Prediction of bike availability.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction demand of bikes at each bike station at different time of the day\n",
    "index = start_date, start_station_id\n",
    "time period = { period 1: 1-3, \n",
    "                period 2: 4-6, \n",
    "                period 3: 7-9, \n",
    "                period 4: 10-12, \n",
    "                period 5: 13-15, \n",
    "                period 6: 16-18, \n",
    "                period 7: 19-21,\n",
    "                period 8: 22-24}\n",
    "peak = {period 3, period 6}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import xgboost as xgb\n",
    "from geopy.distance import geodesic\n",
    "\n",
    "from pandas.tseries.holiday import USFederalHolidayCalendar\n",
    "from pandas.tseries.offsets import CustomBusinessDay\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.pipeline import FeatureUnion\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.cross_validation import KFold\n",
    "from sklearn.metrics import mean_squared_error, median_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  \n",
    "#  \n",
    "# status data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load reduced status data\n",
    "status = pd.read_csv(\"status_reduced.csv\", header=None, \n",
    "                     names = [\"station_id\", \"bikes_available\", \"docks_available\", \"datetime\"],\n",
    "                     parse_dates = [\"datetime\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>bikes_available</th>\n",
       "      <th>docks_available</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>2014-11-11 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-03-20 16:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-06-29 07:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-04-05 15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2013-11-27 06:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   station_id  bikes_available  docks_available            datetime\n",
       "0          67                6               21 2014-11-11 00:00:00\n",
       "1          48               10                4 2015-03-20 16:00:00\n",
       "2          80               11                4 2015-06-29 07:00:00\n",
       "3          58                3               16 2015-04-05 15:00:00\n",
       "4          14                9               10 2013-11-27 06:00:00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to datetime, seperate date and hour\n",
    "# map hour to 8 time periods\n",
    "status[\"date\"] = pd.to_datetime(status.datetime, format='%m/%d/%Y %H:%M').dt.date\n",
    "status[\"time\"]=pd.to_datetime(status.datetime, format='%m/%d/%Y %H:%M').dt.hour\n",
    "status[\"time_period\"]=status.time.map(lambda x: \"period \" + str((x+2)//3) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>bikes_available</th>\n",
       "      <th>docks_available</th>\n",
       "      <th>datetime</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>time_period</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>2014-11-11 00:00:00</td>\n",
       "      <td>2014-11-11</td>\n",
       "      <td>0</td>\n",
       "      <td>period 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-03-20 16:00:00</td>\n",
       "      <td>2015-03-20</td>\n",
       "      <td>16</td>\n",
       "      <td>period 6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2015-06-29 07:00:00</td>\n",
       "      <td>2015-06-29</td>\n",
       "      <td>7</td>\n",
       "      <td>period 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-04-05 15:00:00</td>\n",
       "      <td>2015-04-05</td>\n",
       "      <td>15</td>\n",
       "      <td>period 5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>2013-11-27 06:00:00</td>\n",
       "      <td>2013-11-27</td>\n",
       "      <td>6</td>\n",
       "      <td>period 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   station_id  bikes_available  docks_available            datetime  \\\n",
       "0          67                6               21 2014-11-11 00:00:00   \n",
       "1          48               10                4 2015-03-20 16:00:00   \n",
       "2          80               11                4 2015-06-29 07:00:00   \n",
       "3          58                3               16 2015-04-05 15:00:00   \n",
       "4          14                9               10 2013-11-27 06:00:00   \n",
       "\n",
       "         date  time time_period  \n",
       "0  2014-11-11     0    period 0  \n",
       "1  2015-03-20    16    period 6  \n",
       "2  2015-06-29     7    period 3  \n",
       "3  2015-04-05    15    period 5  \n",
       "4  2013-11-27     6    period 2  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "- period 1: 1-3 \n",
    "- period 2: 4-6 \n",
    "- period 3: 7-9 (AM peak)\n",
    "- period 4: 10-12\n",
    "- period 5: 13-15 \n",
    "- period 6: 16-18 (PM peak)\n",
    "- period 7: 19-21\n",
    "- period 8: 22-24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>station_id</th>\n",
       "      <th>time_period</th>\n",
       "      <th>bikes_available</th>\n",
       "      <th>docks_available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 6</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>24.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 7</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 8</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  station_id time_period  bikes_available  docks_available\n",
       "0  2013-08-29           2    period 4         2.000000        25.000000\n",
       "1  2013-08-29           2    period 5         2.000000        24.666667\n",
       "2  2013-08-29           2    period 6         2.333333        24.333333\n",
       "3  2013-08-29           2    period 7         1.333333        25.000000\n",
       "4  2013-08-29           2    period 8         2.500000        24.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize mean(bikes_available), mean(docks_available) by date, station, start_time_period\n",
    "# this will be data frame used for training\n",
    "status2 = status.groupby(['date','station_id','time_period']).agg({\"bikes_available\": \"mean\", \"docks_available\":\"mean\"}).reset_index()\n",
    "status2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station_id</th>\n",
       "      <th>bikes_available</th>\n",
       "      <th>docks_available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>452045.000000</td>\n",
       "      <td>452045.000000</td>\n",
       "      <td>452045.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.526642</td>\n",
       "      <td>8.178788</td>\n",
       "      <td>9.068467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23.758895</td>\n",
       "      <td>3.832629</td>\n",
       "      <td>4.013591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          station_id  bikes_available  docks_available\n",
       "count  452045.000000    452045.000000    452045.000000\n",
       "mean       42.526642         8.178788         9.068467\n",
       "std        23.758895         3.832629         4.013591\n",
       "min         2.000000         0.000000         0.000000\n",
       "25%        24.000000         5.666667         6.000000\n",
       "50%        42.000000         8.000000         8.666667\n",
       "75%        63.000000        10.000000        11.333333\n",
       "max        84.000000        27.000000        27.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# bikes available: min=0, max=27, mean=8\n",
    "# docks available: min=0, max=27, mean=8.7\n",
    "status2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       8094\n",
       "1      19426\n",
       "2      33695\n",
       "3      54656\n",
       "4      78278\n",
       "5     106073\n",
       "6     129875\n",
       "7     145670\n",
       "8     139642\n",
       "9     116632\n",
       "10     91566\n",
       "11     70491\n",
       "12     54101\n",
       "13     42198\n",
       "14     30971\n",
       "15     22895\n",
       "16     17115\n",
       "17     13544\n",
       "18      9803\n",
       "19      6790\n",
       "20      5268\n",
       "21      3907\n",
       "22      2284\n",
       "23      1050\n",
       "24       410\n",
       "25       283\n",
       "26       107\n",
       "27        10\n",
       "Name: bikes_available, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# out of a total of 452045 rows, 8094 rows have bikes_available = 0 (1.8% of total)\n",
    "# 61215 rows have bikes_available <=3 (13.5% of total) \n",
    "# (when average bikes_available <=3, there could be times within the 3 hour interval that bikes_available = 0)\n",
    "# the situation could get worse in peak hours, peak days, high usage areas\n",
    "# this justify the needs to rebalance\n",
    "status.bikes_available.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12e17aa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot for bikes available\n",
    "# it follows a right-skewed bell curve shape, indicating normal distribution\n",
    "#    - most stations have on average 6-8 bikes available at any 3 hour interval\n",
    "#    - some stations have no bikes available --> shortage\n",
    "#    - some stations have >15 bikes available --> under utilization\n",
    "# how can we solve the problem of shortage and under utilization to maximize revenue\n",
    "\n",
    "plt.figure(figsize=(15,4))\n",
    "plt.subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0)\n",
    "n_bins=28\n",
    "N, bins, patches = plt.hist(status2.bikes_available, bins=n_bins, alpha=0.8)\n",
    "fracs = N / N.max()\n",
    "norm = colors.Normalize(fracs.min(), fracs.max())\n",
    "for thisfrac, thispatch in zip(fracs, patches):\n",
    "    color = plt.cm.viridis(norm(thisfrac))\n",
    "    thispatch.set_facecolor(color)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uaby0/pw5VbUmySuTPHGwPwYAAABgNVrOU6gqybVJvt7dv77oo+1JtkzjLUluWVTfPD1Z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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12edcd748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot for docks available\n",
    "\n",
    "plt.figure(figsize=(15,4))\n",
    "plt.subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0)\n",
    "n_bins=28\n",
    "N, bins, patches = plt.hist(status2.docks_available, bins=n_bins, alpha=0.8)\n",
    "fracs = N / N.max()\n",
    "norm = colors.Normalize(fracs.min(), fracs.max())\n",
    "for thisfrac, thispatch in zip(fracs, patches):\n",
    "    color = plt.cm.viridis(norm(thisfrac))\n",
    "    thispatch.set_facecolor(color)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>station_id</th>\n",
       "      <th>time_period</th>\n",
       "      <th>bikes_available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 4</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 5</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 6</td>\n",
       "      <td>2.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 7</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 8</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  station_id time_period  bikes_available\n",
       "0  2013-08-29           2    period 4         2.000000\n",
       "1  2013-08-29           2    period 5         2.000000\n",
       "2  2013-08-29           2    period 6         2.333333\n",
       "3  2013-08-29           2    period 7         1.333333\n",
       "4  2013-08-29           2    period 8         2.500000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# leave out duration for now\n",
    "status2 = status2.iloc[:,0:4]\n",
    "status2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(452045, 4)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  \n",
    "#  \n",
    "# Weather data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3665, 24)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load weather data\n",
    "weather = pd.read_csv(\"weather.csv\")\n",
    "weather.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # convert to datetime, keep date only\n",
    "weather.date = pd.to_datetime(weather.date, format='%m/%d/%Y').dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([94107, 94063, 94301, 94041, 95113])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#It seems we have one entry per zip code\n",
    "#We need to map the stations to one of the five zip codes later\n",
    "weather.zip_code.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                 0\n",
       "max_temperature_f                    4\n",
       "mean_temperature_f                   4\n",
       "min_temperature_f                    4\n",
       "max_dew_point_f                     54\n",
       "mean_dew_point_f                    54\n",
       "min_dew_point_f                     54\n",
       "max_humidity                        54\n",
       "mean_humidity                       54\n",
       "min_humidity                        54\n",
       "max_sea_level_pressure_inches        1\n",
       "mean_sea_level_pressure_inches       1\n",
       "min_sea_level_pressure_inches        1\n",
       "max_visibility_miles                13\n",
       "mean_visibility_miles               13\n",
       "min_visibility_miles                13\n",
       "max_wind_Speed_mph                   1\n",
       "mean_wind_speed_mph                  1\n",
       "max_gust_speed_mph                 899\n",
       "precipitation_inches                 1\n",
       "cloud_cover                          1\n",
       "events                            3143\n",
       "wind_dir_degrees                     1\n",
       "zip_code                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weather data has a lot of missing values\n",
    "weather.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tidy up events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Fog', 'Rain', 'Fog-Rain', 'rain', 'Rain-Thunderstorm'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some weather events are in capital, some are not\n",
    "# nan should mean normal weather, no special events\n",
    "weather.events.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather.loc[weather.events == 'rain', 'events'] = \"Rain\"\n",
    "weather.loc[weather.events.isnull(), 'events'] = \"Normal\"\n",
    "events = pd.get_dummies(weather.events)\n",
    "weather = weather.merge(events, left_index = True, right_index = True)\n",
    "weather = weather.drop(['events'],1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tidy up max_gust_speed_mph\n",
    "it has the most missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For each value of max_wind, find the median max_gust and use that to fill the null values of max_gust\n",
    "weather.loc[weather.max_gust_speed_mph.isnull(), 'max_gust_speed_mph'] = weather.groupby('max_wind_Speed_mph').max_gust_speed_mph.apply(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### tidy up precipitation_inches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Change this feature from a string to numeric.\n",
    "# Use errors = 'coerce' because some values currently equal 'T' and we want them to become NAs.\n",
    "weather.precipitation_inches = pd.to_numeric(weather.precipitation_inches, errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Change null values to the median, of values > 0, because T, I think, means True. \n",
    "# Therefore we want to find the median amount of precipitation on days when it rained.\n",
    "weather.loc[weather.precipitation_inches.isnull(), \n",
    "            'precipitation_inches'] = weather[weather.precipitation_inches.notnull()].precipitation_inches.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### impute other missing values\n",
    "- fill with median\n",
    "- fill with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# impute other missing value with median first (to be improved later)\n",
    "weather.loc[weather.max_temperature_f.isnull(), 'max_temperature_f'] = weather[weather.max_temperature_f.notnull()].max_temperature_f.median()\n",
    "weather.loc[weather.mean_temperature_f.isnull(), 'mean_temperature_f'] = weather[weather.mean_temperature_f.notnull()].mean_temperature_f.median()\n",
    "weather.loc[weather.min_temperature_f.isnull(), 'min_temperature_f'] = weather[weather.min_temperature_f.notnull()].min_temperature_f.median()\n",
    "\n",
    "weather.loc[weather.max_dew_point_f.isnull(), 'max_dew_point_f'] = weather[weather.max_dew_point_f.notnull()].max_dew_point_f.median()\n",
    "weather.loc[weather.mean_dew_point_f.isnull(), 'mean_dew_point_f'] = weather[weather.mean_dew_point_f.notnull()].mean_dew_point_f.median()\n",
    "weather.loc[weather.min_dew_point_f.isnull(), 'min_dew_point_f'] = weather[weather.min_dew_point_f.notnull()].min_dew_point_f.median()\n",
    "\n",
    "weather.loc[weather.max_humidity.isnull(), 'max_humidity'] = weather[weather.max_humidity.notnull()].max_humidity.median()\n",
    "weather.loc[weather.mean_humidity.isnull(), 'mean_humidity'] = weather[weather.mean_humidity.notnull()].mean_humidity.median()\n",
    "weather.loc[weather.min_humidity.isnull(), 'min_humidity'] = weather[weather.min_humidity.notnull()].min_humidity.median()\n",
    "\n",
    "weather.loc[weather.max_visibility_miles.isnull(), 'max_visibility_miles'] = weather[weather.max_visibility_miles.notnull()].max_visibility_miles.median()\n",
    "weather.loc[weather.mean_visibility_miles.isnull(), 'mean_visibility_miles'] = weather[weather.mean_visibility_miles.notnull()].mean_visibility_miles.median()\n",
    "weather.loc[weather.min_visibility_miles.isnull(), 'min_visibility_miles'] = weather[weather.min_visibility_miles.notnull()].min_visibility_miles.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather = weather.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                              0\n",
       "max_temperature_f                 0\n",
       "mean_temperature_f                0\n",
       "min_temperature_f                 0\n",
       "max_dew_point_f                   0\n",
       "mean_dew_point_f                  0\n",
       "min_dew_point_f                   0\n",
       "max_humidity                      0\n",
       "mean_humidity                     0\n",
       "min_humidity                      0\n",
       "max_sea_level_pressure_inches     0\n",
       "mean_sea_level_pressure_inches    0\n",
       "min_sea_level_pressure_inches     0\n",
       "max_visibility_miles              0\n",
       "mean_visibility_miles             0\n",
       "min_visibility_miles              0\n",
       "max_wind_Speed_mph                0\n",
       "mean_wind_speed_mph               0\n",
       "max_gust_speed_mph                0\n",
       "precipitation_inches              0\n",
       "cloud_cover                       0\n",
       "wind_dir_degrees                  0\n",
       "zip_code                          0\n",
       "Fog                               0\n",
       "Fog-Rain                          0\n",
       "Normal                            0\n",
       "Rain                              0\n",
       "Rain-Thunderstorm                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check there is no more null\n",
    "weather.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>max_temperature_f</th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>min_temperature_f</th>\n",
       "      <th>max_dew_point_f</th>\n",
       "      <th>mean_dew_point_f</th>\n",
       "      <th>min_dew_point_f</th>\n",
       "      <th>max_humidity</th>\n",
       "      <th>mean_humidity</th>\n",
       "      <th>min_humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>max_gust_speed_mph</th>\n",
       "      <th>precipitation_inches</th>\n",
       "      <th>cloud_cover</th>\n",
       "      <th>wind_dir_degrees</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>Fog</th>\n",
       "      <th>Fog-Rain</th>\n",
       "      <th>Normal</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Rain-Thunderstorm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>286.0</td>\n",
       "      <td>94107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>78.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>94107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-31</td>\n",
       "      <td>71.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>94107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>74.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>94107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-09-02</td>\n",
       "      <td>75.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>94107</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  max_temperature_f  mean_temperature_f  min_temperature_f  \\\n",
       "0  2013-08-29               74.0                68.0               61.0   \n",
       "1  2013-08-30               78.0                69.0               60.0   \n",
       "2  2013-08-31               71.0                64.0               57.0   \n",
       "3  2013-09-01               74.0                66.0               58.0   \n",
       "4  2013-09-02               75.0                69.0               62.0   \n",
       "\n",
       "   max_dew_point_f  mean_dew_point_f  min_dew_point_f  max_humidity  \\\n",
       "0             61.0              58.0             56.0          93.0   \n",
       "1             61.0              58.0             56.0          90.0   \n",
       "2             57.0              56.0             54.0          93.0   \n",
       "3             60.0              56.0             53.0          87.0   \n",
       "4             61.0              60.0             58.0          93.0   \n",
       "\n",
       "   mean_humidity  min_humidity        ...          max_gust_speed_mph  \\\n",
       "0           75.0          57.0        ...                        28.0   \n",
