code/team 2 - you are what you eat/cows.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import numpy as np\n",
    "import datetime\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "# Plotlib magic\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"\"\"Add sentiment scores\"\"\"\n",
    "#Load the sentiment emoji data \n",
    "data_str1='Melkopname_ForsterCANBUS.abruf 3.tsv'\n",
    "data_str2='Melkopname_ForsterCANBUS.soll 3.tsv'\n",
    "#data_str3='DeHarne_Calendar20171115083854.csv'\n",
    "water=pd.read_csv(data_str1,sep =\"\\t\",header=-1 )\n",
    "weight=pd.read_csv(data_str2,sep =\"\\t\",header=-1 )\n",
    "water.columns=['Time','tick']\n",
    "weight.columns=['Time','weight']\n",
    "#feed=pd.read_csv(data_str2, sep =\";\", decimal=\",\")\n",
    "#calendar=pd.read_csv(data_str3, sep =\";\", decimal=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>tick</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-17T09:23:08.000</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-17T10:24:16.000</td>\n",
       "      <td>4.2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-17T12:26:19.000</td>\n",
       "      <td>4.2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-17T13:27:29.000</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-19T22:59:00.000</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-01-20T13:32:45.000</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-01-20T13:42:57.000</td>\n",
       "      <td>5.6</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-01-20T19:39:14.000</td>\n",
       "      <td>5.8</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-20T19:49:24.000</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-20T21:41:11.000</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-20T22:11:44.000</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-01-21T00:44:05.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-21T02:45:54.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-21T03:36:49.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-21T03:57:08.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-01-21T05:18:38.000</td>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-01-21T10:03:42.000</td>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-21T11:45:18.000</td>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-01-21T11:55:29.000</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-01-21T12:15:48.000</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-01-21T13:06:36.000</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-21T13:47:12.000</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-01-21T13:57:23.000</td>\n",
       "      <td>5.6</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-01-21T19:53:26.000</td>\n",
       "      <td>7.0</td>\n",
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       "      <th>24</th>\n",
       "      <td>2014-01-21T21:24:55.000</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-01-21T21:45:14.000</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-01-21T22:59:00.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-01-21T23:47:14.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-01-22T02:09:32.000</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>29</th>\n",
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       "      <th>316</th>\n",
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       "      <th>317</th>\n",
       "      <td>2014-02-05T13:20:40.000</td>\n",
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       "      <th>318</th>\n",
       "      <td>2014-02-05T13:30:51.000</td>\n",
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       "      <th>319</th>\n",
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       "      <td>2014-02-05T16:13:44.000</td>\n",
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       "      <th>323</th>\n",
       "      <td>2014-02-05T18:56:41.000</td>\n",
       "      <td>5.6</td>\n",
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       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2014-02-05T20:07:49.000</td>\n",
       "      <td>5.6</td>\n",
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       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>2014-02-05T20:38:21.000</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>2014-02-05T22:59:00.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>2014-02-06T00:01:42.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>2014-02-06T00:11:53.000</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>329</th>\n",
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       "      <th>330</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>2014-02-06T02:24:00.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>2014-02-06T02:34:11.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>2014-02-06T04:15:50.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>2014-02-06T05:06:44.000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2014-02-06T06:58:35.000</td>\n",
       "      <td>2.4</td>\n",
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       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2014-02-06T07:08:46.000</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2014-02-06T07:18:57.000</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2014-02-06T11:23:37.000</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2014-02-06T13:15:37.000</td>\n",
       "      <td>2.6</td>\n",
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       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2014-02-06T14:06:31.000</td>\n",
       "      <td>2.6</td>\n",
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       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>2014-02-06T14:26:54.000</td>\n",
       "      <td>2.6</td>\n",
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       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2014-02-06T14:37:04.000</td>\n",
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       "      <th>343</th>\n",
       "      <td>2014-02-06T15:17:51.000</td>\n",
       "      <td>5.2</td>\n",
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       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>2014-02-06T22:59:00.000</td>\n",
       "      <td>0.0</td>\n",
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       "                        Time  tick\n",
       "0    2014-01-17T09:23:08.000   4.2\n",
       "1    2014-01-17T10:24:16.000   4.2\n",
       "2    2014-01-17T12:26:19.000   4.2\n",
       "3    2014-01-17T13:27:29.000   4.2\n",
       "4    2014-01-19T22:59:00.000   7.0\n",
       "5    2014-01-20T13:32:45.000   2.8\n",
       "6    2014-01-20T13:42:57.000   5.6\n",
       "7    2014-01-20T19:39:14.000   5.8\n",
       "8    2014-01-20T19:49:24.000   7.0\n",
       "9    2014-01-20T21:41:11.000   7.0\n",
       "10   2014-01-20T22:11:44.000   7.0\n",
       "11   2014-01-21T00:44:05.000   0.0\n",
       "12   2014-01-21T02:45:54.000   0.0\n",
       "13   2014-01-21T03:36:49.000   0.0\n",
       "14   2014-01-21T03:57:08.000   0.0\n",
       "15   2014-01-21T05:18:38.000   2.8\n",
       "16   2014-01-21T10:03:42.000   2.8\n",
       "17   2014-01-21T11:45:18.000   2.8\n",
