COPY2016_Prices_by_Zipcode_and_Month.ipynb

{
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  {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zipcode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11211</th>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>18898</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>16109</td>\n",
       "      <td>16106</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
       "      <td>20241</td>\n",
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       "    <tr>\n",
       "      <th>10002</th>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>13254</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>11369</td>\n",
       "      <td>11369</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
       "      <td>13784</td>\n",
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       "    <tr>\n",
       "      <th>11206</th>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>12714</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>10467</td>\n",
       "      <td>10467</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "      <td>13536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10009</th>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>12721</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>10427</td>\n",
       "      <td>10424</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
       "      <td>13079</td>\n",
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       "    <tr>\n",
       "      <th>11221</th>\n",
       "      <td>12465</td>\n",
       "      <td>12461</td>\n",
       "      <td>12465</td>\n",
       "      <td>11959</td>\n",
       "      <td>12465</td>\n",
       "      <td>12465</td>\n",
       "      <td>12465</td>\n",
       "      <td>12465</td>\n",
       "      <td>12465</td>\n",
       "      <td>12465</td>\n",
       "      <td>12465</td>\n",
       "      <td>12465</td>\n",
       "      <td>9627</td>\n",
       "      <td>9626</td>\n",
       "      <td>12465</td>\n",
       "      <td>12465</td>\n",
       "      <td>12465</td>\n",
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       "    <tr>\n",
       "      <th>10003</th>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>11361</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>9568</td>\n",
       "      <td>9567</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
       "      <td>11980</td>\n",
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       "    <tr>\n",
       "      <th>11238</th>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>11387</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>9349</td>\n",
       "      <td>9349</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
       "      <td>11883</td>\n",
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       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>10323</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>8436</td>\n",
       "      <td>8436</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
       "      <td>10834</td>\n",
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       "    <tr>\n",
       "      <th>11222</th>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>9305</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>7181</td>\n",
       "      <td>7180</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
       "      <td>9601</td>\n",
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       "    <tr>\n",
       "      <th>10025</th>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
       "      <td>9221</td>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
       "      <td>6953</td>\n",
       "      <td>6953</td>\n",
       "      <td>9516</td>\n",
       "      <td>9516</td>\n",
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      ],
      "text/plain": [
       "            id   name  host_id  host_name  neighbourhood_group  neighbourhood  \\\n",
       "zipcode                                                                         \n",
       "11211    20241  20241    20241      18898                20241          20241   \n",
       "10002    13784  13784    13784      13254                13784          13784   \n",
       "11206    13536  13536    13536      12714                13536          13536   \n",
       "10009    13079  13079    13079      12721                13079          13079   \n",
       "11221    12465  12461    12465      11959                12465          12465   \n",
       "10003    11980  11980    11980      11361                11980          11980   \n",
       "11238    11883  11883    11883      11387                11883          11883   \n",
       "11216    10834  10834    10834      10323                10834          10834   \n",
       "11222     9601   9601     9601       9305                 9601           9601   \n",
       "10025     9516   9516     9516       9221                 9516           9516   \n",
       "\n",
       "         latitude  longitude  room_type  price  minimum_nights  \\\n",
       "zipcode                                                          \n",
       "11211       20241      20241      20241  20241           20241   \n",
       "10002       13784      13784      13784  13784           13784   \n",
       "11206       13536      13536      13536  13536           13536   \n",
       "10009       13079      13079      13079  13079           13079   \n",
       "11221       12465      12465      12465  12465           12465   \n",
       "10003       11980      11980      11980  11980           11980   \n",
       "11238       11883      11883      11883  11883           11883   \n",
       "11216       10834      10834      10834  10834           10834   \n",
       "11222        9601       9601       9601   9601            9601   \n",
       "10025        9516       9516       9516   9516            9516   \n",
       "\n",
       "         number_of_reviews  last_review  reviews_per_month  \\\n",
       "zipcode                                                      \n",
       "11211                20241        16109              16106   \n",
       "10002                13784        11369              11369   \n",
       "11206                13536        10467              10467   \n",
       "10009                13079        10427              10424   \n",
       "11221                12465         9627               9626   \n",
       "10003                11980         9568               9567   \n",
       "11238                11883         9349               9349   \n",
       "11216                10834         8436               8436   \n",
       "11222                 9601         7181               7180   \n",
       "10025                 9516         6953               6953   \n",
       "\n",
       "         calculated_host_listings_count  availability_365  year_month  \n",
       "zipcode                                                                \n",
       "11211                             20241             20241       20241  \n",
       "10002                             13784             13784       13784  \n",
       "11206                             13536             13536       13536  \n",
       "10009                             13079             13079       13079  \n",
       "11221                             12465             12465       12465  \n",
       "10003                             11980             11980       11980  \n",
       "11238                             11883             11883       11883  \n",
       "11216                             10834             10834       10834  \n",
       "11222                              9601              9601        9601  \n",
       "10025                              9516              9516        9516  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "file_input = './Listings_2016/2016_Listings.csv'\n",
    "file_output = \"Top\"\n",
    "\n",
    "listings = pd.read_csv(file_input)\n",
    "listings['zipcode'] = pd.to_numeric(listings['zipcode'])\n",
    "by_zipcode = listings.groupby('zipcode').count()\n",
    "by_zipcode = by_zipcode.sort_values('id', ascending=False)\n",
    "by_zipcode.head(10)\n",
    "#221 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Mean and Median Prices by Zipcode')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Mean and Median\n",
    "plt.figure(figsize = (10,5))\n",
    "x_axis = np.arange(len(zipcode_prices['Zip Code']))\n",
    "tick_locations = [x for x in x_axis]\n",
    "plt.xticks(tick_locations, zipcode_prices['Zip Code'])\n",
    "plt.bar(x_axis, zipcode_prices['Mean'], label ='Mean',width = 0.5)\n",
    "plt.bar(x_axis, zipcode_prices['Median'], label = 'Median',width = 0.5)\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Zip Code')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Mean and Median Prices by Zipcode')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Mean</th>\n",
       "      <th>Median</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11211</td>\n",
       "      <td>143.768193</td>\n",
       "      <td>119.0</td>\n",
       "      <td>7000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10002</td>\n",
       "      <td>168.815366</td>\n",
       "      <td>135.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11206</td>\n",
       "      <td>94.994238</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10009</td>\n",
       "      <td>165.071030</td>\n",
       "      <td>135.0</td>\n",
       "      <td>5200</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11221</td>\n",
       "      <td>89.587325</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10003</td>\n",