       "1           70.0          50.0        ...                        35.0   \n",
       "2           75.0          57.0        ...                        31.0   \n",
       "3           68.0          49.0        ...                        29.0   \n",
       "4           77.0          61.0        ...                        30.0   \n",
       "\n",
       "   precipitation_inches  cloud_cover  wind_dir_degrees  zip_code  Fog  \\\n",
       "0                   0.0          4.0             286.0     94107    0   \n",
       "1                   0.0          2.0             291.0     94107    0   \n",
       "2                   0.0          4.0             284.0     94107    0   \n",
       "3                   0.0          4.0             284.0     94107    0   \n",
       "4                   0.0          6.0             277.0     94107    0   \n",
       "\n",
       "   Fog-Rain  Normal  Rain  Rain-Thunderstorm  \n",
       "0         0       1     0                  0  \n",
       "1         0       1     0                  0  \n",
       "2         0       1     0                  0  \n",
       "3         0       1     0                  0  \n",
       "4         0       1     0                  0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# weather2 will be used for training later\n",
    "weather2 = weather\n",
    "weather2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  \n",
    "#  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Station data\n",
    "**original attributes in dataset:**\n",
    "- station id: `id`\n",
    "- station name: `name`\n",
    "- station coordinates: `lat`, `long`\n",
    "- dock count: `dock_count`\n",
    "- city: `city`\n",
    "- Installation date: `installation_date`\n",
    "\n",
    "**added attributes:**\n",
    "- coordinate tuple: `coords`\n",
    "- mapped weather zip code: `weather_zip`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>dock_count</th>\n",
       "      <th>city</th>\n",
       "      <th>installation_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>San Jose Civic Center</td>\n",
       "      <td>37.330698</td>\n",
       "      <td>-121.888979</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Santa Clara at Almaden</td>\n",
       "      <td>37.333988</td>\n",
       "      <td>-121.894902</td>\n",
       "      <td>11</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Adobe on Almaden</td>\n",
       "      <td>37.331415</td>\n",
       "      <td>-121.893200</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>San Pedro Square</td>\n",
       "      <td>37.336721</td>\n",
       "      <td>-121.894074</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>Paseo de San Antonio</td>\n",
       "      <td>37.333798</td>\n",
       "      <td>-121.886943</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>San Salvador at 1st</td>\n",
       "      <td>37.330165</td>\n",
       "      <td>-121.885831</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Japantown</td>\n",
       "      <td>37.348742</td>\n",
       "      <td>-121.894715</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>37.337391</td>\n",
       "      <td>-121.886995</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>MLK Library</td>\n",
       "      <td>37.335885</td>\n",
       "      <td>-121.885660</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>SJSU 4th at San Carlos</td>\n",
       "      <td>37.332808</td>\n",
       "      <td>-121.883891</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>St James Park</td>\n",
       "      <td>37.339301</td>\n",
       "      <td>-121.889937</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>Arena Green / SAP Center</td>\n",
       "      <td>37.332692</td>\n",
       "      <td>-121.900084</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>SJSU - San Salvador at 9th</td>\n",
       "      <td>37.333955</td>\n",
       "      <td>-121.877349</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21</td>\n",
       "      <td>Franklin at Maple</td>\n",
       "      <td>37.481758</td>\n",
       "      <td>-122.226904</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/12/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22</td>\n",
       "      <td>Redwood City Caltrain Station</td>\n",
       "      <td>37.486078</td>\n",
       "      <td>-122.232089</td>\n",
       "      <td>25</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/15/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23</td>\n",
       "      <td>San Mateo County Center</td>\n",
       "      <td>37.487616</td>\n",
       "      <td>-122.229951</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/15/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24</td>\n",
       "      <td>Redwood City Public Library</td>\n",
       "      <td>37.484219</td>\n",
       "      <td>-122.227424</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/12/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>25</td>\n",
       "      <td>Stanford in Redwood City</td>\n",
       "      <td>37.485370</td>\n",
       "      <td>-122.203288</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/12/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26</td>\n",
       "      <td>Redwood City Medical Center</td>\n",
       "      <td>37.487682</td>\n",
       "      <td>-122.223492</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/12/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>27</td>\n",
       "      <td>Mountain View City Hall</td>\n",
       "      <td>37.389218</td>\n",
       "      <td>-122.081896</td>\n",
       "      <td>15</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/16/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28</td>\n",
       "      <td>Mountain View Caltrain Station</td>\n",
       "      <td>37.394358</td>\n",
       "      <td>-122.076713</td>\n",
       "      <td>23</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/15/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>29</td>\n",
       "      <td>San Antonio Caltrain Station</td>\n",
       "      <td>37.406940</td>\n",
       "      <td>-122.106758</td>\n",
       "      <td>23</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/15/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30</td>\n",
       "      <td>Evelyn Park and Ride</td>\n",
       "      <td>37.390277</td>\n",
       "      <td>-122.066553</td>\n",
       "      <td>15</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/16/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>31</td>\n",
       "      <td>San Antonio Shopping Center</td>\n",
       "      <td>37.400443</td>\n",
       "      <td>-122.108338</td>\n",
       "      <td>15</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>12/31/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>32</td>\n",
       "      <td>Castro Street and El Camino Real</td>\n",
       "      <td>37.385956</td>\n",
       "      <td>-122.083678</td>\n",
       "      <td>11</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>12/31/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>33</td>\n",
       "      <td>Rengstorff Avenue / California Street</td>\n",
       "      <td>37.400241</td>\n",
       "      <td>-122.099076</td>\n",
       "      <td>15</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/16/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>Palo Alto Caltrain Station</td>\n",
       "      <td>37.443988</td>\n",
       "      <td>-122.164759</td>\n",
       "      <td>23</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>8/14/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35</td>\n",
       "      <td>University and Emerson</td>\n",
       "      <td>37.444521</td>\n",
       "      <td>-122.163093</td>\n",
       "      <td>11</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>8/15/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>36</td>\n",
       "      <td>California Ave Caltrain Station</td>\n",
       "      <td>37.429082</td>\n",
       "      <td>-122.142805</td>\n",
       "      <td>15</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>8/14/2013</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>51</td>\n",
       "      <td>Embarcadero at Folsom</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/20/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>39</td>\n",
       "      <td>Powell Street BART</td>\n",
       "      <td>37.783871</td>\n",
       "      <td>-122.408433</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>54</td>\n",
       "      <td>Embarcadero at Bryant</td>\n",
       "      <td>37.787152</td>\n",
       "      <td>-122.388013</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/20/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>55</td>\n",
       "      <td>Temporary Transbay Terminal (Howard at Beale)</td>\n",
       "      <td>37.789756</td>\n",
       "      <td>-122.394643</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/20/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>56</td>\n",
       "      <td>Beale at Market</td>\n",
       "      <td>37.792251</td>\n",
       "      <td>-122.397086</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/20/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>57</td>\n",
       "      <td>5th at Howard</td>\n",
       "      <td>37.781752</td>\n",
       "      <td>-122.405127</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>58</td>\n",
       "      <td>San Francisco City Hall</td>\n",
       "      <td>37.778650</td>\n",
       "      <td>-122.418235</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>59</td>\n",
       "      <td>Golden Gate at Polk</td>\n",
       "      <td>37.781332</td>\n",
       "      <td>-122.418603</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>60</td>\n",
       "      <td>Embarcadero at Sansome</td>\n",
       "      <td>37.804770</td>\n",
       "      <td>-122.403234</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>61</td>\n",
       "      <td>2nd at Townsend</td>\n",
       "      <td>37.780526</td>\n",
       "      <td>-122.390288</td>\n",
       "      <td>27</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>62</td>\n",
       "      <td>2nd at Folsom</td>\n",
       "      <td>37.785299</td>\n",
       "      <td>-122.396236</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>63</td>\n",
       "      <td>Howard at 2nd</td>\n",
       "      <td>37.786978</td>\n",
       "      <td>-122.398108</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>64</td>\n",
       "      <td>2nd at South Park</td>\n",
       "      <td>37.782259</td>\n",
       "      <td>-122.392738</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>65</td>\n",
       "      <td>Townsend at 7th</td>\n",
       "      <td>37.771058</td>\n",
       "      <td>-122.402717</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>66</td>\n",
       "      <td>South Van Ness at Market</td>\n",
       "      <td>37.774814</td>\n",
       "      <td>-122.418954</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>67</td>\n",
       "      <td>Market at 10th</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>27</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>68</td>\n",
       "      <td>Yerba Buena Center of the Arts (3rd @ Howard)</td>\n",
       "      <td>37.784878</td>\n",
       "      <td>-122.401014</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>69</td>\n",
       "      <td>San Francisco Caltrain 2 (330 Townsend)</td>\n",
       "      <td>37.776600</td>\n",
       "      <td>-122.395470</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>70</td>\n",
       "      <td>San Francisco Caltrain (Townsend at 4th)</td>\n",
       "      <td>37.776617</td>\n",
       "      <td>-122.395260</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>71</td>\n",
       "      <td>Powell at Post (Union Square)</td>\n",
       "      <td>37.788446</td>\n",
       "      <td>-122.408499</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>72</td>\n",
       "      <td>Civic Center BART (7th at Market)</td>\n",
       "      <td>37.781039</td>\n",
       "      <td>-122.411748</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>73</td>\n",
       "      <td>Grant Avenue at Columbus Avenue</td>\n",
       "      <td>37.798522</td>\n",
       "      <td>-122.407245</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>74</td>\n",
       "      <td>Steuart at Market</td>\n",
       "      <td>37.794139</td>\n",
       "      <td>-122.394434</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>75</td>\n",
       "      <td>Mechanics Plaza (Market at Battery)</td>\n",
       "      <td>37.791300</td>\n",
       "      <td>-122.399051</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>76</td>\n",
       "      <td>Market at 4th</td>\n",
       "      <td>37.786305</td>\n",
       "      <td>-122.404966</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>77</td>\n",
       "      <td>Market at Sansome</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>27</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>80</td>\n",
       "      <td>Santa Clara County Civic Center</td>\n",
       "      <td>37.352601</td>\n",
       "      <td>-121.905733</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>12/31/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>82</td>\n",
       "      <td>Broadway St at Battery St</td>\n",
       "      <td>37.798541</td>\n",
       "      <td>-122.400862</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>1/22/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>83</td>\n",
       "      <td>Mezes Park</td>\n",
       "      <td>37.491269</td>\n",
       "      <td>-122.236234</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>2/20/2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>84</td>\n",
       "      <td>Ryland Park</td>\n",
       "      <td>37.342725</td>\n",
       "      <td>-121.895617</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>4/9/2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                                           name        lat        long  \\\n",
       "0    2              San Jose Diridon Caltrain Station  37.329732 -121.901782   \n",
       "1    3                          San Jose Civic Center  37.330698 -121.888979   \n",
       "2    4                         Santa Clara at Almaden  37.333988 -121.894902   \n",
       "3    5                               Adobe on Almaden  37.331415 -121.893200   \n",
       "4    6                               San Pedro Square  37.336721 -121.894074   \n",
       "5    7                           Paseo de San Antonio  37.333798 -121.886943   \n",
       "6    8                            San Salvador at 1st  37.330165 -121.885831   \n",
       "7    9                                      Japantown  37.348742 -121.894715   \n",
       "8   10                             San Jose City Hall  37.337391 -121.886995   \n",
       "9   11                                    MLK Library  37.335885 -121.885660   \n",
       "10  12                         SJSU 4th at San Carlos  37.332808 -121.883891   \n",
       "11  13                                  St James Park  37.339301 -121.889937   \n",
       "12  14                       Arena Green / SAP Center  37.332692 -121.900084   \n",
       "13  16                     SJSU - San Salvador at 9th  37.333955 -121.877349   \n",
       "14  21                              Franklin at Maple  37.481758 -122.226904   \n",
       "15  22                  Redwood City Caltrain Station  37.486078 -122.232089   \n",
       "16  23                        San Mateo County Center  37.487616 -122.229951   \n",
       "17  24                    Redwood City Public Library  37.484219 -122.227424   \n",
       "18  25                       Stanford in Redwood City  37.485370 -122.203288   \n",
       "19  26                    Redwood City Medical Center  37.487682 -122.223492   \n",
       "20  27                        Mountain View City Hall  37.389218 -122.081896   \n",
       "21  28                 Mountain View Caltrain Station  37.394358 -122.076713   \n",
       "22  29                   San Antonio Caltrain Station  37.406940 -122.106758   \n",
       "23  30                           Evelyn Park and Ride  37.390277 -122.066553   \n",
       "24  31                    San Antonio Shopping Center  37.400443 -122.108338   \n",
       "25  32               Castro Street and El Camino Real  37.385956 -122.083678   \n",
       "26  33          Rengstorff Avenue / California Street  37.400241 -122.099076   \n",
       "27  34                     Palo Alto Caltrain Station  37.443988 -122.164759   \n",
       "28  35                         University and Emerson  37.444521 -122.163093   \n",
       "29  36                California Ave Caltrain Station  37.429082 -122.142805   \n",
       "..  ..                                            ...        ...         ...   \n",
       "40  51                          Embarcadero at Folsom  37.791464 -122.391034   \n",
       "41  39                             Powell Street BART  37.783871 -122.408433   \n",
       "42  54                          Embarcadero at Bryant  37.787152 -122.388013   \n",
       "43  55  Temporary Transbay Terminal (Howard at Beale)  37.789756 -122.394643   \n",
       "44  56                                Beale at Market  37.792251 -122.397086   \n",
       "45  57                                  5th at Howard  37.781752 -122.405127   \n",
       "46  58                        San Francisco City Hall  37.778650 -122.418235   \n",
       "47  59                            Golden Gate at Polk  37.781332 -122.418603   \n",
       "48  60                         Embarcadero at Sansome  37.804770 -122.403234   \n",
       "49  61                                2nd at Townsend  37.780526 -122.390288   \n",
       "50  62                                  2nd at Folsom  37.785299 -122.396236   \n",
       "51  63                                  Howard at 2nd  37.786978 -122.398108   \n",
       "52  64                              2nd at South Park  37.782259 -122.392738   \n",
       "53  65                                Townsend at 7th  37.771058 -122.402717   \n",
       "54  66                       South Van Ness at Market  37.774814 -122.418954   \n",
       "55  67                                 Market at 10th  37.776619 -122.417385   \n",
       "56  68  Yerba Buena Center of the Arts (3rd @ Howard)  37.784878 -122.401014   \n",
       "57  69        San Francisco Caltrain 2 (330 Townsend)  37.776600 -122.395470   \n",
       "58  70       San Francisco Caltrain (Townsend at 4th)  37.776617 -122.395260   \n",
       "59  71                  Powell at Post (Union Square)  37.788446 -122.408499   \n",
       "60  72              Civic Center BART (7th at Market)  37.781039 -122.411748   \n",
       "61  73                Grant Avenue at Columbus Avenue  37.798522 -122.407245   \n",
       "62  74                              Steuart at Market  37.794139 -122.394434   \n",
       "63  75            Mechanics Plaza (Market at Battery)  37.791300 -122.399051   \n",
       "64  76                                  Market at 4th  37.786305 -122.404966   \n",
       "65  77                              Market at Sansome  37.789625 -122.400811   \n",
       "66  80                Santa Clara County Civic Center  37.352601 -121.905733   \n",
       "67  82                      Broadway St at Battery St  37.798541 -122.400862   \n",
       "68  83                                     Mezes Park  37.491269 -122.236234   \n",
       "69  84                                    Ryland Park  37.342725 -121.895617   \n",
       "\n",
       "    dock_count           city installation_date  \n",
       "0           27       San Jose          8/6/2013  \n",
       "1           15       San Jose          8/5/2013  \n",
       "2           11       San Jose          8/6/2013  \n",
       "3           19       San Jose          8/5/2013  \n",
       "4           15       San Jose          8/7/2013  \n",
       "5           15       San Jose          8/7/2013  \n",
       "6           15       San Jose          8/5/2013  \n",
       "7           15       San Jose          8/5/2013  \n",
       "8           15       San Jose          8/6/2013  \n",
       "9           19       San Jose          8/6/2013  \n",
       "10          19       San Jose          8/7/2013  \n",
       "11          15       San Jose          8/6/2013  \n",
       "12          19       San Jose          8/5/2013  \n",
       "13          15       San Jose          8/7/2013  \n",
       "14          15   Redwood City         8/12/2013  \n",
       "15          25   Redwood City         8/15/2013  \n",
       "16          15   Redwood City         8/15/2013  \n",
       "17          15   Redwood City         8/12/2013  \n",
       "18          15   Redwood City         8/12/2013  \n",
       "19          15   Redwood City         8/12/2013  \n",
       "20          15  Mountain View         8/16/2013  \n",
       "21          23  Mountain View         8/15/2013  \n",
       "22          23  Mountain View         8/15/2013  \n",
       "23          15  Mountain View         8/16/2013  \n",
       "24          15  Mountain View        12/31/2013  \n",
       "25          11  Mountain View        12/31/2013  \n",
       "26          15  Mountain View         8/16/2013  \n",
       "27          23      Palo Alto         8/14/2013  \n",
       "28          11      Palo Alto         8/15/2013  \n",
       "29          15      Palo Alto         8/14/2013  \n",
       "..         ...            ...               ...  \n",
       "40          19  San Francisco         8/20/2013  \n",
       "41          19  San Francisco         8/25/2013  \n",
       "42          15  San Francisco         8/20/2013  \n",
       "43          23  San Francisco         8/20/2013  \n",