       "18   2014-01-21T11:55:29.000   2.8\n",
       "19   2014-01-21T12:15:48.000   2.8\n",
       "20   2014-01-21T13:06:36.000   2.8\n",
       "21   2014-01-21T13:47:12.000   4.0\n",
       "22   2014-01-21T13:57:23.000   5.6\n",
       "23   2014-01-21T19:53:26.000   7.0\n",
       "24   2014-01-21T21:24:55.000   7.0\n",
       "25   2014-01-21T21:45:14.000   7.0\n",
       "26   2014-01-21T22:59:00.000   0.0\n",
       "27   2014-01-21T23:47:14.000   0.0\n",
       "28   2014-01-22T02:09:32.000   0.0\n",
       "29   2014-01-22T04:11:28.000   0.0\n",
       "..                       ...   ...\n",
       "315  2014-02-05T11:38:57.000   2.6\n",
       "316  2014-02-05T13:00:18.000   2.6\n",
       "317  2014-02-05T13:20:40.000   2.6\n",
       "318  2014-02-05T13:30:51.000   2.6\n",
       "319  2014-02-05T14:31:58.000   2.6\n",
       "320  2014-02-05T14:52:20.000   2.6\n",
       "321  2014-02-05T16:03:32.000   5.4\n",
       "322  2014-02-05T16:13:44.000   5.6\n",
       "323  2014-02-05T18:56:41.000   5.6\n",
       "324  2014-02-05T20:07:49.000   5.6\n",
       "325  2014-02-05T20:38:21.000   5.6\n",
       "326  2014-02-05T22:59:00.000   0.0\n",
       "327  2014-02-06T00:01:42.000   0.0\n",
       "328  2014-02-06T00:11:53.000   0.0\n",
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       "331  2014-02-06T02:24:00.000   0.0\n",
       "332  2014-02-06T02:34:11.000   0.0\n",
       "333  2014-02-06T04:15:50.000   0.0\n",
       "334  2014-02-06T05:06:44.000   0.0\n",
       "335  2014-02-06T06:58:35.000   2.4\n",
       "336  2014-02-06T07:08:46.000   2.6\n",
       "337  2014-02-06T07:18:57.000   2.6\n",
       "338  2014-02-06T11:23:37.000   2.6\n",
       "339  2014-02-06T13:15:37.000   2.6\n",
       "340  2014-02-06T14:06:31.000   2.6\n",
       "341  2014-02-06T14:26:54.000   2.6\n",
       "342  2014-02-06T14:37:04.000   2.6\n",
       "343  2014-02-06T15:17:51.000   5.2\n",
       "344  2014-02-06T22:59:00.000   0.0\n",
       "\n",
       "[345 rows x 2 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "water.ts = pd.to_datetime(water.Time, unit='ms')\n",
    "water.rename(columns={'Time':'ts'}, inplace=True)\n",
    "f= lambda y: y.date()\n",
    "\n",
    "water['date']= water.ts.map(f)\n",
    "\n",
    "weight.ts = pd.to_datetime(weight.Time, unit='ms')\n",
    "weight.rename(columns={'Time':'ts'}, inplace=True)\n",
    "\n",
    "\n",
    "weight['date']= weight.ts.map(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <th>5</th>\n",
       "      <td>2014-01-20T13:32:45.000</td>\n",
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       "      <td>2014-01-20</td>\n",
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       "      <th>6</th>\n",
       "      <td>2014-01-20T13:42:57.000</td>\n",
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       "      <td>2014-01-20</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-01-20T19:39:14.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-20T19:49:24.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-20T21:41:11.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-20T22:11:44.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-01-21T00:44:05.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-21T02:45:54.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-21T03:36:49.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-21T03:57:08.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-01-21T05:18:38.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-01-21T10:03:42.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-21T11:45:18.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-01-21T11:55:29.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-01-21T12:15:48.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-01-21T13:06:36.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-21T13:47:12.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-01-21T13:57:23.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-01-21T19:53:26.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-01-21T21:24:55.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-01-21T21:45:14.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-01-21T22:59:00.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-01-21T23:47:14.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-01-22T02:09:32.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-01-22T04:11:28.000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>2014-02-05T11:38:57.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>2014-02-05T13:00:18.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>2014-02-05T13:20:40.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>2014-02-05T13:30:51.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>2014-02-05T14:31:58.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>2014-02-05T14:52:20.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>2014-02-05T16:03:32.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>2014-02-05T16:13:44.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>2014-02-05T18:56:41.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2014-02-05T20:07:49.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>2014-02-05T20:38:21.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>2014-02-05T22:59:00.000</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2014-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>2014-02-06T00:01:42.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>2014-02-06T00:11:53.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>2014-02-06T00:32:15.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>2014-02-06T02:03:40.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>2014-02-06T02:24:00.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>2014-02-06T02:34:11.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>2014-02-06T04:15:50.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>2014-02-06T05:06:44.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>2014-02-06T06:58:35.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2014-02-06T07:08:46.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>2014-02-06T07:18:57.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2014-02-06T11:23:37.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2014-02-06T13:15:37.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>2014-02-06T14:06:31.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>2014-02-06T14:26:54.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>2014-02-06T14:37:04.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>2014-02-06T15:17:51.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>2014-02-06T22:59:00.000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2014-02-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>345 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          ts  weight        date\n",
       "0    2014-01-17T09:23:08.000     7.0  2014-01-17\n",
       "1    2014-01-17T10:24:16.000     7.0  2014-01-17\n",
       "2    2014-01-17T12:26:19.000     7.0  2014-01-17\n",
       "3    2014-01-17T13:27:29.000     7.0  2014-01-17\n",
       "4    2014-01-19T22:59:00.000     7.0  2014-01-19\n",
       "5    2014-01-20T13:32:45.000     7.0  2014-01-20\n",
       "6    2014-01-20T13:42:57.000     7.0  2014-01-20\n",
