Introduce New Features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bike_df = pd.read_csv('./Bikeshare.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Trip Duration</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Stop Time</th>\n",
       "      <th>Start Station ID</th>\n",
       "      <th>Start Station Name</th>\n",
       "      <th>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "      <th>End Station ID</th>\n",
       "      <th>End Station Name</th>\n",
       "      <th>End Station Latitude</th>\n",
       "      <th>End Station Longitude</th>\n",
       "      <th>Bike ID</th>\n",
       "      <th>User Type</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trip_Duration_in_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-10-01 00:16:26</td>\n",
       "      <td>2015-10-01 00:22:42</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>24470</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>739</td>\n",
       "      <td>2015-10-01 00:27:12</td>\n",
       "      <td>2015-10-01 00:39:32</td>\n",
       "      <td>3207</td>\n",
       "      <td>Oakland Ave</td>\n",
       "      <td>40.737604</td>\n",
       "      <td>-74.052478</td>\n",
       "      <td>3212</td>\n",
       "      <td>Christ Hospital</td>\n",
       "      <td>40.734786</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>24481</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2714</td>\n",
       "      <td>2015-10-01 00:32:46</td>\n",
       "      <td>2015-10-01 01:18:01</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>3193</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>40.724605</td>\n",
       "      <td>-74.078406</td>\n",
       "      <td>24628</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>275</td>\n",
       "      <td>2015-10-01 00:34:31</td>\n",
       "      <td>2015-10-01 00:39:06</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3187</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>-74.038051</td>\n",
       "      <td>24613</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>561</td>\n",
       "      <td>2015-10-01 00:40:12</td>\n",
       "      <td>2015-10-01 00:49:33</td>\n",
       "      <td>3183</td>\n",
       "      <td>Exchange Place</td>\n",
       "      <td>40.716247</td>\n",
       "      <td>-74.033459</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>24668</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Trip Duration           Start Time            Stop Time  \\\n",
       "0           0            376  2015-10-01 00:16:26  2015-10-01 00:22:42   \n",
       "1           1            739  2015-10-01 00:27:12  2015-10-01 00:39:32   \n",
       "2           2           2714  2015-10-01 00:32:46  2015-10-01 01:18:01   \n",
       "3           3            275  2015-10-01 00:34:31  2015-10-01 00:39:06   \n",
       "4           4            561  2015-10-01 00:40:12  2015-10-01 00:49:33   \n",
       "\n",
       "   Start Station ID Start Station Name  Start Station Latitude  \\\n",
       "0              3212    Christ Hospital               40.734786   \n",
       "1              3207        Oakland Ave               40.737604   \n",
       "2              3193       Lincoln Park               40.724605   \n",
       "3              3199       Newport Pkwy               40.728745   \n",
       "4              3183     Exchange Place               40.716247   \n",
       "\n",
       "   Start Station Longitude  End Station ID    End Station Name  \\\n",
       "0               -74.050444            3207         Oakland Ave   \n",
       "1               -74.052478            3212     Christ Hospital   \n",
       "2               -74.078406            3193        Lincoln Park   \n",
       "3               -74.032108            3187           Warren St   \n",