       "44          19  San Francisco         8/20/2013  \n",
       "45          15  San Francisco         8/21/2013  \n",
       "46          19  San Francisco         8/21/2013  \n",
       "47          23  San Francisco         8/21/2013  \n",
       "48          15  San Francisco         8/21/2013  \n",
       "49          27  San Francisco         8/22/2013  \n",
       "50          19  San Francisco         8/22/2013  \n",
       "51          19  San Francisco         8/22/2013  \n",
       "52          15  San Francisco         8/22/2013  \n",
       "53          15  San Francisco         8/22/2013  \n",
       "54          19  San Francisco         8/23/2013  \n",
       "55          27  San Francisco         8/23/2013  \n",
       "56          19  San Francisco         8/23/2013  \n",
       "57          23  San Francisco         8/23/2013  \n",
       "58          19  San Francisco         8/23/2013  \n",
       "59          19  San Francisco         8/23/2013  \n",
       "60          23  San Francisco         8/23/2013  \n",
       "61          15  San Francisco         8/21/2013  \n",
       "62          23  San Francisco         8/25/2013  \n",
       "63          19  San Francisco         8/25/2013  \n",
       "64          19  San Francisco         8/25/2013  \n",
       "65          27  San Francisco         8/25/2013  \n",
       "66          15       San Jose        12/31/2013  \n",
       "67          15  San Francisco         1/22/2014  \n",
       "68          15   Redwood City         2/20/2014  \n",
       "69          15       San Jose          4/9/2014  \n",
       "\n",
       "[70 rows x 7 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load stations data from csv\n",
    "stations = pd.read_csv(\"station.csv\")\n",
    "stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "San Francisco    35\n",
       "San Jose         16\n",
       "Mountain View     7\n",
       "Redwood City      7\n",
       "Palo Alto         5\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stations are distributed across 5 cities in Bay area\n",
    "# most stations are in San Francisco, followed by San Jose\n",
    "stations.city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([94107, 94063, 94301, 94041, 95113])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are also 5 unique zip code in weather data\n",
    "# I suppose 1 zip code corresponds to 1 city\n",
    "# we are going to map the stations to the nearest weather zip code to prove this\n",
    "weather.zip_code.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**coordinate (lat, long) of each zip code**\n",
    "- 94107: 37.7618242, -122.3985871\n",
    "- 94063: 37.5021585, -122.2086579\n",
    "- 94301: 37.4457966, -122.1575745\n",
    "- 94041: 37.386812, -122.0751549\n",
    "- 95113: 37.3326639, -121.8918364\n",
    "\n",
    "source: https://www.freemaptools.com/convert-us-zip-code-to-lat-lng.htm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "coords_1 = (37.7618242, -122.3985871)  # 94107\n",
    "coords_2 = (37.5021585, -122.2086579)  # 94063\n",
    "coords_3 = (37.4457966, -122.1575745)  # 94301\n",
    "coords_4 = (37.3868120, -122.0751549)  # 94041\n",
    "coords_5 = (37.3326639, -121.8918364)  # 95113"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>dock_count</th>\n",
       "      <th>city</th>\n",
       "      <th>installation_date</th>\n",
       "      <th>coords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "      <td>(37.329732, -121.901782)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>San Jose Civic Center</td>\n",
       "      <td>37.330698</td>\n",
       "      <td>-121.888979</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "      <td>(37.330698, -121.888979)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Santa Clara at Almaden</td>\n",
       "      <td>37.333988</td>\n",
       "      <td>-121.894902</td>\n",
       "      <td>11</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "      <td>(37.333988, -121.894902)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Adobe on Almaden</td>\n",
       "      <td>37.331415</td>\n",
       "      <td>-121.893200</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "      <td>(37.331415, -121.8932)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>San Pedro Square</td>\n",
       "      <td>37.336721</td>\n",
       "      <td>-121.894074</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "      <td>(37.336721, -121.894074)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                               name        lat        long  dock_count  \\\n",
       "0   2  San Jose Diridon Caltrain Station  37.329732 -121.901782          27   \n",
       "1   3              San Jose Civic Center  37.330698 -121.888979          15   \n",
       "2   4             Santa Clara at Almaden  37.333988 -121.894902          11   \n",
       "3   5                   Adobe on Almaden  37.331415 -121.893200          19   \n",
       "4   6                   San Pedro Square  37.336721 -121.894074          15   \n",
       "\n",
       "       city installation_date                    coords  \n",
       "0  San Jose          8/6/2013  (37.329732, -121.901782)  \n",
       "1  San Jose          8/5/2013  (37.330698, -121.888979)  \n",
       "2  San Jose          8/6/2013  (37.333988, -121.894902)  \n",
       "3  San Jose          8/5/2013    (37.331415, -121.8932)  \n",
       "4  San Jose          8/7/2013  (37.336721, -121.894074)  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine 'lat' and 'long' into a tuple to be used for distance calculation\n",
    "stations[\"coords\"] = list(zip(stations.lat, stations.long))\n",
    "stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# find the nearest weather zip code using geodesic distance (unit: km)\n",
    "# https://geopy.readthedocs.io/en/stable/\n",
    "stations[\"weather_zip\"] = stations.coords.apply(lambda x: np.argmin((geodesic(coords_1, x).km,\n",
    "                                                                     geodesic(coords_2, x).km,\n",
    "                                                                     geodesic(coords_3, x).km,\n",
    "                                                                     geodesic(coords_4, x).km,\n",
    "                                                                     geodesic(coords_5, x).km)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>dock_count</th>\n",
       "      <th>city</th>\n",
       "      <th>installation_date</th>\n",
       "      <th>coords</th>\n",
       "      <th>weather_zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "      <td>(37.329732, -121.901782)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>San Jose Civic Center</td>\n",
       "      <td>37.330698</td>\n",
       "      <td>-121.888979</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "      <td>(37.330698, -121.888979)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Santa Clara at Almaden</td>\n",
       "      <td>37.333988</td>\n",
       "      <td>-121.894902</td>\n",
       "      <td>11</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "      <td>(37.333988, -121.894902)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Adobe on Almaden</td>\n",
       "      <td>37.331415</td>\n",
       "      <td>-121.893200</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "      <td>(37.331415, -121.8932)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>San Pedro Square</td>\n",
       "      <td>37.336721</td>\n",
       "      <td>-121.894074</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "      <td>(37.336721, -121.894074)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>Paseo de San Antonio</td>\n",
       "      <td>37.333798</td>\n",
       "      <td>-121.886943</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "      <td>(37.333798, -121.886943)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>San Salvador at 1st</td>\n",
       "      <td>37.330165</td>\n",
       "      <td>-121.885831</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "      <td>(37.330165, -121.885831)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>Japantown</td>\n",
       "      <td>37.348742</td>\n",
       "      <td>-121.894715</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "      <td>(37.348742, -121.894715)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>37.337391</td>\n",
       "      <td>-121.886995</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "      <td>(37.337391, -121.886995)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
       "      <td>MLK Library</td>\n",
       "      <td>37.335885</td>\n",
       "      <td>-121.885660</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "      <td>(37.335885, -121.88566)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12</td>\n",
       "      <td>SJSU 4th at San Carlos</td>\n",
       "      <td>37.332808</td>\n",
       "      <td>-121.883891</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "      <td>(37.332808, -121.883891)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13</td>\n",
       "      <td>St James Park</td>\n",
       "      <td>37.339301</td>\n",
       "      <td>-121.889937</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "      <td>(37.339301, -121.889937)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14</td>\n",
       "      <td>Arena Green / SAP Center</td>\n",
       "      <td>37.332692</td>\n",
       "      <td>-121.900084</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "      <td>(37.332692, -121.900084)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>SJSU - San Salvador at 9th</td>\n",
       "      <td>37.333955</td>\n",
       "      <td>-121.877349</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "      <td>(37.333955, -121.877349)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21</td>\n",
       "      <td>Franklin at Maple</td>\n",
       "      <td>37.481758</td>\n",
       "      <td>-122.226904</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/12/2013</td>\n",
       "      <td>(37.481758, -122.226904)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22</td>\n",
       "      <td>Redwood City Caltrain Station</td>\n",
       "      <td>37.486078</td>\n",
       "      <td>-122.232089</td>\n",
       "      <td>25</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/15/2013</td>\n",
       "      <td>(37.486078, -122.232089)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23</td>\n",
       "      <td>San Mateo County Center</td>\n",
       "      <td>37.487616</td>\n",
       "      <td>-122.229951</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/15/2013</td>\n",
       "      <td>(37.487616, -122.229951)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24</td>\n",
       "      <td>Redwood City Public Library</td>\n",
       "      <td>37.484219</td>\n",
       "      <td>-122.227424</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/12/2013</td>\n",
       "      <td>(37.484219, -122.227424)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>25</td>\n",
       "      <td>Stanford in Redwood City</td>\n",
       "      <td>37.485370</td>\n",
       "      <td>-122.203288</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/12/2013</td>\n",
       "      <td>(37.48537, -122.203288)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26</td>\n",
       "      <td>Redwood City Medical Center</td>\n",
       "      <td>37.487682</td>\n",
       "      <td>-122.223492</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>8/12/2013</td>\n",
       "      <td>(37.487682, -122.223492)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>27</td>\n",
       "      <td>Mountain View City Hall</td>\n",
       "      <td>37.389218</td>\n",
       "      <td>-122.081896</td>\n",
       "      <td>15</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/16/2013</td>\n",
       "      <td>(37.389218, -122.081896)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28</td>\n",
       "      <td>Mountain View Caltrain Station</td>\n",
       "      <td>37.394358</td>\n",
       "      <td>-122.076713</td>\n",
       "      <td>23</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/15/2013</td>\n",
       "      <td>(37.394358, -122.076713)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>29</td>\n",
       "      <td>San Antonio Caltrain Station</td>\n",
       "      <td>37.406940</td>\n",
       "      <td>-122.106758</td>\n",
       "      <td>23</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/15/2013</td>\n",
       "      <td>(37.40694, -122.106758)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30</td>\n",
       "      <td>Evelyn Park and Ride</td>\n",
       "      <td>37.390277</td>\n",
       "      <td>-122.066553</td>\n",
       "      <td>15</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/16/2013</td>\n",
       "      <td>(37.390277, -122.066553)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>31</td>\n",
       "      <td>San Antonio Shopping Center</td>\n",
       "      <td>37.400443</td>\n",
       "      <td>-122.108338</td>\n",
       "      <td>15</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>12/31/2013</td>\n",
       "      <td>(37.400443, -122.108338)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>32</td>\n",
       "      <td>Castro Street and El Camino Real</td>\n",
       "      <td>37.385956</td>\n",
       "      <td>-122.083678</td>\n",
       "      <td>11</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>12/31/2013</td>\n",
       "      <td>(37.385956, -122.083678)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>33</td>\n",
       "      <td>Rengstorff Avenue / California Street</td>\n",
       "      <td>37.400241</td>\n",
       "      <td>-122.099076</td>\n",
       "      <td>15</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>8/16/2013</td>\n",
       "      <td>(37.400241, -122.099076)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>Palo Alto Caltrain Station</td>\n",
       "      <td>37.443988</td>\n",
       "      <td>-122.164759</td>\n",
       "      <td>23</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>8/14/2013</td>\n",
       "      <td>(37.443988, -122.164759)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>35</td>\n",
       "      <td>University and Emerson</td>\n",
       "      <td>37.444521</td>\n",
       "      <td>-122.163093</td>\n",
       "      <td>11</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>8/15/2013</td>\n",
       "      <td>(37.444521, -122.163093)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>36</td>\n",
       "      <td>California Ave Caltrain Station</td>\n",
       "      <td>37.429082</td>\n",
       "      <td>-122.142805</td>\n",
       "      <td>15</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>8/14/2013</td>\n",
       "      <td>(37.429082, -122.142805)</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>51</td>\n",
       "      <td>Embarcadero at Folsom</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/20/2013</td>\n",
       "      <td>(37.791464, -122.391034)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>39</td>\n",
       "      <td>Powell Street BART</td>\n",
       "      <td>37.783871</td>\n",
       "      <td>-122.408433</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "      <td>(37.783871, -122.408433)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>54</td>\n",
       "      <td>Embarcadero at Bryant</td>\n",
       "      <td>37.787152</td>\n",
       "      <td>-122.388013</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/20/2013</td>\n",
       "      <td>(37.787152, -122.388013)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>55</td>\n",
       "      <td>Temporary Transbay Terminal (Howard at Beale)</td>\n",
       "      <td>37.789756</td>\n",
       "      <td>-122.394643</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/20/2013</td>\n",
       "      <td>(37.789756, -122.394643)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>56</td>\n",
       "      <td>Beale at Market</td>\n",
       "      <td>37.792251</td>\n",
       "      <td>-122.397086</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/20/2013</td>\n",
       "      <td>(37.792251, -122.397086)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>57</td>\n",
       "      <td>5th at Howard</td>\n",
       "      <td>37.781752</td>\n",
       "      <td>-122.405127</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "      <td>(37.781752, -122.405127)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>58</td>\n",
       "      <td>San Francisco City Hall</td>\n",
       "      <td>37.778650</td>\n",
       "      <td>-122.418235</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "      <td>(37.77865, -122.418235)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>59</td>\n",
       "      <td>Golden Gate at Polk</td>\n",
       "      <td>37.781332</td>\n",
       "      <td>-122.418603</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "      <td>(37.781332, -122.418603)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>60</td>\n",
       "      <td>Embarcadero at Sansome</td>\n",
       "      <td>37.804770</td>\n",
       "      <td>-122.403234</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "      <td>(37.80477, -122.403234)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>61</td>\n",
       "      <td>2nd at Townsend</td>\n",
       "      <td>37.780526</td>\n",
       "      <td>-122.390288</td>\n",
       "      <td>27</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "      <td>(37.780526, -122.390288)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>62</td>\n",
       "      <td>2nd at Folsom</td>\n",
       "      <td>37.785299</td>\n",
       "      <td>-122.396236</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "      <td>(37.785299, -122.396236)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>63</td>\n",
       "      <td>Howard at 2nd</td>\n",
       "      <td>37.786978</td>\n",
       "      <td>-122.398108</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "      <td>(37.786978, -122.398108)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>64</td>\n",
       "      <td>2nd at South Park</td>\n",
       "      <td>37.782259</td>\n",
       "      <td>-122.392738</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "      <td>(37.782259, -122.392738)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>65</td>\n",
       "      <td>Townsend at 7th</td>\n",
       "      <td>37.771058</td>\n",
       "      <td>-122.402717</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/22/2013</td>\n",
       "      <td>(37.771058, -122.402717)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>66</td>\n",
       "      <td>South Van Ness at Market</td>\n",
       "      <td>37.774814</td>\n",
       "      <td>-122.418954</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "      <td>(37.774814, -122.418954)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>67</td>\n",
       "      <td>Market at 10th</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>27</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "      <td>(37.776619, -122.417385)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>68</td>\n",
       "      <td>Yerba Buena Center of the Arts (3rd @ Howard)</td>\n",
       "      <td>37.784878</td>\n",
       "      <td>-122.401014</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "      <td>(37.784878, -122.401014)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>69</td>\n",
       "      <td>San Francisco Caltrain 2 (330 Townsend)</td>\n",
       "      <td>37.776600</td>\n",
       "      <td>-122.395470</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "      <td>(37.7766, -122.39547)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>70</td>\n",
       "      <td>San Francisco Caltrain (Townsend at 4th)</td>\n",
       "      <td>37.776617</td>\n",
       "      <td>-122.395260</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "      <td>(37.776617, -122.39526)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>71</td>\n",
       "      <td>Powell at Post (Union Square)</td>\n",
       "      <td>37.788446</td>\n",
       "      <td>-122.408499</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "      <td>(37.788446, -122.408499)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>72</td>\n",
       "      <td>Civic Center BART (7th at Market)</td>\n",
       "      <td>37.781039</td>\n",
       "      <td>-122.411748</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/23/2013</td>\n",
       "      <td>(37.781039, -122.411748)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>73</td>\n",
       "      <td>Grant Avenue at Columbus Avenue</td>\n",
       "      <td>37.798522</td>\n",
       "      <td>-122.407245</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/21/2013</td>\n",
       "      <td>(37.798522, -122.407245)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>74</td>\n",
       "      <td>Steuart at Market</td>\n",
       "      <td>37.794139</td>\n",
       "      <td>-122.394434</td>\n",
       "      <td>23</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "      <td>(37.794139, -122.394434)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>75</td>\n",
       "      <td>Mechanics Plaza (Market at Battery)</td>\n",
       "      <td>37.791300</td>\n",
       "      <td>-122.399051</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "      <td>(37.7913, -122.399051)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>76</td>\n",
       "      <td>Market at 4th</td>\n",
       "      <td>37.786305</td>\n",
       "      <td>-122.404966</td>\n",
       "      <td>19</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "      <td>(37.786305, -122.404966)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>77</td>\n",