       "7    2014-01-20T19:39:14.000     7.0  2014-01-20\n",
       "8    2014-01-20T19:49:24.000     7.0  2014-01-20\n",
       "9    2014-01-20T21:41:11.000     7.0  2014-01-20\n",
       "10   2014-01-20T22:11:44.000     7.0  2014-01-20\n",
       "11   2014-01-21T00:44:05.000     7.0  2014-01-21\n",
       "12   2014-01-21T02:45:54.000     7.0  2014-01-21\n",
       "13   2014-01-21T03:36:49.000     7.0  2014-01-21\n",
       "14   2014-01-21T03:57:08.000     7.0  2014-01-21\n",
       "15   2014-01-21T05:18:38.000     7.0  2014-01-21\n",
       "16   2014-01-21T10:03:42.000     7.0  2014-01-21\n",
       "17   2014-01-21T11:45:18.000     7.0  2014-01-21\n",
       "18   2014-01-21T11:55:29.000     7.0  2014-01-21\n",
       "19   2014-01-21T12:15:48.000     7.0  2014-01-21\n",
       "20   2014-01-21T13:06:36.000     7.0  2014-01-21\n",
       "21   2014-01-21T13:47:12.000     7.0  2014-01-21\n",
       "22   2014-01-21T13:57:23.000     7.0  2014-01-21\n",
       "23   2014-01-21T19:53:26.000     7.0  2014-01-21\n",
       "24   2014-01-21T21:24:55.000     7.0  2014-01-21\n",
       "25   2014-01-21T21:45:14.000     7.0  2014-01-21\n",
       "26   2014-01-21T22:59:00.000     7.0  2014-01-21\n",
       "27   2014-01-21T23:47:14.000     7.0  2014-01-21\n",
       "28   2014-01-22T02:09:32.000     7.0  2014-01-22\n",
       "29   2014-01-22T04:11:28.000     7.0  2014-01-22\n",
       "..                       ...     ...         ...\n",
       "315  2014-02-05T11:38:57.000     5.9  2014-02-05\n",
       "316  2014-02-05T13:00:18.000     5.9  2014-02-05\n",
       "317  2014-02-05T13:20:40.000     5.9  2014-02-05\n",
       "318  2014-02-05T13:30:51.000     5.9  2014-02-05\n",
       "319  2014-02-05T14:31:58.000     5.9  2014-02-05\n",
       "320  2014-02-05T14:52:20.000     5.9  2014-02-05\n",
       "321  2014-02-05T16:03:32.000     5.9  2014-02-05\n",
       "322  2014-02-05T16:13:44.000     5.9  2014-02-05\n",
       "323  2014-02-05T18:56:41.000     5.9  2014-02-05\n",
       "324  2014-02-05T20:07:49.000     5.9  2014-02-05\n",
       "325  2014-02-05T20:38:21.000     5.9  2014-02-05\n",
       "326  2014-02-05T22:59:00.000     5.9  2014-02-05\n",
       "327  2014-02-06T00:01:42.000     5.8  2014-02-06\n",
       "328  2014-02-06T00:11:53.000     5.8  2014-02-06\n",
       "329  2014-02-06T00:32:15.000     5.8  2014-02-06\n",
       "330  2014-02-06T02:03:40.000     5.8  2014-02-06\n",
       "331  2014-02-06T02:24:00.000     5.8  2014-02-06\n",
       "332  2014-02-06T02:34:11.000     5.8  2014-02-06\n",
       "333  2014-02-06T04:15:50.000     5.8  2014-02-06\n",
       "334  2014-02-06T05:06:44.000     5.8  2014-02-06\n",
       "335  2014-02-06T06:58:35.000     5.8  2014-02-06\n",
       "336  2014-02-06T07:08:46.000     5.8  2014-02-06\n",
       "337  2014-02-06T07:18:57.000     5.8  2014-02-06\n",
       "338  2014-02-06T11:23:37.000     5.8  2014-02-06\n",
       "339  2014-02-06T13:15:37.000     5.8  2014-02-06\n",
       "340  2014-02-06T14:06:31.000     5.8  2014-02-06\n",
       "341  2014-02-06T14:26:54.000     5.8  2014-02-06\n",
       "342  2014-02-06T14:37:04.000     5.8  2014-02-06\n",
       "343  2014-02-06T15:17:51.000     5.8  2014-02-06\n",
       "344  2014-02-06T22:59:00.000     5.8  2014-02-06\n",
       "\n",
       "[345 rows x 3 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "water_copy=water[['date','tick']]\n",
    "water_per_day=water_copy.groupby(['date']).agg(['sum']).reset_index()\n",
    "water_per_day.columns=['date','tick']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "weight_copy=weight[['date','weight']]\n",
    "weight_per_day=weight_copy.groupby(['date']).agg(['sum']).reset_index()\n",
    "weight_per_day.columns=['date','weight']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-17</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-20</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-01-22</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-01-24</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-02-02</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-02-03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-02-04</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-02-05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-02-06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-02-07</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-02-08</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-02-09</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-02-10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-02-11</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-02-12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-02-13</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-02-14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-02-15</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2014-02-16</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014-02-17</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2014-02-18</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2014-02-19</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2014-02-20</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2014-02-21</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2014-02-22</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2014-02-23</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2014-02-24</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2014-02-25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  weight\n",
       "0   2014-01-16    40.5\n",
       "1   2014-01-17    12.5\n",
       "2   2014-01-19     0.0\n",
       "3   2014-01-20     0.0\n",
       "4   2014-01-21     0.0\n",
       "5   2014-01-22     0.0\n",
       "6   2014-01-23     0.0\n",
       "7   2014-01-24     0.0\n",
       "8   2014-01-25     0.0\n",
       "9   2014-01-26     0.0\n",
       "10  2014-01-27     0.0\n",
       "11  2014-01-28     0.0\n",
       "12  2014-01-29     0.0\n",
       "13  2014-01-30     0.0\n",
       "14  2014-01-31     0.0\n",
       "15  2014-02-01     0.0\n",
       "16  2014-02-02     0.0\n",
       "17  2014-02-03     0.0\n",
       "18  2014-02-04     0.0\n",
       "19  2014-02-05     0.0\n",
       "20  2014-02-06     0.0\n",
       "21  2014-02-07     0.0\n",
       "22  2014-02-08     0.0\n",
       "23  2014-02-09     0.0\n",
       "24  2014-02-10     0.0\n",
       "25  2014-02-11     0.0\n",
       "26  2014-02-12     0.0\n",
       "27  2014-02-13     0.0\n",
       "28  2014-02-14     0.0\n",
       "29  2014-02-15     0.0\n",
       "30  2014-02-16     0.0\n",
       "31  2014-02-17     0.0\n",
       "32  2014-02-18     0.0\n",
       "33  2014-02-19     0.0\n",
       "34  2014-02-20     0.0\n",
       "35  2014-02-21     0.0\n",
       "36  2014-02-22     0.0\n",
       "37  2014-02-23     0.0\n",
       "38  2014-02-24     0.0\n",
       "39  2014-02-25     0.0\n",
       "40  2014-02-26     0.0\n",
       "41  2014-02-27     0.0\n",
       "42  2014-02-28     0.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "water_day=water_per_day.set_index('date')\n",
    "weight_day=weight_per_day.set_index('date')\n",
