0SpmhUOtHfxv+7ZwPiaSto7u+gK+tztbdmcLEaxI5mEZSRYvHAmM44dyyPPv8L75k2h/sTJA97GcEq02/L0iNm2r7VHdU+uQg3Txf4bCSAi6OxKnntr2tGS2Rtq8ckzjngOh2Lk3j9hxRmZFXVWlFJXyQwXn7tnPR/9p8e5/uEmPvpPj/O5lesHvI3h1PaxNU+Z8sWLaZjOnagnX1vG7v1tfObOtRzsDNo6ujjYGVxx59oeyzZu2ZtZhny9pGzwOFmMYvkmfxlqv4xLKd8v2ax+/f0pZrazodJp4NXXs8uQL15Mw3T3fNS9J+rJtXHbvswB/TZuOzxCam6vp1z7D7RnXlXUVo/s3ktDmauhRrlCk8uPNI817cobz1clk6W/oSz+Y8PLfOnf+96pXKxSN5ofPy57G/nixTRMd1815CaDK+5c26MarpgrmrnTJrDszNnc3msI7lt+/mLmuotPmck9jduKHovJSsfJwvqdXH6kmTJ+7IDi/ckayqKqQofqw4+kLaMcjeb5upLmi+frBJDbMN3fVcP7TkrmLWvekz3iae/4F5e8i2VnzOFHG1/maw/8st9j+f6aZtrdHj0onCxsVDnSnkH5dCeAP7xpdd7G2GIbhMvVaL5pe3YVW744FHPFma866HC8Nc+Q4C/tPny/Q+5Isy+9Wng4bSeKweNkYaPK6wc7qew1X3Glsqe0LFah2d56t2XkOwEX6p12pNVTbzk+e5j9fPFuuSPB5u4vOdFH5vd4+HM4mKdL6v0bX+b+jS8DUFtdxRvtnX0G/rOhx8nCaNi8+011Ix1OyjH0Rb7Z3saNqaQzokeD8JW97jnIrWLqr9G82OqprITScqA9s9zd8ax1eo8Eu/iU6bxv3lReff0gX3/wl1RWiIoKEZ1BdWUFHV1dSHDZd56itb0DSeSbly63uSHfnNU29DhZjHIX3/w4jzXtBuD6h5v4rbmT+ddPnjHIpSqfDduy5yresG3fESfKfHNN/9F73nLoBFxMg3C+tgKgqOqpfAnlP3dlD7P/n7v2Z64zf/rEPj3GVq3dnneAviC9wojck3+QnaJsuBo2XWclnSvpOUlNkq4e7PKMBA2bdx9KFN0ebdpNw+bdedYY/vLdrftm7uLNmmt61dptPX6pF9ONFJK2gp9ddRbf/uR7+NlVZ7F44cyi7unob4DE/RlDZQDsbW3PXOcHT+cfMiPLQQ+VMSoMi2QhqRL4v8DvAvOBj0maP7ilGv7uadw2oPhIcDBPl8t88WIUczJ/rTX7hJ0Vnzy+hlNmHXco0RRz82R/ZWhpza5i2/v6wcx19uUpazn4ronhY1gkC+B0oCkifhURB4E7gCWDXKZh79ja7FrlfPGR4C2Txw0oXoxiTuYTa7NrfPPFcxVz82R/ZXjr1OyG7LeeMD5znfMXzihYpmKMreo/FVRXivPeNa0k+7LyGy7JYiawJed9cxormXynx5F72oTfP3XWgOIjwZlvm9zn16zS+JEq5mS+YMaxVFf23HN1pVgw49ii9pFVPVVsGT71gXmZ27zid96euU7WsOG5BIyt7v/UUVMlblpWz0X1dT3iFYJjxlRSU1XB1y88hU+f/fbM9f/iQyex+JTpffZ7THUllUrmuq6pquhzAlt8ynTef1L23/IPTp3BRfU9v7faAgltuHv/3ONLti1lDe411Ei6EDgnIj6Zvv84cHpE/Fmv5ZYDywFmz5592osvZt8FmqVpRwtn/90jfeI//h/vG9CdvcPN51au73H37EDmPx6uVjVu5TN3NiIqCLr42wsXlmS02EJdW1c1bu0zAmupR6nNV4aP3/w4j+a0T+V2ZMi3TtOOFhq37D00JEf360njxhy6EfGL926kUhWHJiGqra7q01srdzvd6/boedXPv8F868LhezT2vH7w0DJzp01g9/42TvubH/f5btb85dlMHl/TY5tzp03gnqe2cO/6lznvXb/GhLHVh4ZK3763lZXrtrPk5On87skzaN7Tylfu28Tjm/dkfvenzprIgY7gP3e2kNsT+5hq8XunzGRXywEefq40Y14pfXSfvasqRHtG9+MXrv3IwLctrYmI+j7xYZIszgS+EBHnpO+vAYiIr+Rbp76+PhoaGga0n9F44gT6/M8zGgzWPBSDOf9FObpI5x4PcMTHVup/g6sat3LFirV0RlAp8fWLTilZYs79Ho87Zkxmub/9882HEs3F7z3xUDxfAs59vWHrXu5d/zLTj63h337RjCQigs8vXsA7Zxzb57vOfX3FHU/x882v8t4Tj+fWT555RMc33JNFFfBL4IPAVuAXwH+JiI351jmSZAGj88RpNhINh4mpChmMY8iXLIbFfRYR0SHpU8CPSJoRbukvUbwZc6dNcJIwGwFGwphnQ+kYhkWyAIiI+4D7BrscZmaj0XDpDWVmZoPIycLMzApysjAzs4KcLMzMrKBh0XX2SEjaBRR/V15PU4DRNmO8j3l0GG3HPNqOF978Mb8lIqb2Do7YZPFmSGrI6mc8kvmYR4fRdsyj7XihfMfsaigzMyvIycLMzApyssh202AXYBD4mEeH0XbMo+14oUzH7DYLMzMryFcWZmZWkJOFmZkV5GSRQ9K5kp6T1CTp6sEuTzlImiXpJ5KekbRR0qfT+PGSHpT0fPo8abDLWmqSKiU9Lene9P2IPmZJx0m6S9Kz6d/7zFFwzP8j/Xe9QdL3JI0daccs6RZJOyVtyInlPUZJ16TntOcknXOk+3WySEmqBP4v8LvAfOBjkuYPbqnKogO4IiJ+HTgDuCw9zquBhyJiHvBQ+n6k+TTwTM77kX7M/wf4j4h4B3AKybGP2GOWNBP4c6A+It5JMp3BUkbeMd8KnNsrlnmM6f/bS4EF6To3pOe6AXOyOOx0oCkifhURB4E7gCWDXKaSi4jtEfFU+rqF5AQyk+RYb0sXuw04f1AKWCaS6oCPADfnhEfsMUuaCLwP+GeAiDgYEXsZwcecqgJq0wnTjgG2McKOOSIeAV7tFc53jEuAOyKiLSI2A00k57oBc7I4bCawJed9cxobsSTNAU4FngCmRcR2SBIKcMIgFq0c/h64EujKiY3kY34rsAv4l7Tq7WZJ4xjBxxwRW4G/BV4CtgP7IuIBRvAx58h3jCU7rzlZHKaM2IjtVyxpPHA3cHlEvDbY5SknSecBOyNizWCX5SiqAt4N3BgRpwKvM/yrX/qV1tMvAU4EZgDjJF08uKUadCU7rzlZHNYMzMp5X0dyCTviSKomSRTfiYjvp+Edkqann08Hdg5W+cpgEbBY0gsk1YtnSfo2I/uYm4HmiHgifX8XSfIYycd8NrA5InZFRDvwfeC9jOxj7pbvGEt2XnOyOOwXwDxJJ0oaQ9IotGqQy1RykkRSj/1MRHwj56NVwCXp60uAlUe7bOUSEddERF1EzCH5uz4cERczso/5ZWCLpLenoQ8CmxjBx0xS/XSGpGPSf+cfJGmTG8nH3C3fMa4ClkqqkXQiMA948kh24Du4c0j6MEnddiVwS0R8eXBLVHqSfhN4FFjP4fr7z5K0W6wAZpP8T3dhRPRuRBv2JL0f