       "      <td>Market at Sansome</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>27</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>8/25/2013</td>\n",
       "      <td>(37.789625, -122.400811)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>80</td>\n",
       "      <td>Santa Clara County Civic Center</td>\n",
       "      <td>37.352601</td>\n",
       "      <td>-121.905733</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>12/31/2013</td>\n",
       "      <td>(37.352601, -121.905733)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>82</td>\n",
       "      <td>Broadway St at Battery St</td>\n",
       "      <td>37.798541</td>\n",
       "      <td>-122.400862</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>1/22/2014</td>\n",
       "      <td>(37.798541, -122.400862)</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>83</td>\n",
       "      <td>Mezes Park</td>\n",
       "      <td>37.491269</td>\n",
       "      <td>-122.236234</td>\n",
       "      <td>15</td>\n",
       "      <td>Redwood City</td>\n",
       "      <td>2/20/2014</td>\n",
       "      <td>(37.491269, -122.236234)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>84</td>\n",
       "      <td>Ryland Park</td>\n",
       "      <td>37.342725</td>\n",
       "      <td>-121.895617</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>4/9/2014</td>\n",
       "      <td>(37.342725, -121.895617)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id                                           name        lat        long  \\\n",
       "0    2              San Jose Diridon Caltrain Station  37.329732 -121.901782   \n",
       "1    3                          San Jose Civic Center  37.330698 -121.888979   \n",
       "2    4                         Santa Clara at Almaden  37.333988 -121.894902   \n",
       "3    5                               Adobe on Almaden  37.331415 -121.893200   \n",
       "4    6                               San Pedro Square  37.336721 -121.894074   \n",
       "5    7                           Paseo de San Antonio  37.333798 -121.886943   \n",
       "6    8                            San Salvador at 1st  37.330165 -121.885831   \n",
       "7    9                                      Japantown  37.348742 -121.894715   \n",
       "8   10                             San Jose City Hall  37.337391 -121.886995   \n",
       "9   11                                    MLK Library  37.335885 -121.885660   \n",
       "10  12                         SJSU 4th at San Carlos  37.332808 -121.883891   \n",
       "11  13                                  St James Park  37.339301 -121.889937   \n",
       "12  14                       Arena Green / SAP Center  37.332692 -121.900084   \n",
       "13  16                     SJSU - San Salvador at 9th  37.333955 -121.877349   \n",
       "14  21                              Franklin at Maple  37.481758 -122.226904   \n",
       "15  22                  Redwood City Caltrain Station  37.486078 -122.232089   \n",
       "16  23                        San Mateo County Center  37.487616 -122.229951   \n",
       "17  24                    Redwood City Public Library  37.484219 -122.227424   \n",
       "18  25                       Stanford in Redwood City  37.485370 -122.203288   \n",
       "19  26                    Redwood City Medical Center  37.487682 -122.223492   \n",
       "20  27                        Mountain View City Hall  37.389218 -122.081896   \n",
       "21  28                 Mountain View Caltrain Station  37.394358 -122.076713   \n",
       "22  29                   San Antonio Caltrain Station  37.406940 -122.106758   \n",
       "23  30                           Evelyn Park and Ride  37.390277 -122.066553   \n",
       "24  31                    San Antonio Shopping Center  37.400443 -122.108338   \n",
       "25  32               Castro Street and El Camino Real  37.385956 -122.083678   \n",
       "26  33          Rengstorff Avenue / California Street  37.400241 -122.099076   \n",
       "27  34                     Palo Alto Caltrain Station  37.443988 -122.164759   \n",
       "28  35                         University and Emerson  37.444521 -122.163093   \n",
       "29  36                California Ave Caltrain Station  37.429082 -122.142805   \n",
       "..  ..                                            ...        ...         ...   \n",
       "40  51                          Embarcadero at Folsom  37.791464 -122.391034   \n",
       "41  39                             Powell Street BART  37.783871 -122.408433   \n",
       "42  54                          Embarcadero at Bryant  37.787152 -122.388013   \n",
       "43  55  Temporary Transbay Terminal (Howard at Beale)  37.789756 -122.394643   \n",
       "44  56                                Beale at Market  37.792251 -122.397086   \n",
       "45  57                                  5th at Howard  37.781752 -122.405127   \n",
       "46  58                        San Francisco City Hall  37.778650 -122.418235   \n",
       "47  59                            Golden Gate at Polk  37.781332 -122.418603   \n",
       "48  60                         Embarcadero at Sansome  37.804770 -122.403234   \n",
       "49  61                                2nd at Townsend  37.780526 -122.390288   \n",
       "50  62                                  2nd at Folsom  37.785299 -122.396236   \n",
       "51  63                                  Howard at 2nd  37.786978 -122.398108   \n",
       "52  64                              2nd at South Park  37.782259 -122.392738   \n",
       "53  65                                Townsend at 7th  37.771058 -122.402717   \n",
       "54  66                       South Van Ness at Market  37.774814 -122.418954   \n",
       "55  67                                 Market at 10th  37.776619 -122.417385   \n",
       "56  68  Yerba Buena Center of the Arts (3rd @ Howard)  37.784878 -122.401014   \n",
       "57  69        San Francisco Caltrain 2 (330 Townsend)  37.776600 -122.395470   \n",
       "58  70       San Francisco Caltrain (Townsend at 4th)  37.776617 -122.395260   \n",
       "59  71                  Powell at Post (Union Square)  37.788446 -122.408499   \n",
       "60  72              Civic Center BART (7th at Market)  37.781039 -122.411748   \n",
       "61  73                Grant Avenue at Columbus Avenue  37.798522 -122.407245   \n",
       "62  74                              Steuart at Market  37.794139 -122.394434   \n",
       "63  75            Mechanics Plaza (Market at Battery)  37.791300 -122.399051   \n",
       "64  76                                  Market at 4th  37.786305 -122.404966   \n",
       "65  77                              Market at Sansome  37.789625 -122.400811   \n",
       "66  80                Santa Clara County Civic Center  37.352601 -121.905733   \n",
       "67  82                      Broadway St at Battery St  37.798541 -122.400862   \n",
       "68  83                                     Mezes Park  37.491269 -122.236234   \n",
       "69  84                                    Ryland Park  37.342725 -121.895617   \n",
       "\n",
       "    dock_count           city installation_date                    coords  \\\n",
       "0           27       San Jose          8/6/2013  (37.329732, -121.901782)   \n",
       "1           15       San Jose          8/5/2013  (37.330698, -121.888979)   \n",
       "2           11       San Jose          8/6/2013  (37.333988, -121.894902)   \n",
       "3           19       San Jose          8/5/2013    (37.331415, -121.8932)   \n",
       "4           15       San Jose          8/7/2013  (37.336721, -121.894074)   \n",
       "5           15       San Jose          8/7/2013  (37.333798, -121.886943)   \n",
       "6           15       San Jose          8/5/2013  (37.330165, -121.885831)   \n",
       "7           15       San Jose          8/5/2013  (37.348742, -121.894715)   \n",
       "8           15       San Jose          8/6/2013  (37.337391, -121.886995)   \n",
       "9           19       San Jose          8/6/2013   (37.335885, -121.88566)   \n",
       "10          19       San Jose          8/7/2013  (37.332808, -121.883891)   \n",
       "11          15       San Jose          8/6/2013  (37.339301, -121.889937)   \n",
       "12          19       San Jose          8/5/2013  (37.332692, -121.900084)   \n",
       "13          15       San Jose          8/7/2013  (37.333955, -121.877349)   \n",
       "14          15   Redwood City         8/12/2013  (37.481758, -122.226904)   \n",
       "15          25   Redwood City         8/15/2013  (37.486078, -122.232089)   \n",
       "16          15   Redwood City         8/15/2013  (37.487616, -122.229951)   \n",
       "17          15   Redwood City         8/12/2013  (37.484219, -122.227424)   \n",
       "18          15   Redwood City         8/12/2013   (37.48537, -122.203288)   \n",
       "19          15   Redwood City         8/12/2013  (37.487682, -122.223492)   \n",
       "20          15  Mountain View         8/16/2013  (37.389218, -122.081896)   \n",
       "21          23  Mountain View         8/15/2013  (37.394358, -122.076713)   \n",
       "22          23  Mountain View         8/15/2013   (37.40694, -122.106758)   \n",
       "23          15  Mountain View         8/16/2013  (37.390277, -122.066553)   \n",
       "24          15  Mountain View        12/31/2013  (37.400443, -122.108338)   \n",
       "25          11  Mountain View        12/31/2013  (37.385956, -122.083678)   \n",
       "26          15  Mountain View         8/16/2013  (37.400241, -122.099076)   \n",
       "27          23      Palo Alto         8/14/2013  (37.443988, -122.164759)   \n",
       "28          11      Palo Alto         8/15/2013  (37.444521, -122.163093)   \n",
       "29          15      Palo Alto         8/14/2013  (37.429082, -122.142805)   \n",
       "..         ...            ...               ...                       ...   \n",
       "40          19  San Francisco         8/20/2013  (37.791464, -122.391034)   \n",
       "41          19  San Francisco         8/25/2013  (37.783871, -122.408433)   \n",
       "42          15  San Francisco         8/20/2013  (37.787152, -122.388013)   \n",
       "43          23  San Francisco         8/20/2013  (37.789756, -122.394643)   \n",
       "44          19  San Francisco         8/20/2013  (37.792251, -122.397086)   \n",
       "45          15  San Francisco         8/21/2013  (37.781752, -122.405127)   \n",
       "46          19  San Francisco         8/21/2013   (37.77865, -122.418235)   \n",
       "47          23  San Francisco         8/21/2013  (37.781332, -122.418603)   \n",
       "48          15  San Francisco         8/21/2013   (37.80477, -122.403234)   \n",
       "49          27  San Francisco         8/22/2013  (37.780526, -122.390288)   \n",
       "50          19  San Francisco         8/22/2013  (37.785299, -122.396236)   \n",
       "51          19  San Francisco         8/22/2013  (37.786978, -122.398108)   \n",
       "52          15  San Francisco         8/22/2013  (37.782259, -122.392738)   \n",
       "53          15  San Francisco         8/22/2013  (37.771058, -122.402717)   \n",
       "54          19  San Francisco         8/23/2013  (37.774814, -122.418954)   \n",
       "55          27  San Francisco         8/23/2013  (37.776619, -122.417385)   \n",
       "56          19  San Francisco         8/23/2013  (37.784878, -122.401014)   \n",
       "57          23  San Francisco         8/23/2013     (37.7766, -122.39547)   \n",
       "58          19  San Francisco         8/23/2013   (37.776617, -122.39526)   \n",
       "59          19  San Francisco         8/23/2013  (37.788446, -122.408499)   \n",
       "60          23  San Francisco         8/23/2013  (37.781039, -122.411748)   \n",
       "61          15  San Francisco         8/21/2013  (37.798522, -122.407245)   \n",
       "62          23  San Francisco         8/25/2013  (37.794139, -122.394434)   \n",
       "63          19  San Francisco         8/25/2013    (37.7913, -122.399051)   \n",
       "64          19  San Francisco         8/25/2013  (37.786305, -122.404966)   \n",
       "65          27  San Francisco         8/25/2013  (37.789625, -122.400811)   \n",
       "66          15       San Jose        12/31/2013  (37.352601, -121.905733)   \n",
       "67          15  San Francisco         1/22/2014  (37.798541, -122.400862)   \n",
       "68          15   Redwood City         2/20/2014  (37.491269, -122.236234)   \n",
       "69          15       San Jose          4/9/2014  (37.342725, -121.895617)   \n",
       "\n",
       "    weather_zip  \n",
       "0             4  \n",
       "1             4  \n",
       "2             4  \n",
       "3             4  \n",
       "4             4  \n",
       "5             4  \n",
       "6             4  \n",
       "7             4  \n",
       "8             4  \n",
       "9             4  \n",
       "10            4  \n",
       "11            4  \n",
       "12            4  \n",
       "13            4  \n",
       "14            1  \n",
       "15            1  \n",
       "16            1  \n",
       "17            1  \n",
       "18            1  \n",
       "19            1  \n",
       "20            3  \n",
       "21            3  \n",
       "22            3  \n",
       "23            3  \n",
       "24            3  \n",
       "25            3  \n",
       "26            3  \n",
       "27            2  \n",
       "28            2  \n",
       "29            2  \n",
       "..          ...  \n",
       "40            0  \n",
       "41            0  \n",
       "42            0  \n",
       "43            0  \n",
       "44            0  \n",
       "45            0  \n",
       "46            0  \n",
       "47            0  \n",
       "48            0  \n",
       "49            0  \n",
       "50            0  \n",
       "51            0  \n",
       "52            0  \n",
       "53            0  \n",
       "54            0  \n",
       "55            0  \n",
       "56            0  \n",
       "57            0  \n",
       "58            0  \n",
       "59            0  \n",
       "60            0  \n",
       "61            0  \n",
       "62            0  \n",
       "63            0  \n",
       "64            0  \n",
       "65            0  \n",
       "66            4  \n",
       "67            0  \n",
       "68            1  \n",
       "69            4  \n",
       "\n",
       "[70 rows x 9 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all stations are now mapped to one of the 5 weather zip areas, using an index (0,4) representing each area\n",
    "# we can see that each city corresponds to one index: \n",
    "#    0-San Francisco, 1-Redwood City, 2-Palo Alto, 3-Mountain View, 4-San Jose\n",
    "# we can use either station city name or weather zip to map weather data to trips\n",
    "stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use a dictionary to map weather area index back to weather zip code\n",
    "dic = {0:94107, 1:94063, 2:94301, 3:94041, 4:95113}\n",
    "stations.replace({\"weather_zip\": dic}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>dock_count</th>\n",
       "      <th>city</th>\n",
       "      <th>installation_date</th>\n",
       "      <th>coords</th>\n",
       "      <th>weather_zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "      <td>(37.329732, -121.901782)</td>\n",
       "      <td>95113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>San Jose Civic Center</td>\n",
       "      <td>37.330698</td>\n",
       "      <td>-121.888979</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "      <td>(37.330698, -121.888979)</td>\n",
       "      <td>95113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Santa Clara at Almaden</td>\n",
       "      <td>37.333988</td>\n",
       "      <td>-121.894902</td>\n",
       "      <td>11</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/6/2013</td>\n",
       "      <td>(37.333988, -121.894902)</td>\n",
       "      <td>95113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Adobe on Almaden</td>\n",
       "      <td>37.331415</td>\n",
       "      <td>-121.893200</td>\n",
       "      <td>19</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/5/2013</td>\n",
       "      <td>(37.331415, -121.8932)</td>\n",
       "      <td>95113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>San Pedro Square</td>\n",
       "      <td>37.336721</td>\n",
       "      <td>-121.894074</td>\n",
       "      <td>15</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>8/7/2013</td>\n",
       "      <td>(37.336721, -121.894074)</td>\n",
       "      <td>95113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                               name        lat        long  dock_count  \\\n",
       "0   2  San Jose Diridon Caltrain Station  37.329732 -121.901782          27   \n",
       "1   3              San Jose Civic Center  37.330698 -121.888979          15   \n",
       "2   4             Santa Clara at Almaden  37.333988 -121.894902          11   \n",
       "3   5                   Adobe on Almaden  37.331415 -121.893200          19   \n",
       "4   6                   San Pedro Square  37.336721 -121.894074          15   \n",
       "\n",
       "       city installation_date                    coords  weather_zip  \n",
       "0  San Jose          8/6/2013  (37.329732, -121.901782)        95113  \n",
       "1  San Jose          8/5/2013  (37.330698, -121.888979)        95113  \n",
       "2  San Jose          8/6/2013  (37.333988, -121.894902)        95113  \n",
       "3  San Jose          8/5/2013    (37.331415, -121.8932)        95113  \n",
       "4  San Jose          8/7/2013  (37.336721, -121.894074)        95113  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# installation date is not important for our prediction: if a station is not installed a certain date, \n",
    "# there will be no trip data for that station on that date.\n",
    "# we also don't need coordinates any more, we'll drop these columns\n",
    "stations2 = stations.drop(['installation_date', 'lat', 'long', 'coords'],1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  \n",
    "#  \n",
    "# merge all data into train\n",
    "1. merge trip with stations, using station id as the key\n",
    "2. merge trip-station with weather, using date and weather_zip as the key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge trip data with station data\n",
    "train = pd.merge(status2, stations2, left_on=['station_id'], right_on=['id'])\n",
    "\n",
    "# merge again with weather data\n",
    "train = pd.merge(train, weather2, left_on=['date', 'weather_zip'], right_on=['date', 'zip_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>station_id</th>\n",
       "      <th>time_period</th>\n",
       "      <th>bikes_available</th>\n",
       "      <th>name</th>\n",
       "      <th>dock_count</th>\n",
       "      <th>city</th>\n",
       "      <th>max_temperature_f</th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>min_temperature_f</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_wind_speed_mph</th>\n",
       "      <th>max_gust_speed_mph</th>\n",
       "      <th>precipitation_inches</th>\n",
       "      <th>cloud_cover</th>\n",
       "      <th>wind_dir_degrees</th>\n",
       "      <th>Fog</th>\n",
       "      <th>Fog-Rain</th>\n",
       "      <th>Normal</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Rain-Thunderstorm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 6</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 7</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 8</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  station_id time_period  bikes_available  \\\n",
       "0  2013-08-29           2    period 4         2.000000   \n",
       "1  2013-08-29           2    period 5         2.000000   \n",
       "2  2013-08-29           2    period 6         2.333333   \n",
       "3  2013-08-29           2    period 7         1.333333   \n",
       "4  2013-08-29           2    period 8         2.500000   \n",
       "\n",
       "                                name  dock_count      city  max_temperature_f  \\\n",
       "0  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "1  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "2  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "3  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "4  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "\n",
       "   mean_temperature_f  min_temperature_f        ...          \\\n",
       "0                72.0               63.0        ...           \n",
       "1                72.0               63.0        ...           \n",
       "2                72.0               63.0        ...           \n",
       "3                72.0               63.0        ...           \n",
       "4                72.0               63.0        ...           \n",
       "\n",
       "   mean_wind_speed_mph  max_gust_speed_mph  precipitation_inches  cloud_cover  \\\n",
       "0                  7.0                24.0                   0.0          4.0   \n",
       "1                  7.0                24.0                   0.0          4.0   \n",
       "2                  7.0                24.0                   0.0          4.0   \n",
       "3                  7.0                24.0                   0.0          4.0   \n",
       "4                  7.0                24.0                   0.0          4.0   \n",
       "\n",
       "   wind_dir_degrees  Fog  Fog-Rain  Normal  Rain  Rain-Thunderstorm  \n",
       "0             320.0    0         0       1     0                  0  \n",
       "1             320.0    0         0       1     0                  0  \n",
       "2             320.0    0         0       1     0                  0  \n",
       "3             320.0    0         0       1     0                  0  \n",