       "4               -74.033459            3192  Liberty Light Rail   \n",
       "\n",
       "   End Station Latitude  End Station Longitude  Bike ID   User Type  \\\n",
       "0             40.737604             -74.052478    24470  Subscriber   \n",
       "1             40.734786             -74.050444    24481  Subscriber   \n",
       "2             40.724605             -74.078406    24628  Subscriber   \n",
       "3             40.721124             -74.038051    24613  Subscriber   \n",
       "4             40.711242             -74.055701    24668    Customer   \n",
       "\n",
       "   Birth Year  Gender  Trip_Duration_in_min  \n",
       "0      1960.0       1                     6  \n",
       "1      1960.0       1                    12  \n",
       "2      1983.0       1                    45  \n",
       "3      1975.0       1                     5  \n",
       "4      1984.0       0                     9  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_lon=bike_df[['Start Station Latitude','Start Station Longitude']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_lon=lat_lon.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Start Station Latitude</th>\n",
       "      <th>Start Station Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.722391</td>\n",
       "      <td>-74.056281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.013041</td>\n",
       "      <td>0.017047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>40.692640</td>\n",
       "      <td>-74.096937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>40.714252</td>\n",
       "      <td>-74.067272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.721525</td>\n",
       "      <td>-74.050444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.728170</td>\n",
       "      <td>-74.043918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.752559</td>\n",
       "      <td>-74.032108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Start Station Latitude  Start Station Longitude\n",
       "count               51.000000                51.000000\n",
       "mean                40.722391               -74.056281\n",
       "std                  0.013041                 0.017047\n",
       "min                 40.692640               -74.096937\n",
       "25%                 40.714252               -74.067272\n",
       "50%                 40.721525               -74.050444\n",
       "75%                 40.728170               -74.043918\n",
       "max                 40.752559               -74.032108"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_lon.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 57,
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H3+Ta2ENNz2d1+tg+3ZkvaWa5toZJG9vjn2Pg1fT/frmhSs0zHQnAFkBRDgAA8A7msgWdv35PXx7ZqzP3s6oHW/MIbwAAAAAAtjJL0khPRgOtMZ2/MbZma2IvFcr6/uwVnTo8rIEd7fptenlTj6q2LelAX4u6064u3hzTQp6p5wFsHNt0AAAAgGazkC/qzLW7Oj3UqYTLr1MAAAAAAGwl3Zm4vt3brUohqz+fvbpmJfljQRjpzLVRjU1M6ZvhLrWnYmv6/I1iR0dS3450a2F+Xt+fvUpJDmDDMaIcAADgPSwtl/TzlTv66vh+nb2f3bJrhwEAAAAAsFUkXFvHB9tUr1b1w/lrqnn+ur7e1HxOC/lr+vr4fmVrCd2eK67r622UjlRMRwdaNbuQ1Z/PjCoI2acCwAyKcgAAgPeULZT14+Xb+ubkAZ17kFPlA9YhAwAAAAAAjenJNOttMV24Ma6F/MYV1tW6rx8u3tDB3f36enibLk3mm3b/Q9K1dXSgVZHv6R+/3FS5VjcdCcAWR1EOAADwAfKliv5x+Za+PXlQ5ydyKteb88sqAAAAAAB4UXcmrqP9rRqdmtWfr88ay3HrwaxmFnL66vh+jS1VNJWrGsvyrmxL2t/bot6Mqws3N/ZAAwB4HRbVBAAA+EDLpar+/stNndrVoUyc4xABAAAAAGh2CdfWp7s6tLPF1g8XrunWhLmS/LF8qarvz15Vq+3rs10dcm3LdKQ32t6+sg750uKC/nz2KiU5gIbCnlwAAIA1UKjU9D+XbupfPjmoi5PLKtbWd50yAAAAAACw9h5Psz7YFtf5G2MNV+yGUaQLN8fV39Wmrw8N6+pMQYulxpvC/PE65I8WWYccQONiRDkAAMAaKVVr+tvFm/pkR5vakhyPCAAAAABAM+lKx/Xt3m5Vizn9+cyVhivJnza7tKy/nLuq4faYjvS3qlHGlidcW5/sbNfezph+/PWmfr0zQUkOoGGxBxcAAGANlWt1/e3iDf3x08P69eGylquMLAcAAAAAoNFYltSRiqsrHVN3OqaEaylfLOuH89dU85rju