Airbnb-Project-(Patrica-Manuela-Sura-Gilbert).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import seaborn as sns #!pip install seaborn\n",
    "import matplotlib.pyplot as plt\n",
    "import mpl_toolkits\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import requests\n",
    "import pprint\n",
    "from config import map_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://www.mapquestapi.com/geocoding/v1/address?key=AVGKdlu1IdakAmuexFuRz46D89fhRGZj&location='"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url='http://www.mapquestapi.com/geocoding/v1/address?key=' + map_key + '&location='\n",
    "url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >id</th>        <th class=\"col_heading level0 col1\" >name</th>        <th class=\"col_heading level0 col2\" >host_id</th>        <th class=\"col_heading level0 col3\" >host_name</th>        <th class=\"col_heading level0 col4\" >neighbourhood_group</th>        <th class=\"col_heading level0 col5\" >neighbourhood</th>        <th class=\"col_heading level0 col6\" >latitude</th>        <th class=\"col_heading level0 col7\" >longitude</th>        <th class=\"col_heading level0 col8\" >room_type</th>        <th class=\"col_heading level0 col9\" >price</th>        <th class=\"col_heading level0 col10\" >minimum_nights</th>        <th class=\"col_heading level0 col11\" >number_of_reviews</th>        <th class=\"col_heading level0 col12\" >last_review</th>        <th class=\"col_heading level0 col13\" >reviews_per_month</th>        <th class=\"col_heading level0 col14\" >calculated_host_listings_count</th>        <th class=\"col_heading level0 col15\" >availability_365</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col0\" class=\"data row0 col0\" >2539</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col1\" class=\"data row0 col1\" >Clean & quiet apt home by the park</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col2\" class=\"data row0 col2\" >2787</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col3\" class=\"data row0 col3\" >John</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col4\" class=\"data row0 col4\" >Brooklyn</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col5\" class=\"data row0 col5\" >Kensington</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col6\" class=\"data row0 col6\" >40.6475</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col7\" class=\"data row0 col7\" >-73.9724</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col8\" class=\"data row0 col8\" >Private room</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col9\" class=\"data row0 col9\" >149</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col10\" class=\"data row0 col10\" >1</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col11\" class=\"data row0 col11\" >9</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col12\" class=\"data row0 col12\" >2018-10-19</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col13\" class=\"data row0 col13\" >0.21</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col14\" class=\"data row0 col14\" >6</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row0_col15\" class=\"data row0 col15\" >365</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col0\" class=\"data row1 col0\" >2595</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col1\" class=\"data row1 col1\" >Skylit Midtown Castle</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col2\" class=\"data row1 col2\" >2845</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col3\" class=\"data row1 col3\" >Jennifer</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col4\" class=\"data row1 col4\" >Manhattan</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col5\" class=\"data row1 col5\" >Midtown</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col6\" class=\"data row1 col6\" >40.7536</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col7\" class=\"data row1 col7\" >-73.9838</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col8\" class=\"data row1 col8\" >Entire home/apt</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col9\" class=\"data row1 col9\" >225</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col10\" class=\"data row1 col10\" >1</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col11\" class=\"data row1 col11\" >45</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col12\" class=\"data row1 col12\" >2019-05-21</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col13\" class=\"data row1 col13\" >0.38</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col14\" class=\"data row1 col14\" >2</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row1_col15\" class=\"data row1 col15\" >355</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col0\" class=\"data row2 col0\" >3647</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col1\" class=\"data row2 col1\" >THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col2\" class=\"data row2 col2\" >4632</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col3\" class=\"data row2 col3\" >Elisabeth</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col4\" class=\"data row2 col4\" >Manhattan</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col5\" class=\"data row2 col5\" >Harlem</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col6\" class=\"data row2 col6\" >40.809</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col7\" class=\"data row2 col7\" >-73.9419</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col8\" class=\"data row2 col8\" >Private room</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col9\" class=\"data row2 col9\" >150</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col10\" class=\"data row2 col10\" >3</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col11\" class=\"data row2 col11\" >0</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col12\" class=\"data row2 col12\" >nan</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col13\" class=\"data row2 col13\" >nan</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col14\" class=\"data row2 col14\" >1</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row2_col15\" class=\"data row2 col15\" >365</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col0\" class=\"data row3 col0\" >3831</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col1\" class=\"data row3 col1\" >Cozy Entire Floor of Brownstone</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col2\" class=\"data row3 col2\" >4869</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col3\" class=\"data row3 col3\" >LisaRoxanne</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col4\" class=\"data row3 col4\" >Brooklyn</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col5\" class=\"data row3 col5\" >Clinton Hill</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col6\" class=\"data row3 col6\" >40.6851</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col7\" class=\"data row3 col7\" >-73.9598</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col8\" class=\"data row3 col8\" >Entire home/apt</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col9\" class=\"data row3 col9\" >89</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col10\" class=\"data row3 col10\" >1</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col11\" class=\"data row3 col11\" >270</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col12\" class=\"data row3 col12\" >2019-07-05</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col13\" class=\"data row3 col13\" >4.64</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col14\" class=\"data row3 col14\" >1</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row3_col15\" class=\"data row3 col15\" >194</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col0\" class=\"data row4 col0\" >5022</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col1\" class=\"data row4 col1\" >Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col2\" class=\"data row4 col2\" >7192</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col3\" class=\"data row4 col3\" >Laura</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col4\" class=\"data row4 col4\" >Manhattan</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col5\" class=\"data row4 col5\" >East Harlem</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col6\" class=\"data row4 col6\" >40.7985</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col7\" class=\"data row4 col7\" >-73.944</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col8\" class=\"data row4 col8\" >Entire home/apt</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col9\" class=\"data row4 col9\" >80</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col10\" class=\"data row4 col10\" >10</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col11\" class=\"data row4 col11\" >9</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col12\" class=\"data row4 col12\" >2018-11-19</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col13\" class=\"data row4 col13\" >0.1</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col14\" class=\"data row4 col14\" >1</td>\n",