       "4             320.0    0         0       1     0                  0  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.drop(['id','weather_zip','zip_code'], 1)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  \n",
    "#  \n",
    "# Special Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.date(2013, 9, 2), datetime.date(2013, 10, 14),\n",
       "       datetime.date(2013, 11, 11), datetime.date(2013, 11, 28),\n",
       "       datetime.date(2013, 12, 25), datetime.date(2014, 1, 1),\n",
       "       datetime.date(2014, 1, 20), datetime.date(2014, 2, 17),\n",
       "       datetime.date(2014, 5, 26), datetime.date(2014, 7, 4),\n",
       "       datetime.date(2014, 9, 1), datetime.date(2014, 10, 13),\n",
       "       datetime.date(2014, 11, 11), datetime.date(2014, 11, 27),\n",
       "       datetime.date(2014, 12, 25), datetime.date(2015, 1, 1),\n",
       "       datetime.date(2015, 1, 19), datetime.date(2015, 2, 16),\n",
       "       datetime.date(2015, 5, 25), datetime.date(2015, 7, 3)], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find all of the holidays during our time span\n",
    "calendar = USFederalHolidayCalendar()\n",
    "holidays = calendar.holidays(start=train.date.min(), end=train.date.max())\n",
    "holidays = pd.to_datetime(holidays, format='%Y/%m/%d').date\n",
    "holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.date(2013, 8, 29), datetime.date(2013, 8, 30),\n",
       "       datetime.date(2013, 9, 3), datetime.date(2013, 9, 4),\n",
       "       datetime.date(2013, 9, 5), datetime.date(2013, 9, 6),\n",
       "       datetime.date(2013, 9, 9), datetime.date(2013, 9, 10),\n",
       "       datetime.date(2013, 9, 11), datetime.date(2013, 9, 12),\n",
       "       datetime.date(2013, 9, 13), datetime.date(2013, 9, 16),\n",
       "       datetime.date(2013, 9, 17), datetime.date(2013, 9, 18),\n",
       "       datetime.date(2013, 9, 19), datetime.date(2013, 9, 20),\n",
       "       datetime.date(2013, 9, 23), datetime.date(2013, 9, 24),\n",
       "       datetime.date(2013, 9, 25), datetime.date(2013, 9, 26),\n",
       "       datetime.date(2013, 9, 27), datetime.date(2013, 9, 30),\n",
       "       datetime.date(2013, 10, 1), datetime.date(2013, 10, 2),\n",
       "       datetime.date(2013, 10, 3), datetime.date(2013, 10, 4),\n",
       "       datetime.date(2013, 10, 7), datetime.date(2013, 10, 8),\n",
       "       datetime.date(2013, 10, 9), datetime.date(2013, 10, 10),\n",
       "       datetime.date(2013, 10, 11), datetime.date(2013, 10, 15),\n",
       "       datetime.date(2013, 10, 16), datetime.date(2013, 10, 17),\n",
       "       datetime.date(2013, 10, 18), datetime.date(2013, 10, 21),\n",
       "       datetime.date(2013, 10, 22), datetime.date(2013, 10, 23),\n",
       "       datetime.date(2013, 10, 24), datetime.date(2013, 10, 25),\n",
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       "       datetime.date(2013, 10, 30), datetime.date(2013, 10, 31),\n",
       "       datetime.date(2013, 11, 1), datetime.date(2013, 11, 4),\n",
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       "       datetime.date(2013, 11, 18), datetime.date(2013, 11, 19),\n",
       "       datetime.date(2013, 11, 20), datetime.date(2013, 11, 21),\n",
       "       datetime.date(2013, 11, 22), datetime.date(2013, 11, 25),\n",
       "       datetime.date(2013, 11, 26), datetime.date(2013, 11, 27),\n",
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       "       datetime.date(2013, 12, 3), datetime.date(2013, 12, 4),\n",
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       "       datetime.date(2013, 12, 19), datetime.date(2013, 12, 20),\n",
       "       datetime.date(2013, 12, 23), datetime.date(2013, 12, 24),\n",
       "       datetime.date(2013, 12, 26), datetime.date(2013, 12, 27),\n",
       "       datetime.date(2013, 12, 30), datetime.date(2013, 12, 31),\n",
       "       datetime.date(2014, 1, 2), datetime.date(2014, 1, 3),\n",
       "       datetime.date(2014, 1, 6), datetime.date(2014, 1, 7),\n",
       "       datetime.date(2014, 1, 8), datetime.date(2014, 1, 9),\n",
       "       datetime.date(2014, 1, 10), datetime.date(2014, 1, 13),\n",
       "       datetime.date(2014, 1, 14), datetime.date(2014, 1, 15),\n",
       "       datetime.date(2014, 1, 16), datetime.date(2014, 1, 17),\n",
       "       datetime.date(2014, 1, 21), datetime.date(2014, 1, 22),\n",
       "       datetime.date(2014, 1, 23), datetime.date(2014, 1, 24),\n",
       "       datetime.date(2014, 1, 27), datetime.date(2014, 1, 28),\n",
       "       datetime.date(2014, 1, 29), datetime.date(2014, 1, 30),\n",
       "       datetime.date(2014, 1, 31), datetime.date(2014, 2, 3),\n",
       "       datetime.date(2014, 2, 4), datetime.date(2014, 2, 5),\n",
       "       datetime.date(2014, 2, 6), datetime.date(2014, 2, 7),\n",
       "       datetime.date(2014, 2, 10), datetime.date(2014, 2, 11),\n",
       "       datetime.date(2014, 2, 12), datetime.date(2014, 2, 13),\n",
       "       datetime.date(2014, 2, 14), datetime.date(2014, 2, 18),\n",
       "       datetime.date(2014, 2, 19), datetime.date(2014, 2, 20),\n",
       "       datetime.date(2014, 2, 21), datetime.date(2014, 2, 24),\n",
       "       datetime.date(2014, 2, 25), datetime.date(2014, 2, 26),\n",
       "       datetime.date(2014, 2, 27), datetime.date(2014, 2, 28),\n",
       "       datetime.date(2014, 3, 3), datetime.date(2014, 3, 4),\n",
       "       datetime.date(2014, 3, 5), datetime.date(2014, 3, 6),\n",
       "       datetime.date(2014, 3, 7), datetime.date(2014, 3, 10),\n",
       "       datetime.date(2014, 3, 11), datetime.date(2014, 3, 12),\n",
       "       datetime.date(2014, 3, 13), datetime.date(2014, 3, 14),\n",
       "       datetime.date(2014, 3, 17), datetime.date(2014, 3, 18),\n",
       "       datetime.date(2014, 3, 19), datetime.date(2014, 3, 20),\n",
       "       datetime.date(2014, 3, 21), datetime.date(2014, 3, 24),\n",
       "       datetime.date(2014, 3, 25), datetime.date(2014, 3, 26),\n",
       "       datetime.date(2014, 3, 27), datetime.date(2014, 3, 28),\n",
       "       datetime.date(2014, 3, 31), datetime.date(2014, 4, 1),\n",
       "       datetime.date(2014, 4, 2), datetime.date(2014, 4, 3),\n",
       "       datetime.date(2014, 4, 4), datetime.date(2014, 4, 7),\n",
       "       datetime.date(2014, 4, 8), datetime.date(2014, 4, 9),\n",
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       "       datetime.date(2014, 4, 16), datetime.date(2014, 4, 17),\n",
       "       datetime.date(2014, 4, 18), datetime.date(2014, 4, 21),\n",
       "       datetime.date(2014, 4, 22), datetime.date(2014, 4, 23),\n",
       "       datetime.date(2014, 4, 24), datetime.date(2014, 4, 25),\n",
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       "       datetime.date(2014, 4, 30), datetime.date(2014, 5, 1),\n",
       "       datetime.date(2014, 5, 2), datetime.date(2014, 5, 5),\n",
       "       datetime.date(2014, 5, 6), datetime.date(2014, 5, 7),\n",
       "       datetime.date(2014, 5, 8), datetime.date(2014, 5, 9),\n",
       "       datetime.date(2014, 5, 12), datetime.date(2014, 5, 13),\n",
       "       datetime.date(2014, 5, 14), datetime.date(2014, 5, 15),\n",
       "       datetime.date(2014, 5, 16), datetime.date(2014, 5, 19),\n",
       "       datetime.date(2014, 5, 20), datetime.date(2014, 5, 21),\n",
       "       datetime.date(2014, 5, 22), datetime.date(2014, 5, 23),\n",
       "       datetime.date(2014, 5, 27), datetime.date(2014, 5, 28),\n",
       "       datetime.date(2014, 5, 29), datetime.date(2014, 5, 30),\n",
       "       datetime.date(2014, 6, 2), datetime.date(2014, 6, 3),\n",
       "       datetime.date(2014, 6, 4), datetime.date(2014, 6, 5),\n",
       "       datetime.date(2014, 6, 6), datetime.date(2014, 6, 9),\n",
       "       datetime.date(2014, 6, 10), datetime.date(2014, 6, 11),\n",
       "       datetime.date(2014, 6, 12), datetime.date(2014, 6, 13),\n",
       "       datetime.date(2014, 6, 16), datetime.date(2014, 6, 17),\n",
       "       datetime.date(2014, 6, 18), datetime.date(2014, 6, 19),\n",
       "       datetime.date(2014, 6, 20), datetime.date(2014, 6, 23),\n",
       "       datetime.date(2014, 6, 24), datetime.date(2014, 6, 25),\n",
       "       datetime.date(2014, 6, 26), datetime.date(2014, 6, 27),\n",
       "       datetime.date(2014, 6, 30), datetime.date(2014, 7, 1),\n",
       "       datetime.date(2014, 7, 2), datetime.date(2014, 7, 3),\n",
       "       datetime.date(2014, 7, 7), datetime.date(2014, 7, 8),\n",
       "       datetime.date(2014, 7, 9), datetime.date(2014, 7, 10),\n",
       "       datetime.date(2014, 7, 11), datetime.date(2014, 7, 14),\n",
       "       datetime.date(2014, 7, 15), datetime.date(2014, 7, 16),\n",
       "       datetime.date(2014, 7, 17), datetime.date(2014, 7, 18),\n",
       "       datetime.date(2014, 7, 21), datetime.date(2014, 7, 22),\n",
       "       datetime.date(2014, 7, 23), datetime.date(2014, 7, 24),\n",
       "       datetime.date(2014, 7, 25), datetime.date(2014, 7, 28),\n",
       "       datetime.date(2014, 7, 29), datetime.date(2014, 7, 30),\n",
       "       datetime.date(2014, 7, 31), datetime.date(2014, 8, 1),\n",
       "       datetime.date(2014, 8, 4), datetime.date(2014, 8, 5),\n",
       "       datetime.date(2014, 8, 6), datetime.date(2014, 8, 7),\n",
       "       datetime.date(2014, 8, 8), datetime.date(2014, 8, 11),\n",
       "       datetime.date(2014, 8, 12), datetime.date(2014, 8, 13),\n",
       "       datetime.date(2014, 8, 14), datetime.date(2014, 8, 15),\n",
       "       datetime.date(2014, 8, 18), datetime.date(2014, 8, 19),\n",
       "       datetime.date(2014, 8, 20), datetime.date(2014, 8, 21),\n",
       "       datetime.date(2014, 8, 22), datetime.date(2014, 8, 25),\n",
       "       datetime.date(2014, 8, 26), datetime.date(2014, 8, 27),\n",
       "       datetime.date(2014, 8, 28), datetime.date(2014, 8, 29),\n",
       "       datetime.date(2014, 9, 2), datetime.date(2014, 9, 3),\n",
       "       datetime.date(2014, 9, 4), datetime.date(2014, 9, 5),\n",
       "       datetime.date(2014, 9, 8), datetime.date(2014, 9, 9),\n",
       "       datetime.date(2014, 9, 10), datetime.date(2014, 9, 11),\n",
       "       datetime.date(2014, 9, 12), datetime.date(2014, 9, 15),\n",
       "       datetime.date(2014, 9, 16), datetime.date(2014, 9, 17),\n",
       "       datetime.date(2014, 9, 18), datetime.date(2014, 9, 19),\n",
       "       datetime.date(2014, 9, 22), datetime.date(2014, 9, 23),\n",
       "       datetime.date(2014, 9, 24), datetime.date(2014, 9, 25),\n",
       "       datetime.date(2014, 9, 26), datetime.date(2014, 9, 29),\n",
       "       datetime.date(2014, 9, 30), datetime.date(2014, 10, 1),\n",
       "       datetime.date(2014, 10, 2), datetime.date(2014, 10, 3),\n",
       "       datetime.date(2014, 10, 6), datetime.date(2014, 10, 7),\n",
       "       datetime.date(2014, 10, 8), datetime.date(2014, 10, 9),\n",
       "       datetime.date(2014, 10, 10), datetime.date(2014, 10, 14),\n",
       "       datetime.date(2014, 10, 15), datetime.date(2014, 10, 16),\n",
       "       datetime.date(2014, 10, 17), datetime.date(2014, 10, 20),\n",
       "       datetime.date(2014, 10, 21), datetime.date(2014, 10, 22),\n",
       "       datetime.date(2014, 10, 23), datetime.date(2014, 10, 24),\n",
       "       datetime.date(2014, 10, 27), datetime.date(2014, 10, 28),\n",
       "       datetime.date(2014, 10, 29), datetime.date(2014, 10, 30),\n",
       "       datetime.date(2014, 10, 31), datetime.date(2014, 11, 3),\n",
       "       datetime.date(2014, 11, 4), datetime.date(2014, 11, 5),\n",
       "       datetime.date(2014, 11, 6), datetime.date(2014, 11, 7),\n",
       "       datetime.date(2014, 11, 10), datetime.date(2014, 11, 12),\n",
       "       datetime.date(2014, 11, 13), datetime.date(2014, 11, 14),\n",
       "       datetime.date(2014, 11, 17), datetime.date(2014, 11, 18),\n",
       "       datetime.date(2014, 11, 19), datetime.date(2014, 11, 20),\n",
       "       datetime.date(2014, 11, 21), datetime.date(2014, 11, 24),\n",
       "       datetime.date(2014, 11, 25), datetime.date(2014, 11, 26),\n",
       "       datetime.date(2014, 11, 28), datetime.date(2014, 12, 1),\n",
       "       datetime.date(2014, 12, 2), datetime.date(2014, 12, 3),\n",
       "       datetime.date(2014, 12, 4), datetime.date(2014, 12, 5),\n",
       "       datetime.date(2014, 12, 8), datetime.date(2014, 12, 9),\n",
       "       datetime.date(2014, 12, 10), datetime.date(2014, 12, 11),\n",
       "       datetime.date(2014, 12, 12), datetime.date(2014, 12, 15),\n",
       "       datetime.date(2014, 12, 16), datetime.date(2014, 12, 17),\n",
       "       datetime.date(2014, 12, 18), datetime.date(2014, 12, 19),\n",
       "       datetime.date(2014, 12, 22), datetime.date(2014, 12, 23),\n",
       "       datetime.date(2014, 12, 24), datetime.date(2014, 12, 26),\n",
       "       datetime.date(2014, 12, 29), datetime.date(2014, 12, 30),\n",
       "       datetime.date(2014, 12, 31), datetime.date(2015, 1, 2),\n",
       "       datetime.date(2015, 1, 5), datetime.date(2015, 1, 6),\n",
       "       datetime.date(2015, 1, 7), datetime.date(2015, 1, 8),\n",
       "       datetime.date(2015, 1, 9), datetime.date(2015, 1, 12),\n",
       "       datetime.date(2015, 1, 13), datetime.date(2015, 1, 14),\n",
       "       datetime.date(2015, 1, 15), datetime.date(2015, 1, 16),\n",
       "       datetime.date(2015, 1, 20), datetime.date(2015, 1, 21),\n",
       "       datetime.date(2015, 1, 22), datetime.date(2015, 1, 23),\n",
       "       datetime.date(2015, 1, 26), datetime.date(2015, 1, 27),\n",
       "       datetime.date(2015, 1, 28), datetime.date(2015, 1, 29),\n",
       "       datetime.date(2015, 1, 30), datetime.date(2015, 2, 2),\n",
       "       datetime.date(2015, 2, 3), datetime.date(2015, 2, 4),\n",
       "       datetime.date(2015, 2, 5), datetime.date(2015, 2, 6),\n",
       "       datetime.date(2015, 2, 9), datetime.date(2015, 2, 10),\n",
       "       datetime.date(2015, 2, 11), datetime.date(2015, 2, 12),\n",
       "       datetime.date(2015, 2, 13), datetime.date(2015, 2, 17),\n",
       "       datetime.date(2015, 2, 18), datetime.date(2015, 2, 19),\n",
       "       datetime.date(2015, 2, 20), datetime.date(2015, 2, 23),\n",
       "       datetime.date(2015, 2, 24), datetime.date(2015, 2, 25),\n",
       "       datetime.date(2015, 2, 26), datetime.date(2015, 2, 27),\n",
       "       datetime.date(2015, 3, 2), datetime.date(2015, 3, 3),\n",
       "       datetime.date(2015, 3, 4), datetime.date(2015, 3, 5),\n",
       "       datetime.date(2015, 3, 6), datetime.date(2015, 3, 9),\n",
       "       datetime.date(2015, 3, 10), datetime.date(2015, 3, 11),\n",
       "       datetime.date(2015, 3, 12), datetime.date(2015, 3, 13),\n",
       "       datetime.date(2015, 3, 16), datetime.date(2015, 3, 17),\n",
       "       datetime.date(2015, 3, 18), datetime.date(2015, 3, 19),\n",
       "       datetime.date(2015, 3, 20), datetime.date(2015, 3, 23),\n",
       "       datetime.date(2015, 3, 24), datetime.date(2015, 3, 25),\n",
       "       datetime.date(2015, 3, 26), datetime.date(2015, 3, 27),\n",
       "       datetime.date(2015, 3, 30), datetime.date(2015, 3, 31),\n",
       "       datetime.date(2015, 4, 1), datetime.date(2015, 4, 2),\n",
       "       datetime.date(2015, 4, 3), datetime.date(2015, 4, 6),\n",
       "       datetime.date(2015, 4, 7), datetime.date(2015, 4, 8),\n",
       "       datetime.date(2015, 4, 9), datetime.date(2015, 4, 10),\n",
       "       datetime.date(2015, 4, 13), datetime.date(2015, 4, 14),\n",
       "       datetime.date(2015, 4, 15), datetime.date(2015, 4, 16),\n",
       "       datetime.date(2015, 4, 17), datetime.date(2015, 4, 20),\n",
       "       datetime.date(2015, 4, 21), datetime.date(2015, 4, 22),\n",
       "       datetime.date(2015, 4, 23), datetime.date(2015, 4, 24),\n",
       "       datetime.date(2015, 4, 27), datetime.date(2015, 4, 28),\n",
       "       datetime.date(2015, 4, 29), datetime.date(2015, 4, 30),\n",
       "       datetime.date(2015, 5, 1), datetime.date(2015, 5, 4),\n",
       "       datetime.date(2015, 5, 5), datetime.date(2015, 5, 6),\n",
       "       datetime.date(2015, 5, 7), datetime.date(2015, 5, 8),\n",
       "       datetime.date(2015, 5, 11), datetime.date(2015, 5, 12),\n",
       "       datetime.date(2015, 5, 13), datetime.date(2015, 5, 14),\n",
       "       datetime.date(2015, 5, 15), datetime.date(2015, 5, 18),\n",
       "       datetime.date(2015, 5, 19), datetime.date(2015, 5, 20),\n",
       "       datetime.date(2015, 5, 21), datetime.date(2015, 5, 22),\n",
       "       datetime.date(2015, 5, 26), datetime.date(2015, 5, 27),\n",
       "       datetime.date(2015, 5, 28), datetime.date(2015, 5, 29),\n",
       "       datetime.date(2015, 6, 1), datetime.date(2015, 6, 2),\n",
       "       datetime.date(2015, 6, 3), datetime.date(2015, 6, 4),\n",
       "       datetime.date(2015, 6, 5), datetime.date(2015, 6, 8),\n",
       "       datetime.date(2015, 6, 9), datetime.date(2015, 6, 10),\n",
       "       datetime.date(2015, 6, 11), datetime.date(2015, 6, 12),\n",
       "       datetime.date(2015, 6, 15), datetime.date(2015, 6, 16),\n",
       "       datetime.date(2015, 6, 17), datetime.date(2015, 6, 18),\n",
       "       datetime.date(2015, 6, 19), datetime.date(2015, 6, 22),\n",
       "       datetime.date(2015, 6, 23), datetime.date(2015, 6, 24),\n",
       "       datetime.date(2015, 6, 25), datetime.date(2015, 6, 26),\n",
       "       datetime.date(2015, 6, 29), datetime.date(2015, 6, 30),\n",
       "       datetime.date(2015, 7, 1), datetime.date(2015, 7, 2),\n",
       "       datetime.date(2015, 7, 6), datetime.date(2015, 7, 7),\n",
       "       datetime.date(2015, 7, 8), datetime.date(2015, 7, 9),\n",
       "       datetime.date(2015, 7, 10), datetime.date(2015, 7, 13),\n",
       "       datetime.date(2015, 7, 14), datetime.date(2015, 7, 15),\n",
       "       datetime.date(2015, 7, 16), datetime.date(2015, 7, 17),\n",
       "       datetime.date(2015, 7, 20), datetime.date(2015, 7, 21),\n",
       "       datetime.date(2015, 7, 22), datetime.date(2015, 7, 23),\n",
       "       datetime.date(2015, 7, 24), datetime.date(2015, 7, 27),\n",
       "       datetime.date(2015, 7, 28), datetime.date(2015, 7, 29),\n",
       "       datetime.date(2015, 7, 30), datetime.date(2015, 7, 31),\n",
       "       datetime.date(2015, 8, 3), datetime.date(2015, 8, 4),\n",
       "       datetime.date(2015, 8, 5), datetime.date(2015, 8, 6),\n",
       "       datetime.date(2015, 8, 7), datetime.date(2015, 8, 10),\n",
       "       datetime.date(2015, 8, 11), datetime.date(2015, 8, 12),\n",
       "       datetime.date(2015, 8, 13), datetime.date(2015, 8, 14),\n",
       "       datetime.date(2015, 8, 17), datetime.date(2015, 8, 18),\n",
       "       datetime.date(2015, 8, 19), datetime.date(2015, 8, 20),\n",
       "       datetime.date(2015, 8, 21), datetime.date(2015, 8, 24),\n",
       "       datetime.date(2015, 8, 25), datetime.date(2015, 8, 26),\n",
       "       datetime.date(2015, 8, 27), datetime.date(2015, 8, 28),\n",
       "       datetime.date(2015, 8, 31)], dtype=object)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find all of the business days in our time span\n",
    "us_bd = CustomBusinessDay(calendar=USFederalHolidayCalendar())\n",
    "business_days = pd.DatetimeIndex(start=train.date.min(), end=train.date.max(), freq=us_bd)\n",
    "business_days = pd.to_datetime(business_days, format='%Y/%m/%d').date\n",
    "business_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# map date to business_day and holiday, convert to binary variables\n",
    "train['business_day'] = train.date.isin(business_days)\n",
    "train['holiday'] = train.date.isin(holidays)\n",
    "#Convert True to 1 and False to 0\n",
    "train.business_day = train.business_day.map(lambda x: 1 if x == True else 0)\n",
    "train.holiday = train.holiday.map(lambda x: 1 if x == True else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>station_id</th>\n",
       "      <th>time_period</th>\n",
       "      <th>bikes_available</th>\n",
       "      <th>name</th>\n",
       "      <th>dock_count</th>\n",
       "      <th>city</th>\n",
       "      <th>max_temperature_f</th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>min_temperature_f</th>\n",
       "      <th>...</th>\n",
       "      <th>Fog</th>\n",
       "      <th>Fog-Rain</th>\n",
       "      <th>Normal</th>\n",
       "      <th>Rain</th>\n",
       "      <th>Rain-Thunderstorm</th>\n",
       "      <th>business_day</th>\n",
       "      <th>holiday</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 6</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 7</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-29</td>\n",
       "      <td>2</td>\n",
       "      <td>period 8</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>San Jose Diridon Caltrain Station</td>\n",
       "      <td>27</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>81.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  station_id time_period  bikes_available  \\\n",
       "0  2013-08-29           2    period 4         2.000000   \n",
       "1  2013-08-29           2    period 5         2.000000   \n",
       "2  2013-08-29           2    period 6         2.333333   \n",
       "3  2013-08-29           2    period 7         1.333333   \n",
       "4  2013-08-29           2    period 8         2.500000   \n",
       "\n",
       "                                name  dock_count      city  max_temperature_f  \\\n",