3zdD/Q/v97S3u19+mZkUJem8ioZmuHOtqS9vRlta4np0s37mssVjOQAgHdBUQ4AALDGKnVPP1y4rn/77Kj+PrpoOg4AAAAAAFteOu6oKx1XdzqmtqSjMAy1tFzUo/msbucKqtY90xHf272Hc5pdyuubE/s1ma/r/lJ5Q19/sD2p/b0Z3bw/rV+uzm3oawPAh6AoBwAAWAc1z1cQBKZjAAAAAACw5bi2pa7MymjxrlRMjiUVK1U9Wsrr+sNl5YoV0xHXXLFS0/fnrurkvl36YnenLk3l5QXrO+V5eyqmY/2tms/m9P3ZUfnr/HoAsNYoygEAANZJzfMUc+x1/2IKAAAAAACkpGvr010d8j1P87llTU3N6XK+uGUK3CiSfr0zaHdNPAAAIABJREFUod6OJX11ZETXZ0uaL9be+/lsS4o59urJkuvYitm2XMdSTyYmJ/T14+WbKlXra/guAGDjUJQDAACsk2KlpkzcUa6yNb6QAwAAAABgSsK19cWeTv302+1NOWL8Xcznivr+7FWdPrpXA21tmivWFXNsufbKaPuYYylmPy6/Lbm2LSmSIkmKFEUrzxOEoTzPV90P5Pm+6l5d9aqvqu/r+sOKFpdLBt8lAHw4inIAAIB1UipXlU60Kldp3nXOAAAAAABodAnX1mlK8mf4Qagff7ujHX0dasuk5JUDlX1fnh+sFt+B6qvXt8qIewB4HkU5AADAOilWqupsaTcdAwAAAACATetxSf7P3+5Qkr/E1FxOUs50DABoSLbpAAAAAJtVsVJXOsavWwAAAAAArIcnJfmVO8oWy6bjAACaDHtuAQAA1kmpWlMq5piOAQAAAADAphN/uiQvUJIDAN4dRTkAAMA6qXm+4i6/bgEAAAAAsJbizkpJ/jMlOQDgA7DnFgAAYJ3s29Gnmh+ajgEAAAAAwKYRd2ydHurUmSt3tURJDgD4AK7pAAAAAJuNJenU4WG5yZQuTuRMxwEAAAAAYFN4tiQvmY4DAGhyFOUAAABrKO46+u6jg5ou+nrwcNl0HAAAAAAANoUnJflVSnIAwNqgKAcAAFgj7ZmUvj6xX1dnCloqe6bjAAAAAACwKcRWS/KzV+9qaZmSHACwNijKAQAA1sCOvk4d37tb5yZyqnqB6TgAAAAAAGwKMcfWl6sl+SIlOQBgDVGUAwAAfKDjIzvV3dWhn8YWFUam0wAAAAAAsDnEHEtfDnXq3DVKcgDA2qMoBwAAeE+ObeubE/uU9yxdmMiZjgMAAAAAwKYRcyx9uadL567d00KekhwAsPYoygEAAN5DOhnXHz46qNvzZT0q1EzHAQAAAABg04g5lk7v6dK56/e0kC+ajgMA2KQoygEAAN5RX0erTh0Z0cXJvIo133QcAAAAAAA2jccl+XlKcgDAOqMoBwAAeAf7d27Tnu39+mlsST4LkgMAAAAAsGZce6Ukv3CDkhwAsP4oygEAAN6CZUlfHBmR3ITOjC+JihwAAAAAgLXj2pa+HFopyedzlOQAgPVHUQ4AALYcx7blOrZcx1k9f/6yLdd1Vrc5cl1HfR2tup+taXK+YDo+AAAAAACbyuOS/OKNUUpyAMCGoSgHAABNZWigW+lk4qki21bMceQ8VXZLlizr8SOs3x9srfwnCEP5oRSEkfwwVLB6OYgi+WGkMJL8MFItjFQKIwW1UA8ml1X1ww1/vwAAAAAAbGauben0UKcu3hjVXI6D0wEAG4eiHAAANJXje3fr+mxhpcD2HxfdnvwwWim7WTccAAAAAICm8GQk+U1KcgDAxqMoBwAATaXmB5pdrpqOAQAAAAAAPsDKSPIuXbo5qrksJTkAYOPZpgMAAAC8LcuyFEWMGAcAAAAAoJk9Lsl/uTmmR5TkAABDKMoBAEDTcB2bqdUBAAAAAGhijm3p9J5O/XprTI+yy6bjAAC2MIpyAADQNFzbVsCIcgAAAAAAmpJjW/pyT6d+vT2u2SVKcgCAWRTlAACgaTiOrSA0nQIAAAAAALwrx7b05RAlOQCgcbimAwAAALwtx2bqdQAAAAAAmollSXu60trdmdTFm5TkAIDGQVEOAACahusw9ToAAAAAAM3AtqQ9XRnt6kzq3uSs/uvmHYV8pwcANBCKcgAA0DQYUQ4AAAAAQGOzLWmoO6OdHQndnZjV/7k5p4iCHADQgCjKAQBA03BZoxwAAAAAgIZkW9Jwd0Y7OhK6MzGr/3ODghwA0NgoygEAQNNwmHodAAAAAICGYlvSSE9Gg+0J3Xkws1qQm04FAMCbUZQDAICmwRrlAAAAAAA0BseyNNyT1mBbQrcnZvRf1ynIAQDNhaIcAAA0Dce2xRLlAAAAAACY83RBfuvBtC5fmxdf1QEAzYiiHAAANA3HcRTQlAMAAAAAsOEc29JId0YDbXHdvE9BDgBofhTlAACgacQcW1WKcgAAAAAANoxjW9rbk1Z/a0I37z/UL9cWTEcCAGBNUJQDAICm4TiOfIpyAAAAAADWnWtb2tuT0bbWuG6MT+nS1UXTkQAAWFMU5QAAoGm4jq2wTlEOAAAAAMB6cW1Le3sz2paJ6/r9KV2kIAcAbFIU5QAAoGkwohwAAAAAgPXh2pb29WbUm4np+viULs4umY4EAMC6oigHAABNw3VsBVFgOgYAAAAAAJtGzLG0r7dFPWlX18amdOERBTkAYGugKAcAAE3DsW2FoW86BgAAAAAATS/mWNrf26LutKurY5M6/yhrOhIAABuKohwAADQN17GZeh0AAAAAgA8Qc2zt78uoO+Xqyr1JnZunIAcAbE0U5QAAoGk4jqMgDE3HAAAAAACg6cRXC/LOpKOro5M6N58zHQkAAKMoygEAQNNwbEsMKAcAAAAA4O3FHVsH+jLqSDq6cm9SZxcoyAEAkCjKAQBAU7FMBwAAAAAAoCnYlnSkv1XtSUdX7k7ozGLedCQAABoKRTkAAAAAAAAAAJtMd0tCfrWs7y/fNx0FAICGZJsOAAAA8LYsBpQDAAAAAPBWOpMxPVzImo4BAEDDoigHAAAAAAAAAGCT6Uy7WsyXTMcAAKBhUZQDAIAmwpByAAAAAADeRtyxVPN80zEAAGhYFOUAAAAAAAAAAGwirm3J8wPTMQAAaGgU5QAAoHkwoBwAAAAAgDfqSMe1mC+YjgEAQEOjKAcAAE2EphwAAAAAgDfpTLmazy6bjgEAQEOjKAcAAE2BihwAAAAAgLfTkYppMV8yHQMAgIZGUQ4AAJqC49gKwtB0DAAAAAAAGl7StVSpe6ZjAADQ0CjKAQBAU3BsWwE9OQAAAAAAr+VYlvwgMB0DAICGR1EOAACaguvYCsLIdAwAAAAAABpaezqmpXzRdAwAABoeRTkAAGgKjmMriCjKAQAAAAB4nc5UTPPZgukYAAA0PIpyAADQFFybEeUAAAAAALxJZ8rVwjIjygEAeBOKcgAA0BQcpl4HAAAAAOCNUjFH5WrddAwAABoeRTkAAGgKjs3U6wAAAAAAvI5tSUEQmI4BAEBToCgHAABNwXVsBfTkAAAAAAC8UnsqrmyBadcBAHgbFOUAAKApuI7D1OsAAAAAALxGZ8rVfLZgOgYAAE2BohwAADQFx7YYUQ4AAAAAwGt0pGJayDOiHACAt0FRDgAAmoLjOAoZUQ4AAAAAwCtl4o6KlZrpGAAANAWKcgAA0BRc12bqdQAAAAAAXsGypDAMTMcAAKBpUJQDAICm4NqOgoiiHAAAAACAl2lLxpQtlE3HAACgaVCUAwCApuA6jCgHAAAAAOBVOtMxLWSXTccAAKBpUJQDAICm4DgORTkAAAAAAK/QmXK1kC+ajgEAQNOgKAcAAE2BEeUAAAAAALxaS9zRcrlqOgYAAE2DohwAADQFx7ZZoxwAAAAAgJewJEUcXA4AwDuhKAcAAE3BcWz5fOkHAAAAAOAFrUlXuVLZdAwAAJoKRTkAAGgKLmuUAwAAAAD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\n",