    "df_outer_join=pd.merge(water_day, weight_day, how='inner', left_index=True, right_index=True).reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tick</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>7.5</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-01-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-02-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-02-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-02-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-02-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-02-06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-02-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-02-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-02-09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-02-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-02-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-02-12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-02-13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-02-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-02-15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2014-02-16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014-02-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2014-02-18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2014-02-19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2014-02-20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2014-02-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2014-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2014-02-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2014-02-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2014-02-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2014-02-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2014-02-28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  tick  weight\n",
       "0   2014-01-16   7.5    40.5\n",
       "1   2014-01-17   0.0    12.5\n",
       "2   2014-01-19   0.0     0.0\n",
       "3   2014-01-20   0.0     0.0\n",
       "4   2014-01-21   0.0     0.0\n",
       "5   2014-01-22   0.0     0.0\n",
       "6   2014-01-23   0.0     0.0\n",
       "7   2014-01-24   0.0     0.0\n",
       "8   2014-01-25   0.0     0.0\n",
       "9   2014-01-26   0.0     0.0\n",
       "10  2014-01-27   0.0     0.0\n",
       "11  2014-01-28   0.0     0.0\n",
       "12  2014-01-29   0.0     0.0\n",
       "13  2014-01-30   0.0     0.0\n",
       "14  2014-01-31   0.0     0.0\n",
       "15  2014-02-01   0.0     0.0\n",
       "16  2014-02-02   0.0     0.0\n",
       "17  2014-02-03   0.0     0.0\n",
       "18  2014-02-04   0.0     0.0\n",
       "19  2014-02-05   0.0     0.0\n",
       "20  2014-02-06   0.0     0.0\n",
       "21  2014-02-07   0.0     0.0\n",
       "22  2014-02-08   0.0     0.0\n",
       "23  2014-02-09   0.0     0.0\n",
       "24  2014-02-10   0.0     0.0\n",
       "25  2014-02-11   0.0     0.0\n",
       "26  2014-02-12   0.0     0.0\n",
       "27  2014-02-13   0.0     0.0\n",
       "28  2014-02-14   0.0     0.0\n",
       "29  2014-02-15   0.0     0.0\n",
       "30  2014-02-16   0.0     0.0\n",
       "31  2014-02-17   0.0     0.0\n",
       "32  2014-02-18   0.0     0.0\n",
       "33  2014-02-19   0.0     0.0\n",
       "34  2014-02-20   0.0     0.0\n",
       "35  2014-02-21   0.0     0.0\n",
       "36  2014-02-22   0.0     0.0\n",
       "37  2014-02-23   0.0     0.0\n",
       "38  2014-02-24   0.0     0.0\n",
       "39  2014-02-25   0.0     0.0\n",
       "40  2014-02-26   0.0     0.0\n",
       "41  2014-02-27   0.0     0.0\n",
       "42  2014-02-28   0.0     0.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_outer_join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11c29cdd8>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f83860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BwS3+G2RNZ/epp55y2NfVkMu7wF6l1D+t2lYAN5sfzwaWW7VfKyJBItIT6A3Y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\nLfEuf1z9R2475zbO7lAdDhkWP4xl1yz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f83cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)                # the first figure\n",
    "fig, ax = plt.subplots()            # the first subplot in the first figure\n",
    "#ax=plt.subplot(211)\n",
    "ax.plot(water_day.tick, 'g--', label='data')\n",
    "ax.plot(weight_day.weight, 'r-', label='estimated mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>tick</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-12-17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-12-23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-12-24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-12-25</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-12-27</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-12-28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-12-29</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-12-30</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-04</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-05</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-01-11</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-12</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-01-13</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-01-16</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-01-17</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-01-18</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-01-19</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-01-20</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2014-05-13</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2014-05-14</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2014-05-15</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2014-05-16</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2014-05-17</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2014-05-18</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2014-05-19</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2014-05-20</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2014-05-21</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2014-05-22</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2014-05-23</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2014-06-02</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2014-06-03</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2014-06-04</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2014-06-05</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2014-06-06</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2014-06-07</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2014-06-08</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2014-06-09</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2014-06-10</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2014-06-12</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2014-06-13</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2014-06-14</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>2014-06-15</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2014-06-16</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>2014-06-17</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>2014-06-18</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2014-06-19</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>164 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  tick\n",