       "0  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "1  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "2  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "3  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "4  San Jose Diridon Caltrain Station          27  San Jose               81.0   \n",
       "\n",
       "   mean_temperature_f  min_temperature_f   ...     Fog  Fog-Rain  Normal  \\\n",
       "0                72.0               63.0   ...       0         0       1   \n",
       "1                72.0               63.0   ...       0         0       1   \n",
       "2                72.0               63.0   ...       0         0       1   \n",
       "3                72.0               63.0   ...       0         0       1   \n",
       "4                72.0               63.0   ...       0         0       1   \n",
       "\n",
       "   Rain  Rain-Thunderstorm  business_day  holiday  year  month  weekday  \n",
       "0     0                  0             1        0  2013      8        3  \n",
       "1     0                  0             1        0  2013      8        3  \n",
       "2     0                  0             1        0  2013      8        3  \n",
       "3     0                  0             1        0  2013      8        3  \n",
       "4     0                  0             1        0  2013      8        3  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert date to the important features, year, month, weekday (0 = Monday, 1 = Tuesday...)\n",
    "train['year'] = pd.to_datetime(train['date']).dt.year\n",
    "train['month'] = pd.to_datetime(train['date']).dt.month\n",
    "train['weekday'] = pd.to_datetime(train['date']).dt.weekday\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(452045, 38)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['date',\n",
       " 'station_id',\n",
       " 'time_period',\n",
       " 'bikes_available',\n",
       " 'name',\n",
       " 'dock_count',\n",
       " 'city',\n",
       " 'max_temperature_f',\n",
       " 'mean_temperature_f',\n",
       " 'min_temperature_f',\n",
       " 'max_dew_point_f',\n",
       " 'mean_dew_point_f',\n",
       " 'min_dew_point_f',\n",
       " 'max_humidity',\n",
       " 'mean_humidity',\n",
       " 'min_humidity',\n",
       " 'max_sea_level_pressure_inches',\n",
       " 'mean_sea_level_pressure_inches',\n",
       " 'min_sea_level_pressure_inches',\n",
       " 'max_visibility_miles',\n",
       " 'mean_visibility_miles',\n",
       " 'min_visibility_miles',\n",
       " 'max_wind_Speed_mph',\n",
       " 'mean_wind_speed_mph',\n",
       " 'max_gust_speed_mph',\n",
       " 'precipitation_inches',\n",
       " 'cloud_cover',\n",
       " 'wind_dir_degrees',\n",
       " 'Fog',\n",
       " 'Fog-Rain',\n",
       " 'Normal',\n",
       " 'Rain',\n",
       " 'Rain-Thunderstorm',\n",
       " 'business_day',\n",
       " 'holiday',\n",
       " 'year',\n",
       " 'month',\n",
       " 'weekday']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here's the list of 38 columns in train\n",
    "list(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  \n",
    "#  \n",
    "# Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert categorical variables to one-hot representation, as required by scikit learn\n",
    "time_period = pd.get_dummies(train.time_period)\n",
    "station_id = pd.get_dummies(train.station_id)\n",
    "city = pd.get_dummies(train.city)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.merge(time_period, left_index = True, right_index = True)\n",
    "train = train.merge(city, left_index = True, right_index = True)\n",
    "train = train.merge(station_id, left_index = True, right_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.drop(['time_period','station_id','city','name','date'],1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bikes_available</th>\n",
       "      <th>dock_count</th>\n",
       "      <th>max_temperature_f</th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>min_temperature_f</th>\n",
       "      <th>max_dew_point_f</th>\n",
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       "      <th>min_dew_point_f</th>\n",
       "      <th>max_humidity</th>\n",
       "      <th>mean_humidity</th>\n",
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       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
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       "      <th>83</th>\n",
       "      <th>84</th>\n",
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       "      <td>452045.000000</td>\n",
       "      <td>452045.000000</td>\n",
       "      <td>452045.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.178788</td>\n",
       "      <td>17.715336</td>\n",
       "      <td>69.945981</td>\n",
       "      <td>61.095988</td>\n",
       "      <td>51.906122</td>\n",
       "      <td>52.759772</td>\n",
       "      <td>48.753836</td>\n",
       "      <td>44.50143</td>\n",
       "      <td>85.673055</td>\n",
       "      <td>67.529536</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014549</td>\n",
       "      <td>0.014463</td>\n",
       "      <td>0.014549</td>\n",
       "      <td>0.014549</td>\n",
       "      <td>0.014549</td>\n",
       "      <td>0.014549</td>\n",
       "      <td>0.012081</td>\n",
       "      <td>0.011647</td>\n",
       "      <td>0.011065</td>\n",
       "      <td>0.010132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.832629</td>\n",
       "      <td>3.990183</td>\n",
       "      <td>8.114152</td>\n",
       "      <td>6.852748</td>\n",
       "      <td>7.022960</td>\n",
       "      <td>6.322607</td>\n",
       "      <td>7.353652</td>\n",
       "      <td>9.01759</td>\n",
       "      <td>8.821702</td>\n",
       "      <td>10.567253</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119740</td>\n",
       "      <td>0.119390</td>\n",
       "      <td>0.119740</td>\n",
       "      <td>0.119740</td>\n",
       "      <td>0.119740</td>\n",
       "      <td>0.119740</td>\n",
       "      <td>0.109246</td>\n",
       "      <td>0.107291</td>\n",
       "      <td>0.104608</td>\n",
       "      <td>0.100145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.666667</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>40.00000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>46.00000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>52.00000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>63.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bikes_available     dock_count  max_temperature_f  mean_temperature_f  \\\n",
       "count    452045.000000  452045.000000      452045.000000       452045.000000   \n",
       "mean          8.178788      17.715336          69.945981           61.095988   \n",
       "std           3.832629       3.990183           8.114152            6.852748   \n",
       "min           0.000000      11.000000          44.000000           38.000000   \n",
       "25%           5.666667      15.000000          64.000000           56.000000   \n",
       "50%           8.000000      15.000000          70.000000           61.000000   \n",
       "75%          10.000000      19.000000          75.000000           67.000000   \n",
       "max          27.000000      27.000000         102.000000           84.000000   \n",
       "\n",
       "       min_temperature_f  max_dew_point_f  mean_dew_point_f  min_dew_point_f  \\\n",
       "count      452045.000000    452045.000000     452045.000000     452045.00000   \n",
       "mean           51.906122        52.759772         48.753836         44.50143   \n",
       "std             7.022960         6.322607          7.353652          9.01759   \n",
       "min            25.000000        20.000000         13.000000          2.00000   \n",
       "25%            47.000000        49.000000         45.000000         40.00000   \n",
       "50%            53.000000        54.000000         49.000000         46.00000   \n",
       "75%            57.000000        57.000000         55.000000         52.00000   \n",
       "max            75.000000        68.000000         65.000000         63.00000   \n",
       "\n",
       "        max_humidity  mean_humidity      ...                   72  \\\n",
       "count  452045.000000  452045.000000      ...        452045.000000   \n",
       "mean       85.673055      67.529536      ...             0.014549   \n",
       "std         8.821702      10.567253      ...             0.119740   \n",
       "min        24.000000      24.000000      ...             0.000000   \n",
       "25%        82.000000      62.000000      ...             0.000000   \n",
       "50%        86.000000      69.000000      ...             0.000000   \n",
       "75%        93.000000      74.000000      ...             0.000000   \n",
       "max       100.000000      96.000000      ...             1.000000   \n",
       "\n",
       "                  73             74             75             76  \\\n",
       "count  452045.000000  452045.000000  452045.000000  452045.000000   \n",
       "mean        0.014463       0.014549       0.014549       0.014549   \n",
       "std         0.119390       0.119740       0.119740       0.119740   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                  77             80             82             83  \\\n",
       "count  452045.000000  452045.000000  452045.000000  452045.000000   \n",
       "mean        0.014549       0.012081       0.011647       0.011065   \n",
       "std         0.119740       0.109246       0.107291       0.104608   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "                  84  \n",
       "count  452045.000000  \n",
       "mean        0.010132  \n",
       "std         0.100145  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 117 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.000000\n",
       "1    2.000000\n",
       "2    2.333333\n",
       "3    1.333333\n",
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       "Name: bikes_available, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = train.iloc[:,0]\n",
    "labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
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       "</table>\n",
       "<p>5 rows × 116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   dock_count  max_temperature_f  mean_temperature_f  min_temperature_f  \\\n",
       "0          27               81.0                72.0               63.0   \n",
       "1          27               81.0                72.0               63.0   \n",
       "2          27               81.0                72.0               63.0   \n",
       "3          27               81.0                72.0               63.0   \n",
       "4          27               81.0                72.0               63.0   \n",
       "\n",
       "   max_dew_point_f  mean_dew_point_f  min_dew_point_f  max_humidity  \\\n",
       "0             62.0              61.0             59.0          87.0   \n",
       "1             62.0              61.0             59.0          87.0   \n",
       "2             62.0              61.0             59.0          87.0   \n",
       "3             62.0              61.0             59.0          87.0   \n",
       "4             62.0              61.0             59.0          87.0   \n",
       "\n",
       "   mean_humidity  min_humidity ...  72  73  74  75  76  77  80  82  83  84  \n",
       "0           69.0          51.0 ...   0   0   0   0   0   0   0   0   0   0  \n",
       "1           69.0          51.0 ...   0   0   0   0   0   0   0   0   0   0  \n",
       "2           69.0          51.0 ...   0   0   0   0   0   0   0   0   0   0  \n",
       "3           69.0          51.0 ...   0   0   0   0   0   0   0   0   0   0  \n",
       "4           69.0          51.0 ...   0   0   0   0   0   0   0   0   0   0  \n",
       "\n",
       "[5 rows x 116 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = train.iloc[:,1:]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split into training 80% and test 20%\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(361636, 116)\n",
      "(90409, 116)\n"
     ]
    }
   ],
   "source": [
    "# our data has 116 features\n",
    "# training data has 361636 samples\n",
    "# test data has 90409 samples\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#10 fold cross validation. Multiply by -1 to make values positive.\n",
    "#Used median absolute error to learn how many trips my predictions are off by.\n",
    "\n",
    "def scoring(clf):\n",
    "    scores = cross_val_score(clf, X_train, y_train, cv=10, n_jobs=1, scoring = 'neg_median_absolute_error')\n",
    "    print (np.median(scores) * -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.04727032227\n"
     ]
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(n_estimators = 10, # to be tuned\n",
    "                            min_samples_leaf = 3,\n",
    "                            random_state = 2)\n",
    "scoring(rfr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.66163764712\n"
     ]
    }
   ],
   "source": [
    "gbr = GradientBoostingRegressor(learning_rate = 0.12,\n",
    "                                n_estimators = 100,\n",
    "                                max_depth = 8,\n",
    "                                min_samples_leaf = 1,\n",
    "                                random_state = 2)\n",
    "scoring(gbr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.86616394665\n"
     ]
    }
   ],
   "source": [
    "dtr = DecisionTreeRegressor(min_samples_leaf = 3,\n",
    "                            max_depth = 8,\n",
    "                            random_state = 2)\n",
    "scoring(dtr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.07161247021\n"
     ]
    }
   ],
   "source": [
    "abr = AdaBoostRegressor(n_estimators = 100,\n",
    "                        learning_rate = 0.1,\n",
    "                        loss = 'linear',\n",
    "                        random_state = 2)\n",
    "scoring(abr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Fold 1\n",
      "[0]\ttrain-rmse:7.48979\teval-rmse:7.45756\n",
      "Multiple eval metrics have been passed: 'eval-rmse' will be used for early stopping.\n",
      "\n",
      "Will train until eval-rmse hasn't improved in 100 rounds.\n",
      "[50]\ttrain-rmse:2.9649\teval-rmse:2.97511\n",
      "[100]\ttrain-rmse:2.87753\teval-rmse:2.89973\n",
      "[150]\ttrain-rmse:2.80309\teval-rmse:2.83722\n",
      "[200]\ttrain-rmse:2.74613\teval-rmse:2.78952\n",
      "[250]\ttrain-rmse:2.69527\teval-rmse:2.74965\n",
      "[300]\ttrain-rmse:2.6454\teval-rmse:2.71154\n",
      "[350]\ttrain-rmse:2.60556\teval-rmse:2.68211\n",
      "[400]\ttrain-rmse:2.56655\teval-rmse:2.65309\n",
      "[450]\ttrain-rmse:2.5297\teval-rmse:2.62524\n",
      "[500]\ttrain-rmse:2.49749\teval-rmse:2.59918\n",
      "[550]\ttrain-rmse:2.46654\teval-rmse:2.57608\n",
      "[600]\ttrain-rmse:2.43964\teval-rmse:2.55598\n",
      "[650]\ttrain-rmse:2.41116\teval-rmse:2.53585\n",
      "[700]\ttrain-rmse:2.3828\teval-rmse:2.51612\n",
      "[750]\ttrain-rmse:2.36208\teval-rmse:2.50346\n",
      "[800]\ttrain-rmse:2.34011\teval-rmse:2.48863\n",
      "[850]\ttrain-rmse:2.31891\teval-rmse:2.47527\n",
      "[900]\ttrain-rmse:2.29798\teval-rmse:2.4602\n",
      "[950]\ttrain-rmse:2.27751\teval-rmse:2.446\n",
      "[1000]\ttrain-rmse:2.25939\teval-rmse:2.43424\n",
      "[1050]\ttrain-rmse:2.23678\teval-rmse:2.41769\n",
      "[1100]\ttrain-rmse:2.2194\teval-rmse:2.40639\n",
      "[1150]\ttrain-rmse:2.20185\teval-rmse:2.39589\n",
      "[1200]\ttrain-rmse:2.18653\teval-rmse:2.38539\n",
      "[1250]\ttrain-rmse:2.17063\teval-rmse:2.37557\n",
      "[1300]\ttrain-rmse:2.15786\teval-rmse:2.36745\n",
      "[1350]\ttrain-rmse:2.14217\teval-rmse:2.35786\n",
      "[1400]\ttrain-rmse:2.12671\teval-rmse:2.34892\n",
      "[1450]\ttrain-rmse:2.11439\teval-rmse:2.34039\n",
      "[1500]\ttrain-rmse:2.1019\teval-rmse:2.33307\n",
      "[1550]\ttrain-rmse:2.08811\teval-rmse:2.32563\n",
      "[1600]\ttrain-rmse:2.07524\teval-rmse:2.3183\n",
      "[1650]\ttrain-rmse:2.06398\teval-rmse:2.3117\n",
      "[1700]\ttrain-rmse:2.0541\teval-rmse:2.30644\n",
      "[1750]\ttrain-rmse:2.04204\teval-rmse:2.30017\n",
      "[1800]\ttrain-rmse:2.03173\teval-rmse:2.29508\n",
      "[1850]\ttrain-rmse:2.01881\teval-rmse:2.28818\n",
      "[1900]\ttrain-rmse:2.00956\teval-rmse:2.28348\n",
      "[1950]\ttrain-rmse:1.99898\teval-rmse:2.27761\n",
      "[2000]\ttrain-rmse:1.98852\teval-rmse:2.27237\n",
      "[2050]\ttrain-rmse:1.97725\teval-rmse:2.26579\n",
      "[2100]\ttrain-rmse:1.96602\teval-rmse:2.2596\n",
      "[2150]\ttrain-rmse:1.95777\teval-rmse:2.25519\n",
      "[2200]\ttrain-rmse:1.94881\teval-rmse:2.25074\n",
      "[2250]\ttrain-rmse:1.93884\teval-rmse:2.24621\n",
      "[2300]\ttrain-rmse:1.92914\teval-rmse:2.24209\n",
      "[2350]\ttrain-rmse:1.91905\teval-rmse:2.23849\n",
      "[2400]\ttrain-rmse:1.91017\teval-rmse:2.23478\n",
      "[2450]\ttrain-rmse:1.90155\teval-rmse:2.2311\n",
      "[2500]\ttrain-rmse:1.89222\teval-rmse:2.22657\n",
      "[2550]\ttrain-rmse:1.88438\teval-rmse:2.2231\n",
      "[2600]\ttrain-rmse:1.87664\teval-rmse:2.21961\n",
      "[2650]\ttrain-rmse:1.86994\teval-rmse:2.21702\n",
      "[2700]\ttrain-rmse:1.86227\teval-rmse:2.21449\n",
      "[2750]\ttrain-rmse:1.85638\teval-rmse:2.21252\n",
      "[2800]\ttrain-rmse:1.84836\teval-rmse:2.20892\n",
      "[2850]\ttrain-rmse:1.84095\teval-rmse:2.20624\n",
      "[2900]\ttrain-rmse:1.83518\teval-rmse:2.20447\n",
      "[2950]\ttrain-rmse:1.82826\teval-rmse:2.20242\n",
      "[3000]\ttrain-rmse:1.8206\teval-rmse:2.19997\n",
      "[3050]\ttrain-rmse:1.8137\teval-rmse:2.19803\n",
      "[3100]\ttrain-rmse:1.80747\teval-rmse:2.19559\n",
      "[3150]\ttrain-rmse:1.80166\teval-rmse:2.19363\n",
      "[3200]\ttrain-rmse:1.79473\teval-rmse:2.1912\n",
      "[3250]\ttrain-rmse:1.78776\teval-rmse:2.18959\n",
      "[3300]\ttrain-rmse:1.78195\teval-rmse:2.18765\n",
      "[3350]\ttrain-rmse:1.77637\teval-rmse:2.18528\n",
      "[3400]\ttrain-rmse:1.77026\teval-rmse:2.18294\n",
      "[3450]\ttrain-rmse:1.76427\teval-rmse:2.18133\n",
      "[3500]\ttrain-rmse:1.75864\teval-rmse:2.17984\n",
      "[3550]\ttrain-rmse:1.75294\teval-rmse:2.17854\n",
      "[3600]\ttrain-rmse:1.74799\teval-rmse:2.17756\n",
      "[3650]\ttrain-rmse:1.7425\teval-rmse:2.17583\n",
      "[3700]\ttrain-rmse:1.73627\teval-rmse:2.17444\n",
      "[3750]\ttrain-rmse:1.73153\teval-rmse:2.17362\n",
      "[3800]\ttrain-rmse:1.72589\teval-rmse:2.17311\n",
      "[3850]\ttrain-rmse:1.72162\teval-rmse:2.17252\n",
      "[3900]\ttrain-rmse:1.71605\teval-rmse:2.17126\n",
      "[3950]\ttrain-rmse:1.70973\teval-rmse:2.17003\n",
      "[4000]\ttrain-rmse:1.70446\teval-rmse:2.16934\n",
      "[4050]\ttrain-rmse:1.69869\teval-rmse:2.16793\n",
      "[4100]\ttrain-rmse:1.69318\teval-rmse:2.16636\n",
      "[4150]\ttrain-rmse:1.68795\teval-rmse:2.16561\n",
      "[4200]\ttrain-rmse:1.68368\teval-rmse:2.16526\n",
      "[4250]\ttrain-rmse:1.67912\teval-rmse:2.1646\n",
      "[4300]\ttrain-rmse:1.67447\teval-rmse:2.16405\n",
      "[4350]\ttrain-rmse:1.67033\teval-rmse:2.16343\n",
      "[4400]\ttrain-rmse:1.66554\teval-rmse:2.16255\n",
      "[4450]\ttrain-rmse:1.66033\teval-rmse:2.16221\n",
      "[4500]\ttrain-rmse:1.655\teval-rmse:2.1617\n",
      "[4550]\ttrain-rmse:1.64958\teval-rmse:2.16079\n",
      "[4600]\ttrain-rmse:1.64474\teval-rmse:2.16072\n",
      "[4650]\ttrain-rmse:1.64035\teval-rmse:2.16048\n",
      "[4700]\ttrain-rmse:1.63475\teval-rmse:2.16007\n",
      "[4750]\ttrain-rmse:1.63071\teval-rmse:2.15938\n",
      "[4800]\ttrain-rmse:1.62711\teval-rmse:2.15923\n",
      "[4850]\ttrain-rmse:1.62228\teval-rmse:2.15903\n",
      "[4900]\ttrain-rmse:1.61771\teval-rmse:2.15922\n",
      "[4950]\ttrain-rmse:1.61299\teval-rmse:2.15891\n",
      "[5000]\ttrain-rmse:1.6093\teval-rmse:2.15867\n",
      "[5050]\ttrain-rmse:1.60514\teval-rmse:2.15829\n",
      "[5100]\ttrain-rmse:1.60127\teval-rmse:2.15809\n",
      "[5150]\ttrain-rmse:1.59689\teval-rmse:2.15779\n",
      "[5200]\ttrain-rmse:1.59275\teval-rmse:2.15764\n",
      "[5250]\ttrain-rmse:1.58929\teval-rmse:2.15752\n",
      "[5300]\ttrain-rmse:1.58407\teval-rmse:2.15756\n",
      "[5350]\ttrain-rmse:1.57915\teval-rmse:2.15758\n",
      "[5400]\ttrain-rmse:1.57416\teval-rmse:2.15676\n",
      "[5450]\ttrain-rmse:1.57017\teval-rmse:2.15641\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-89-ba8c320be014>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     39\u001b[0m                          \u001b[0mwatchlist\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m                          \u001b[0mverbose_eval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprintN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m                          \u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m                         )\n\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xinyi/anaconda/lib/python3.6/site-packages/xgboost/training.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, learning_rates)\u001b[0m\n\u001b[1;32m    202\u001b[0m                            \u001b[0mevals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mevals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m                            \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m                            xgb_model=xgb_model, callbacks=callbacks)\n\u001b[0m\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xinyi/anaconda/lib/python3.6/site-packages/xgboost/training.py\u001b[0m in \u001b[0;36m_train_internal\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;31m# Skip the first update if it is a recovery step.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mversion\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m             \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_rabit_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mversion\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/xinyi/anaconda/lib/python3.6/site-packages/xgboost/core.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m    893\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfobj\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, ctypes.c_int(iteration),\n\u001b[0;32m--> 895\u001b[0;31m                                                     dtrain.handle))\n\u001b[0m\u001b[1;32m    896\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m             \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# xgboost\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "random_state = 2\n",