      "text/plain": [
       "<Figure size 5000x5000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "llon = -74.1\n",
    "llat = 40.6\n",
    "rlon = -74.0\n",
    "rlat = 40.8\n",
    "\n",
    "plt.figure(figsize=(10, 10), dpi=500)\n",
    "\n",
    "m=Basemap(llcrnrlon=llon,llcrnrlat=llat,urcrnrlon=rlon,urcrnrlat=rlat,resolution='f')\n",
    "m.drawmapboundary(fill_color='#A6CAE0', linewidth=0)\n",
    "m.fillcontinents(color='grey', alpha=0.3)\n",
    "m.drawcoastlines(linewidth=0.1, color=\"white\")\n",
    "\n",
    "m.scatter(lat_lon['Start Station Longitude'],lat_lon['Start Station Latitude'],s=4,alpha=1,color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduce Weather Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather = pd.read_csv('./weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather['date'] = pd.to_datetime(weather['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>42</td>\n",
       "      <td>34</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-01</td>\n",
       "      <td>29</td>\n",
       "      <td>11</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  maximum temperature  minimum temperature  average temperature  \\\n",
       "0 2016-01-01                   42                   34                 38.0   \n",
       "1 2016-02-01                   40                   32                 36.0   \n",
       "2 2016-03-01                   45                   35                 40.0   \n",
       "3 2016-04-01                   36                   14                 25.0   \n",
       "4 2016-05-01                   29                   11                 20.0   \n",
       "\n",
       "  precipitation snow fall snow depth  \n",
       "0          0.00       0.0          0  \n",
       "1          0.00       0.0          0  \n",
       "2          0.00       0.0          0  \n",
       "3          0.00       0.0          0  \n",
       "4          0.00       0.0          0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather.rename(columns={'date':'day'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2377394</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-06-12 00:43:35</td>\n",
       "      <td>2016-06-12 00:54:38</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.980415</td>\n",
       "      <td>40.738564</td>\n",
       "      <td>-73.999481</td>\n",
       "      <td>40.731152</td>\n",
       "      <td>N</td>\n",
       "      <td>663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id3858529</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-19 11:35:24</td>\n",
       "      <td>2016-01-19 12:10:48</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.979027</td>\n",
       "      <td>40.763939</td>\n",
       "      <td>-74.005333</td>\n",
       "      <td>40.710087</td>\n",
       "      <td>N</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3504673</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-06 19:32:31</td>\n",
       "      <td>2016-04-06 19:39:40</td>\n",
       "      <td>1</td>\n",
       "      <td>-74.010040</td>\n",
       "      <td>40.719971</td>\n",
       "      <td>-74.012268</td>\n",
       "      <td>40.706718</td>\n",
       "      <td>N</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id2181028</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-26 13:30:55</td>\n",
       "      <td>2016-03-26 13:38:10</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.973053</td>\n",
       "      <td>40.793209</td>\n",
       "      <td>-73.972923</td>\n",
       "      <td>40.782520</td>\n",
       "      <td>N</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