+ExEnCdpMiP4mCUtJGnQHwP8CvivJD8QR/Ix/zXwhyS9/p4GPgmMZwQds6TvAe8nGYp8B/B54B7yHKOk/wX8Ccl3cnlE3H9E+3WyMDOzQlwNZWZmBTlZmJlZQU4WZmZWkJOFmZkV5GRhZmYFOVmYmVlBThY24kmaIemuwS7HUCPpcknH5Ly/T9Jxg1gkG8J8n4UNO+nduYqIroILDyOSqiKio4Tb6/d7Soc/qY+IV0q1Txu5fGVhw4KkOekEPjcATwF/JekXktald+0i6TpJ/z1nnS9IuiJdd0Maq5T0tZx1/zSN3yBpcfr6B5JuSV9/QtLf5CnTOEn/LmltOtnOH6bx35D08zT+pKQJ6SQ8/yJpfToK7AfSZf9Y0p2Sfgg8kG7zlrR8T0taki63IN1WY1rueUV+T7Mk3SipQcmkQN3f1Z+TDLb3E0k/SWMvSJqSs41vpes8IKk259jWSVqdfo8bssphI1BE+OHHkH8Ac0iGJzkD+BBwE8mImhXAvSRzN5wK/L+cdTaRDH8wB9iQxpYDf5m+rgEaSEYpXQp8LY0/CTyevv4X4Jw8ZboA+FbO+2M5PLTGb6SxiSQjwF4B/EsaewfJkAxjgT8mGezt+PSz/w1cnL4+DvglMA74JvBHaXwMUFvoe8qJdW+7EvgpcHL6/gVgSs5yL5AMITGHZGiIhWl8RU6ZNgDvTV9f2/29+jHyH76ysOHkxYh4nCRZfIhk7J+nSE6+8yLiaeCEtI3iFGBPRLzUaxsfApZJaiQZD2syyeBqjwK/pWRmsU0cHsXzTODnecqzHjg7vaL5rYjYB7wd2B4RvwCIiNciqVr6TeBf09izwIvASel2HozDYxV9CLg6Ld9PSRLKbGA18FlJVwFviYjWIr6nbhdJeir9vhaQzARZyOaIaExfrwHmpO0ZEyKi+/v4bhHbsRGiarALYDYAr6fPAr4SEf+UscxdwEeBXyMZjrw3AX8WET/q80EyH8K5wCPA8cBFwP5IZhTsIyJ+Kek04MPAVyQ9QDKgW1ZDYNa8Ar2Pq3u5CyLiuV7LPCPpCZLZ/n4k6ZMR8XCh7aUjjX6G5Epnj6RbSRJQIW05rzuB2gLHYCOcryxsOPoR8CdKJnBC0kxJ3TOD3UFSpfRRksSRte6lSub0QNJJSmaQg+TX++UkyeJRkpPso/kKIWkG8EZEfJtkhrZ3A88CMyT9RrrMBCVTfD4C/FH3PkmuFnonhO7y/VnaOI2kU9PntwK/iojrSYadPrm/LyjHRJLksU/SNJI55ru1ABOK3A4RsQdokXRGGlpa7Lo2/PnKwoadiHhA0q8Dq9Nz6n7gYpLZ8DZKmgBsjXSayV5uJqmTfyo9Ie/i8HzFjwIfiogmSS+SXF3kTRbAu4CvSeoC2oFLI+Jg2tD9zbRRuJVkUp4bgH+UtJ6kPeCPI6ItLX+uL5EMk78uLd8LwHkkw25fLKkdeBn4YuFvCiJiraSngY0kbSk/y/n4JuB+Sdsj4gPFbA/4BPAtSa+TVJPtK3I9G+bcddbMiiZpfETsT19fDUyPiE8PcrHsKPCVhZkNxEckXUNy7niRpDeXjQK+sjArQMmMeg9lfPTBiNh9tMsDQ7NMNrI5WZiZWUHuDWVmZgU5WZiZWUFOFmZmVpCThZmZFfT/AVUrs0ue0f7NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_scores_boston.plot.scatter(x='review_scores_rating', y='price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='review_scores_rating'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_scores_boston.groupby('review_scores_rating').mean().plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that there is no significant correlation between the score rating and the prices\n",
    "- The highest average prices are not in the best rated places"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analyzing Seattle data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>241032</td>\n",
       "      <td>https://www.airbnb.com/rooms/241032</td>\n",
       "      <td>20160104002432</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>Stylish Queen Anne Apartment</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>Make your self at home in this charming one-be...</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>f</td>\n",
       "      <td>moderate</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 92 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                          listing_url       scrape_id last_scraped  \\\n",
       "0  241032  https://www.airbnb.com/rooms/241032  20160104002432   2016-01-04   \n",
       "\n",
       "                           name summary  \\\n",
       "0  Stylish Queen Anne Apartment     NaN   \n",
       "\n",
       "                                               space  \\\n",
       "0  Make your self at home in this charming one-be...   \n",
       "\n",
       "                                         description experiences_offered  \\\n",
       "0  Make your self at home in this charming one-be...                none   \n",
       "\n",
       "  neighborhood_overview  ... review_scores_value requires_license license  \\\n",