       "      <td>218.895993</td>\n",
       "      <td>169.0</td>\n",
       "      <td>6000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11238</td>\n",
       "      <td>138.259867</td>\n",
       "      <td>106.0</td>\n",
       "      <td>6500</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11216</td>\n",
       "      <td>110.022522</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11222</td>\n",
       "      <td>128.623164</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1600</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10025</td>\n",
       "      <td>158.431274</td>\n",
       "      <td>119.0</td>\n",
       "      <td>9998</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10019</td>\n",
       "      <td>215.508512</td>\n",
       "      <td>175.0</td>\n",
       "      <td>2695</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10011</td>\n",
       "      <td>225.796276</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2700</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11237</td>\n",
       "      <td>86.114981</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2250</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10012</td>\n",
       "      <td>230.587597</td>\n",
       "      <td>190.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10014</td>\n",
       "      <td>239.303179</td>\n",
       "      <td>200.0</td>\n",
       "      <td>5000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10036</td>\n",
       "      <td>212.077208</td>\n",
       "      <td>165.0</td>\n",
       "      <td>5400</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10031</td>\n",
       "      <td>99.883374</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9900</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11215</td>\n",
       "      <td>150.024911</td>\n",
       "      <td>130.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10027</td>\n",
       "      <td>121.078350</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1222</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10016</td>\n",
       "      <td>214.245595</td>\n",
       "      <td>175.0</td>\n",
       "      <td>3000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10029</td>\n",
       "      <td>127.426550</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9999</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11249</td>\n",
       "      <td>172.929537</td>\n",
       "      <td>130.0</td>\n",
       "      <td>5999</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11205</td>\n",
       "      <td>154.145494</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8000</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11217</td>\n",
       "      <td>164.352042</td>\n",
       "      <td>130.0</td>\n",
       "      <td>3400</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11225</td>\n",
       "      <td>94.748649</td>\n",
       "      <td>80.0</td>\n",
       "      <td>950</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11226</td>\n",
       "      <td>84.754190</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1100</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11233</td>\n",
       "      <td>105.371874</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10023</td>\n",
       "      <td>223.957771</td>\n",
       "      <td>180.0</td>\n",
       "      <td>9900</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10024</td>\n",
       "      <td>199.247779</td>\n",
       "      <td>165.0</td>\n",
       "      <td>2000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11201</td>\n",
       "      <td>166.963918</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1500</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Zip Code        Mean  Median   Max  Min\n",
       "0      11211  143.768193   119.0  7000   25\n",
       "1      10002  168.815366   135.0  9999   23\n",
       "2      11206   94.994238    75.0  9000   10\n",
       "3      10009  165.071030   135.0  5200   23\n",
       "4      11221   89.587325    70.0  2000   10\n",
       "5      10003  218.895993   169.0  6000   10\n",
       "6      11238  138.259867   106.0  6500   10\n",
       "7      11216  110.022522    85.0  2000   10\n",
       "8      11222  128.623164   109.0  1600   25\n",
       "9      10025  158.431274   119.0  9998   10\n",
       "10     10019  215.508512   175.0  2695   10\n",
       "11     10011  225.796276   199.0  2700   30\n",
       "12     11237   86.114981    69.0  2250   18\n",
       "13     10012  230.587597   190.0  5000   25\n",
       "14     10014  239.303179   200.0  5000   10\n",
       "15     10036  212.077208   165.0  5400   10\n",
       "16     10031   99.883374    75.0  9900   10\n",
       "17     11215  150.024911   130.0  2000   25\n",
       "18     10027  121.078350   100.0  1222   22\n",
       "19     10016  214.245595   175.0  3000   25\n",
       "20     10029  127.426550   100.0  9999   20\n",
       "21     11249  172.929537   130.0  5999   35\n",
       "22     11205  154.145494   100.0  8000   21\n",
       "23     11217  164.352042   130.0  3400   25\n",
       "24     11225   94.748649    80.0   950   20\n",
       "25     11226   84.754190    70.0  1100   17\n",
       "26     11233  105.371874    95.0  1000   18\n",
       "27     10023  223.957771   180.0  9900   29\n",
       "28     10024  199.247779   165.0  2000   11\n",
       "29     11201  166.963918   150.0  1500   15"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#top 30 zipcode 2016\n",
    "\n",
    "zip_list = list(by_zipcode.index.values)\n",
    "zipcodes = zip_list[0:30]\n",
    "means = []\n",
    "medians = []\n",
    "maxs = []\n",
    "mins = []\n",
    "\n",
    "for zipcode in zipcodes:\n",
    "    median = listings.loc[listings['zipcode']==zipcode]['price'].median()\n",
    "    mean = listings.loc[listings['zipcode']==zipcode]['price'].mean()\n",
    "    maxe = listings.loc[listings['zipcode']==zipcode]['price'].max()\n",
    "    mine = listings.loc[listings['zipcode']==zipcode]['price'].min()\n",
    "    means.append(mean)\n",
    "    medians.append(median)\n",
    "    maxs.append(maxe)\n",
    "    mins.append(mine)\n",
    "\n",
    "zipcode_prices = pd.DataFrame({\n",
    "    'Zip Code':zipcodes,\n",
    "    'Mean': means,\n",
    "    'Median': medians, \n",
    "    'Max': maxs, \n",
    "    'Min': mins\n",
    "})\n",
    "zipcode_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_name</th>\n",
       "      <th>neighbourhood_group</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>room_type</th>\n",
       "      <th>price</th>\n",
       "      <th>minimum_nights</th>\n",
       "      <th>number_of_reviews</th>\n",
       "      <th>last_review</th>\n",
       "      <th>reviews_per_month</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>availability_365</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>5258</td>\n",
       "      <td>Artist's Loft with indoor Hammock!</td>\n",
       "      <td>7611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.710302</td>\n",
       "      <td>-73.964961</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>175</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>8/28/2015</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1</td>\n",
       "      <td>308</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24</td>\n",
       "      <td>5857</td>\n",
       "      <td>Amazing Apt in WILLIAMSBURG, NY!!</td>\n",
       "      <td>10176</td>\n",
       "      <td>Juan</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.717358</td>\n",
       "      <td>-73.942063</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>250</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>12/7/2015</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>6111</td>\n",
       "      <td>Dream Come True! Williamsburg, BK</td>\n",
       "      <td>12169</td>\n",
       "      <td>Miko &amp; Lauren</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Greenpoint</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.719211</td>\n",
       "      <td>-73.951231</td>\n",
       "      <td>Private room</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>96</td>\n",
       "      <td>10/26/2015</td>\n",
       "      <td>1.19</td>\n",
       "      <td>2</td>\n",
       "      <td>363</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>7064</td>\n",
       "      <td>Bright, peaceful loft, Williamsburg</td>\n",
       "      <td>17297</td>\n",
       "      <td>Joelle</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.712643</td>\n",
       "      <td>-73.958624</td>\n",
       "      <td>Private room</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>8/31/2015</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84</td>\n",
       "      <td>13002</td>\n",
       "      <td>CBG Williamsburg  loft room</td>\n",
       "      <td>22486</td>\n",
       "      <td>Lisel</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.710961</td>\n",
       "      <td>-73.946840</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>145</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12/12/2015</td>\n",
       "      <td>0.15</td>\n",
       "      <td>5</td>\n",
       "      <td>365</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>87</td>\n",
       "      <td>13362</td>\n",
       "      <td>Landmark Williamsburg</td>\n",
       "      <td>52043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.715022</td>\n",
       "      <td>-73.958098</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>190</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>10/7/2015</td>\n",