       "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
       "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
       "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
       "3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
       "4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.980415        40.738564         -73.999481   \n",
       "2                1        -73.979027        40.763939         -74.005333   \n",
       "3                1        -74.010040        40.719971         -74.012268   \n",
       "4                1        -73.973053        40.793209         -73.972923   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
       "0         40.765602                  N            455  \n",
       "1         40.731152                  N            663  \n",
       "2         40.710087                  N           2124  \n",
       "3         40.706718                  N            429  \n",
       "4         40.782520                  N            435  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_train = pd.read_csv(\"train.csv\")\n",
    "raw_data_test = pd.read_csv(\"train.csv\")\n",
    "raw_data=pd.concat([raw_data_train,raw_data_test])\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data['pickup_datetime'] = pd.to_datetime(raw_data['pickup_datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_day_and_hour():\n",
    "    day = []\n",
    "    hour = []\n",
    "    for i in range(len(raw_data)):\n",
    "        day.append(raw_data['pickup_datetime'].iloc[i].date())\n",
    "        hour.append(raw_data['pickup_datetime'].iloc[i].hour)\n",
    "    day_series = pd.Series(day)\n",
    "    hour_series = pd.Series(hour)\n",
    "    raw_data['day'] = day_series\n",
    "    raw_data['hour'] = hour_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_day_and_hour()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data['day'] = pd.to_datetime(raw_data['day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_data = pd.merge(raw_data,weather,on='day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>trip_duration</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>maximum temperature</th>\n",
       "      <th>minimum temperature</th>\n",
       "      <th>average temperature</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>snow fall</th>\n",
       "      <th>snow depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id2875421</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 17:24:55</td>\n",
       "      <td>2016-03-14 17:32:30</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.982155</td>\n",
       "      <td>40.767937</td>\n",
       "      <td>-73.964630</td>\n",
       "      <td>40.765602</td>\n",
       "      <td>N</td>\n",
       "      <td>455</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>17</td>\n",
       "      <td>51</td>\n",
       "      <td>40</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id2129090</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 14:05:39</td>\n",
       "      <td>2016-03-14 14:28:05</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.975090</td>\n",
       "      <td>40.758766</td>\n",
       "      <td>-73.953201</td>\n",
       "      <td>40.765068</td>\n",
       "      <td>N</td>\n",
       "      <td>1346</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>14</td>\n",
       "      <td>51</td>\n",
       "      <td>40</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id0256505</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-03-14 15:04:38</td>\n",
       "      <td>2016-03-14 15:16:13</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.994484</td>\n",
       "      <td>40.745087</td>\n",
       "      <td>-73.998993</td>\n",
       "      <td>40.722710</td>\n",