       "                        <td id=\"T_c82039b4_08b7_11ea_8723_086d41dd6a90row4_col15\" class=\"data row4 col15\" >0</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11567cc90>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a reference to the CSV and import it into a Pandas DataFrame\n",
    "nyc_airbnb_data = \"nyc_airbnd_data.csv\"\n",
    "nyc_airbnb_df = pd.read_csv(nyc_airbnb_data)\n",
    "nyc_airbnb_df.head().style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>2539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>355</td>\n",
       "      <td>2595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>3647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Cozy Entire Floor of Brownstone</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "      <td>3831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>East Harlem</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>5022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               name neighbourhood_group  \\\n",
       "0                Clean & quiet apt home by the park            Brooklyn   \n",
       "1                             Skylit Midtown Castle           Manhattan   \n",
       "2               THE VILLAGE OF HARLEM....NEW YORK !           Manhattan   \n",
       "3                   Cozy Entire Floor of Brownstone            Brooklyn   \n",
       "4  Entire Apt: Spacious Studio/Loft by central park           Manhattan   \n",
       "\n",
       "  neighbourhood        room_type  price  minimum_nights  availability_365  \\\n",
       "0    Kensington     Private room    149               1               365   \n",
       "1       Midtown  Entire home/apt    225               1               355   \n",
       "2        Harlem     Private room    150               3               365   \n",
       "3  Clinton Hill  Entire home/apt     89               1               194   \n",
       "4   East Harlem  Entire home/apt     80              10                 0   \n",
       "\n",
       "     id  \n",
       "0  2539  \n",
       "1  2595  \n",
       "2  3647  \n",
       "3  3831  \n",
       "4  5022  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create DataFrama\n",
    "nyc_airbnb = nyc_airbnb_df[[\"name\", \"neighbourhood_group\", \"neighbourhood\", \"room_type\", \"price\", \n",
    "                            \"minimum_nights\", \"availability_365\", \"id\"]]\n",
    "nyc_airbnb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Name</th>        <th class=\"col_heading level0 col1\" >Borough</th>        <th class=\"col_heading level0 col2\" >Neighborhood</th>        <th class=\"col_heading level0 col3\" >Room Type</th>        <th class=\"col_heading level0 col4\" >Price</th>        <th class=\"col_heading level0 col5\" >Minimum Nights</th>        <th class=\"col_heading level0 col6\" >Yearly Availability</th>        <th class=\"col_heading level0 col7\" >ID</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row0_col0\" class=\"data row0 col0\" >Clean & quiet apt home by the park</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row0_col1\" class=\"data row0 col1\" >Brooklyn</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row0_col2\" class=\"data row0 col2\" >Kensington</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row0_col3\" class=\"data row0 col3\" >Private room</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row0_col4\" class=\"data row0 col4\" >149</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row0_col5\" class=\"data row0 col5\" >1</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row0_col6\" class=\"data row0 col6\" >365</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row0_col7\" class=\"data row0 col7\" >2539</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row1_col0\" class=\"data row1 col0\" >Skylit Midtown Castle</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row1_col1\" class=\"data row1 col1\" >Manhattan</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row1_col2\" class=\"data row1 col2\" >Midtown</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row1_col3\" class=\"data row1 col3\" >Entire home/apt</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row1_col4\" class=\"data row1 col4\" >225</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row1_col5\" class=\"data row1 col5\" >1</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row1_col6\" class=\"data row1 col6\" >355</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row1_col7\" class=\"data row1 col7\" >2595</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row2_col0\" class=\"data row2 col0\" >THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row2_col1\" class=\"data row2 col1\" >Manhattan</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row2_col2\" class=\"data row2 col2\" >Harlem</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row2_col3\" class=\"data row2 col3\" >Private room</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row2_col4\" class=\"data row2 col4\" >150</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row2_col5\" class=\"data row2 col5\" >3</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row2_col6\" class=\"data row2 col6\" >365</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row2_col7\" class=\"data row2 col7\" >3647</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row3_col0\" class=\"data row3 col0\" >Cozy Entire Floor of Brownstone</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row3_col1\" class=\"data row3 col1\" >Brooklyn</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row3_col2\" class=\"data row3 col2\" >Clinton Hill</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row3_col3\" class=\"data row3 col3\" >Entire home/apt</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row3_col4\" class=\"data row3 col4\" >89</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row3_col5\" class=\"data row3 col5\" >1</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row3_col6\" class=\"data row3 col6\" >194</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row3_col7\" class=\"data row3 col7\" >3831</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row4_col0\" class=\"data row4 col0\" >Entire Apt: Spacious Studio/Loft by central park</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row4_col1\" class=\"data row4 col1\" >Manhattan</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row4_col2\" class=\"data row4 col2\" >East Harlem</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row4_col3\" class=\"data row4 col3\" >Entire home/apt</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row4_col4\" class=\"data row4 col4\" >80</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row4_col5\" class=\"data row4 col5\" >10</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row4_col6\" class=\"data row4 col6\" >0</td>\n",
       "                        <td id=\"T_c832de02_08b7_11ea_8723_086d41dd6a90row4_col7\" class=\"data row4 col7\" >5022</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11c833410>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns\n",
    "nyc_airbnb = nyc_airbnb.rename(columns={\"name\": \"Name\",\n",
    "                                        \"neighbourhood_group\": \"Borough\",\n",