       "      <td>0.76</td>\n",
       "      <td>1</td>\n",
       "      <td>359</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>88</td>\n",
       "      <td>13380</td>\n",
       "      <td>1 br Williamsburg Brooklyn Condo,</td>\n",
       "      <td>52233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.712523</td>\n",
       "      <td>-73.958105</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>100</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6/7/2015</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>101</td>\n",
       "      <td>14377</td>\n",
       "      <td>Williamsburg 1 bedroom Apartment</td>\n",
       "      <td>56512</td>\n",
       "      <td>Joanna</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.708808</td>\n",
       "      <td>-73.959302</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>12/27/2015</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>131</td>\n",
       "      <td>16008</td>\n",
       "      <td>Apt  in Williamsburg</td>\n",
       "      <td>62132</td>\n",
       "      <td>Rachel</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.714282</td>\n",
       "      <td>-73.945124</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>11/29/2015</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1</td>\n",
       "      <td>341</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>166</td>\n",
       "      <td>18764</td>\n",
       "      <td>Cozy 2 BR in Williamsburg</td>\n",
       "      <td>72014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.711540</td>\n",
       "      <td>-73.961120</td>\n",
       "      <td>Private room</td>\n",
       "      <td>129</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>12/16/2015</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2</td>\n",
       "      <td>351</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>174</td>\n",
       "      <td>20611</td>\n",
       "      <td>Cozy Bedroom in Williamsburg 2 BR</td>\n",
       "      <td>72014</td>\n",
       "      <td>Lulu</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.711558</td>\n",
       "      <td>-73.962178</td>\n",
       "      <td>Private room</td>\n",
       "      <td>84</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>12/20/2015</td>\n",
       "      <td>1.41</td>\n",
       "      <td>2</td>\n",
       "      <td>359</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>179</td>\n",
       "      <td>20793</td>\n",
       "      <td>Charming Carriage House Suite</td>\n",
       "      <td>42756</td>\n",
       "      <td>Leslie</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.710545</td>\n",
       "      <td>-73.962100</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>165</td>\n",
       "      <td>1</td>\n",
       "      <td>235</td>\n",
       "      <td>12/20/2015</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3</td>\n",
       "      <td>341</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>181</td>\n",
       "      <td>20913</td>\n",
       "      <td>Charming 1 bed GR8 WBurg LOCATION!</td>\n",
       "      <td>79402</td>\n",
       "      <td>Christiana</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.709843</td>\n",
       "      <td>-73.957750</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>12/12/2015</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1</td>\n",
       "      <td>357</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>182</td>\n",
       "      <td>21218</td>\n",
       "      <td>Roof-Top Room in Williamsburg</td>\n",
       "      <td>42756</td>\n",
       "      <td>Leslie</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.710529</td>\n",
       "      <td>-73.960866</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>281</td>\n",
       "      <td>12/16/2015</td>\n",
       "      <td>4.04</td>\n",
       "      <td>3</td>\n",
       "      <td>280</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>183</td>\n",
       "      <td>21220</td>\n",
       "      <td>Gorgeous Williamsburg 1BR Apartment</td>\n",
       "      <td>42756</td>\n",
       "      <td>Leslie</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.710121</td>\n",
       "      <td>-73.961302</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "      <td>216</td>\n",
       "      <td>12/27/2015</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3</td>\n",
       "      <td>310</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>205</td>\n",
       "      <td>24187</td>\n",
       "      <td>Large furnished private bedroom!</td>\n",
       "      <td>97476</td>\n",
       "      <td>Sebastian</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.709455</td>\n",
       "      <td>-73.962172</td>\n",
       "      <td>Private room</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8/23/2015</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>214</td>\n",
       "      <td>25406</td>\n",
       "      <td>Modern Traveler's Suite in Brooklyn</td>\n",
       "      <td>105538</td>\n",
       "      <td>Erik</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.714591</td>\n",
       "      <td>-73.948443</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>169</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>11/17/2015</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>224</td>\n",
       "      <td>26864</td>\n",
       "      <td>Nice room in Prime W.Burg Location</td>\n",
       "      <td>114590</td>\n",
       "      <td>Juan Pablo</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.721012</td>\n",
       "      <td>-73.961206</td>\n",
       "      <td>Private room</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5/2/2014</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>240</td>\n",
       "      <td>28083</td>\n",
       "      <td>SPECTACULAR view in a modern condo</td>\n",
       "      <td>120952</td>\n",
       "      <td>Kayvon</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Greenpoint</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.719356</td>\n",
       "      <td>-73.951183</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>11/8/2015</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>244</td>\n",
       "      <td>28396</td>\n",
       "      <td>Modern Apt with Spectacular Views</td>\n",
       "      <td>6197784</td>\n",
       "      <td>Jo</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.719226</td>\n",
       "      <td>-73.964679</td>\n",
       "      <td>Private room</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>9/18/2011</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1</td>\n",
       "      <td>122</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>250</td>\n",
       "      <td>29594</td>\n",
       "      <td>Sunny private house in trendy area</td>\n",
       "      <td>127428</td>\n",
       "      <td>Shira</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.712565</td>\n",
       "      <td>-73.939520</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>8/18/2015</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1</td>\n",
       "      <td>214</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>256</td>\n",
       "      <td>30589</td>\n",
       "      <td>Sunny Williamsburg Penthouse</td>\n",
       "      <td>131716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.712599</td>\n",
       "      <td>-73.950012</td>\n",
       "      <td>Private room</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>10/17/2015</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>257</td>\n",
       "      <td>30970</td>\n",
       "      <td>Classic Williamsburg</td>\n",
       "      <td>132918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.715924</td>\n",
       "      <td>-73.938550</td>\n",
       "      <td>Private room</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>119</td>\n",
       "      <td>12/14/2015</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>264</td>\n",
       "      <td>32289</td>\n",
       "      <td>Sun-drenched, artsy modernist 1+ BR</td>\n",
       "      <td>139612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.715611</td>\n",
       "      <td>-73.948346</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6/23/2014</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1</td>\n",
       "      <td>364</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>268</td>\n",
       "      <td>33009</td>\n",
       "      <td>Retreat in Williamsburg</td>\n",
       "      <td>143027</td>\n",
       "      <td>Ming</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.714920</td>\n",
       "      <td>-73.959351</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>185</td>\n",
       "      <td>4</td>\n",
       "      <td>151</td>\n",
       "      <td>12/18/2015</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1</td>\n",
       "      <td>345</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>272</td>\n",
       "      <td>33223</td>\n",
       "      <td>Cozy BR in Wiliamsburg 2 Bedroom</td>\n",
       "      <td>72014</td>\n",
       "      <td>Lulu</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.711652</td>\n",
       "      <td>-73.960869</td>\n",
       "      <td>Private room</td>\n",
       "      <td>78</td>\n",
       "      <td>3</td>\n",
       "      <td>92</td>\n",
       "      <td>11/28/2015</td>\n",
       "      <td>1.42</td>\n",
       "      <td>2</td>\n",
       "      <td>352</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>275</td>\n",
       "      <td>33998</td>\n",
       "      <td>1100 sq/ft loft-apt in williamsburg</td>\n",
       "      <td>130313</td>\n",