       "      <td>N</td>\n",
       "      <td>695</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>15</td>\n",
       "      <td>51</td>\n",
       "      <td>40</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id3863815</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 04:24:36</td>\n",
       "      <td>2016-03-14 04:37:11</td>\n",
       "      <td>3</td>\n",
       "      <td>-73.944359</td>\n",
       "      <td>40.714489</td>\n",
       "      <td>-73.910530</td>\n",
       "      <td>40.709492</td>\n",
       "      <td>N</td>\n",
       "      <td>755</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>4</td>\n",
       "      <td>51</td>\n",
       "      <td>40</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id3817493</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-03-14 14:57:56</td>\n",
       "      <td>2016-03-14 15:15:26</td>\n",
       "      <td>1</td>\n",
       "      <td>-73.952881</td>\n",
       "      <td>40.766468</td>\n",
       "      <td>-73.978630</td>\n",
       "      <td>40.761921</td>\n",
       "      <td>N</td>\n",
       "      <td>1050</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>14</td>\n",
       "      <td>51</td>\n",
       "      <td>40</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  vendor_id     pickup_datetime     dropoff_datetime  \\\n",
       "0  id2875421          2 2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
       "1  id2129090          1 2016-03-14 14:05:39  2016-03-14 14:28:05   \n",
       "2  id0256505          1 2016-03-14 15:04:38  2016-03-14 15:16:13   \n",
       "3  id3863815          2 2016-03-14 04:24:36  2016-03-14 04:37:11   \n",
       "4  id3817493          2 2016-03-14 14:57:56  2016-03-14 15:15:26   \n",
       "\n",
       "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "0                1        -73.982155        40.767937         -73.964630   \n",
       "1                1        -73.975090        40.758766         -73.953201   \n",
       "2                1        -73.994484        40.745087         -73.998993   \n",
       "3                3        -73.944359        40.714489         -73.910530   \n",
       "4                1        -73.952881        40.766468         -73.978630   \n",
       "\n",
       "   dropoff_latitude store_and_fwd_flag  trip_duration        day  hour  \\\n",
       "0         40.765602                  N            455 2016-03-14    17   \n",
       "1         40.765068                  N           1346 2016-03-14    14   \n",
       "2         40.722710                  N            695 2016-03-14    15   \n",
       "3         40.709492                  N            755 2016-03-14     4   \n",
       "4         40.761921                  N           1050 2016-03-14    14   \n",
       "\n",
       "   maximum temperature  minimum temperature  average temperature  \\\n",
       "0                   51                   40                 45.5   \n",
       "1                   51                   40                 45.5   \n",
       "2                   51                   40                 45.5   \n",
       "3                   51                   40                 45.5   \n",
       "4                   51                   40                 45.5   \n",
       "\n",
       "  precipitation snow fall snow depth  \n",
       "0          0.29       0.0          0  \n",
       "1          0.29       0.0          0  \n",
       "2          0.29       0.0          0  \n",
       "3          0.29       0.0          0  \n",
       "4          0.29       0.0          0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_data.to_csv(\"./data_with_weather.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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