       "0                   NaN  ...                10.0                f     NaN   \n",
       "\n",
       "  jurisdiction_names instant_bookable cancellation_policy  \\\n",
       "0         WASHINGTON                f            moderate   \n",
       "\n",
       "   require_guest_profile_picture require_guest_phone_verification  \\\n",
       "0                              f                                f   \n",
       "\n",
       "  calculated_host_listings_count reviews_per_month  \n",
       "0                              2              4.07  \n",
       "\n",
       "[1 rows x 92 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# looking into data\n",
    "df_seattle_listings.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "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>id</th>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.818000e+03</td>\n",
       "      <td>3818</td>\n",
       "      <td>3171.000000</td>\n",
       "      <td>3818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Broadway</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.550111e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.539262</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.962660e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.606083</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.335000e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.258256e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.118244e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.035127e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.034016e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id neighbourhood_cleansed  review_scores_rating    price\n",
       "count   3.818000e+03                   3818           3171.000000     3818\n",
       "unique           NaN                     87                   NaN      273\n",
       "top              NaN               Broadway                   NaN  $150.00\n",
       "freq             NaN                    397                   NaN      162\n",
       "mean    5.550111e+06                    NaN             94.539262      NaN\n",
       "std     2.962660e+06                    NaN              6.606083      NaN\n",
       "min     3.335000e+03                    NaN             20.000000      NaN\n",
       "25%     3.258256e+06                    NaN             93.000000      NaN\n",
       "50%     6.118244e+06                    NaN             96.000000      NaN\n",
       "75%     8.035127e+06                    NaN             99.000000      NaN\n",
       "max     1.034016e+07                    NaN            100.000000      NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seattle_listings[['id','neighbourhood_cleansed','review_scores_rating','price']].describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter dataframe keeping the columns that are interesting \n",
    "df_seattle_values = df_seattle_listings[['review_scores_rating','neighbourhood_cleansed','price']]\n",
    "df_seattle_values = df_seattle_values.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "review_scores_rating      float64\n",
       "neighbourhood_cleansed     object\n",
       "price                     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert price in float\n",
    "df_seattle_values['price'] = df_seattle_values['price'].str.replace(',','').str.replace('$','').astype('float')\n",
    "df_seattle_values.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adams</th>\n",
       "      <td>121.671429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alki</th>\n",