       "      <td>Emily</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.710081</td>\n",
       "      <td>-73.961231</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>165</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>5/18/2015</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>282</td>\n",
       "      <td>35963</td>\n",
       "      <td>Great 1 br Apt! Next to Manhattan!</td>\n",
       "      <td>154377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.709483</td>\n",
       "      <td>-73.953150</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>6/25/2015</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1</td>\n",
       "      <td>153</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>284</td>\n",
       "      <td>36133</td>\n",
       "      <td>Classic Artist Loft Williamsburg</td>\n",
       "      <td>142684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.715356</td>\n",
       "      <td>-73.960568</td>\n",
       "      <td>Private room</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>97</td>\n",
       "      <td>10/24/2015</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>287</td>\n",
       "      <td>36934</td>\n",
       "      <td>In the heart of Williamsburg</td>\n",
       "      <td>159370</td>\n",
       "      <td>Viviana</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Williamsburg</td>\n",
       "      <td>11211</td>\n",
       "      <td>40.713632</td>\n",
       "      <td>-73.963980</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>125</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>12/19/2015</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267396</th>\n",
       "      <td>379227</td>\n",
       "      <td>15885483</td>\n",
       "      <td>Brooklyn - Corner, One Bedroom in New Building</td>\n",
       "      <td>33611129</td>\n",
       "      <td>James</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Fort Greene</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.692873</td>\n",
       "      <td>-73.981909</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267397</th>\n",
       "      <td>379238</td>\n",
       "      <td>6952302</td>\n",
       "      <td>2BR- Downtown Brooklyn</td>\n",
       "      <td>31883140</td>\n",
       "      <td>Fabiana</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Fort Greene</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.691727</td>\n",
       "      <td>-73.977558</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>7/4/2016</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267398</th>\n",
       "      <td>379245</td>\n",
       "      <td>6685873</td>\n",
       "      <td>Sunny &amp; Quiet Luxury NYC Oasis</td>\n",
       "      <td>1030526</td>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Fort Greene</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.690231</td>\n",
       "      <td>-73.980865</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267399</th>\n",
       "      <td>379255</td>\n",
       "      <td>13781431</td>\n",
       "      <td>Best location in Brooklyn . Luxury high rise</td>\n",
       "      <td>10229330</td>\n",
       "      <td>Tamara</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Fort Greene</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.694513</td>\n",
       "      <td>-73.981902</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8/11/2016</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267400</th>\n",
       "      <td>379257</td>\n",
       "      <td>9061445</td>\n",
       "      <td>Brooklyn Downtown</td>\n",
       "      <td>47267614</td>\n",
       "      <td>Vitali</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Fort Greene</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.691474</td>\n",
       "      <td>-73.980725</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267401</th>\n",
       "      <td>379273</td>\n",
       "      <td>9711001</td>\n",
       "      <td>One bedroom Sublet Available</td>\n",
       "      <td>50136185</td>\n",
       "      <td>Nehal</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Fort Greene</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.695562</td>\n",
       "      <td>-73.982295</td>\n",
       "      <td>Private room</td>\n",
       "      <td>125</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>12/22/2015</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267402</th>\n",
       "      <td>389486</td>\n",
       "      <td>5725963</td>\n",
       "      <td>Private Room in Dumbo Artist's Loft</td>\n",
       "      <td>21742218</td>\n",
       "      <td>Ashley</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Navy Yard</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.701609</td>\n",
       "      <td>-73.979967</td>\n",
       "      <td>Private room</td>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5/30/2016</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1</td>\n",
       "      <td>363</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267403</th>\n",
       "      <td>389491</td>\n",
       "      <td>14842966</td>\n",
       "      <td>Charming Duplex in Brooklyn</td>\n",
       "      <td>23502626</td>\n",
       "      <td>Fun</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Navy Yard</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.702418</td>\n",
       "      <td>-73.980162</td>\n",
       "      <td>Shared room</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267404</th>\n",
       "      <td>393165</td>\n",
       "      <td>15948728</td>\n",
       "      <td>Bright Private Room in the Heart of Chinatown</td>\n",
       "      <td>19124849</td>\n",
       "      <td>Carol</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Two Bridges</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.711814</td>\n",
       "      <td>-73.996149</td>\n",
       "      <td>Private room</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>85</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267405</th>\n",
       "      <td>393224</td>\n",
       "      <td>14028586</td>\n",
       "      <td>Perfect Luxury Studio in DUMBO</td>\n",
       "      <td>83977725</td>\n",
       "      <td>Me</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.701249</td>\n",
       "      <td>-73.985822</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>12/1/2016</td>\n",
       "      <td>6.09</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267406</th>\n",
       "      <td>393225</td>\n",
       "      <td>7799741</td>\n",
       "      <td>Under the Brooklyn Sun</td>\n",
       "      <td>41053947</td>\n",
       "      <td>Nicole</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.698931</td>\n",
       "      <td>-73.985158</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>180</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>9/14/2016</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267407</th>\n",
       "      <td>393226</td>\n",
       "      <td>14413987</td>\n",
       "      <td>Cozy Bedroom, private bathroom, astonishing view!</td>\n",
       "      <td>4166168</td>\n",
       "      <td>Nick And Noémie</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.700753</td>\n",
       "      <td>-73.982756</td>\n",
       "      <td>Private room</td>\n",
       "      <td>85</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12/1/2016</td>\n",
       "      <td>2.93</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267408</th>\n",
       "      <td>393227</td>\n",
       "      <td>14403893</td>\n",
       "      <td>DUMBO Experience</td>\n",
       "      <td>88554743</td>\n",
       "      <td>Dumbo</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.703344</td>\n",
       "      <td>-73.983046</td>\n",
       "      <td>Private room</td>\n",
       "      <td>120</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1</td>\n",
       "      <td>336</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267409</th>\n",
       "      <td>393228</td>\n",
       "      <td>14926698</td>\n",
       "      <td>Comfy DUMBO Apt near the Brooklyn Bridge and Etsy</td>\n",
       "      <td>2809938</td>\n",
       "      <td>Phillip</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.701208</td>\n",
       "      <td>-73.982959</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>250</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>11/22/2016</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267410</th>\n",
       "      <td>393229</td>\n",
       "      <td>950777</td>\n",
       "      <td>Big 2 bdrm in DUMBO, Brooklyn, NY</td>\n",
       "      <td>1126015</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.701555</td>\n",
       "      <td>-73.983434</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>8/13/2015</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267411</th>\n",
       "      <td>393230</td>\n",
       "      <td>8866335</td>\n",
       "      <td>Cozy apartment in DUMBO</td>\n",
       "      <td>4383624</td>\n",
       "      <td>Ferran</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.703792</td>\n",
       "      <td>-73.982196</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>100</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>10/19/2016</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267412</th>\n",
       "      <td>393231</td>\n",
       "      <td>2715985</td>\n",
       "      <td>*EPIC VIEW* En-suite room w/balcony</td>\n",
       "      <td>4166168</td>\n",
       "      <td>Nick And Noémie</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.700517</td>\n",
       "      <td>-73.982793</td>\n",