    "                                        \"neighbourhood\": \"Neighborhood\",\n",
    "                                        \"room_type\": \"Room Type\",\n",
    "                                        \"price\": \"Price\",\n",
    "                                        \"minimum_nights\": \"Minimum Nights\",\n",
    "                                        \"availability_365\": \"Yearly Availability\",\n",
    "                                        \"id\": \"ID\"})\n",
    "\n",
    "nyc_airbnb.head().style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Name</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Room Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Minimum Nights</th>\n",
       "      <th>Yearly Availability</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Clean &amp; quiet apt home by the park</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Kensington</td>\n",
       "      <td>Private room</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>2539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Skylit Midtown Castle</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Midtown</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>1</td>\n",
       "      <td>355</td>\n",
       "      <td>2595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Harlem</td>\n",
       "      <td>Private room</td>\n",
       "      <td>150</td>\n",
       "      <td>3</td>\n",
       "      <td>365</td>\n",
       "      <td>3647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Cozy Entire Floor of Brownstone</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Clinton Hill</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>194</td>\n",
       "      <td>3831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Large Cozy 1 BR Apartment In Midtown East</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Murray Hill</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>200</td>\n",
       "      <td>3</td>\n",
       "      <td>129</td>\n",
       "      <td>5099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name    Borough  Neighborhood  \\\n",
       "0         Clean & quiet apt home by the park   Brooklyn    Kensington   \n",
       "1                      Skylit Midtown Castle  Manhattan       Midtown   \n",
       "2        THE VILLAGE OF HARLEM....NEW YORK !  Manhattan        Harlem   \n",
       "3            Cozy Entire Floor of Brownstone   Brooklyn  Clinton Hill   \n",
       "4  Large Cozy 1 BR Apartment In Midtown East  Manhattan   Murray Hill   \n",
       "\n",
       "         Room Type  Price  Minimum Nights  Yearly Availability    ID  \n",
       "0     Private room    149               1                  365  2539  \n",
       "1  Entire home/apt    225               1                  355  2595  \n",
       "2     Private room    150               3                  365  3647  \n",
       "3  Entire home/apt     89               1                  194  3831  \n",
       "4  Entire home/apt    200               3                  129  5099  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DROP ROWS WHERE YEARLY AVAILABILITY = 0\n",
    "airbnb_df = nyc_airbnb[nyc_airbnb[\"Yearly Availability\"] != 0]\n",
    "# Reset Index\n",
    "airbnb_df.reset_index(inplace=True,drop=True)\n",
    "airbnb_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                            PATRICIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = airbnb_df.sort_values(\n",
    "    ['Borough', 'Neighborhood'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Room Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>125</td>\n",
       "      <td>182177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Private room</td>\n",
       "      <td>35</td>\n",
       "      <td>498120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Private room</td>\n",
       "      <td>39</td>\n",
       "      <td>715270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>49</td>\n",
       "      <td>755528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Private room</td>\n",
       "      <td>38</td>\n",
       "      <td>773041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Borough Neighborhood        Room Type  Price      ID\n",
       "0   Bronx     Allerton  Entire home/apt    125  182177\n",
       "1   Bronx     Allerton     Private room     35  498120\n",
       "2   Bronx     Allerton     Private room     39  715270\n",
       "3   Bronx     Allerton  Entire home/apt     49  755528\n",
       "4   Bronx     Allerton     Private room     38  773041"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BnN = B.drop(['Name', 'Minimum Nights', 'Yearly Availability'], axis=1).reset_index(drop=True)\n",
    "BnN.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "terms=[]\n",
    "for _,row in BnN.iterrows():\n",
    "   term=row['Neighborhood']+','+row['Borough']+', NYC'\n",
    "   if term not in terms:\n",
    "       terms.append(term)\n",
    "len(terms)\n",
    "lats=[]\n",
    "lngs=[]\n",
    "locs=[]\n",
    "for term in terms:\n",
    "   try:\n",
    "       queryurl=url+term\n",
    "       response=requests.get(queryurl).json()\n",
    "       pair=response['results'][0]['locations'][0]['latLng']\n",
    "       lats.append(pair['lat'])\n",
    "       lngs.append(pair['lng'])\n",
    "       locs.append(term)\n",
    "   except:\n",
    "       continue\n",
    "Ns=[]\n",
    "Bs=[]\n",
    "for loc in locs:\n",
    "   s=loc.split(',')\n",
    "   Ns.append(s[0])\n",
    "   Bs.append(s[1])\n",
    "newdf=pd.DataFrame({'lats':lats,'lngs':lngs,'Neighborhood':Ns,'Borough':Bs})\n",
    "BnN=BnN.merge(newdf,how='left',on=['Neighborhood','Borough'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "response['results'][0]['locations'][0]['latLng']\n",
    "price = BnN[\"Price\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "lats = BnN['lats']\n",
    "\n",
    "lngs = BnN['lngs']\n",
    "prices=BnN['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipyleaflet import Map, Heatmap, Marker, MarkerCluster\n",
    "\n",
    "m = Map(center=(40.730610, -73.935242), zoom=10, dragging=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "marker_locs=[]\n",
    "for i in range(0,len(prices)):\n",
    "    marker_loc =  lats[i],lngs[i],prices[i]\n",
    "    marker_locs.append(marker_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40.86553, -73.86247, 125)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5446e27ffa4e4c1e84ef6e5b224664c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(basemap={'url': 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', 'max_zoom': 19, 'attribution': 'Map …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap = Heatmap(\n",
    "    locations=marker_locs,\n",
    "\n",
    "    \n",
    ")\n",
    "\n",
    " #Change some attributes of the heatmap\n",
    "heatmap.radius = 10\n",
    "heatmap.blur = 5\n",
    "heatmap.max = 1000\n",
    "heatmap.gradient = {0.4: 'lime', 0.6: 'cyan', 0.7: 'yellow', 0.8: 'orange', 1.0: 'red'}\n",
    "\n",
    "m.add_layer(heatmap);\n",
    "\n",
    "print(marker_locs[0])\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Borough</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Room Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>ID</th>\n",
       "      <th>lats</th>\n",
       "      <th>lngs</th>\n",
       "      <th>Price Summary per Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>125</td>\n",
       "      <td>182177</td>\n",
       "      <td>40.86553</td>\n",
       "      <td>-73.86247</td>\n",
       "      <td>100-149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Private room</td>\n",
       "      <td>35</td>\n",
       "      <td>498120</td>\n",
       "      <td>40.86553</td>\n",
       "      <td>-73.86247</td>\n",
       "      <td>&lt; 50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Private room</td>\n",
       "      <td>39</td>\n",
       "      <td>715270</td>\n",
       "      <td>40.86553</td>\n",
       "      <td>-73.86247</td>\n",
       "      <td>&lt; 50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>49</td>\n",
       "      <td>755528</td>\n",
       "      <td>40.86553</td>\n",
       "      <td>-73.86247</td>\n",