       "0    2013-12-17     3\n",
       "1    2013-12-23     2\n",
       "2    2013-12-24     7\n",
       "3    2013-12-25     9\n",
       "4    2013-12-26     3\n",
       "5    2013-12-27     8\n",
       "6    2013-12-28    28\n",
       "7    2013-12-29     7\n",
       "8    2013-12-30    20\n",
       "9    2013-12-31    16\n",
       "10   2014-01-01    18\n",
       "11   2014-01-02    89\n",
       "12   2014-01-03    24\n",
       "13   2014-01-04    20\n",
       "14   2014-01-05    29\n",
       "15   2014-01-06    59\n",
       "16   2014-01-07    17\n",
       "17   2014-01-08    18\n",
       "18   2014-01-09    14\n",
       "19   2014-01-10    25\n",
       "20   2014-01-11    24\n",
       "21   2014-01-12    27\n",
       "22   2014-01-13    24\n",
       "23   2014-01-14    23\n",
       "24   2014-01-15    23\n",
       "25   2014-01-16    21\n",
       "26   2014-01-17   131\n",
       "27   2014-01-18    40\n",
       "28   2014-01-19    26\n",
       "29   2014-01-20    34\n",
       "..          ...   ...\n",
       "134  2014-05-13   252\n",
       "135  2014-05-14   109\n",
       "136  2014-05-15   227\n",
       "137  2014-05-16   340\n",
       "138  2014-05-17   346\n",
       "139  2014-05-18   413\n",
       "140  2014-05-19   505\n",
       "141  2014-05-20   388\n",
       "142  2014-05-21   341\n",
       "143  2014-05-22   203\n",
       "144  2014-05-23    19\n",
       "145  2014-06-02    97\n",
       "146  2014-06-03    84\n",
       "147  2014-06-04    81\n",
       "148  2014-06-05    85\n",
       "149  2014-06-06    95\n",
       "150  2014-06-07    96\n",
       "151  2014-06-08   119\n",
       "152  2014-06-09   117\n",
       "153  2014-06-10    89\n",
       "154  2014-06-11   115\n",
       "155  2014-06-12   106\n",
       "156  2014-06-13   102\n",
       "157  2014-06-14    82\n",
       "158  2014-06-15   115\n",
       "159  2014-06-16    98\n",
       "160  2014-06-17   115\n",
       "161  2014-06-18    82\n",
       "162  2014-06-19    85\n",
       "163  2014-06-20    22\n",
       "\n",
       "[164 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "water_day.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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QVBN2ThmFhMhiRD5FZAYiH3vLmiAyAZH5iIxHpFHocjiSM2WK/QBybcXcoYP98BYvDkQs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\nxn1Wiur2VTcRvf7667f837d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75z8HvP/f/MaiXOLqPZV8+C27Nllt/RgcjiIlbMuoDPgj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\nxQgA0BzFCADQHMUIANDcL/6JAPPbGVIsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11806a198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def two_scales(ax1, time, data1, data2, c1, c2):\n",
    "    \"\"\"\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax : axis\n",
    "        Axis to put two scales on\n",
    "\n",
    "    time : array-like\n",
    "        x-axis values for both datasets\n",
    "\n",
    "    data1: array-like\n",
    "        Data for left hand scale\n",
    "\n",
    "    data2 : array-like\n",
    "        Data for right hand scale\n",
    "\n",
    "    c1 : color\n",
    "        Color for line 1\n",
    "\n",
    "    c2 : color\n",
    "        Color for line 2\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ax : axis\n",
    "        Original axis\n",
    "    ax2 : axis\n",
    "        New twin axis\n",
    "    \"\"\"\n",
    "    ax2 = ax1.twinx()\n",
    "\n",
    "    ax1.plot(time, data1, color=c1)\n",
    "    ax1.set_xlabel('time')\n",
    "    ax1.set_ylabel('water')\n",
    "\n",
    "    ax2.plot(time, data2, color=c2)\n",
    "    ax2.set_ylabel('weight')\n",
    "    return ax1, ax2\n",
    "\n",
    "\n",
    "# Create some mock data\n",
    "t = df_outer_join.date\n",
    "s1 = df_outer_join.tick\n",
    "s2 = df_outer_join.weight\n",
    "\n",
    "# Create axes\n",
    "fig, ax = plt.subplots()\n",
    "ax1, ax2 = two_scales(ax, t, s1, s2, 'r', 'b')\n",
    "\n",
    "\n",
    "# Change color of each axis\n",
    "def color_y_axis(ax, color):\n",
    "    \"\"\"Color your axes.\"\"\"\n",
    "    for t in ax.get_yticklabels():\n",
    "        t.set_color(color)\n",
    "    return None\n",
    "color_y_axis(ax1, 'r')\n",
    "color_y_axis(ax2, 'b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TSq9aFW66jiKKlIf7nJKyeDGMGQOvvw4jR1ofbvv29mrXrvavqk0xcfzxNkVF9+75X7t5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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b62b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def two_scales(ax1, time, data1, data2, c1, c2):\n",
    "    \"\"\"\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax : axis\n",
    "        Axis to put two scales on\n",
    "\n",
    "    time : array-like\n",
    "        x-axis values for both datasets\n",
    "\n",
    "    data1: array-like\n",
    "        Data for left hand scale\n",
    "\n",
    "    data2 : array-like\n",
    "        Data for right hand scale\n",
    "\n",
    "    c1 : color\n",
    "        Color for line 1\n",
    "\n",
    "    c2 : color\n",
    "        Color for line 2\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ax : axis\n",
    "        Original axis\n",
    "    ax2 : axis\n",
    "        New twin axis\n",
    "    \"\"\"\n",
    "    ax2 = ax1.twinx()\n",
    "\n",
    "    ax1.plot(time, data1, color=c1)\n",
    "    ax1.set_xlabel('time (s)')\n",
    "    ax1.set_ylabel('water')\n",
    "\n",
    "    ax2.plot(time, data2, color=c2)\n",
    "    ax2.set_ylabel('weight')\n",
    "    return ax1, ax2\n",
    "\n",
    "\n",
    "# Create some mock data\n",
    "t = df_outer_join.date\n",
    "s1 = df_outer_join.tick\n",
    "s2 = df_outer_join.weight\n",
    "\n",
    "# Create axes\n",
    "fig, ax = plt.subplots()\n",
    "ax1, ax2 = two_scales(ax, t, s1, s2, 'r', 'b')\n",
    "\n",
    "\n",
    "# Change color of each axis\n",
    "def color_y_axis(ax, color):\n",
    "    \"\"\"Color your axes.\"\"\"\n",
    "    for t in ax.get_yticklabels():\n",
    "        t.set_color(color)\n",
    "    return None\n",
    "color_y_axis(ax1, 'r')\n",
    "color_y_axis(ax2, 'b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rbfhwr17mYa1cmfoZDB5sOYgxYxIdbiYPIxvDhsGQIfY3ZPRoS6wDPPUUHH98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\n4ziOEwkXDMdxHCcSLhiO4zhOJP4/vI2LsdyieCoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1184deeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y=water_per_day.tick\n",
    "plt.xlabel('days')\n",
    "plt.ylabel('ticks')\n",
    "#ax.set_yticks([])\n",
    "#fig.savefig(\"histogram2.png\", dpi=25)  # results in 160x120 px image\n",
    "plt.title(\"ticks per day\");\n",
    "plt.plot(Y)\n",
    "plt.savefig(\"time_series_of_ticks_per_day.png\", dpi=105)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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doRrujepEzYwZsN9+sP768MYbzQXjk0/Mc+jXr3GsTx+b9d2/f9MPeiIslSwY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\nE/Va0vc1jwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1173a4240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y=weight_per_day.weight\n",
    "plt.xlabel('days')\n",