       "      <td>Private room</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>11/20/2016</td>\n",
       "      <td>1.43</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267413</th>\n",
       "      <td>393232</td>\n",
       "      <td>11885997</td>\n",
       "      <td>Beautiful loft in DUMBO Brooklyn</td>\n",
       "      <td>17782625</td>\n",
       "      <td>David</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.702107</td>\n",
       "      <td>-73.984181</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>245</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4/5/2016</td>\n",
       "      <td>0.12</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267414</th>\n",
       "      <td>393233</td>\n",
       "      <td>15148038</td>\n",
       "      <td>Bright Guest Room in Luxury Apt</td>\n",
       "      <td>12587313</td>\n",
       "      <td>Raphaela</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.699183</td>\n",
       "      <td>-73.984303</td>\n",
       "      <td>Private room</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>10/31/2016</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267415</th>\n",
       "      <td>393234</td>\n",
       "      <td>4698707</td>\n",
       "      <td>NY TIMES Featured Dumbo Penthouse</td>\n",
       "      <td>5352419</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.702127</td>\n",
       "      <td>-73.983799</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>550</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267416</th>\n",
       "      <td>393235</td>\n",
       "      <td>6390777</td>\n",
       "      <td>Bkln (Vinegar Hill) duplex w/garden</td>\n",
       "      <td>33317997</td>\n",
       "      <td>Anna</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.700980</td>\n",
       "      <td>-73.982114</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>375</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267417</th>\n",
       "      <td>393236</td>\n",
       "      <td>14747746</td>\n",
       "      <td>Private room in luxury buiding in DUMBO</td>\n",
       "      <td>82170330</td>\n",
       "      <td>Ludovica</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.699541</td>\n",
       "      <td>-73.985291</td>\n",
       "      <td>Private room</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>67</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267418</th>\n",
       "      <td>393237</td>\n",
       "      <td>7275122</td>\n",
       "      <td>Stunning NYC Views</td>\n",
       "      <td>21336136</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.701068</td>\n",
       "      <td>-73.983909</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>235</td>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>11/27/2016</td>\n",
       "      <td>3.35</td>\n",
       "      <td>1</td>\n",
       "      <td>258</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267419</th>\n",
       "      <td>393238</td>\n",
       "      <td>13444166</td>\n",
       "      <td>Spacious 1 bedroom in downtown Brookyn</td>\n",
       "      <td>10455134</td>\n",
       "      <td>Vera</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.699105</td>\n",
       "      <td>-73.982498</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>161</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>11/14/2016</td>\n",
       "      <td>2.87</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267420</th>\n",
       "      <td>393239</td>\n",
       "      <td>7516218</td>\n",
       "      <td>Inviting, Modern 1 Bedroom in Dumbo</td>\n",
       "      <td>60046</td>\n",
       "      <td>John</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.701461</td>\n",
       "      <td>-73.981879</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>225</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>10/31/2016</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1</td>\n",
       "      <td>365</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267421</th>\n",
       "      <td>393240</td>\n",
       "      <td>9739230</td>\n",
       "      <td>2 BR Dumbo Brooklyn. Amazing views!</td>\n",
       "      <td>48112217</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.702660</td>\n",
       "      <td>-73.983500</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>11/27/2016</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267422</th>\n",
       "      <td>393241</td>\n",
       "      <td>8292794</td>\n",
       "      <td>Spectacular 1 Bdrm Condo in DUMBO</td>\n",
       "      <td>43724240</td>\n",
       "      <td>Dc</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.698544</td>\n",
       "      <td>-73.984333</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>205</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>11/19/2016</td>\n",
       "      <td>1.54</td>\n",
       "      <td>1</td>\n",
       "      <td>348</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267423</th>\n",
       "      <td>393242</td>\n",
       "      <td>14050340</td>\n",
       "      <td>BK GETAWAY</td>\n",
       "      <td>33575686</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.698433</td>\n",
       "      <td>-73.983619</td>\n",
       "      <td>Private room</td>\n",
       "      <td>125</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1</td>\n",
       "      <td>359</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267424</th>\n",
       "      <td>393243</td>\n",
       "      <td>3411815</td>\n",
       "      <td>Sunny 1 Bdrm Vinegar Hill/DUMBO</td>\n",
       "      <td>1649108</td>\n",
       "      <td>Allie</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.704399</td>\n",
       "      <td>-73.981530</td>\n",
       "      <td>Private room</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>10/2/2016</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267425</th>\n",
       "      <td>393244</td>\n",
       "      <td>11927624</td>\n",
       "      <td>Light filled modern APT in DUMBO</td>\n",
       "      <td>5251483</td>\n",
       "      <td>Sasha</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Vinegar Hill</td>\n",
       "      <td>11201</td>\n",
       "      <td>40.703284</td>\n",
       "      <td>-73.983138</td>\n",
       "      <td>Entire home/apt</td>\n",
       "      <td>170</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>7/23/2016</td>\n",
       "      <td>0.38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>201612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>267426 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index        id                                               name  \\\n",
       "0           16      5258                 Artist's Loft with indoor Hammock!   \n",
       "1           24      5857                  Amazing Apt in WILLIAMSBURG, NY!!   \n",
       "2           28      6111                  Dream Come True! Williamsburg, BK   \n",
       "3           34      7064                Bright, peaceful loft, Williamsburg   \n",
       "4           84     13002                        CBG Williamsburg  loft room   \n",
       "5           87     13362                              Landmark Williamsburg   \n",
       "6           88     13380                  1 br Williamsburg Brooklyn Condo,   \n",
       "7          101     14377                   Williamsburg 1 bedroom Apartment   \n",
       "8          131     16008                               Apt  in Williamsburg   \n",
       "9          166     18764                         Cozy 2 BR in Williamsburg    \n",
       "10         174     20611                  Cozy Bedroom in Williamsburg 2 BR   \n",
       "11         179     20793                      Charming Carriage House Suite   \n",
       "12         181     20913                 Charming 1 bed GR8 WBurg LOCATION!   \n",
       "13         182     21218                      Roof-Top Room in Williamsburg   \n",
       "14         183     21220                Gorgeous Williamsburg 1BR Apartment   \n",
       "15         205     24187                   Large furnished private bedroom!   \n",
       "16         214     25406                Modern Traveler's Suite in Brooklyn   \n",
       "17         224     26864                 Nice room in Prime W.Burg Location   \n",
       "18         240     28083                 SPECTACULAR view in a modern condo   \n",
       "19         244     28396                  Modern Apt with Spectacular Views   \n",
       "20         250     29594                 Sunny private house in trendy area   \n",
       "21         256     30589                       Sunny Williamsburg Penthouse   \n",
       "22         257     30970                               Classic Williamsburg   \n",
       "23         264     32289                Sun-drenched, artsy modernist 1+ BR   \n",
       "24         268     33009                            Retreat in Williamsburg   \n",
       "25         272     33223                   Cozy BR in Wiliamsburg 2 Bedroom   \n",
       "26         275     33998                1100 sq/ft loft-apt in williamsburg   \n",
       "27         282     35963                 Great 1 br Apt! Next to Manhattan!   \n",
       "28         284     36133                  Classic Artist Loft Williamsburg    \n",
       "29         287     36934                       In the heart of Williamsburg   \n",
       "...        ...       ...                                                ...   \n",
       "267396  379227  15885483     Brooklyn - Corner, One Bedroom in New Building   \n",
       "267397  379238   6952302                             2BR- Downtown Brooklyn   \n",
       "267398  379245   6685873                     Sunny & Quiet Luxury NYC Oasis   \n",