       "      <td>&lt; 50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Private room</td>\n",
       "      <td>38</td>\n",
       "      <td>773041</td>\n",
       "      <td>40.86553</td>\n",
       "      <td>-73.86247</td>\n",
       "      <td>&lt; 50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Borough Neighborhood        Room Type  Price      ID      lats      lngs  \\\n",
       "0   Bronx     Allerton  Entire home/apt    125  182177  40.86553 -73.86247   \n",
       "1   Bronx     Allerton     Private room     35  498120  40.86553 -73.86247   \n",
       "2   Bronx     Allerton     Private room     39  715270  40.86553 -73.86247   \n",
       "3   Bronx     Allerton  Entire home/apt     49  755528  40.86553 -73.86247   \n",
       "4   Bronx     Allerton     Private room     38  773041  40.86553 -73.86247   \n",
       "\n",
       "  Price Summary per Neighborhood  \n",
       "0                        100-149  \n",
       "1                           < 50  \n",
       "2                           < 50  \n",
       "3                           < 50  \n",
       "4                           < 50  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the bins in which Data will be held\n",
    "# Bins are 0, 59, 69, 79, 89, 100.   \n",
    "bins = [0, 49, 99, 149, 199, 249, 299, 10000]\n",
    "\n",
    "# Create the names for the four bins\n",
    "group_names = [\"< 50\", \"50-99\", \"100-149\", \"150-199\", \"200-249\", \"250-299\",\"> 300\"]\n",
    "\n",
    "BnN[\"Price Summary per Neighborhood\"] = pd.cut(BnN[\"Price\"], bins, labels=group_names)\n",
    "BnN.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price Summary per Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>&lt; 50</td>\n",
       "      <td>3103</td>\n",
       "      <td>3103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50-99</td>\n",
       "      <td>10426</td>\n",
       "      <td>10426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100-149</td>\n",
       "      <td>6207</td>\n",
       "      <td>6207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150-199</td>\n",
       "      <td>4645</td>\n",
       "      <td>4645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200-249</td>\n",
       "      <td>2408</td>\n",
       "      <td>2408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Price     ID\n",
       "Price Summary per Neighborhood              \n",
       "< 50                             3103   3103\n",
       "50-99                           10426  10426\n",
       "100-149                          6207   6207\n",
       "150-199                          4645   4645\n",
       "200-249                          2408   2408"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of apartments available per price group\n",
    "# Split up our data into groups based upon 'price'\n",
    "# Find out how many apts are available is each price range\n",
    "BnN_Price = BnN.groupby(\"Price Summary per Neighborhood\").count()\n",
    "apt_price = BnN_Price.drop(['Borough','Neighborhood','Room Type','lats','lngs'], axis=1)\n",
    "apt_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dBnwoIibl8kuBPYA/Nmp/zMygtia5fe211177QFlHRwcPPfTQImXTpk1bMDxhwgQmTJiwyPSVV16ZK664ok9ja3YT31Uj4vk8PAtYNQ+vAUwvm29GLuuqfEaV8qokHSJpsqTJs2fP7t0emJnZAi27TySfdUSTtnVeRIyNiLHDhlV9wqOZmdWh2UnkhXyZivz/xVw+ExhRNt/wXNZV+fAq5WZm1kTNTiLXAqWLdBOAa8rKx+dWWlsAL+fLXjcAO0haKVeo7wDckKe9ImmL3CprfNm6zMysSRpWsS7p16SK8ZUlzSC1sjoZuFLSwcAzwN559uuBnYGpwBvAQQARMVfSj4B78nw/LFWyA18htQAbRKpQd6W6mVmTNbJ11hc7mbR9lXkDOKyT9VwEXFSlfDKwYW9iNDOz3nEHjGZmba7ULcm0adMYNGgQG2+8Meuvvz6bbbYZl1xySUtjc7cnZmY9cfwKfby+nj1S4SMf+Qj3338/AE899RSf//zniQgOOuigvo2rRj4TMTMrqLXXXpvTTjuNM888s2UxOImYmRXYJptswmOPPday7TuJmJkVWGqX1DpOImZmBXb//fez/vrrt2z7TiJmZgU1bdo0jj76aA4//PCWxeDWWWZmBfLkk0+y8cYb89ZbbzF48GCOOOIIDjzwwJbF4yRiZtYTPWyS2xdKXcF3dHTw5ptvNn37XfHlLDMzq5uTiJmZ1c1JxMzM6uYkYmbWjVbfi9Es9eynk4iZWRcGDhzInDlzFvtEEhHMmTOHgQMH9mg5t84yM+vC8OHDmTFjBrNnz251KA03cOBAhg8f3v2MZZxEzMy6MGDAAEaOHNnqMNqWL2eZmVndnETMzKxuTiJmZlY3JxEzM6ubk4iZmdXNScTMzOrmJGJmZnVzEjEzs7o5iZiZWd2cRMzMrG5OImZmVjcnETMzq5uTiJmZ1c1JxMzM6uYkYmZmdWtJEpF0lKSHJT0k6deSBkoaKekuSVMlXSFp6TzvMnl8ap7eUbaeY3P545J2bMW+mJn1Z01PIpLWAI4AxkbEhsCSwL7AKcDpEbEOMA84OC9yMDAvl5+e50PSqLzcBsA44GxJSzZzX8zM+ruak4ikZftwu0sBgyQtBSwLPA9sB/wmT58I7JGHd8/j5OnbS1Iuvzwi3o6Ip4GpwGZ9GKOZmXWj2yQi6ZOSHgEey+MbSTq73g1GxEzgVOBZUvJ4GbgXmB8R7+bZZgBr5OE1gOl52Xfz/EPLy6ssU7kPh0iaLGlyf3hOsplZs9RyJnI6sCMwByAiHgA+Xe8GJa1EOosYCawOLEe6HNUwEXFeRIyNiLHDhg1r5KbMzPqVmi5nRcT0iqL3erHNzwBPR8TsiHgHuBr4FLBivrwFMByYmYdnAiMA8vQVSAltQXmVZczMrAlqSSLTJX0SCEkDJB0NPNqLbT4LbCFp2Vy3sT3wCHAr8IU8zwTgmjx8bR4nT78lIiKX75tbb40E1gXu7kVcZmbWQ0t1PwuHAmeQ6htmAjcCh9W7wYi4S9JvgPuAd4H7gfOA64DLJZ2Qyy7Mi1wI/I+kqcBcUossIuJhSVeSEtC7wGER0ZszJDMz66Fuk0hEvATs35cbjYjjgOMqip+iSuuqiHgL2KuT9ZwInNiXsZmZWe26TSKSzqxS/DIwOSKuqTLNzMz6iVrqRAYCY4An8t9oUiX2wZJ+1sDYzMyszdVSJzIa+FSpvkHSOcAdwJbAgw2MzczM2lwtZyIrAcuXjS8HDMlJ5e2GRGVmZoVQy5nIT4Apkm4DRLrR8CRJywF/bmBsZmbW5mppnXWhpOtZ2HLqOxHxXB7+ZsMiMzOztldrB4xLALNJveuuI6nubk/MzGzxUUsT31OAfYCHgfdzcQB/aWBcZmZWALXUiewBrBcRrkQ3M7NF1HI56ylgQKMDMTOz4qnlTOQNUuusmylr0hsRRzQsKjMzK4Raksi1+c/MzGwRtTTxndjdPGZm1j/V0jprXeDHwChSP1oARMTaDYzLzMwKoJaK9YuBc0jP7NgWuBT4ZSODMjOzYqgliQyKiJsBRcQzEXE8sEtjwzIzsyKopWL9bUlLAE9I+irp6YbLd7OMmZn1A7WciRwJLAscAXwCOICFzzw3M7N+rJbWWffkwdeAgwAkrdnIoMzMrBi6PBOR9C+SviBplTw+WtJlwN+aEp2ZmbW1TpOIpP8CLgL2BK6TdAJwI3AXsG5zwjMzs3bW1eWsXYCNI+ItSSsB04ENI2JaUyIzM7O219XlrLci4i2AiJgHPOEEYmZm5bo6E1lbUnmfWSPLxyNit8aFZWZmRdBVEtm9YvynjQzEzMyKp9MkEhG3NzMQMzMrnlqfsW5mZvYBTiJmZla3mpOIpGUbGYiZmRVPt0lE0iclPQI8lsc3knR2wyMzM7O2V0svvqcDO5IfkRsRD0j6dG82KmlF4AJgQyCALwGPA1cAHcA0YO+ImCdJwBnAzqTnvR8YEffl9UwAvpdXe4Kfwrh46Tjmuh7NP23gfj3bwPEv92x+M/uAmi5nRcT0iqL3erndM4A/RcTHgI2AR4FjgJsjYl3g5jwOsBOpm5V1gUNID8hC0hDgOGBzYDPguHxnvZmZNUktSWS6pE8CIWmApKNJB/26SFoB+DRwIUBE/DMi5pPuSymdSUwE9sjDuwOXRjIJWFHSaqSzo5siYm6+o/4mYFy9cZmZWc/VkkQOBQ4D1iA9kGpMHq/XSGA2cLGk+yVdIGk5YNWIeD7PMwtYNQ+vQeq3q2RGLuus3MzMmqSW54m8BOzfx9vcBDg8Iu6SdAYLL12VthmSoq82KOkQ0qUw1lzTj0IxM+srtbTOmpgrwkvjK0m6qBfbnAHMiIi78vhvSEnlhXyZivz/xTx9JjCibPnhuayz8g+IiPMiYmxEjB02bFgvQjczs3K1XM4anessgAU9+m5c7wYjYhapnmW9XLQ98Aip9VfpsbsTgGvy8LXAeCVbAC/ny143ADvkpLYSsEMuMzOzJqmlie8SklbKyaPUKqqW5bpyOPArSUsDT5Eeu7sEcKWkg4FngL3zvNeTmvdOJTXxPQggIuZK+hFQenzvDyNibi/jMjOzHqglGfwU+LukqwABXwBO7M1GI2IKMLbKpO2rzBt0UpEfEReRnr5oZmYtUEvF+qWSJgPb5aLPR8QjjQ3LzMyKoNMkIulDEfFKvnw1C7isbNoQXzoyM7OuzkQuA3YF7iV1TVKiPL52A+MyM7MC6OqhVLvmfqu2johnmxiTmZkVRJdNfHOlds96wTMzs36jlvtE7pO0acMjMTOzwqmlie/mwL9Jmga8Tq4TiYjRjQzMzMzaXy1JZMeGR2FmZoXUVRPfgaQefNcBHgQujIh3mxWYmZm1v67qRCaS7ip/kPRgqJ82JSIzMyuMri5njYqIjwNIuhC4uzkhmZlZUXR1JvJOacCXsczMrJquzkQ2kvRKHhYwKI+XWmd9qOHRmZlZW+vqjvUlmxmImZkVTy03G5qZmVXlJGJmZnVzEjEzs7p1mUQkLSnp1mYFY2ZmxdJdL77vAe9LWqFJ8ZiZWYHU0nfWa8CDkm4idcAIQEQc0bCozMysEGpJIlfnPzMzs0V0m0QiYqKkQcCaEfF4E2IyM7OC6DaJSPoscCqwNDBS0hjghxGxW6ODMyuyjmN69lDQaQP369kGjn+5Z/ObNUAtTXyPBzYD5gNExBRg7QbGZGZmBVFLEnknIip/8rzfiGDMzKxYaqlYf1jSfsCSktYFjgDubGxYZmZWBLWciRwObAC8DfwaeAX4WiODMjOzYqilddYbwHclnZJG49XGh2VmZkXQ7ZmIpE0lPQj8g3TT4QOSPtH40MzMrN3VUidyIfCViLgDQNKWwMXA6EYGZmZm7a+WOpH3SgkEICL+CvhxuWZmVlMSuV3SLyRtI2lrSWcDt0naRNIm9W449xB8v6Q/5PGRku6SNFXSFZKWzuXL5PGpeXpH2TqOzeWPS9qx3ljMzKw+tVzO2ij/P66ifGMggO3q3PaRwKNA6VntpwCnR8Tlks4FDgbOyf/nRcQ6kvbN8+0jaRSwL6nl2OrAnyV9NPc8bGZmTVBL66xt+3qjkoYDuwAnAl+XJFIyKvX7MJF0p/w5wO55GOA3wH/n+XcHLo+It4GnJU0l3Vn/976O18zMqmvVkw1/BnyLhXe+DwXmR0SprmUGsEYeXgOYDpCnv5znX1BeZRkzM2uCpicRSbsCL0bEvU3c5iGSJkuaPHv27GZt1sxssdeKM5FPAbtJmgZcTrqMdQawoqTS5bXhwMw8PBMYAZCnrwDMKS+vsswiIuK8iBgbEWOHDRvWt3tjZtaP1XKz4V6SBufh70m6ujetsiLi2IgYHhEdpIrxWyJif+BW4At5tgnANXn42jxOnn5LREQu3ze33hoJrAvcXW9cZmbWc7WciXw/Il7NNxl+hnTz4TkNiOXbpEr2qaQ6jwtz+YXA0Fz+deAYgIh4GLgSeAT4E3CYW2aZmTVXLU18SwfmXYDzIuI6SSf0xcYj4jbgtjz8FKl1VeU8bwF7dbL8iaQWXmZm1gK1nInMlPQLYB/geknL1LicmZkt5mpJBnsDNwA7RsR8YAjwzYZGZWZmhVBLEvlFRFwdEU8ARMTzwAGNDcvMzIqgliSyQfmIpCUBdwVvZmadJ5HcueGrwGhJr0h6NY+/yMLmt2Zm1o91mkQi4scRMRj4r4j4UEQMzn9DI+LYJsZoZmZtqpYmvt+R9HlgS1KvvXdExO8bG5aZmRVBLXUiZwGHAg8CDwGHSjqroVGZmVkh1HImsh2wfu5qBEkTgYcbGpWZmRVCLWciU4E1y8ZH5DIzM+vnajkTGQw8KuluUp3IZsBkSdcCRMRuDYzPzMzaWC1J5AcNj8LMzAqplsfj3l4+nnvz/WJEHNawqNpIxzHX1TzvtIH7dT9TueNf7mE0ZmbtpZYzESRtTHr++V7A08BvGxmUmZkVQ6dJRNJHgS/mv5eAKwBFxLZNis3MzNpcV2cijwF3ALtGxFQASUc1JSozs36uKJfSu2ri+3ngeeBWSedL2h5Qn23ZzMwKr6u+s34fEfsCHyM9//xrwCqSzpG0Q7MCNDOz9tXtzYYR8XpEXBYRnwWGA/eTnoduZmb9XI