    "plt.ylabel('weight')\n",
    "#ax.set_yticks([])\n",
    "#fig.savefig(\"histogram2.png\", dpi=25)  # results in 160x120 px image\n",
    "plt.title(\"weight per day\");\n",
    "plt.plot(Y)\n",
    "plt.savefig(\"time_series_of_weight_per_day.png\", dpi=105)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cows=pd.read_csv('cow_features (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['animal_number', ' wd60', ' wd120', ' avg_milkintake', ' max_milk_day',\n",
       "       ' bv_kgMilkn', ' total_kgMilk_305d', ' total_kgFat_305d',\n",
       "       ' total_kgProtein_305d'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cows.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cows[' wd120 sq']=cows[' wd120']**2\n",
    "cows[' wd60 sq']=cows[' wd60']**2\n",
    "cows[' wd120 ln']=np.log(cows[' wd120'])\n",
    "cows[' wd60 ln']=np.log(cows[' wd60'])\n",
    "cows[' total_kgMilk_305d ln']= np.log(cows[' total_kgMilk_305d'])\n",
    "cows[' avg_milkintake ln']= np.log(cows[' avg_milkintake'])\n",
    "cows[' avg_milkintake sq']=cows[' avg_milkintake']**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FfGNvLb39lYg4rvz9iYhHKJYMODLlmUvx/TIbkIORtYNPSlpNMQ/W/hR/PH8N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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11daec978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "X = cows[' total_kgMilk_305d'].values\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(X, normed=True) # Normed = True -> Makes sure the integral is 1\n",
    "#ax.set_xticks(range(1, 17))\n",
    "#plt.ylim((0, 710));\n",
    "ax.relim()\n",
    "#ax.set_ylim(0, 1010)\n",
    "#plt.ylim((0, 12310));\n",
    "plt.xlabel('average milk per day')\n",
    "plt.ylabel('number of cows')\n",
    "#ax.set_yticks([])\n",
    "fig.savefig(\"hist_average milk production across cows.png\", dpi=105)  # results in 160x120 px image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hD6dlCyVdUVPmTEmL0/R/TQMW3SfpKklKy38v6fOp99bZkv5HGrBqnaQlNes6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\nK9T/BxA5sR8d/Xz9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dc81cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "X = cows[' total_kgProtein_305d'].values\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(X, normed=True) # Normed = True -> Makes sure the integral is 1\n",
    "#ax.set_xticks(range(1, 17))\n",
    "#plt.ylim((0, 710));\n",
    "ax.relim()\n",
    "#ax.set_ylim(0, 1010)\n",
    "#plt.ylim((0, 12310));\n",
    "plt.xlabel('average protein per day')\n",
    "plt.ylabel('number of cows')\n",
    "#ax.set_yticks([])\n",
    "fig.savefig(\"hist_average protein production across cows.png\", dpi=105)  # results in 160x120 px image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mPOl3P+CQ1GX2VuAfaHELdH9//+75vr4++vr6CgjRzKx31Go1arVaYdtrmVjSWyMBvi3p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\nM7NCObGYmVmhnFjMzKxQ/x/hpO5s32HVcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dc4c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "X = cows[' total_kgFat_305d'].values\n",
    "fig, ax = plt.subplots()\n",
    "ax.hist(X, normed=True) # Normed = True -> Makes sure the integral is 1\n",
    "#ax.set_xticks(range(1, 17))\n",
    "#plt.ylim((0, 710));\n",
    "ax.relim()\n",
    "#ax.set_ylim(0, 1010)\n",
    "#plt.ylim((0, 12310));\n",
    "plt.xlabel('average fat per day')\n",
    "plt.ylabel('number of cows')\n",
    "#ax.set_yticks([])\n",
    "fig.savefig(\"hist_average fat production across cows.png\", dpi=105)  # results in 160x120 px image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "y = cows[' total_kgMilk_305d']# response\n",
    "\n",
    "X = cows[[' wd120 sq',' wd120',' bv_kgMilkn']] # predictor\n",
    "#X = cows[[' avg_milkintake',' bv_kgMilkn']] # predictor\n",
    "X = sm.add_constant(X)  # Adds a constant term to the predictor\n",
    "#X.head()\n",
    "est = sm.OLS(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td> total_kgMilk_305d</td> <th>  R-squared:         </th> <td>   0.545</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.508</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   14.76</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Fri, 01 Dec 2017</td>  <th>  Prob (F-statistic):</th> <td>1.78e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>09:10:51</td>      <th>  Log-Likelihood:    </th> <td> -324.37</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>    41</td>       <th>  AIC:               </th> <td>   656.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td>    37</td>       <th>  BIC:               </th> <td>   663.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     3</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td>  779.2583</td> <td> 2596.544</td> <td>    0.300</td> <td> 0.766</td> <td>-4481.839  6040.355</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> wd120 sq</th>   <td>   -0.4828</td> <td>    0.218</td> <td>   -2.212</td> <td> 0.033</td> <td>   -0.925    -0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> wd120</th>      <td>  114.1904</td> <td>   47.515</td> <td>    2.403</td> <td> 0.021</td> <td>   17.915   210.466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> bv_kgMilkn</th> <td>    1.3243</td> <td>    0.227</td> <td>    5.845</td> <td> 0.000</td> <td>    0.865     1.783</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.349</td> <th>  Durbin-Watson:     </th> <td>   1.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.840</td> <th>  Jarque-Bera (JB):  </th> <td>   0.393</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.200</td> <th>  Prob(JB):          </th> <td>   0.822</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.737</td> <th>  Cond. No.          </th> <td>3.64e+05</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:      total_kgMilk_305d   R-squared:                       0.545\n",
       "Model:                            OLS   Adj. R-squared:                  0.508\n",
       "Method:                 Least Squares   F-statistic:                     14.76\n",
       "Date:                Fri, 01 Dec 2017   Prob (F-statistic):           1.78e-06\n",
       "Time:                        09:10:51   Log-Likelihood:                -324.37\n",
       "No. Observations:                  41   AIC:                             656.7\n",
       "Df Residuals:                      37   BIC:                             663.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------\n",
       "const         779.2583   2596.544      0.300      0.766     -4481.839  6040.355\n",
       " wd120 sq      -0.4828      0.218     -2.212      0.033        -0.925    -0.041\n",
       " wd120        114.1904     47.515      2.403      0.021        17.915   210.466\n",
       " bv_kgMilkn     1.3243      0.227      5.845      0.000         0.865     1.783\n",
       "==============================================================================\n",
       "Omnibus:                        0.349   Durbin-Watson:                   1.941\n",
       "Prob(Omnibus):                  0.840   Jarque-Bera (JB):                0.393\n",
       "Skew:                          -0.200   Prob(JB):                        0.822\n",