       "267399  379255  13781431       Best location in Brooklyn . Luxury high rise   \n",
       "267400  379257   9061445                                  Brooklyn Downtown   \n",
       "267401  379273   9711001                       One bedroom Sublet Available   \n",
       "267402  389486   5725963                Private Room in Dumbo Artist's Loft   \n",
       "267403  389491  14842966                        Charming Duplex in Brooklyn   \n",
       "267404  393165  15948728      Bright Private Room in the Heart of Chinatown   \n",
       "267405  393224  14028586                     Perfect Luxury Studio in DUMBO   \n",
       "267406  393225   7799741                             Under the Brooklyn Sun   \n",
       "267407  393226  14413987  Cozy Bedroom, private bathroom, astonishing view!   \n",
       "267408  393227  14403893                                   DUMBO Experience   \n",
       "267409  393228  14926698  Comfy DUMBO Apt near the Brooklyn Bridge and Etsy   \n",
       "267410  393229    950777                  Big 2 bdrm in DUMBO, Brooklyn, NY   \n",
       "267411  393230   8866335                            Cozy apartment in DUMBO   \n",
       "267412  393231   2715985                *EPIC VIEW* En-suite room w/balcony   \n",
       "267413  393232  11885997                   Beautiful loft in DUMBO Brooklyn   \n",
       "267414  393233  15148038                    Bright Guest Room in Luxury Apt   \n",
       "267415  393234   4698707                  NY TIMES Featured Dumbo Penthouse   \n",
       "267416  393235   6390777                Bkln (Vinegar Hill) duplex w/garden   \n",
       "267417  393236  14747746            Private room in luxury buiding in DUMBO   \n",
       "267418  393237   7275122                                 Stunning NYC Views   \n",
       "267419  393238  13444166             Spacious 1 bedroom in downtown Brookyn   \n",
       "267420  393239   7516218                Inviting, Modern 1 Bedroom in Dumbo   \n",
       "267421  393240   9739230                2 BR Dumbo Brooklyn. Amazing views!   \n",
       "267422  393241   8292794                  Spectacular 1 Bdrm Condo in DUMBO   \n",
       "267423  393242  14050340                                         BK GETAWAY   \n",
       "267424  393243   3411815                    Sunny 1 Bdrm Vinegar Hill/DUMBO   \n",
       "267425  393244  11927624                   Light filled modern APT in DUMBO   \n",
       "\n",
       "         host_id        host_name neighbourhood_group neighbourhood  zipcode  \\\n",
       "0           7611              NaN            Brooklyn  Williamsburg    11211   \n",
       "1          10176             Juan            Brooklyn  Williamsburg    11211   \n",
       "2          12169    Miko & Lauren            Brooklyn    Greenpoint    11211   \n",
       "3          17297           Joelle            Brooklyn  Williamsburg    11211   \n",
       "4          22486            Lisel            Brooklyn  Williamsburg    11211   \n",
       "5          52043              NaN            Brooklyn  Williamsburg    11211   \n",
       "6          52233              NaN            Brooklyn  Williamsburg    11211   \n",
       "7          56512           Joanna            Brooklyn  Williamsburg    11211   \n",
       "8          62132           Rachel            Brooklyn  Williamsburg    11211   \n",
       "9          72014              NaN            Brooklyn  Williamsburg    11211   \n",
       "10         72014             Lulu            Brooklyn  Williamsburg    11211   \n",
       "11         42756           Leslie            Brooklyn  Williamsburg    11211   \n",
       "12         79402       Christiana            Brooklyn  Williamsburg    11211   \n",
       "13         42756           Leslie            Brooklyn  Williamsburg    11211   \n",
       "14         42756           Leslie            Brooklyn  Williamsburg    11211   \n",
       "15         97476        Sebastian            Brooklyn  Williamsburg    11211   \n",
       "16        105538             Erik            Brooklyn  Williamsburg    11211   \n",
       "17        114590       Juan Pablo            Brooklyn  Williamsburg    11211   \n",
       "18        120952           Kayvon            Brooklyn    Greenpoint    11211   \n",
       "19       6197784               Jo            Brooklyn  Williamsburg    11211   \n",
       "20        127428            Shira            Brooklyn  Williamsburg    11211   \n",
       "21        131716              NaN            Brooklyn  Williamsburg    11211   \n",
       "22        132918              NaN            Brooklyn  Williamsburg    11211   \n",
       "23        139612              NaN            Brooklyn  Williamsburg    11211   \n",
       "24        143027             Ming            Brooklyn  Williamsburg    11211   \n",
       "25         72014             Lulu            Brooklyn  Williamsburg    11211   \n",
       "26        130313            Emily            Brooklyn  Williamsburg    11211   \n",
       "27        154377              NaN            Brooklyn  Williamsburg    11211   \n",
       "28        142684              NaN            Brooklyn  Williamsburg    11211   \n",
       "29        159370          Viviana            Brooklyn  Williamsburg    11211   \n",
       "...          ...              ...                 ...           ...      ...   \n",
       "267396  33611129            James            Brooklyn   Fort Greene    11201   \n",
       "267397  31883140          Fabiana            Brooklyn   Fort Greene    11201   \n",
       "267398   1030526        Elizabeth            Brooklyn   Fort Greene    11201   \n",
       "267399  10229330           Tamara            Brooklyn   Fort Greene    11201   \n",
       "267400  47267614           Vitali            Brooklyn   Fort Greene    11201   \n",
       "267401  50136185            Nehal            Brooklyn   Fort Greene    11201   \n",
       "267402  21742218           Ashley            Brooklyn     Navy Yard    11201   \n",
       "267403  23502626              Fun            Brooklyn     Navy Yard    11201   \n",
       "267404  19124849            Carol           Manhattan   Two Bridges    11201   \n",
       "267405  83977725               Me            Brooklyn  Vinegar Hill    11201   \n",
       "267406  41053947           Nicole            Brooklyn  Vinegar Hill    11201   \n",
       "267407   4166168  Nick And Noémie            Brooklyn  Vinegar Hill    11201   \n",
       "267408  88554743            Dumbo            Brooklyn  Vinegar Hill    11201   \n",
       "267409   2809938          Phillip            Brooklyn  Vinegar Hill    11201   \n",
       "267410   1126015         Patricia            Brooklyn  Vinegar Hill    11201   \n",
       "267411   4383624           Ferran            Brooklyn  Vinegar Hill    11201   \n",
       "267412   4166168  Nick And Noémie            Brooklyn  Vinegar Hill    11201   \n",
       "267413  17782625            David            Brooklyn  Vinegar Hill    11201   \n",
       "267414  12587313         Raphaela            Brooklyn  Vinegar Hill    11201   \n",
       "267415   5352419            Brian            Brooklyn  Vinegar Hill    11201   \n",
       "267416  33317997             Anna            Brooklyn  Vinegar Hill    11201   \n",
       "267417  82170330         Ludovica            Brooklyn  Vinegar Hill    11201   \n",
       "267418  21336136         Brooklyn            Brooklyn  Vinegar Hill    11201   \n",
       "267419  10455134             Vera            Brooklyn  Vinegar Hill    11201   \n",
       "267420     60046             John            Brooklyn  Vinegar Hill    11201   \n",
       "267421  48112217             Paul            Brooklyn  Vinegar Hill    11201   \n",
       "267422  43724240               Dc            Brooklyn  Vinegar Hill    11201   \n",
       "267423  33575686           Thomas            Brooklyn  Vinegar Hill    11201   \n",
       "267424   1649108            Allie            Brooklyn  Vinegar Hill    11201   \n",
       "267425   5251483            Sasha            Brooklyn  Vinegar Hill    11201   \n",
       "\n",
       "         latitude  longitude        room_type  price  minimum_nights  \\\n",
       "0       40.710302 -73.964961  Entire home/apt    175               2   \n",
       "1       40.717358 -73.942063  Entire home/apt    250               2   \n",
       "2       40.719211 -73.951231     Private room    109               2   \n",
       "3       40.712643 -73.958624     Private room    125               2   \n",
       "4       40.710961 -73.946840  Entire home/apt    145               2   \n",
       "5       40.715022 -73.958098  Entire home/apt    190               2   \n",
       "6       40.712523 -73.958105  Entire home/apt    100               7   \n",
       "7       40.708808 -73.959302  Entire home/apt    150               2   \n",
       "8       40.714282 -73.945124  Entire home/apt    125               2   \n",
       "9       40.711540 -73.961120     Private room    129               3   \n",
       "10      40.711558 -73.962178     Private room     84               3   \n",
       "11      40.710545 -73.962100  Entire home/apt    165               1   \n",
       "12      40.709843 -73.957750  Entire home/apt    100               1   \n",
       "13      40.710529 -73.960866  Entire home/apt    140               1   \n",
       "14      40.710121 -73.961302  Entire home/apt    238               1   \n",
       "15      40.709455 -73.962172     Private room     65               2   \n",
       "16      40.714591 -73.948443  Entire home/apt    169               3   \n",
