Airbnb Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48895 entries, 0 to 48894\n",
      "Data columns (total 16 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   id                              48895 non-null  int64  \n",
      " 1   name                            48879 non-null  object \n",
      " 2   host_id                         48895 non-null  int64  \n",
      " 3   host_name                       48874 non-null  object \n",
      " 4   neighbourhood_group             48895 non-null  object \n",
      " 5   neighbourhood                   48895 non-null  object \n",
      " 6   latitude                        48895 non-null  float64\n",
      " 7   longitude                       48895 non-null  float64\n",
      " 8   room_type                       48895 non-null  object \n",
      " 9   price                           48895 non-null  int64  \n",
      " 10  minimum_nights                  48895 non-null  int64  \n",
      " 11  number_of_reviews               48895 non-null  int64  \n",
      " 12  last_review                     38843 non-null  object \n",
      " 13  reviews_per_month               38843 non-null  float64\n",
      " 14  calculated_host_listings_count  48895 non-null  int64  \n",
      " 15  availability_365                48895 non-null  int64  \n",
      "dtypes: float64(3), int64(7), object(6)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Inputing the data\n",
    "df = pd.read_csv('airbnb_2.csv')\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood_group\n",
       "Manhattan        21661\n",
       "Brooklyn         20104\n",
       "Queens            5666\n",
       "Bronx             1091\n",
       "Staten Island      373\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.value_counts('neighbourhood_group')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "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['neighbourhood_group'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "neighbourhood_group\n",
       "Manhattan        0.443011\n",
       "Brooklyn         0.411167\n",
       "Queens           0.115881\n",
       "Bronx            0.022313\n",
       "Staten Island    0.007629\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.value_counts('neighbourhood_group', normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a calculated field\n",
    "df['Revenues'] = df['price']*df['number_of_reviews']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Compare the counts bar graph above with the revenue graph below\n",
    "df.groupby(\"neighbourhood_group\").mean()['Revenues'].sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Comparing individual neighbourhoods within three specific groups\n",
    "condition = df['neighbourhood_group'].isin(['Manhattan','Brooklyn','Queens'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "MBQ = df[condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "revenue = MBQ.groupby(['neighbourhood_group', 'neighbourhood'])['Revenues'].sum().sort_values(ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "grouped_df = revenue.groupby('neighbourhood_group').head(3)\n",
    "grouped_df.groupby('neighbourhood').sum().sort_values(by='Revenues', ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Williamsburg',\n",
       " 'Bedford-Stuyvesant',\n",
       " 'Harlem',\n",
       " \"Hell's Kitchen\",\n",
       " 'East Village',\n",
       " 'Bushwick',\n",
       " 'Astoria',\n",
       " 'Long Island City',\n",
       " 'Flushing']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top9 = list(grouped_df['neighbourhood'])\n",
    "top9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         neighbourhood        room_type     Revenues\n",
      "0              Astoria  Entire home/apt  2845.629921\n",
      "1              Astoria     Private room  1549.394737\n",
      "2              Astoria      Shared room  1250.160000\n",
      "3   Bedford-Stuyvesant  Entire home/apt  5923.995600\n",
      "4   Bedford-Stuyvesant     Private room  1409.256624\n",
      "5   Bedford-Stuyvesant      Shared room   650.764706\n",
      "6             Bushwick  Entire home/apt  4128.241888\n",
      "7             Bushwick     Private room  1124.728912\n",
      "8             Bushwick      Shared room   439.220588\n",
      "9         East Village  Entire home/apt  4871.841880\n",
      "10        East Village     Private room  2796.284848\n",
      "11        East Village      Shared room  1257.913043\n",
      "12            Flushing  Entire home/apt  4879.349057\n",
      "13            Flushing     Private room  2027.403909\n",
      "14            Flushing      Shared room    63.538462\n",
      "15              Harlem  Entire home/apt  4991.899614\n",
      "16              Harlem     Private room  2132.832804\n",
      "17              Harlem      Shared room  1472.204082\n",
      "18      Hell's Kitchen  Entire home/apt  4116.112890\n",
      "19      Hell's Kitchen     Private room  4713.443452\n",
      "20      Hell's Kitchen      Shared room  1876.070707\n",
      "21    Long Island City  Entire home/apt  3303.504167\n",
      "22    Long Island City     Private room  2032.819788\n",
      "23    Long Island City      Shared room   486.857143\n",
      "24        Williamsburg  Entire home/apt  4659.608673\n",
      "25        Williamsburg     Private room  1770.604907\n",
      "26        Williamsburg      Shared room  1306.031250\n"
     ]
    }
   ],
   "source": [
    "# Looking at revenues for different room types in the top 9 neighbourhoods\n",
    "top9df = df[df['neighbourhood'].isin(top9)] #You are returning a Boolean Matrix here, not a Dataframe. \n",
    "top9df \n",
    "room_revenues = top9df.groupby(['neighbourhood', 'room_type'])['Revenues'].mean().reset_index()\n",
    "print(room_revenues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>neighbourhood</th>\n",
       "      <th>room_type</th>\n",
       "      <th>Revenues</th>\n",
       "      <th>neighbourhood_room_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Astoria</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>2845.629921</td>\n",
       "      <td>Astoria, Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Astoria</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1549.394737</td>\n",
       "      <td>Astoria, Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Astoria</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>1250.160000</td>\n",
       "      <td>Astoria, Shared room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>5923.995600</td>\n",
       "      <td>Bedford-Stuyvesant, Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1409.256624</td>\n",
       "      <td>Bedford-Stuyvesant, Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bedford-Stuyvesant</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>650.764706</td>\n",
       "      <td>Bedford-Stuyvesant, Shared room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bushwick</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4128.241888</td>\n",
       "      <td>Bushwick, Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bushwick</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1124.728912</td>\n",