8ecxsR8yLivIjYvlEBmZlZcfhZ6WZmVjcnETMzq5uTiJmZ1c1JxMzM6uYkYmZmdXMSMTOzutXUAaOZ9T896XYD3It1f+UzETMzq5uTiJmZ1c1JxMzM6uYkYmZmdWt6EpE0QtKtkh6R9LCkI3P5EEk3SXoi/18pl0vSmZKmSvqHpE3K1jUhz/+EpAnN3hczs/6uFWci7wLfiIhRwBbAYZJGAccAN0fEusDNeRxgJ2Dd/HcIcA6kpAMcB2wObAYcV0o8ZmbWHE1PIhHxfETcl4dfBR4F1gB2Bybm2SYCe+Th3YFLI5kErChpNWBH4KaImBsR84CbgHFN3BUzs36vpXUikjqAjYG7gFUj4vk8aRawah5eA5hettiMXNZZebXtHCJpsqTJs2fP7rP4zcz6u5YlEUnLA78FvhYRr5RPi4gAoq+2lZ+BMjYixg4bNqyvVmtm1u+1JIlIGkBKIL+KiKtz8Qv5MhX5/4u5fCYwomzx4bmss3IzM2uSVrTOEnAh8GhEnFY26Vqg1MJqAnBNWfn43EprC+DlfNnrBmAHSSvlCvUdcpmZmTVJK/rO+hRwAPCgpCm57DvAycCVkg4GngH2ztOuB3YGpgJvAAcBRMRcST8C7snz/TAi5jZnF8zMDFqQRCLir4A6mfyBZ7fn+pHDOlnXRcBFfRedmZn1hO9YNzOzujmJmJlZ3ZxEzMysbn4olZktlvxQrebwmYiZmdXNScTMzOrmJGJmZnVzEjEzs7o5iZiZWd2cRMzMrG5OImZmVjcnETMzq5uTiJmZ1c1JxMzM6uYkYmZmdXMSMTOzujmJmJlZ3ZxEzMysbk4iZmZWNycRMzOrm5OImZnVzUnEzMzq5iRiZmZ1cxIxM7O6OYmYmVndnETMzKxuTiJmZlY3JxEzM6ubk4iZmdXNScTMzOrmJGJmZnUrfBKRNE7S45KmSjqm1fGYmfUnhU4ikpYEzgJ2AkYBX5Q0qrVRmZn1H4VOIsBmwNSIeCoi/hJeoxQAAA+NSURBVAlcDuze4pjMzPoNRUSrY6ibpC8A4yLi3/P4AcDmEfHVivkOAQ7Jo+sBjzcopJWBlxq07mZw/K3l+FuryPE3Ova1ImJYtQlLNXCjbSMizgPOa/R2JE2OiLGN3k6jOP7WcvytVeT4Wxl70S9nzQRGlI0Pz2VmZtYERU8i9wDrShopaWlgX+DaFsdkZtZvFPpyVkS8K+mrwA3AksBFEfFwC0Nq+CWzBnP8reX4W6vI8bcs9kJXrJuZWWsV/XKWmZm1kJOImZnVzUnEzMzqVuiK9VaSJNId82vkopnA3eFKJjNrIEkrAONY9NhzQ0TMb0k8Pub1nKQdgLOBJ1h4X8pwYB3gKxFxY6ti6ylJw0ixvwc8FRGvtTgkKwhJ2wJ7ku7Veg/4P+CCiJja0sC6IWl54Fuk2IcD/wSeBM6NiEtaGFq3JI0HjgNuZNFjz78C/xkRlzY9JieRnpP0KLBTREyrKB8JXB8R67cksB7IHVWeCXQAawL3A6sAtwNHRsTLrYuuNkU9iEGxD2QAkn4MfBi4GdgDeJr0+n8FOCkirmpheF2SdA3wO+DPwN7AcqR+974HzIyI77QwvC5JepzUtdP8ivKVgLsi4qPNjsl1IvVZCphRpXwmMKDJsdTrIuCwiFgH2BJ4LCJGAn8DLmxpZDXIB7HxwCTgHdIB+EngKkl7tTK2Gv0KeArYEfhPUkI/ANhW0kmtDKxGu0bEQRHxS9JNvp+MiPOB7Ui/lNtZR0RcEhEzIuI0YLeIeAI4CPh8i2PrjoBqv/zfz9OaznUi9bkIuEfS5cD0XDaC9GVq+wNwNigiHgeIiLslnZuHz5f09daGVpNdI+LjAPl9uD0ivinpN8AdQNv+Es46ys44TpN0T0T8SNJBwCNA2/4azt6XNCQi5gKrk272JSLm5frCdva6pC0j4q+SdgPmAkTE+wWI/UTgPkk3svDYsybpctaPWhGQk0gdIuLHkn5P6nb+X3LxTGD/iHikdZH1yJOSvg/cQvr1NQVA0gCKcYZa5IMYFPtABnAScL+k/yP1jP0fsKCO7YFWBlaDQ4ELJH0UeAg4GBbEflYrA+tOREyUdC3pDLZUsX4bcGxEzGtFTK4T6ackrUj6tTuK9KU/OSJezS0/1o+ISS0NsBuS9gF+QroOvx7wHxFxXT4QnBER+7U0wG5IGg1cACw4kEXE4zn+L0bEmS0NsAaShgBrk57p05KWQf2VpFUpa50VES+0LBYnkZ6TNC4i/pSHVwB+Smru+xBwVCvf0P7EB7H2Iumkdq6ULidpbdIZeHmjjMsi4pWWBtYNSWOAc4EVSPWyIjXMmE9qGXpf02NyEuk5SfdFxCZ5+AJgFnA+6UO5dUTs0cr4aiFpCWACH2zddG5E3NbC0OpWpIMYFPdABiCp8kxJpIYBlwJExBFND6pGko4APktqibgzqWXifOBzpAPxba2LrmuSpgD/LyLuqijfAvhFRGzU7JhcJ9J7YyNiTB4+XdKElkZTuwuBZ4CTgS8Ar5AqpL8n6eMR8fNWBtedzg5iuelsWx/E4AMHsk1JB7IRwCRJbX0gyz5Hiv1GFrYK2he4t2UR1e7LwJiIeE/SaaRm+dtI+gVwDbBxa8Pr0nKVCQQgIiZJWq4VAflMpA6SZgCnkb48hwEfKd2pLukfETG6lfHVojJOSZMiYgtJywBT2v1eF0nT+eBB7FTgaEgVkC0KrSaSHmThgWxZFh7I1gSuiYh2PpAhaTCpNdAqwNER8ZykpyJi7RaH1q382o+NiLfz/RU3lZ4KKOmhiNiwtRF2Lv94+gjpjK+8Zeh44OnKR4M3g89E6nM+MDgPTyQ933i2pA+TWzkVwDuSPhIRT0rahHSzG/mLVYRfFqNIB7FxLDyIHdfuyaPCUqTLWMsApTOoZ3MLubYWEa8CX5P0CeBXkq6jGK36IDVouEfSXcBWwCmwoHXW3FYG1p2IOELSzsBuLNrtyVkRcX0rYvKZSD8laTvgEuBt0sFs34i4K3+RvhkR32plfLXKB7FTgeuAr0ZER2sjqo2kI0lNSxccyCLi4vz6/zYiPt3SAHsgN0n+CvAvEfFvrY6nFpI2ANYHHoqIx1odT5E5ifRj+cs/NCJeanUsvVHEgxj4QGY9l692HEe6Q/0HwOGkxhmPkborer7ZMRXl9NMaIJIFCURSIR8PmvfjrCIlEICIeDgiflOZQEqNA9qZpBGSLpd0h6TvlF+Cyzfiti1JoyVNkjRd0nm5XqQ07e5WxlaDS0g9GkwHbgXeBHYhNYo5txUBOYlYubGtDqAv5IrTIitCrwcXke6UPhxYDbhd0tA8ba1WBVWjs4HjgY+TmlX/VdJH8rR2r49aNSJ+HhEnAytGxCkRMT23pmzJ6+6K9TrlFjXrRsQDZWVrAu9FxMzOl2xrL7Y6gFpJ6qyjPJF6l21rXfRPJnIle5sbFhGlX76HS/o34C+5C5d2v0Y+uHSzMHCqpHuBP0k6gPaPvfyHf2W37y05KXASqd87wNWSRkfE67nsAlJXIoVMIhExrtUx9MAVpJ5wq33pBzY5lnqcBPwX8G6VaUW4QjBA0sCIeAsgIn4paRZwA6lr9bYmaYXIjzuIiFsl7Qn8FhjS2si6dY2k5SPitYj4XqlQ0jqks6qmc8V6L0g6FXg4t6opRPv+ktxdy7GkZ0GsQjoYv0i62erkdu9GJP96nBARD1WZNj0iRrQgrJpJuhM4PCI+cHNeQeI/CrgvIm6vKN8Y+ElE/GtrIuuepP1ID2CbVFG+JvD9iPhyayIrJieRXpD0MeC8iPi0pO8BrxSh4zwASTeQevCdGBGzctmHSV2hbB8RO7Qyvu5I2gp4JiKerTJtbERMbkFYNZO0HjCnWss4Sau6/7XmkvTh0vfAesZJpJck3UFq7381sFW0qDvmnpL0eESs19Np1jhFP5CV9ylXNEWOvdWKcO213V1Iqgt5sCgJJHtG0rdyl9JA+gUs6dss7E6hUCQ1vQfTPtaSO477UBGeg9KZIsfeUk4ivXclsBHFeaJhyT7AUFLTzHmS5pKabA4hPXe6iIp+ICh6/Ne1OoBeOL/VAdRD0lhJS7cyBrfO6qWIeIPUt3+h5LOmb+e/Uh3DZqQzqrbuP6gLRT6IQQEPZFr04Uht3fNzudzLwWYsjP1eSYoCXd+XtBpwJ+nZ8L9qWRwFes2sD0m6OyI2y8P/TuqN+PfADsD/5puZ2p7a6AlvPVXlQDYTuLsIBzIt+nCkUpP2lj4cqVaSdiDdcPgEi8a+Din2G1sVW09IOobUo+86EbFty+IowOfVGkDS/aXmyJLuAXaOiNn5mQSTIuLjrY2wa0U+iEHxD2Rqw4cj1UrSo8BOETGtonwkqUv+tn4MQomkh4GtgWuBAyLiyVbE4ctZ/dcSuc+gJUg/JmYDRMTrkqrdANduLqHzg9jFpHqqdnYG8JnODmSkjhnbWds9HKkHliI9WrbSTNq/2xMAJG0LPBYRL0m6hNRCtCVP9XQS6b9WID2FTkBIWi0ins+d/xWhgrfIBzEo/oHsj/kZItUejvSnTpdqDxeRnidyOYvGvi/FaSDzJRbGejmpTud7EfF+swPx5SxbRO4TbNWIeLrVsXRFbfiEt56QdCypFVy1A9mVEfHjVsVWK0k7AbuzaJ3Ota16OFJPSFqf6rG3feeXklYEJpP67is9UfV/gCsi4g9Nj8dJxIqqyAcxKPaBzKzEScTMeqys77XdgVUpUN9rksaVevHN+/FTUiu5h4CjitTCrx34ZkMrJEkrSDpZ0qOS5kqak4dPzqf7bU3SuLLhFSRdIOkfki4r70WgjV0JzAO2jYghETEU2JbUOu7KlkbWvZPKhn8KzAI+C9wD/KIlERWYz0SskLroQPJAYLsCdCC5oK8mSReQDmTnkx51unVE7NHK+LpT5L7XKl77KRExpmzaIuPWPbfOsqLqiIhTygtyMjlZ0kEtiqleY8sOXKdLmtDSaGrzjKRvkZL4C7Dgxs8Daf++11bJDwUT8KGKO9V9daaH/IJZURW9A8lVJH1d0jfIB7KyaUX4Xha577XzgcGkJ0hOBFaGBWeyU1oYVyH5cpYVUr5R8hhSxe4qufgF0t27J7d7j8qSjqsoOjv3GPBh0kOdxrcirp7Iz9MZTurh4LWy8gUV1+0qx74GcFfRYm83TiK22JF0UERc3Oo46lWE+CUdQepv7VFgDHBkRFyTp7X1szkkHQ58lQLG3o6cRGyxI+nZiFiz1XHUqwjxS3oQ+JeIeE1SB/Ab4H8i4ozyftnaUZFjb0euWLdCkvSPziaR7ltoa0WPH1iidBkoIqZJ2gb4jaS1aP9uc4oce9txErGiWhXYkXSvQjmRnrHQ7ooe/wuSxkTEFID8q35XUr9Ubd0DNMWOve04iVhR/QFYvnQgKCfptuaH02NFj388sEhvzxHxLjBeUrvfsFfk2NuO60TMzKxuRWiPbmZmbcpJxMzM6uYkYlaFpPckTZH0kKSr8nNWqs13fW87fJS0jaSX8/Yek3Rqb9Zn1kxOImbVvRkRYyJiQ+CfwKHlE5UsERE791G353fk/rM2BnaV9Kk+WKdZwzmJmHXvDmAdSR2SHpd0KenZEyMkTZNU6ntpfO7O/YH8pDkkDZP0W0n35L8uk0NEvEnqv2mNvPxmkv4u6X5Jd0paL5cfKOlqSX+S9ISkn5TWIelgSf8n6W5J50v673piMauFm/iadUHSUsBOLHxu+LrAhIiYlKeX5tsA+B7wyYh4SdKQPP8ZwOkR8VdJawI3AOt3sb2V8jb+koseA7aKiHclfYb0LIw987TSmcvbwOOSfg68B3wf2AR4ldRd/gP1xGJWCycRs+oGSSrdw3EHcCGwOvBMKYFU2A64KiJeAoiIubn8M8Cosk56PyRp+fJO/7KtJD1ASiA/Kz0jBVgBmChpXdLTAweULXNzRLwMIOkRYC1Sj7S3l7Yv6Srgoz2MxaxmTiJm1b1Z+XCifPB9vYfrWQLYIiLe6ma+OyJiV0kjgUmSrsw3Iv4IuDUiPpf7ebqtbJm3y4bfo/vvc62xmNXMdSJmfeMWYC9JQwHKLmfdCBxemklSl0/Ni4ingZOBb+eiFYCZefjAGuK4B9ha0kr5UtyeZdN6FItZLZxEzPpARDwMnEh6SNMDwGl50hHA2Fzh/ggVrbw6cS7w6Xzm8RPgx5Lup4YrBxExk1RvcjfwN2Aa8HIvYjHrkrs9MVvMlOo58pnI74CLIuJ3rY7LFk8+EzFb/ByfGwU8BDwN/L7F8dhizGciZmZWN5+JmJlZ3ZxEzMysbk4iZmZWNycRMzOrm5OImZnVzUnEzMzq9v8Byt++QdKHQy4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Chart our data, give it a title, and label the axes\n",
    "Apt_price = apt_price.plot(kind=\"bar\", title=\"Amount of Apartments Available per Price Range\")\n",
    "Apt_price.set_xlabel(\"Price Range\")\n",
    "Apt_price.set_ylabel(\"Apts per Price Range\")\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                        MANUELA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Yearly Availability</th>\n",
       "      <th>Room Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>365</td>\n",
       "      <td>Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>355</td>\n",
       "      <td>Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>365</td>\n",
       "      <td>Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>194</td>\n",
       "      <td>Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>129</td>\n",
       "      <td>Entire home/apt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Yearly Availability        Room Type\n",
       "0                  365     Private room\n",
       "1                  355  Entire home/apt\n",
       "2                  365     Private room\n",
       "3                  194  Entire home/apt\n",
       "4                  129  Entire home/apt"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_df = airbnb_df[[\"Yearly Availability\", \"Room Type\"]]\n",
    "first_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>Room Type</th>\n",
       "      <th>Sum of Days Available During the Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>16533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Private room</td>\n",
       "      <td>13965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Room Type  Sum of Days Available During the Year\n",
       "0  Entire home/apt                                  16533\n",
       "1     Private room                                  13965\n",