    "params = {\n",
    "        'eta': 0.15,\n",
    "        'max_depth': 6,\n",
    "        'min_child_weight': 2,\n",
    "        'subsample': 1,\n",
    "        'colsample_bytree': 1,\n",
    "        'verbose_eval': True,\n",
    "        'seed': random_state,\n",
    "    }\n",
    "\n",
    "n_folds = 10 #number of Kfolds\n",
    "cv_scores = [] #The sum of the mean_absolute_error for each fold.\n",
    "early_stopping_rounds = 100\n",
    "iterations = 10000\n",
    "printN = 50\n",
    "fpred = [] #stores the sums of predicted values for each fold.\n",
    "\n",
    "testFinal = xgb.DMatrix(X_test)\n",
    "\n",
    "kf = KFold(len(X_train), n_folds=n_folds)\n",
    "\n",
    "for i, (train_index, test_index) in enumerate(kf):\n",
    "    print('\\n Fold %d' % (i+1))\n",
    "    Xtrain, Xval = X_train.iloc[train_index], X_train.iloc[test_index]\n",
    "    Ytrain, Yval = y_train.iloc[train_index], y_train.iloc[test_index]\n",
    "    \n",
    "    xgtrain = xgb.DMatrix(Xtrain, label = Ytrain)\n",
    "    xgtest = xgb.DMatrix(Xval, label = Yval)\n",
    "    watchlist = [(xgtrain, 'train'), (xgtest, 'eval')] \n",
    "    \n",
    "    xgbModel = xgb.train(params, \n",
    "                         xgtrain, \n",
    "                         iterations, \n",
    "                         watchlist,\n",
    "                         verbose_eval = printN,\n",
    "                         early_stopping_rounds=early_stopping_rounds\n",
    "                        )\n",
    "    \n",
    "    scores_val = xgbModel.predict(xgtest, ntree_limit=xgbModel.best_ntree_limit)\n",
    "    cv_score = median_absolute_error(Yval, scores_val)\n",
    "    print('eval-MSE: %.6f' % cv_score)\n",
    "    y_pred = xgbModel.predict(testFinal, ntree_limit=xgbModel.best_ntree_limit)\n",
    "    print(xgbModel.best_ntree_limit)\n",
    "\n",
    "    if i > 0:\n",
    "        fpred = pred + y_pred #sum predictions\n",
    "    else:\n",
    "        fpred = y_pred\n",
    "    pred = fpred\n",
    "    cv_scores.append(cv_score)\n",
    "\n",
    "xgb_preds = pred / n_folds #find the average values for the predictions\n",
    "score = np.median(cv_scores)\n",
    "print('Median error: %.6f' % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daily error of trip count: 1.27660695292\n"
     ]
    }
   ],
   "source": [
    "#Train and make predictions with the best models.\n",
    "rfr = rfr.fit(X_train, y_train)\n",
    "gbr = gbr.fit(X_train, y_train)\n",
    "\n",
    "rfr_preds = rfr.predict(X_test)\n",
    "gbr_preds = gbr.predict(X_test)\n",
    "\n",
    "#Weight the top models to find the best prediction\n",
    "final_preds = rfr_preds*0.5 + gbr_preds*0.5\n",
    "print (\"Daily error of trip count:\", median_absolute_error(y_test, final_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    452045.000000\n",
       "mean          8.178788\n",
       "std           3.832629\n",
       "min           0.000000\n",
       "25%           5.666667\n",
       "50%           8.000000\n",
       "75%          10.000000\n",
       "max          27.000000\n",
       "Name: bikes_available, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#A reminder of the range of values in number of daily trips.\n",
    "labels.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_test.reset_index(drop = True, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SJElpiDHtCCRJkiRJkiRJXVTUxOMY4yggZHnrqWLGIUmSJEl5t3kN1IyEA76e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\nfb71QNu7F+V8aqP1GXz2R4vjI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BCaT73r0Lv1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\ndFrfN5qWDpWurZJM42k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Nf8RU6DcEdjqy/LgTd4WNz8P840uPM3p2tr53PqadZU3Lctkl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\n8fEmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x130921eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs = 16\n",
    "plt.figure(figsize=(50,10))\n",
    "plt.plot(final_preds)\n",
    "plt.plot(y_test)\n",
    "plt.legend(['Prediction', 'Acutal'])\n",
    "plt.ylabel(\"Number of Trips\", fontsize = fs)\n",
    "plt.xlabel(\"Predicted Date\", fontsize = fs)\n",
    "plt.title(\"Predicted Values vs Actual Values\", fontsize = fs)\n",
    "plt.show()\n",
    "# too much data point, hard to see\n",
    "# need to find better way to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create a plot that ranks the features by importance.\n",
    "def plot_importances(model, model_name):\n",
    "    importances = model.feature_importances_\n",
    "    std = np.std([model.feature_importances_ for feature in model.estimators_],\n",
    "                 axis=0)\n",
    "    indices = np.argsort(importances)[::-1]    \n",
    "\n",
    "    # Plot the feature importances of the forest\n",
    "    plt.figure(figsize = (50,5))\n",
    "    plt.title(\"Feature importances of \" + model_name)\n",
    "    plt.bar(range(X_train.shape[1]), importances[indices], color=\"r\", align=\"center\")\n",
    "    plt.xticks(range(X_train.shape[1]), indices)\n",
    "    plt.xlim([-1, X_train.shape[1]])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "0 dock_count\n",
      "1 max_temperature_f\n",
      "2 mean_temperature_f\n",
      "3 min_temperature_f\n",
      "4 max_dew_point_f\n",
      "5 mean_dew_point_f\n",
      "6 min_dew_point_f\n",
      "7 max_humidity\n",
      "8 mean_humidity\n",
      "9 min_humidity\n",
      "10 max_sea_level_pressure_inches\n",
      "11 mean_sea_level_pressure_inches\n",
      "12 min_sea_level_pressure_inches\n",
      "13 max_visibility_miles\n",
      "14 mean_visibility_miles\n",
      "15 min_visibility_miles\n",
      "16 max_wind_Speed_mph\n",
      "17 mean_wind_speed_mph\n",
      "18 max_gust_speed_mph\n",
      "19 precipitation_inches\n",
      "20 cloud_cover\n",
      "21 wind_dir_degrees\n",
      "22 Fog\n",
      "23 Fog-Rain\n",
      "24 Normal\n",
      "25 Rain\n",
      "26 Rain-Thunderstorm\n",
      "27 business_day\n",
      "28 holiday\n",
      "29 year\n",
      "30 month\n",
      "31 weekday\n",
      "32 period 0\n",
      "33 period 1\n",
      "34 period 2\n",
      "35 period 3\n",
      "36 period 4\n",
      "37 period 5\n",
      "38 period 6\n",
      "39 period 7\n",
      "40 period 8\n",
      "41 Mountain View\n",
      "42 Palo Alto\n",
      "43 Redwood City\n",
      "44 San Francisco\n",
      "45 San Jose\n",
      "46 2\n",
      "47 3\n",
      "48 4\n",
      "49 5\n",
      "50 6\n",
      "51 7\n",
      "52 8\n",
      "53 9\n",
      "54 10\n",
      "55 11\n",
      "56 12\n",
      "57 13\n",
      "58 14\n",
      "59 16\n",
      "60 21\n",
      "61 22\n",
      "62 23\n",
      "63 24\n",
      "64 25\n",
      "65 26\n",
      "66 27\n",
      "67 28\n",
      "68 29\n",
      "69 30\n",
      "70 31\n",
      "71 32\n",
      "72 33\n",
      "73 34\n",
      "74 35\n",
      "75 36\n",
      "76 37\n",
      "77 38\n",
      "78 39\n",
      "79 41\n",
      "80 42\n",
      "81 45\n",
      "82 46\n",
      "83 47\n",
      "84 48\n",
      "85 49\n",
      "86 50\n",
      "87 51\n",
      "88 54\n",
      "89 55\n",
      "90 56\n",
      "91 57\n",
      "92 58\n",
      "93 59\n",
      "94 60\n",
      "95 61\n",
      "96 62\n",
      "97 63\n",
      "98 64\n",
      "99 65\n",
      "100 66\n",
      "101 67\n",
      "102 68\n",
      "103 69\n",
      "104 70\n",
      "105 71\n",
      "106 72\n",
      "107 73\n",
      "108 74\n",
      "109 75\n",
      "110 76\n",
      "111 77\n",
      "112 80\n",
      "113 82\n",
      "114 83\n",
      "115 84\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ae3d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12eed2ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print the feature ranking\n",
    "print(\"Feature ranking:\")\n",
    "\n",
    "i = 0\n",
    "for feature in X_train:\n",
    "    print (i, feature)\n",
    "    i += 1\n",
    "    \n",
    "plot_importances(rfr, \"Random Forest Regressor\")\n",
    "plot_importances(gbr, \"Gradient Boosting Regressor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "For random forest model, top 10 important features are:\n",
    "- 0: dock_count\n",
    "- 21: **wind_dir_degree**\n",
    "- 30: **month**\n",
    "- 31: weekday\n",
    "- 18: **max_gust_speed_mph**\n",
    "- 12: min_sea_level_pressure_inches\n",
    "- 1: max_temperature\n",
    "- 10: max_sea_level_pressure_inches\n",
    "- 3: min_temperature\n",
    "- 29: year\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For gradient boosting model, top 10 important features are:\n",
    "- 0: dock_count\n",
    "- 30: month\n",
    "- 29: year\n",
    "- 31: weekday\n",
    "- 21: wind_dir_degree\n",
    "- 36: period 4\n",
    "- 3: min_temperature\n",
    "- 12: min_sea_level_pressure_inches\n",
    "- 10: max_sea_level_pressure_inches\n",
    "- 1: max_temperature\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90409,)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output = X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output[\"bikes_available_actual\"] = y_test.values\n",
    "output[\"bikes_available_predicted\"] = final_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dock_count</th>\n",
       "      <th>max_temperature_f</th>\n",
       "      <th>mean_temperature_f</th>\n",
       "      <th>min_temperature_f</th>\n",
       "      <th>max_dew_point_f</th>\n",
       "      <th>mean_dew_point_f</th>\n",
       "      <th>min_dew_point_f</th>\n",
       "      <th>max_humidity</th>\n",
       "      <th>mean_humidity</th>\n",
       "      <th>min_humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "      <th>80</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>bikes_available_actual</th>\n",
       "      <th>bikes_available_predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>447998</th>\n",
       "      <td>19</td>\n",
       "      <td>74.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>7.467833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252530</th>\n",
       "      <td>15</td>\n",
       "      <td>56.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.235819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187605</th>\n",
       "      <td>15</td>\n",
       "      <td>79.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.368054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222744</th>\n",
       "      <td>23</td>\n",
       "      <td>71.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.932964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210121</th>\n",
       "      <td>15</td>\n",
       "      <td>66.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>6.164502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334071</th>\n",
       "      <td>19</td>\n",
       "      <td>75.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.720888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48910</th>\n",
       "      <td>15</td>\n",
       "      <td>77.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.703687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297148</th>\n",
       "      <td>19</td>\n",
       "      <td>63.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>6.949081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83812</th>\n",
       "      <td>19</td>\n",
       "      <td>72.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>9.202847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163278</th>\n",
       "      <td>11</td>\n",
       "      <td>75.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.984040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173970</th>\n",
       "      <td>23</td>\n",
       "      <td>61.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>11.781151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222959</th>\n",
       "      <td>15</td>\n",
       "      <td>75.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.506112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139195</th>\n",
       "      <td>15</td>\n",
       "      <td>66.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.012161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72009</th>\n",
       "      <td>19</td>\n",
       "      <td>72.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.411190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263245</th>\n",
       "      <td>15</td>\n",
       "      <td>61.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>7.391361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350760</th>\n",
       "      <td>23</td>\n",
       "      <td>72.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.333333</td>\n",
       "      <td>18.855416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87324</th>\n",
       "      <td>11</td>\n",
       "      <td>68.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.163009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329079</th>\n",
       "      <td>23</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.997955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119744</th>\n",
       "      <td>15</td>\n",
       "      <td>75.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>6.939301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446043</th>\n",
       "      <td>27</td>\n",
       "      <td>78.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.169032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32396</th>\n",
       "      <td>11</td>\n",
       "      <td>71.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.081764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219280</th>\n",
       "      <td>23</td>\n",
       "      <td>71.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.687127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413599</th>\n",
       "      <td>19</td>\n",
       "      <td>76.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>10.313117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382536</th>\n",
       "      <td>15</td>\n",
       "      <td>64.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>7.579229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75371</th>\n",
       "      <td>11</td>\n",
       "      <td>63.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>6.687045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10964</th>\n",
       "      <td>15</td>\n",
       "      <td>65.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>8.440400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304329</th>\n",
       "      <td>19</td>\n",
       "      <td>68.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.108036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356698</th>\n",
       "      <td>15</td>\n",
       "      <td>66.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>5.515297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132199</th>\n",
       "      <td>15</td>\n",
       "      <td>59.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.454338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134391</th>\n",
       "      <td>15</td>\n",
       "      <td>62.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.042790</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404300</th>\n",
       "      <td>19</td>\n",
       "      <td>69.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8.344893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19860</th>\n",
       "      <td>19</td>\n",
       "      <td>60.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>7.801566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181555</th>\n",
       "      <td>15</td>\n",
       "      <td>67.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.430534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327128</th>\n",
       "      <td>15</td>\n",
       "      <td>73.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>6.256373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409454</th>\n",
       "      <td>15</td>\n",
       "      <td>67.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>8.070772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35440</th>\n",
       "      <td>15</td>\n",
       "      <td>83.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>6.511050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431601</th>\n",
       "      <td>15</td>\n",
       "      <td>74.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>6.186994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327264</th>\n",
       "      <td>15</td>\n",
       "      <td>71.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.159311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162824</th>\n",
       "      <td>15</td>\n",
       "      <td>75.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>6.911511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43739</th>\n",
       "      <td>15</td>\n",
       "      <td>79.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>6.758716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201401</th>\n",
       "      <td>11</td>\n",
       "      <td>78.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.923807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122480</th>\n",
       "      <td>15</td>\n",
       "      <td>78.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.573893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23002</th>\n",
       "      <td>15</td>\n",
       "      <td>73.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.971225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242273</th>\n",
       "      <td>27</td>\n",
       "      <td>65.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>11.061221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308145</th>\n",
       "      <td>19</td>\n",
       "      <td>69.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>8.082001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215846</th>\n",
       "      <td>11</td>\n",
       "      <td>75.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.899762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145836</th>\n",
       "      <td>25</td>\n",
       "      <td>73.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.174635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86526</th>\n",
       "      <td>19</td>\n",
       "      <td>65.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.628112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229882</th>\n",
       "      <td>15</td>\n",
       "      <td>73.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.850814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101021</th>\n",
       "      <td>15</td>\n",
       "      <td>77.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>9.660880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435350</th>\n",
       "      <td>19</td>\n",
       "      <td>73.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>11.992588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258005</th>\n",
       "      <td>19</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>10.857564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139751</th>\n",
       "      <td>15</td>\n",
       "      <td>60.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.944772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205190</th>\n",
       "      <td>15</td>\n",
       "      <td>93.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.840895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247824</th>\n",
       "      <td>19</td>\n",
       "      <td>63.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>9.872159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149977</th>\n",
       "      <td>15</td>\n",
       "      <td>71.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>8.605511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351069</th>\n",
       "      <td>23</td>\n",
       "      <td>71.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>18.232788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161729</th>\n",
       "      <td>23</td>\n",
       "      <td>73.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.514915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352075</th>\n",
       "      <td>19</td>\n",
       "      <td>75.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.657037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332956</th>\n",
       "      <td>15</td>\n",
       "      <td>68.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.033458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90409 rows × 118 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        dock_count  max_temperature_f  mean_temperature_f  min_temperature_f  \\\n",
       "447998          19               74.0                69.0               63.0   \n",
       "252530          15               56.0                51.0               46.0   \n",
       "187605          15               79.0                70.0               61.0   \n",
       "222744          23               71.0                70.0               64.0   \n",
       "210121          15               66.0                62.0               59.0   \n",
       "334071          19               75.0                68.0               61.0   \n",