       "17      40.721012 -73.961206     Private room     90               1   \n",
       "18      40.719356 -73.951183  Entire home/apt    150               2   \n",
       "19      40.719226 -73.964679     Private room     90               1   \n",
       "20      40.712565 -73.939520  Entire home/apt    120               4   \n",
       "21      40.712599 -73.950012     Private room    109               2   \n",
       "22      40.715924 -73.938550     Private room     80               2   \n",
       "23      40.715611 -73.948346  Entire home/apt    225               3   \n",
       "24      40.714920 -73.959351  Entire home/apt    185               4   \n",
       "25      40.711652 -73.960869     Private room     78               3   \n",
       "26      40.710081 -73.961231  Entire home/apt    165               5   \n",
       "27      40.709483 -73.953150  Entire home/apt     69              13   \n",
       "28      40.715356 -73.960568     Private room    100               3   \n",
       "29      40.713632 -73.963980  Entire home/apt    125               6   \n",
       "...           ...        ...              ...    ...             ...   \n",
       "267396  40.692873 -73.981909  Entire home/apt    200               2   \n",
       "267397  40.691727 -73.977558  Entire home/apt    120               4   \n",
       "267398  40.690231 -73.980865  Entire home/apt     95               2   \n",
       "267399  40.694513 -73.981902  Entire home/apt    300               7   \n",
       "267400  40.691474 -73.980725  Entire home/apt     95               1   \n",
       "267401  40.695562 -73.982295     Private room    125               4   \n",
       "267402  40.701609 -73.979967     Private room    135               2   \n",
       "267403  40.702418 -73.980162      Shared room    185               1   \n",
       "267404  40.711814 -73.996149     Private room     80               1   \n",
       "267405  40.701249 -73.985822  Entire home/apt    135               1   \n",
       "267406  40.698931 -73.985158  Entire home/apt    180               2   \n",
       "267407  40.700753 -73.982756     Private room     85               2   \n",
       "267408  40.703344 -73.983046     Private room    120               2   \n",
       "267409  40.701208 -73.982959  Entire home/apt    250              30   \n",
       "267410  40.701555 -73.983434  Entire home/apt    225               3   \n",
       "267411  40.703792 -73.982196  Entire home/apt    100              11   \n",
       "267412  40.700517 -73.982793     Private room    100               2   \n",
       "267413  40.702107 -73.984181  Entire home/apt    245               2   \n",
       "267414  40.699183 -73.984303     Private room     55               1   \n",
       "267415  40.702127 -73.983799  Entire home/apt    550              20   \n",
       "267416  40.700980 -73.982114  Entire home/apt    375              14   \n",
       "267417  40.699541 -73.985291     Private room     90               5   \n",
       "267418  40.701068 -73.983909  Entire home/apt    235               3   \n",
       "267419  40.699105 -73.982498  Entire home/apt    161               2   \n",
       "267420  40.701461 -73.981879  Entire home/apt    225              30   \n",
       "267421  40.702660 -73.983500  Entire home/apt    160               3   \n",
       "267422  40.698544 -73.984333  Entire home/apt    205               2   \n",
       "267423  40.698433 -73.983619     Private room    125               1   \n",
       "267424  40.704399 -73.981530     Private room     80               1   \n",
       "267425  40.703284 -73.983138  Entire home/apt    170               4   \n",
       "\n",
       "        number_of_reviews last_review  reviews_per_month  \\\n",
       "0                      51   8/28/2015               0.63   \n",
       "1                      66   12/7/2015               0.81   \n",
       "2                      96  10/26/2015               1.19   \n",
       "3                       9   8/31/2015               0.14   \n",
       "4                      10  12/12/2015               0.15   \n",
       "5                      56   10/7/2015               0.76   \n",
       "6                       4    6/7/2015               0.15   \n",
       "7                      60  12/27/2015               0.82   \n",
       "8                      30  11/29/2015               0.41   \n",
       "9                      54  12/16/2015               0.77   \n",
       "10                     99  12/20/2015               1.41   \n",
       "11                    235  12/20/2015               3.33   \n",
       "12                    132  12/12/2015               2.06   \n",
       "13                    281  12/16/2015               4.04   \n",
       "14                    216  12/27/2015               3.08   \n",
       "15                      5   8/23/2015               0.08   \n",
       "16                     18  11/17/2015               0.60   \n",
       "17                      5    5/2/2014               0.10   \n",
       "18                     31   11/8/2015               0.58   \n",
       "19                     10   9/18/2011               0.16   \n",
       "20                     16   8/18/2015               0.25   \n",
       "21                     78  10/17/2015               1.17   \n",
       "22                    119  12/14/2015               1.75   \n",
       "23                      8   6/23/2014               0.13   \n",
       "24                    151  12/18/2015               2.26   \n",
       "25                     92  11/28/2015               1.42   \n",
       "26                     19   5/18/2015               0.29   \n",
       "27                     11   6/25/2015               0.18   \n",
       "28                     97  10/24/2015               1.82   \n",
       "29                     14  12/19/2015               0.22   \n",
       "...                   ...         ...                ...   \n",
       "267396                  0         NaN                NaN   \n",
       "267397                  1    7/4/2016               0.20   \n",
       "267398                  0         NaN                NaN   \n",
       "267399                  2   8/11/2016               0.50   \n",
       "267400                  0         NaN                NaN   \n",
       "267401                  1  12/22/2015               0.09   \n",
       "267402                  4   5/30/2016               0.27   \n",
       "267403                  0         NaN                NaN   \n",
       "267404                  0         NaN                NaN   \n",
       "267405                 14   12/1/2016               6.09   \n",
       "267406                  9   9/14/2016               0.58   \n",
       "267407                  9   12/1/2016               2.93   \n",
       "267408                  4   10/9/2016               1.38   \n",
       "267409                  4  11/22/2016               1.62   \n",
       "267410                 31   8/13/2015               0.69   \n",
       "267411                  5  10/19/2016               0.44   \n",
       "267412                 44  11/20/2016               1.43   \n",
       "267413                  1    4/5/2016               0.12   \n",
       "267414                  7  10/31/2016               3.00   \n",
       "267415                  0         NaN                NaN   \n",
       "267416                  0         NaN                NaN   \n",
       "267417                  0         NaN                NaN   \n",
       "267418                 55  11/27/2016               3.35   \n",
       "267419                 16  11/14/2016               2.87   \n",
       "267420                 13  10/31/2016               0.85   \n",
       "267421                 27  11/27/2016               2.21   \n",
       "267422                 23  11/19/2016               1.54   \n",
       "267423                 14   11/6/2016               3.50   \n",
       "267424                  8   10/2/2016               0.34   \n",
       "267425                  2   7/23/2016               0.38   \n",
       "\n",
       "        calculated_host_listings_count  availability_365  year_month  \n",
       "0                                    1               308      201601  \n",
       "1                                    1               351      201601  \n",
       "2                                    2               363      201601  \n",
       "3                                    1               365      201601  \n",
       "4                                    5               365      201601  \n",
       "5                                    1               359      201601  \n",
       "6                                    1               118      201601  \n",
       "7                                    1                36      201601  \n",
       "8                                    1               341      201601  \n",
       "9                                    2               351      201601  \n",
       "10                                   2               359      201601  \n",
       "11                                   3               341      201601  \n",
       "12                                   1               357      201601  \n",
       "13                                   3               280      201601  \n",
       "14                                   3               310      201601  \n",
       "15                                   2               118      201601  \n",
       "16                                   1                 6      201601  \n",
       "17                                   1               365      201601  \n",