       "2      Shared room                                    864"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roomtgrouped = first_df.groupby([\"Room Type\"]).count()\n",
    "roomtgroupeddf = pd.DataFrame(roomtgrouped)\n",
    "roomtgroupeddf.reset_index(level = None, inplace = True)\n",
    "roomtgroupeddf = roomtgroupeddf.rename(columns = {\"Yearly Availability\" : \"Sum of Days Available During the Year\"})\n",
    "roomtgroupeddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_axis = roomtgroupeddf[\"Sum of Days Available During the Year\"]\n",
    "x_axis = roomtgroupeddf[\"Room Type\"]\n",
    "plt.bar(x_axis, y_axis, color=\"b\", align=\"center\")\n",
    "plt.title(\"Room Type vs. Sum of Days Available During the Year\")\n",
    "plt.xlabel(\"Room Types\")\n",
    "plt.ylabel(\"Sum of Days Available During the Year\")\n",
    "\n",
    "\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IN THE NEW YORK AREA, THERE'S HIGHER AVAILABILITY OF ENTIRE APARTMENTS THROUGHOUT THE YEAR (GREATER AMOUNT OF DAYS AVAILABLE). SECOND IS PRIVATE ROOMS, AND THIRD IS A SMALL AMOUNT OF SHARED ROOMS AVAILABLE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Borough</th>\n",
       "      <th>Room Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Private room</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Entire home/apt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Entire home/apt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Borough        Room Type\n",
       "0   Brooklyn     Private room\n",
       "1  Manhattan  Entire home/apt\n",
       "2  Manhattan     Private room\n",
       "3   Brooklyn  Entire home/apt\n",
       "4  Manhattan  Entire home/apt"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_df = airbnb_df[[\"Borough\", \"Room Type\"]]\n",
    "second_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>Borough</th>\n",
       "      <th>Total Number of Rooms/ Apts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>12260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>13559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Queens</td>\n",
       "      <td>4298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Borough  Total Number of Rooms/ Apts\n",
       "0          Bronx                          914\n",
       "1       Brooklyn                        12260\n",
       "2      Manhattan                        13559\n",
       "3         Queens                         4298\n",
       "4  Staten Island                          331"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boroughgrouped = second_df.groupby([\"Borough\"]).count()\n",
    "boroughgroupeddf = pd.DataFrame(boroughgrouped)\n",
    "boroughgroupeddf.reset_index(level = None, inplace = True)\n",
    "boroughgroupeddf = boroughgroupeddf.rename(columns = {\"Room Type\" : \"Total Number of Rooms/ Apts\"})\n",
    "boroughgroupeddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2_axis = boroughgroupeddf[\"Borough\"]\n",
    "x2_axis = boroughgroupeddf[\"Total Number of Rooms/ Apts\"]\n",
    "colors = [\"red\", \"orange\", \"lightcoral\", \"lightskyblue\", \"blue\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x11fdbbdd0>,\n",
       "  <matplotlib.patches.Wedge at 0x11fdc96d0>,\n",
       "  <matplotlib.patches.Wedge at 0x11fdd41d0>,\n",
       "  <matplotlib.patches.Wedge at 0x11fdd4c90>,\n",
       "  <matplotlib.patches.Wedge at 0x11fd1e9d0>],\n",
       " [Text(-0.9037660939152232, 0.6270620762645593, 'Bronx'),\n",
       "  Text(-0.8319780034936637, -0.7195919688981371, 'Brooklyn'),\n",
       "  Text(1.086326435919438, 0.17290134361123816, 'Manhattan'),\n",
       "  Text(-0.40373054229104743, 1.023230985272327, 'Queens'),\n",
       "  Text(-0.8187458979751604, 0.734612247753091, 'Staten Island')],\n",
       " [Text(-0.4929633239537581, 0.3420338597806687, '2.91435%'),\n",
       "  Text(-0.4538061837238165, -0.39250471030807477, '39.09189%'),\n",
       "  Text(0.5925416923196933, 0.09430982378794807, '43.23385%'),\n",
       "  Text(-0.2202166594314804, 0.5581259919667237, '13.70448%'),\n",
       "  Text(-0.44658867162281474, 0.4006975896835041, '1.05542%')])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(x2_axis, labels=y2_axis, colors=colors,\n",
    "        autopct='%1.5f%%', shadow=True, startangle=140)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# THE HIGHEST NUMBER OF AVAILABLE ROOMS/ APTS ARE LOCATED IN MANHATTAN, EVEN THOUGH IS THE SMALLEST BOROUGH. NEXT IS BROOKLYN, FOLLOWED BY QUEENS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                            SURA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Room Type</th>        <th class=\"col_heading level0 col1\" >Price</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90row0_col0\" class=\"data row0 col0\" >Entire home/apt</td>\n",
       "                        <td id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90row0_col1\" class=\"data row0 col1\" >$211.79</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90row1_col0\" class=\"data row1 col0\" >Private room</td>\n",
       "                        <td id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90row1_col1\" class=\"data row1 col1\" >$89.78</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90row2_col0\" class=\"data row2 col0\" >Shared room</td>\n",
       "                        <td id=\"T_f05ff180_08b7_11ea_8723_086d41dd6a90row2_col1\" class=\"data row2 col1\" >$70.13</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x120191fd0>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby room type by Price average\n",
    "mean_price_of_rooms = nyc_airbnb.groupby([\"Room Type\"],as_index=False)\n",
    "\n",
    "# Create DataFrame\n",
    "avrg_price = pd.DataFrame(mean_price_of_rooms[\"Price\"].mean())\n",
    "\n",
    "# Formating currency\n",
    "avrg_price[\"Price\"] = avrg_price[\"Price\"].astype(float).map(\"${:,.2f}\".format)\n",
    "#pivot = avrg_price.pivot()\n",
    "\n",
    "avrg_price.style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Total Rooms</th>        <th class=\"col_heading level0 col1\" >Per of Room Type</th>    </tr>    <tr>        <th class=\"index_name level0\" >Room Type</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90level0_row0\" class=\"row_heading level0 row0\" >Entire home/apt</th>\n",
       "                        <td id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90row0_col0\" class=\"data row0 col0\" >25409</td>\n",
       "                        <td id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90row0_col1\" class=\"data row0 col1\" >51.97%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90level0_row1\" class=\"row_heading level0 row1\" >Private room</th>\n",
       "                        <td id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90row1_col0\" class=\"data row1 col0\" >22326</td>\n",
       "                        <td id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90row1_col1\" class=\"data row1 col1\" >45.66%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90level0_row2\" class=\"row_heading level0 row2\" >Shared room</th>\n",
       "                        <td id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90row2_col0\" class=\"data row2 col0\" >1160</td>\n",
       "                        <td id=\"T_f066b0f6_08b7_11ea_8723_086d41dd6a90row2_col1\" class=\"data row2 col1\" >2.37%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11fd40810>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Data Frame: Total Rooms and Percentage showing which rooms we have more\n",
    "room_categ = pd.DataFrame({\"Total Rooms\" : nyc_airbnb[\"Room Type\"].value_counts(),\n",
    "                           \"Per of Room Type\" : nyc_airbnb[\"Room Type\"].value_counts()/nyc_airbnb[\"Room Type\"].count()/1})\n",
    "\n",
    "\n",
    "# Set Index and Index name\n",
    "index = [\"Entire home/apt\", \"Private room\", \"Shared room\"]\n",
    "room_categ.index.name = \"Room Type\"\n",
    "\n",
    "# Format with percentage and currency style\n",
    "room_categ[\"Per of Room Type\"] = room_categ[\"Per of Room Type\"].astype(float).map(\"{:.2%}\".format)\n",