       "48910           15               77.0                67.0               56.0   \n",
       "297148          19               63.0                53.0               44.0   \n",
       "83812           19               72.0                60.0               48.0   \n",
       "163278          11               75.0                68.0               61.0   \n",
       "173970          23               61.0                53.0               44.0   \n",
       "222959          15               75.0                68.0               62.0   \n",
       "139195          15               66.0                58.0               50.0   \n",
       "72009           19               72.0                60.0               48.0   \n",
       "263245          15               61.0                54.0               46.0   \n",
       "350760          23               72.0                65.0               58.0   \n",
       "87324           11               68.0                62.0               55.0   \n",
       "329079          23               74.0                68.0               62.0   \n",
       "119744          15               75.0                67.0               59.0   \n",
       "446043          27               78.0                67.0               55.0   \n",
       "32396           11               71.0                62.0               52.0   \n",
       "219280          23               71.0                62.0               53.0   \n",
       "413599          19               76.0                65.0               53.0   \n",
       "382536          15               64.0                52.0               39.0   \n",
       "75371           11               63.0                57.0               51.0   \n",
       "10964           15               65.0                53.0               41.0   \n",
       "304329          19               68.0                62.0               56.0   \n",
       "356698          15               66.0                55.0               44.0   \n",
       "132199          15               59.0                50.0               42.0   \n",
       "134391          15               62.0                56.0               50.0   \n",
       "...            ...                ...                 ...                ...   \n",
       "404300          19               69.0                59.0               48.0   \n",
       "19860           19               60.0                52.0               44.0   \n",
       "181555          15               67.0                60.0               52.0   \n",
       "327128          15               73.0                66.0               59.0   \n",
       "409454          15               67.0                58.0               49.0   \n",
       "35440           15               83.0                69.0               54.0   \n",
       "431601          15               74.0                66.0               58.0   \n",
       "327264          15               71.0                65.0               59.0   \n",
       "162824          15               75.0                66.0               56.0   \n",
       "43739           15               79.0                71.0               62.0   \n",
       "201401          11               78.0                66.0               53.0   \n",
       "122480          15               78.0                68.0               59.0   \n",
       "23002           15               73.0                59.0               44.0   \n",
       "242273          27               65.0                57.0               48.0   \n",
       "308145          19               69.0                61.0               53.0   \n",
       "215846          11               75.0                62.0               48.0   \n",
       "145836          25               73.0                68.0               62.0   \n",
       "86526           19               65.0                59.0               52.0   \n",
       "229882          15               73.0                65.0               56.0   \n",
       "101021          15               77.0                70.0               62.0   \n",
       "435350          19               73.0                67.0               61.0   \n",
       "258005          19               60.0                50.0               40.0   \n",
       "139751          15               60.0                55.0               50.0   \n",
       "205190          15               93.0                76.0               59.0   \n",
       "247824          19               63.0                56.0               49.0   \n",
       "149977          15               71.0                60.0               50.0   \n",
       "351069          23               71.0                63.0               54.0   \n",
       "161729          23               73.0                64.0               54.0   \n",
       "352075          19               75.0                65.0               54.0   \n",
       "332956          15               68.0                64.0               60.0   \n",
       "\n",
       "        max_dew_point_f  mean_dew_point_f  min_dew_point_f  max_humidity  \\\n",
       "447998             57.0              56.0             54.0          78.0   \n",
       "252530             54.0              38.0             25.0          93.0   \n",
       "187605             59.0              57.0             55.0          84.0   \n",
       "222744             61.0              60.0             57.0          78.0   \n",
       "210121             50.0              49.0             46.0          72.0   \n",
       "334071             59.0              57.0             56.0          84.0   \n",
       "48910              57.0              54.0             51.0          80.0   \n",
       "297148             44.0              42.0             39.0          86.0   \n",
       "83812              51.0              43.0             37.0          80.0   \n",
       "163278             58.0              55.0             51.0          90.0   \n",
       "173970             48.0              44.0             43.0          93.0   \n",
       "222959             61.0              59.0             57.0          82.0   \n",
       "139195             50.0              44.0             41.0          82.0   \n",
       "72009              51.0              48.0             44.0          86.0   \n",
       "263245             52.0              45.0             39.0          86.0   \n",
       "350760             57.0              55.0             54.0          87.0   \n",
       "87324              52.0              48.0             46.0          80.0   \n",
       "329079             59.0              57.0             55.0          84.0   \n",
       "119744             57.0              55.0             46.0          88.0   \n",
       "446043             61.0              57.0             50.0          93.0   \n",
       "32396              50.0              48.0             46.0          83.0   \n",
       "219280             61.0              54.0             50.0          88.0   \n",
       "413599             54.0              51.0             48.0          93.0   \n",
       "382536             48.0              42.0             38.0          89.0   \n",
       "75371              50.0              46.0             41.0          89.0   \n",
       "10964              41.0              39.0             36.0          89.0   \n",
       "304329             51.0              49.0             48.0          77.0   \n",
       "356698             50.0              45.0             42.0          93.0   \n",
       "132199             50.0              45.0             41.0          93.0   \n",
       "134391             55.0              52.0             48.0         100.0   \n",
       "...                 ...               ...              ...           ...   \n",
       "404300             45.0              38.0             29.0          80.0   \n",
       "19860              45.0              40.0             30.0          86.0   \n",
       "181555             50.0              47.0             39.0          83.0   \n",
       "327128             57.0              56.0             55.0          84.0   \n",
       "409454             50.0              48.0             47.0          89.0   \n",
       "35440              50.0              48.0             42.0          83.0   \n",
       "431601             57.0              55.0             53.0          86.0   \n",
       "327264             57.0              56.0             55.0          87.0   \n",
       "162824             55.0              53.0             51.0          86.0   \n",
       "43739              60.0              57.0             56.0          78.0   \n",
       "201401             57.0              53.0             46.0          77.0   \n",
       "122480             59.0              56.0             54.0          82.0   \n",
       "23002              48.0              43.0             40.0          89.0   \n",
       "242273             50.0              45.0             42.0          83.0   \n",
       "308145             52.0              49.0             48.0          89.0   \n",
       "215846             57.0              53.0             45.0          93.0   \n",
       "145836             57.0              56.0             55.0          77.0   \n",
       "86526              50.0              47.0             45.0          83.0   \n",
       "229882             59.0              56.0             50.0          87.0   \n",
       "101021             59.0              57.0             57.0          84.0   \n",
       "435350             56.0              55.0             54.0          84.0   \n",
       "258005             43.0              32.0             21.0          71.0   \n",
       "139751             48.0              47.0             45.0          87.0   \n",
       "205190             66.0              62.0             54.0          82.0   \n",
       "247824             49.0              45.0             42.0          89.0   \n",
       "149977             53.0              48.0             42.0          78.0   \n",
       "351069             57.0              55.0             53.0          86.0   \n",
       "161729             52.0              50.0             47.0          86.0   \n",
       "352075             58.0              54.0             49.0          84.0   \n",
       "332956             57.0              55.0             55.0          84.0   \n",
       "\n",
       "        mean_humidity  min_humidity            ...              74  75  76  \\\n",
       "447998           66.0          53.0            ...               0   0   0   \n",
       "252530           69.0          45.0            ...               0   0   0   \n",
       "187605           67.0          50.0            ...               0   0   0   \n",
       "222744           70.0          64.0            ...               0   0   0   \n",
       "210121           59.0          52.0            ...               0   0   0   \n",
       "334071           71.0          57.0            ...               0   0   0   \n",
       "48910            63.0          46.0            ...               0   0   0   \n",
       "297148           63.0          44.0            ...               0   0   0   \n",
       "83812            55.0          29.0            ...               0   0   0   \n",
       "163278           68.0          46.0            ...               0   0   0   \n",
       "173970           74.0          55.0            ...               0   0   0   \n",
       "222959           68.0          57.0            ...               0   0   0   \n",
       "139195           71.0          45.0            ...               0   0   0   \n",
       "72009            65.0          44.0            ...               0   0   0   \n",
       "263245           70.0          53.0            ...               0   0   0   \n",
       "350760           70.0          53.0            ...               0   0   0   \n",
       "87324            64.0          48.0            ...               0   0   0   \n",
       "329079           71.0          57.0            ...               0   0   0   \n",
       "119744           75.0          36.0            ...               0   0   0   \n",
       "446043           67.0          41.0            ...               0   0   0   \n",
       "32396            65.0          47.0            ...               0   0   0   \n",
       "219280           66.0          56.0            ...               0   0   0   \n",
       "413599           71.0          49.0            ...               0   0   0   \n",
       "382536           66.0          43.0            ...               0   0   0   \n",
       "75371            72.0          55.0            ...               0   0   0   \n",
       "10964            62.0          34.0            ...               0   0   0   \n",
       "304329           65.0          52.0            ...               0   0   0   \n",
       "356698           71.0          48.0            ...               0   0   0   \n",
       "132199           86.0          63.0            ...               0   0   0   \n",
       "134391           90.0          77.0            ...               0   0   0   \n",
       "...               ...           ...            ...              ..  ..  ..   \n",
       "404300           52.0          24.0            ...               0   0   0   \n",
       "19860            60.0          33.0            ...               0   0   0   \n",
       "181555           63.0          42.0            ...               0   0   0   \n",
       "327128           71.0          57.0            ...               0   0   0   \n",
       "409454           69.0          48.0            ...               0   0   0   \n",
       "35440            55.0          26.0            ...               0   0   0   \n",
       "431601           70.0          53.0            ...               0   0   0   \n",
       "327264           72.0          57.0            ...               0   0   0   \n",
       "162824           70.0          53.0            ...               0   0   0   \n",
       "43739            63.0          48.0            ...               0   0   0   \n",
       "201401           59.0          42.0            ...               0   0   0   \n",
       "122480           74.0          47.0            ...               0   0   0   \n",
       "23002            61.0          33.0            ...               0   0   0   \n",
       "242273           66.0          48.0            ...               0   0   0   \n",
       "308145           71.0          53.0            ...               0   0   0   \n",
       "215846           68.0          47.0            ...               0   0   0   \n",
       "145836           71.0          53.0            ...               0   0   0   \n",
       "86526            68.0          52.0            ...               0   0   0   \n",
       "229882           70.0          53.0            ...               0   0   0   \n",
       "101021           67.0          50.0            ...               0   0   0   \n",
       "435350           69.0          53.0            ...               0   0   0   \n",
       "258005           50.0          29.0            ...               0   1   0   \n",
       "139751           74.0          59.0            ...               0   0   0   \n",
       "205190           56.0          34.0            ...               0   0   0   \n",
       "247824           69.0          48.0            ...               0   0   0   \n",
       "149977           67.0          48.0            ...               0   0   0   \n",
       "351069           72.0          57.0            ...               0   0   0   \n",
       "161729           65.0          44.0            ...               0   0   0   \n",
       "352075           64.0          43.0            ...               0   0   0   \n",
       "332956           76.0          68.0            ...               0   0   0   \n",
       "\n",
       "        77  80  82  83  84  bikes_available_actual  bikes_available_predicted  \n",
       "447998   0   0   0   0   0               13.333333                   7.467833  \n",
       "252530   0   0   0   0   0               12.000000                   7.235819  \n",
       "187605   0   0   0   0   0                7.000000                   7.368054  \n",
       "222744   0   0   0   0   0                9.000000                   9.932964  \n",
       "210121   0   0   0   0   0                5.666667                   6.164502  \n",
       "334071   0   0   0   0   0                6.000000                   7.720888  \n",
       "48910    0   0   0   0   0                7.000000                   7.703687  \n",
       "297148   0   0   0   0   0                5.666667                   6.949081  \n",
       "83812    0   0   0   0   0               11.333333                   9.202847  \n",
       "163278   0   0   0   0   0                2.000000                   3.984040  \n",
       "173970   0   0   0   0   0               10.333333                  11.781151  \n",
       "222959   0   0   0   0   0                7.000000                   7.506112  \n",
       "139195   0   0   0   0   0                5.000000                   4.012161  \n",
       "72009    0   0   0   0   0                7.000000                   7.411190  \n",
       "263245   0   0   0   0   0                8.666667                   7.391361  \n",
       "350760   0   0   0   0   0               20.333333                  18.855416  \n",
       "87324    0   0   0   0   0                3.000000                   5.163009  \n",
       "329079   0   0   0   0   0                7.000000                   7.997955  \n",
       "119744   0   0   0   0   0                6.666667                   6.939301  \n",
       "446043   1   0   0   0   0               10.000000                  11.169032  \n",
       "32396    0   0   0   0   0                8.000000                   6.081764  \n",
       "219280   0   0   0   0   0                4.000000                   7.687127  \n",
       "413599   0   0   0   0   0               13.000000                  10.313117  \n",
       "382536   0   0   0   0   0                8.333333                   7.579229  \n",
       "75371    0   0   0   0   0                8.333333                   6.687045  \n",
       "10964    0   0   0   0   0                8.666667                   8.440400  \n",
       "304329   0   0   0   0   0                6.000000                   7.108036  \n",
       "356698   0   0   0   0   0                6.333333                   5.515297  \n",
       "132199   0   0   0   0   0                4.666667                   4.454338  \n",
       "134391   0   0   0   0   0                6.000000                   6.042790  \n",
       "...     ..  ..  ..  ..  ..                     ...                        ...  \n",
       "404300   0   0   0   0   0                7.000000                   8.344893  \n",
       "19860    0   0   0   0   0                6.333333                   7.801566  \n",
       "181555   0   0   0   0   0                6.000000                   6.430534  \n",
       "327128   0   0   0   0   0                8.666667                   6.256373  \n",
       "409454   0   0   0   0   0                7.666667                   8.070772  \n",
       "35440    0   0   0   0   0                5.666667                   6.511050  \n",
       "431601   0   0   1   0   0                8.666667                   6.186994  \n",
       "327264   0   0   0   0   0                5.000000                   4.159311  \n",
       "162824   0   0   0   0   0                6.666667                   6.911511  \n",
       "43739    0   1   0   0   0                6.666667                   6.758716  \n",
       "201401   0   0   0   0   0                5.000000                   4.923807  \n",
       "122480   0   0   0   0   0                6.000000                   6.573893  \n",
       "23002    0   0   0   0   0                8.000000                   7.971225  \n",
       "242273   0   0   0   0   0               13.500000                  11.061221  \n",
       "308145   0   0   0   0   0               15.000000                   8.082001  \n",
       "215846   0   0   0   0   0                6.000000                   4.899762  \n",
       "145836   0   0   0   0   0                9.000000                   8.174635  \n",
       "86526    0   0   0   0   0                5.000000                   6.628112  \n",
       "229882   0   0   0   0   0                6.000000                   6.850814  \n",
       "101021   0   0   0   0   0               11.000000                   9.660880  \n",
       "435350   0   0   0   0   0               12.000000                  11.992588  \n",
       "258005   0   0   0   0   0                6.500000                  10.857564  \n",
       "139751   0   0   0   0   0                4.000000                   4.944772  \n",
       "205190   0   0   0   0   0                5.000000                   6.840895  \n",
       "247824   0   0   0   0   0                9.666667                   9.872159  \n",
       "149977   0   0   0   0   0                8.333333                   8.605511  \n",
       "351069   0   0   0   0   0               20.000000                  18.232788  \n",
       "161729   0   0   0   0   0               10.000000                  10.514915  \n",
       "352075   0   0   0   0   0                6.000000                   8.657037  \n",
       "332956   0   0   0   0   0                3.000000                   6.033458  \n",
       "\n",
       "[90409 rows x 118 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "output.to_csv(\"prediction output/Bike availability prediction output.csv\", encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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