       "      <td>Bushwick, Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bushwick</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>439.220588</td>\n",
       "      <td>Bushwick, Shared room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>East Village</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4871.841880</td>\n",
       "      <td>East Village, Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>East Village</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2796.284848</td>\n",
       "      <td>East Village, Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>East Village</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>1257.913043</td>\n",
       "      <td>East Village, Shared room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Flushing</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4879.349057</td>\n",
       "      <td>Flushing, Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Flushing</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2027.403909</td>\n",
       "      <td>Flushing, Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Flushing</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>63.538462</td>\n",
       "      <td>Flushing, Shared room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Harlem</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4991.899614</td>\n",
       "      <td>Harlem, Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Harlem</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2132.832804</td>\n",
       "      <td>Harlem, Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Harlem</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>1472.204082</td>\n",
       "      <td>Harlem, Shared room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4116.112890</td>\n",
       "      <td>Hell's Kitchen, Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>Private room</td>\n",
       "      <td>4713.443452</td>\n",
       "      <td>Hell's Kitchen, Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Hell's Kitchen</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>1876.070707</td>\n",
       "      <td>Hell's Kitchen, Shared room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Long Island City</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>3303.504167</td>\n",
       "      <td>Long Island City, Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Long Island City</td>\n",
       "      <td>Private room</td>\n",
       "      <td>2032.819788</td>\n",
       "      <td>Long Island City, Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Long Island City</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>486.857143</td>\n",
       "      <td>Long Island City, Shared room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>4659.608673</td>\n",
       "      <td>Williamsburg, Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>Private room</td>\n",
       "      <td>1770.604907</td>\n",
       "      <td>Williamsburg, Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>1306.031250</td>\n",
       "      <td>Williamsburg, Shared room</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         neighbourhood        room_type     Revenues  \\\n",
       "0              Astoria  Entire home/apt  2845.629921   \n",
       "1              Astoria     Private room  1549.394737   \n",
       "2              Astoria      Shared room  1250.160000   \n",
       "3   Bedford-Stuyvesant  Entire home/apt  5923.995600   \n",
       "4   Bedford-Stuyvesant     Private room  1409.256624   \n",
       "5   Bedford-Stuyvesant      Shared room   650.764706   \n",
       "6             Bushwick  Entire home/apt  4128.241888   \n",
       "7             Bushwick     Private room  1124.728912   \n",
       "8             Bushwick      Shared room   439.220588   \n",
       "9         East Village  Entire home/apt  4871.841880   \n",
       "10        East Village     Private room  2796.284848   \n",
       "11        East Village      Shared room  1257.913043   \n",
       "12            Flushing  Entire home/apt  4879.349057   \n",
       "13            Flushing     Private room  2027.403909   \n",
       "14            Flushing      Shared room    63.538462   \n",
       "15              Harlem  Entire home/apt  4991.899614   \n",
       "16              Harlem     Private room  2132.832804   \n",
       "17              Harlem      Shared room  1472.204082   \n",
       "18      Hell's Kitchen  Entire home/apt  4116.112890   \n",
       "19      Hell's Kitchen     Private room  4713.443452   \n",
       "20      Hell's Kitchen      Shared room  1876.070707   \n",
       "21    Long Island City  Entire home/apt  3303.504167   \n",
       "22    Long Island City     Private room  2032.819788   \n",
       "23    Long Island City      Shared room   486.857143   \n",
       "24        Williamsburg  Entire home/apt  4659.608673   \n",
       "25        Williamsburg     Private room  1770.604907   \n",
       "26        Williamsburg      Shared room  1306.031250   \n",
       "\n",
       "                neighbourhood_room_type  \n",
       "0              Astoria, Entire home/apt  \n",
       "1                 Astoria, Private room  \n",
       "2                  Astoria, Shared room  \n",
       "3   Bedford-Stuyvesant, Entire home/apt  \n",
       "4      Bedford-Stuyvesant, Private room  \n",
       "5       Bedford-Stuyvesant, Shared room  \n",
       "6             Bushwick, Entire home/apt  \n",
       "7                Bushwick, Private room  \n",
       "8                 Bushwick, Shared room  \n",
       "9         East Village, Entire home/apt  \n",
       "10           East Village, Private room  \n",
       "11            East Village, Shared room  \n",
       "12            Flushing, Entire home/apt  \n",
       "13               Flushing, Private room  \n",
       "14                Flushing, Shared room  \n",
       "15              Harlem, Entire home/apt  \n",
       "16                 Harlem, Private room  \n",
       "17                  Harlem, Shared room  \n",
       "18      Hell's Kitchen, Entire home/apt  \n",
       "19         Hell's Kitchen, Private room  \n",
       "20          Hell's Kitchen, Shared room  \n",
       "21    Long Island City, Entire home/apt  \n",
       "22       Long Island City, Private room  \n",
       "23        Long Island City, Shared room  \n",
       "24        Williamsburg, Entire home/apt  \n",
       "25           Williamsburg, Private room  \n",
       "26            Williamsburg, Shared room  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "room_revenues['neighbourhood_room_type'] = room_revenues['neighbourhood'] + ', ' + room_revenues['room_type']\n",
    "room_revenues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "room_revenues_sorted = room_revenues.sort_values(by='Revenues', ascending = False)\n",
    "room_revenues_distinct = room_revenues_sorted.drop_duplicates(subset = ['neighbourhood'])\n",
    "top9types = room_revenues_distinct.head(9)\n",
    "indexed9types = top9types.set_index('neighbourhood_room_type')\n",
    "indexed9types.plot(kind='bar')\n",
    "plt.xlabel('neighbourhood, room type')\n",
    "plt.title('Popular Airbnb Neighbourhoods by Room Type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}