    "room_categ.style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Borough</th>        <th class=\"col_heading level0 col1\" >Price</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row0_col0\" class=\"data row0 col0\" >Bronx</td>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row0_col1\" class=\"data row0 col1\" >$87.50</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row1_col0\" class=\"data row1 col0\" >Brooklyn</td>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row1_col1\" class=\"data row1 col1\" >$124.38</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row2_col0\" class=\"data row2 col0\" >Manhattan</td>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row2_col1\" class=\"data row2 col1\" >$196.88</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row3_col0\" class=\"data row3 col0\" >Queens</td>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row3_col1\" class=\"data row3 col1\" >$99.52</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row4_col0\" class=\"data row4 col0\" >Staten Island</td>\n",
       "                        <td id=\"T_f06d0ae6_08b7_11ea_8723_086d41dd6a90row4_col1\" class=\"data row4 col1\" >$114.81</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11c6492d0>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by Cities\n",
    "group_city = nyc_airbnb.groupby([\"Borough\"],as_index=False)\n",
    "\n",
    "# Create Data Frame by Price\n",
    "city_avrg_prices = pd.DataFrame(group_city[\"Price\"].mean())\n",
    "\n",
    "# Create formating currency for Price column\n",
    "city_avrg_prices[\"Price\"] = city_avrg_prices[\"Price\"].astype(float).map(\"${:,.2f}\".format)\n",
    "\n",
    "# Show Data Frame by style (bold index)\n",
    "city_avrg_prices.style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Room Category:\n",
      "-----------------\n"
     ]
    },
    {
     "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>Total Rooms</th>\n",
       "      <th>Per of Room Type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Room Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>25409</td>\n",
       "      <td>51.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Private room</td>\n",
       "      <td>22326</td>\n",
       "      <td>45.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Shared room</td>\n",
       "      <td>1160</td>\n",
       "      <td>2.37%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Total Rooms Per of Room Type\n",
       "Room Type                                    \n",
       "Entire home/apt        25409           51.97%\n",
       "Private room           22326           45.66%\n",
       "Shared room             1160            2.37%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avarage Price:\n",
      "-----------------\n"
     ]
    },
    {
     "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>Room Type</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>$211.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Private room</td>\n",
       "      <td>$89.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>$70.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Room Type    Price\n",
       "0  Entire home/apt  $211.79\n",
       "1     Private room   $89.78\n",
       "2      Shared room   $70.13"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Borough:\n",
      "-----------------\n"
     ]
    },
    {
     "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>Borough</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>$87.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>$124.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>$196.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Queens</td>\n",
       "      <td>$99.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>$114.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Borough    Price\n",
       "0          Bronx   $87.50\n",
       "1       Brooklyn  $124.38\n",
       "2      Manhattan  $196.88\n",
       "3         Queens   $99.52\n",
       "4  Staten Island  $114.81"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print both datas inside the window for preview\n",
    "pd.set_option(\"display.max_rows\", None)\n",
    "print (\"Room Category:\")\n",
    "print (\"-----------------\")\n",
    "display(room_categ)\n",
    "print (\"Avarage Price:\")\n",
    "print (\"-----------------\")\n",
    "display(avrg_price)\n",
    "print (\"Borough:\")\n",
    "print (\"-----------------\")\n",
    "display(city_avrg_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Room Type</th>        <th class=\"col_heading level0 col1\" >Price</th>        <th class=\"col_heading level0 col2\" >Total Rooms</th>        <th class=\"col_heading level0 col3\" >Per of Room Type</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row0_col0\" class=\"data row0 col0\" >Entire home/apt</td>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row0_col1\" class=\"data row0 col1\" >$211.79</td>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row0_col2\" class=\"data row0 col2\" >25409</td>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row0_col3\" class=\"data row0 col3\" >51.97%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row1_col0\" class=\"data row1 col0\" >Private room</td>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row1_col1\" class=\"data row1 col1\" >$89.78</td>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row1_col2\" class=\"data row1 col2\" >22326</td>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row1_col3\" class=\"data row1 col3\" >45.66%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row2_col0\" class=\"data row2 col0\" >Shared room</td>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row2_col1\" class=\"data row2 col1\" >$70.13</td>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row2_col2\" class=\"data row2 col2\" >1160</td>\n",
       "                        <td id=\"T_f07d5536_08b7_11ea_8723_086d41dd6a90row2_col3\" class=\"data row2 col3\" >2.37%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x120186710>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge both DataFrames\n",
    "merge_room_type = pd.merge(avrg_price, room_categ, on=\"Room Type\")\n",
    "pd.DataFrame(merge_room_type)\n",
    "merge_room_type.style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>Room Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total Rooms</th>\n",
       "      <th>Per of Room Type</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>$211.79</td>\n",
       "      <td>25409</td>\n",
       "      <td>51.97%</td>\n",
       "      <td>Brooklyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Private room</td>\n",
       "      <td>$89.78</td>\n",
       "      <td>22326</td>\n",
       "      <td>45.66%</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>$70.13</td>\n",
       "      <td>1160</td>\n",
       "      <td>2.37%</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Room Type    Price  Total Rooms Per of Room Type    Borough\n",
       "0  Entire home/apt  $211.79        25409           51.97%   Brooklyn\n",
       "1     Private room   $89.78        22326           45.66%  Manhattan\n",
       "2      Shared room   $70.13         1160            2.37%  Manhattan"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding Cities column to Data Frame\n",
    "city_list = nyc_airbnb[\"Borough\"]\n",
    "merge_room_type[\"Borough\"] = city_list\n",
    "  \n",
    "# Lock table\n",
    "locked_table = merge_room_type.loc[:, [\"Room Type\", \"Price\", \"Total Rooms\", \"Per of Room Type\", \"Borough\"]]\n",
    "locked_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove $ from Price column - for be able to create the error bar\n",
    "locked_table[\"Price\"] = locked_table[\"Price\"].apply(lambda x:float(x.strip('$')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11fd51fd0>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouping by Cities and Room Type\n",
    "# Setting index by false to see nice Data Frame\n",
    "airbnb_new = locked_table.groupby([\"Borough\", \"Room Type\"],as_index=False)[\"Price\"].mean()\n",
    "\n",
    "# Show Data Frame by index style\n",
    "airbnb_new.style\n",
    "\n",