       "      <td>171.619048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arbor Heights</th>\n",
       "      <td>126.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atlantic</th>\n",
       "      <td>111.396226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belltown</th>\n",
       "      <td>162.893162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             price\n",
       "neighbourhood_cleansed            \n",
       "Adams                   121.671429\n",
       "Alki                    171.619048\n",
       "Arbor Heights           126.800000\n",
       "Atlantic                111.396226\n",
       "Belltown                162.893162"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the average price of properties by neighbourhood\n",
    "df_neighbour_seattle = df_seattle_values[['neighbourhood_cleansed','price']].groupby('neighbourhood_cleansed').mean()\n",
    "df_neighbour_seattle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column with the average price of dataset for comparison\n",
    "df_neighbour_seattle['mean'] = df_neighbour_seattle['price'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_neighbour_seattle=df_neighbour_seattle.sort_values(by='price', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = df_neighbour_seattle[['price']].plot(figsize=(25,5), kind='bar')\n",
    "plt.xticks(rotation=0)\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(ax.get_xticks(), df_neighbour_seattle[['mean']], color='orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>price</th>\n",
       "      <th>mean</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Southeast Magnolia</th>\n",
       "      <td>231.705882</td>\n",
       "      <td>123.623676</td>\n",
       "      <td>0.874284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portage Bay</th>\n",
       "      <td>227.857143</td>\n",
       "      <td>123.623676</td>\n",
       "      <td>0.843151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             price        mean   percent\n",
       "neighbourhood_cleansed                                  \n",
       "Southeast Magnolia      231.705882  123.623676  0.874284\n",
       "Portage Bay             227.857143  123.623676  0.843151"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a column with the percentage difference between the average price by neighbourhood and the average price of dataset\n",
    "df_neighbour_seattle['percent'] = (df_neighbour_seattle['price']/df_neighbour_seattle['mean'])-1\n",
    "# look at the highest positive difference\n",
    "df_neighbour_seattle.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>price</th>\n",
       "      <th>mean</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neighbourhood_cleansed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Georgetown</th>\n",
       "      <td>76.125000</td>\n",
       "      <td>123.623676</td>\n",
       "      <td>-0.384220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Delridge</th>\n",
       "      <td>74.666667</td>\n",
       "      <td>123.623676</td>\n",
       "      <td>-0.396016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olympic Hills</th>\n",
       "      <td>71.687500</td>\n",
       "      <td>123.623676</td>\n",
       "      <td>-0.420115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rainier Beach</th>\n",
       "      <td>68.555556</td>\n",
       "      <td>123.623676</td>\n",
       "      <td>-0.445450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            price        mean   percent\n",