       "Kurtosis:                       2.737   Cond. No.                     3.64e+05\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.64e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = est.fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "y = cows[' total_kgMilk_305d']# response\n",
    "\n",
    "#y= y[0:38]\n",
    "X = cows[[' wd60',' wd120', ' avg_milkintake', ' bv_kgMilkn']] # predictor\n",
    "\n",
    "#X =X[0:38]\n",
    "#X = cows[[' avg_milkintake',' bv_kgMilkn']] # predictor\n",
    "X = sm.add_constant(X)  # Adds a constant term to the predictor\n",
    "#X.head()\n",
    "est2 = sm.OLS(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td> total_kgMilk_305d</td> <th>  R-squared:         </th> <td>   0.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.497</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   10.87</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Fri, 01 Dec 2017</td>  <th>  Prob (F-statistic):</th> <td>7.00e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>11:11:50</td>      <th>  Log-Likelihood:    </th> <td> -324.26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td>    41</td>       <th>  AIC:               </th> <td>   658.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td>    36</td>       <th>  BIC:               </th> <td>   667.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>     4</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>           <td> 6603.5874</td> <td> 1430.358</td> <td>    4.617</td> <td> 0.000</td> <td> 3702.687  9504.488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> wd60</th>           <td>  -30.4550</td> <td>   18.939</td> <td>   -1.608</td> <td> 0.117</td> <td>  -68.866     7.956</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> wd120</th>          <td>   17.6736</td> <td>    8.358</td> <td>    2.115</td> <td> 0.041</td> <td>    0.723    34.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> avg_milkintake</th> <td>   10.5646</td> <td>    6.617</td> <td>    1.597</td> <td> 0.119</td> <td>   -2.856    23.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> bv_kgMilkn</th>     <td>    1.3195</td> <td>    0.233</td> <td>    5.666</td> <td> 0.000</td> <td>    0.847     1.792</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.186</td> <th>  Durbin-Watson:     </th> <td>   1.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.911</td> <th>  Jarque-Bera (JB):  </th> <td>   0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.052</td> <th>  Prob(JB):          </th> <td>   0.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.966</td> <th>  Cond. No.          </th> <td>7.23e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:      total_kgMilk_305d   R-squared:                       0.547\n",
       "Model:                            OLS   Adj. R-squared:                  0.497\n",
       "Method:                 Least Squares   F-statistic:                     10.87\n",
       "Date:                Fri, 01 Dec 2017   Prob (F-statistic):           7.00e-06\n",
       "Time:                        11:11:50   Log-Likelihood:                -324.26\n",
       "No. Observations:                  41   AIC:                             658.5\n",
       "Df Residuals:                      36   BIC:                             667.1\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-----------------------------------------------------------------------------------\n",
       "const            6603.5874   1430.358      4.617      0.000      3702.687  9504.488\n",
       " wd60             -30.4550     18.939     -1.608      0.117       -68.866     7.956\n",
       " wd120             17.6736      8.358      2.115      0.041         0.723    34.624\n",
       " avg_milkintake    10.5646      6.617      1.597      0.119        -2.856    23.985\n",
       " bv_kgMilkn         1.3195      0.233      5.666      0.000         0.847     1.792\n",
       "==============================================================================\n",
       "Omnibus:                        0.186   Durbin-Watson:                   1.944\n",
       "Prob(Omnibus):                  0.911   Jarque-Bera (JB):                0.020\n",
       "Skew:                          -0.052   Prob(JB):                        0.990\n",
       "Kurtosis:                       2.966   Cond. No.                     7.23e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 7.23e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est2 = est2.fit()\n",
    "est2.summary()\n",
    "#ypred2 = est2.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ypred2 = est2.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>wd60</th>\n",
       "      <th>wd120</th>\n",
       "      <th>avg_milkintake</th>\n",
       "      <th>bv_kgMilkn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>76.940666</td>\n",
       "      <td>127.036639</td>\n",
       "      <td>85.12439</td>\n",
       "      <td>849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const       wd60       wd120   avg_milkintake   bv_kgMilkn\n",
       "0      1  76.940666  127.036639         85.12439          849"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9014\n",
       "Name:  total_kgMilk_305d, dtype: float64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8525.09087423])"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred2[0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11859aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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j5+AorNQYIQRndT2L2NBYu9dq62r57P2/Ufbmy02wuL22yWiAuDjlOTRzmOtE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\nYJOFWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11859a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)                # the first figure\n",
    "fig, ax = plt.subplots()            # the first subplot in the first figure\n",
    "#ax=plt.subplot(211)\n",
    "ax.plot(y, 'g--', label='milk')\n",
    "ax.plot(ypred2, 'r-', label='predicted milk')\n",
    "plt.legend()\n",
    "plt.savefig(\"milk_prediction.png\", dpi=305)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x1, y, 'o', label=\"Data\")\n",
    "ax.plot(x1, y_true, 'b-', label=\"True\")\n",
    "ax.plot(np.hstack((x1, x1n)), np.hstack((ypred, ynewpred)), 'r', label=\"OLS prediction\")\n",
    "ax.legend(loc=\"best\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11e23b940>]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e2266a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# regress \"expression\" onto \"motifScore\" (plus an intercept)\n",
    "model = sm.OLS(cows[' total_kgMilk_305d'], sm.add_constant(cows[[' wd60',' wd120', ' avg_milkintake', ' bv_kgMilkn']]))\n",
    "p = model.fit().params\n",
    "\n",
    "#print(p.FeedIntake)\n",
    "# generate x-values for your regression line (two is sufficient)\n",
    "x1 = np.arange(40, 160)\n",
    "x2 = np.arange(40, 160)\n",
    "x3 = np.arange(40, 160)\n",
    "x4 = np.arange(40, 160)\n",
    "# scatter-plot data\n",
    "ax = cows.plot(x=' wd120', y=' total_kgMilk_305d', kind='scatter')\n",
    "\n",
    "# plot regression line on the same axes, set x-axis limits\n",
    "ax.plot(x1, p.const + p[' wd60'] * x1 + p[' wd120'] * x2+ p[' avg_milkintake'] * x3 + p[' bv_kgMilkn']* x4)\n",
    "#ax.set_xlim([1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y = cows[' total_kgFat_305d']# response\n",