    "# Show Bar by seaborn (it's will shows you matplotlib errors bar by different color)\n",
    "sns.barplot(airbnb_new[\"Room Type\"], airbnb_new[\"Price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defind y values by price\n",
    "y = airbnb_new[\"Price\"].values\n",
    "\n",
    "# Create plt bar\n",
    "airbnb_bar = plt.bar(airbnb_new[\"Room Type\"],airbnb_new[\"Price\"], color = \"r\")\n",
    "\n",
    "#Creating title \n",
    "plt.title(\"Room Type -vs- Price\")\n",
    "\n",
    "# Set up price by Roofor each bar\n",
    "def autolabel(rects):\n",
    "    for i,rect in enumerate(rects):\n",
    "        plt.text(rect.get_x() + rect.get_width()/2., 2, '$%s'% (y[i]), ha=\"center\", va=\"bottom\", \n",
    "                 color=\"white\", weight=\"bold\")\n",
    "                  \n",
    "\n",
    "autolabel(airbnb_bar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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": [
    "# Set price and room type for new DataFrame\n",
    "prices = airbnb_new[\"Price\"].values\n",
    "type_of_r = airbnb_new[\"Room Type\"]\n",
    "\n",
    "# Create new Data Frame\n",
    "data = pd.DataFrame({\"in\": [1,2,3], \"r\": type_of_r, \"p\": prices})\n",
    "\n",
    "# Set 3D projection \n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Show projection\n",
    "ax\n",
    "\n",
    "# Create pivot table\n",
    "piv_tbl = pd.pivot_table(data, values=\"p\", index=\"in\", columns=\"r\")\n",
    "\n",
    "# Set all x, y  for 3D graph\n",
    "xpos = np.arange(piv_tbl.shape[0])\n",
    "ypos = np.arange(piv_tbl.shape[1])\n",
    "xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25)\n",
    "\n",
    "xpos = xpos.flatten()\n",
    "ypos = ypos.flatten()\n",
    "zpos = np.zeros(piv_tbl.shape).flatten()\n",
    "\n",
    "# Set delta x, y, z\n",
    "dx = 0.5 * np.ones_like(zpos)\n",
    "dy = 0.5 * np.ones_like(zpos)\n",
    "dz = piv_tbl.values.ravel()\n",
    "# Setting \n",
    "dz[np.isnan(dz)] = 0.\n",
    "\n",
    "# Set 3D graph - adding color and shape \n",
    "    # with alpha showing dept(thickness)\n",
    "ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color=\"c\", alpha=0.9)\n",
    "\n",
    "\n",
    "ax.set_xticks([.5, 1.5, 2.5])\n",
    "ax.set_yticks([1.5, 2.5, 3.5])\n",
    "\n",
    "# Set index and columns of graph\n",
    "ax.w_xaxis.set_ticklabels(piv_tbl.index)\n",
    "ax.w_yaxis.set_ticklabels(piv_tbl.columns, rotation=6)\n",
    "\n",
    "# Set grid colors and linestyle\n",
    "    #ax.zaxis._axinfo[\"grid\"]['color'] = \"c\"\n",
    "    #ax.zaxis._axinfo[\"grid\"]['linestyle'] = \"-\"\n",
    "\n",
    "# Formating for label and tight\n",
    "fig.tight_layout()\n",
    "ax.set_zlabel(\"Price\")\n",
    "\n",
    "# Show 3D histogram\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "                Gilbert\n",
    "\n",
    "        Yearly Availability By Borough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Yearly Availability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Allerton</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>185.783784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Arden Heights</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>125.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Arrochar</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>255.809524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Arverne</td>\n",
       "      <td>Queens</td>\n",
       "      <td>198.753425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Astoria</td>\n",
       "      <td>Queens</td>\n",
       "      <td>179.328467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Bath Beach</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>222.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Battery Park City</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>206.558824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Bay Ridge</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>190.122642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Bay Terrace</td>\n",
       "      <td>Queens</td>\n",
       "      <td>222.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Baychester</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>184.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Neighborhood        Borough  Yearly Availability\n",
       "0           Allerton          Bronx           185.783784\n",
       "1      Arden Heights  Staten Island           125.666667\n",
       "2           Arrochar  Staten Island           255.809524\n",
       "3            Arverne         Queens           198.753425\n",
       "4            Astoria         Queens           179.328467\n",
       "5         Bath Beach       Brooklyn           222.266667\n",
       "6  Battery Park City      Manhattan           206.558824\n",
       "7          Bay Ridge       Brooklyn           190.122642\n",
       "8        Bay Terrace         Queens           222.500000\n",
       "9         Baychester          Bronx           184.166667"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = airbnb_df.groupby(['Neighborhood','Borough'],as_index=False)['Yearly Availability'].mean()\n",
    "newdf.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Yearly Availability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Bay Terrace</td>\n",
       "      <td>Queens</td>\n",
       "      <td>222.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Baychester</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>184.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Belmont</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>264.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Bushwick</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>162.548654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Carroll Gardens</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>136.877049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Neighborhood   Borough  Yearly Availability\n",
       "0      Bay Terrace    Queens           222.500000\n",
       "1       Baychester     Bronx           184.166667\n",
       "2          Belmont     Bronx           264.941176\n",
       "3         Bushwick  Brooklyn           162.548654\n",
       "4  Carroll Gardens  Brooklyn           136.877049"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newnew = newdf.drop([0, 1, 2, 3,4,5,6,11,12,13,16,17,18,20,23,24,25,26, \n",
    "                     34,32,33,36,37,38,39,43,44,42,45,46,48,56,57,58,59,\n",
    "                     53,52, 188,21,31,41,51,61,65,67,66,68,69,70,71,72,73,\n",
    "                     74,75,76,77, 196,78,79,89,88,87,86,85,84,81,92,93,94,95,\n",
    "                     96,97,98,101,100,111,123,124,112,114,143,154,165,129,130,\n",
    "                     140,150,160,161,171,14,22,162,165,167,166,186,176,156,\n",
    "                     189,200,212,213,214,215,216,203,204,205,206,207,209,191,\n",
    "                     192,193,194,195,197,156,157,158,159,10,19,28,29,7,50,55,\n",
    "                     60,62,82,90,99,64,102,103,107,113,115,141,142,144,135,\n",
    "                     155,164,168,170,172,177,185,187,190,183,202,47,49,54,80,\n",
    "                     109, 104,105,106,107,119,120,121,122,151,152,153,161,199,\n",
    "                     145,146,147,148,174,175,176,169,181,198,91,117,110,83,35])\n",
    "\n",
    "newnew = newnew.reset_index(drop=True)\n",
    "newnew.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set x axis and tick locations\n",
    "x_axis = np.arange(len(newnew))\n",
    "tick_locations = [value+0.8 for value in x_axis]\n",
    "y = newnew[\"Yearly Availability\"]\n",
    "\n",
    "# Create a list indicating where to write x labels and set figure size to adjust for space\n",
    "plt.figure(figsize=(11,7))\n",
    "plt.bar(x_axis, y, color='b', alpha=0.7, align=\"center\")\n",
    "plt.xticks(newnew.index, newnew[\"Borough\"], rotation=\"vertical\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "plt.bar(x_axis, y, color='g', alpha=0.5, align=\"center\")\n",
    "plt.xticks(newnew.index, newnew[\"Neighborhood\"], rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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