       "18                                   1                29      201601  \n",
       "19                                   1               122      201601  \n",
       "20                                   1               214      201601  \n",
       "21                                   1                90      201601  \n",
       "22                                   1               364      201601  \n",
       "23                                   1               364      201601  \n",
       "24                                   1               345      201601  \n",
       "25                                   2               352      201601  \n",
       "26                                   1               118      201601  \n",
       "27                                   1               153      201601  \n",
       "28                                   1               351      201601  \n",
       "29                                   1                89      201601  \n",
       "...                                ...               ...         ...  \n",
       "267396                               1                50      201612  \n",
       "267397                               1                 0      201612  \n",
       "267398                               1                 0      201612  \n",
       "267399                               1                 0      201612  \n",
       "267400                               1                 0      201612  \n",
       "267401                               1                 0      201612  \n",
       "267402                               1               363      201612  \n",
       "267403                               1               365      201612  \n",
       "267404                               2                85      201612  \n",
       "267405                               1               157      201612  \n",
       "267406                               1               349      201612  \n",
       "267407                               2                67      201612  \n",
       "267408                               1               336      201612  \n",
       "267409                               1                60      201612  \n",
       "267410                               1                 0      201612  \n",
       "267411                               1                19      201612  \n",
       "267412                               2               150      201612  \n",
       "267413                               2                 6      201612  \n",
       "267414                               1                69      201612  \n",
       "267415                               1               365      201612  \n",
       "267416                               1                 0      201612  \n",
       "267417                               2                67      201612  \n",
       "267418                               1               258      201612  \n",
       "267419                               1                39      201612  \n",
       "267420                               1               365      201612  \n",
       "267421                               1               184      201612  \n",
       "267422                               1               348      201612  \n",
       "267423                               1               359      201612  \n",
       "267424                               1                 0      201612  \n",
       "267425                               1                 0      201612  \n",
       "\n",
       "[267426 rows x 19 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 10 zipcodes = [11211,11221,11206,10019,11216,10002,10009,10003,11222,11238]\n",
    "\n",
    "# create Data Frame with specified zipcodes\n",
    "# data from all months (except march-missing) per top 30 zipcodes(based on most concetration of listings)\n",
    "frames = []\n",
    "\n",
    "for zipcode in zipcodes:\n",
    "    one_zip = listings.loc[listings['zipcode']==zipcode]\n",
    "    frames.append(one_zip)\n",
    "\n",
    "#print(frames)\n",
    "by_zip = pd.concat(frames)\n",
    "by_zip.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "year_month\n",
      "201601    147.207304\n",
      "201602    145.121072\n",
      "201604    146.060921\n",
      "201605    147.868601\n",
      "201606    147.559228\n",
      "201607    145.153404\n",
      "201608    145.032514\n",
      "201609    145.325450\n",
      "201610    146.669895\n",
      "201611    146.869331\n",
      "201612    144.462298\n",
      "Name: price, dtype: float64\n",
      "year_month\n",
      "201601    6500\n",
      "201602    6500\n",
      "201604    6500\n",
      "201605    6500\n",
      "201606    7000\n",
      "201607    6500\n",
      "201608    6500\n",
      "201609    9000\n",
      "201610    9999\n",
      "201611    9999\n",
      "201612    9999\n",
      "Name: price, dtype: int64\n",
      "year_month\n",
      "201601    10\n",
      "201602    15\n",
      "201604    20\n",
      "201605    15\n",
      "201606    10\n",
      "201607    10\n",
      "201608    10\n",
      "201609    10\n",
      "201610    10\n",
      "201611    10\n",
      "201612    14\n",
      "Name: price, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Avg price per month of 2016 for top 10 zipcodes (based on most concetration of listings)\n",
    "\n",
    "by_month = by_zip.groupby('year_month')['price'].mean()\n",
    "print(by_month)\n",
    "\n",
    "by_m_max = by_zip.groupby('year_month')['price'].max()\n",
    "print(by_m_max)\n",
    "\n",
    "by_m_min = by_zip.groupby('year_month')['price'].min()\n",
    "print(by_m_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot averages\n",
    "plt.figure(figsize=(10,5))\n",
    "x_axis = np.arange(len(by_month))\n",
    "tick_locations = [x for x in x_axis]\n",
    "plt.xticks(tick_locations, ['Jan','Feb','Apr','May','Jun',\n",
    "                            'Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "plt.plot(x_axis, by_month, color='r', linewidth=3)\n",
    "#plt.scatter(x_axis, by_m_min, color='b', linewidth=3)\n",
    "#plt.scatter(x_axis, by_m_max, color='g', linewidth=3)\n",
    "plt.grid()\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Price $')\n",
    "plt.title('Avg. Price / Night vs. Month for 2016')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "x_axis = np.arange(len(by_month))\n",
    "tick_locations = [x for x in x_axis]\n",
    "plt.xticks(tick_locations, ['Jan','Feb','Apr','May','Jun',\n",
    "                            'Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "#plt.scatter(x_axis, by_month, color='r', linewidth=3)\n",
    "plt.plot(x_axis, by_m_min, color='b', linewidth=3)\n",
    "#plt.scatter(x_axis, by_m_max, color='g', linewidth=3)\n",
    "plt.grid()\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Price $')\n",
    "plt.title('Min Price / Night vs. Month for 2016')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "x_axis = np.arange(len(by_month))\n",
    "tick_locations = [x for x in x_axis]\n",
    "plt.xticks(tick_locations, ['Jan','Feb','Apr','May','Jun',\n",
    "                            'Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "#plt.scatter(x_axis, by_month, color='r', linewidth=3)\n",
    "#plt.scatter(x_axis, by_m_min, color='b', linewidth=3)\n",
    "plt.plot(x_axis, by_m_max, color='g', linewidth=3)\n",
    "plt.grid()\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Price $')\n",
    "plt.title('Max Price / Night vs. Month for 2016')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Number of Listings   Avg Price\n",
      "year_month                                \n",
      "201601                   11582  147.207304\n",
      "201602                   11489  145.121072\n",
      "201604                   10210  146.060921\n",
      "201605                    9924  147.868601\n",
      "201606                   11498  147.559228\n",
      "201607                   12014  145.153404\n",
      "201608                   10949  145.032514\n",
      "201609                   12346  145.325450\n",
      "201610                   12287  146.669895\n",
      "201611                   12061  146.869331\n",
      "201612                   12559  144.462298\n"
     ]
    }
   ],
   "source": [
    "#find number of listings per month - top zipcodes only\n",
    "listing_bymonth = by_zip.groupby('year_month')['id'].count()\n",
    "#print(listing_bymonth)\n",
    "\n",
    "grouped = pd.DataFrame({\"Number of Listings\":listing_bymonth, \"Avg Price\":by_month})\n",
    "print(grouped)\n",
    "\n",
    "#original data - all zipcodes\n",
    "#listing_bymonth_original = listings.groupby('year_month')['id'].count()\n",
    "#print(listing_bymonth_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BUBBLE PLOT - S==Correlating to number of listings/month 2016\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "x_axis = np.arange(len(by_month))\n",
    "tick_locations = [x for x in x_axis]\n",
    "plt.xticks(tick_locations, ['Jan','Feb','Apr','May','Jun',\n",
    "                            'Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "plt.scatter(x_axis, grouped[\"Avg Price\"], s=grouped[\"Number of Listings\"]*.15, alpha=.8, linewidths=1.0, edgecolors='black',\n",
    "            color='gold', label =\"Listing Concentrations\")\n",
    "#plt.scatter(x_axis, by_m_min, color='b', linewidth=3)\n",
    "#plt.plot(x_axis, by_m_max, color='g', linewidth=3)\n",
    "plt.grid()\n",
    "plt.xlabel('Month (missing \"March\" data)')\n",
    "plt.ylabel('Avg Price $')\n",
    "plt.title('Air B&B Avg Price Data (2016)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}