       "neighbourhood_cleansed                                 \n",
       "Georgetown              76.125000  123.623676 -0.384220\n",
       "South Delridge          74.666667  123.623676 -0.396016\n",
       "Olympic Hills           71.687500  123.623676 -0.420115\n",
       "Rainier Beach           68.555556  123.623676 -0.445450"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at the highest negative difference\n",
    "df_neighbour_seattle.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look the number of neighbourhoods with average prices higher than the average price of Seattle\n",
    "len(df_neighbour_seattle[df_neighbour_seattle['price']>df_neighbour_seattle['mean']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look the number of neighbourhoods with average prices lower than the average price of Seattle\n",
    "len(df_neighbour_seattle[df_neighbour_seattle['price']<df_neighbour_seattle['mean']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that the average prices also vary according to the neighborhood\n",
    "- We have 41 neighborhoods with an average price above the Seattle average and 46 neighborhoods with an average price below the Seattle average\n",
    "- The neighborhoods with the highest average prices are Southeast Magnolia and Portage Bay, costing on average \\ $ 231 and \\ $ 227, and are 87 \\% and 84 \\% higher than the Seattle average respectively\n",
    "- The neighborhood with the lowest average price is Rainier Beach costing an average of \\ $ 68, which is 44 \\% less than the Seattle average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>review_scores_rating</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>96.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97.0</td>\n",
       "      <td>975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92.0</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_scores_rating  price\n",
       "0                  95.0   85.0\n",
       "1                  96.0  150.0\n",
       "2                  97.0  975.0\n",
       "3                   0.0  100.0\n",
       "4                  92.0  450.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter dataframe keeping the columns that are interesting \n",
    "df_scores_seattle = df_seattle_values[['review_scores_rating','price']]\n",
    "df_scores_seattle.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='review_scores_rating', ylabel='price'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_scores_seattle.plot.scatter(x='review_scores_rating', y='price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='review_scores_rating'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_scores_seattle.groupby('review_scores_rating').mean().plot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that there is no significant correlation between the score rating and the prices\n",
    "- The highest average prices are not in the best rated places"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing Boston and Seattle:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Both have differences in the average price depending on the neighborhood\n",
    "- Seattle has many more neighborhoods than Boston\n",
    "- The average price in the most expensive neighborhoods is between 3 and 4 times higher than in the cheapest neighborhoods\n",
    "- In none of the cities is there a significant correlation between the score rating and prices."
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}