    "\n",
    "X = cows[[ ' avg_milkintake', ' bv_kgMilkn']] # predictor\n",
    "#X = cows[[' avg_milkintake',' bv_kgMilkn']] # predictor\n",
    "X = sm.add_constant(X)  # Adds a constant term to the predictor\n",
    "#X.head()\n",
    "est3 = sm.OLS(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td> total_kgFat_305d</td> <th>  R-squared:         </th> <td>   0.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared:    </th> <td>   0.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th> <td>   6.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 01 Dec 2017</td>  <th>  Prob (F-statistic):</th>  <td>0.00524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>09:29:25</td>      <th>  Log-Likelihood:    </th> <td> -200.36</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    41</td>       <th>  AIC:               </th> <td>   406.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    38</td>       <th>  BIC:               </th> <td>   411.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>           <td>  274.3413</td> <td>   25.573</td> <td>   10.728</td> <td> 0.000</td> <td>  222.572   326.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> avg_milkintake</th> <td>    0.5918</td> <td>    0.311</td> <td>    1.905</td> <td> 0.064</td> <td>   -0.037     1.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> bv_kgMilkn</th>     <td>    0.0293</td> <td>    0.011</td> <td>    2.746</td> <td> 0.009</td> <td>    0.008     0.051</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.216</td> <th>  Durbin-Watson:     </th> <td>   1.837</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.330</td> <th>  Jarque-Bera (JB):  </th> <td>   1.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.232</td> <th>  Prob(JB):          </th> <td>   0.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.701</td> <th>  Cond. No.          </th> <td>2.71e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:       total_kgFat_305d   R-squared:                       0.241\n",
       "Model:                            OLS   Adj. R-squared:                  0.202\n",
       "Method:                 Least Squares   F-statistic:                     6.048\n",
       "Date:                Fri, 01 Dec 2017   Prob (F-statistic):            0.00524\n",
       "Time:                        09:29:25   Log-Likelihood:                -200.36\n",
       "No. Observations:                  41   AIC:                             406.7\n",
       "Df Residuals:                      38   BIC:                             411.9\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-----------------------------------------------------------------------------------\n",
       "const             274.3413     25.573     10.728      0.000       222.572   326.111\n",
       " avg_milkintake     0.5918      0.311      1.905      0.064        -0.037     1.221\n",
       " bv_kgMilkn         0.0293      0.011      2.746      0.009         0.008     0.051\n",
       "==============================================================================\n",
       "Omnibus:                        2.216   Durbin-Watson:                   1.837\n",
       "Prob(Omnibus):                  0.330   Jarque-Bera (JB):                1.210\n",
       "Skew:                          -0.232   Prob(JB):                        0.546\n",
       "Kurtosis:                       3.701   Cond. No.                     2.71e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.71e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est3 = est3.fit()\n",
    "est3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = cows[' total_kgProtein_305d']# response\n",
    "\n",
    "X = cows[[' wd60',' wd120', ' bv_kgMilkn']] # predictor\n",
    "#X = cows[[' avg_milkintake',' bv_kgMilkn']] # predictor\n",
    "X = sm.add_constant(X)  # Adds a constant term to the predictor\n",
    "#X.head()\n",
    "est4= sm.OLS(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td> total_kgProtein_305d</td> <th>  R-squared:         </th> <td>   0.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                     <td>OLS</td>          <th>  Adj. R-squared:    </th> <td>   0.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>Least Squares</td>     <th>  F-statistic:       </th> <td>   11.72</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>               <td>Fri, 01 Dec 2017</td>    <th>  Prob (F-statistic):</th> <td>1.53e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                   <td>09:21:51</td>        <th>  Log-Likelihood:    </th> <td> -182.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>        <td>    41</td>         <th>  AIC:               </th> <td>   372.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>            <td>    37</td>         <th>  BIC:               </th> <td>   379.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                <td>     3</td>         <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>nonrobust</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td>  285.3877</td> <td>   39.746</td> <td>    7.180</td> <td> 0.000</td> <td>  204.854   365.921</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> wd60</th>       <td>   -1.2537</td> <td>    0.584</td> <td>   -2.147</td> <td> 0.038</td> <td>   -2.437    -0.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> wd120</th>      <td>    0.5455</td> <td>    0.255</td> <td>    2.135</td> <td> 0.039</td> <td>    0.028     1.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> bv_kgMilkn</th> <td>    0.0378</td> <td>    0.007</td> <td>    5.283</td> <td> 0.000</td> <td>    0.023     0.052</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.819</td> <th>  Durbin-Watson:     </th> <td>   1.972</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.403</td> <th>  Jarque-Bera (JB):  </th> <td>   1.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.075</td> <th>  Prob(JB):          </th> <td>   0.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.183</td> <th>  Cond. No.          </th> <td>6.49e+03</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              OLS Regression Results                             \n",
       "=================================================================================\n",
       "Dep. Variable:      total_kgProtein_305d   R-squared:                       0.487\n",
       "Model:                               OLS   Adj. R-squared:                  0.446\n",
       "Method:                    Least Squares   F-statistic:                     11.72\n",
       "Date:                   Fri, 01 Dec 2017   Prob (F-statistic):           1.53e-05\n",
       "Time:                           09:21:51   Log-Likelihood:                -182.21\n",
       "No. Observations:                     41   AIC:                             372.4\n",
       "Df Residuals:                         37   BIC:                             379.3\n",
       "Df Model:                              3                                         \n",
       "Covariance Type:               nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------\n",
       "const         285.3877     39.746      7.180      0.000       204.854   365.921\n",
       " wd60          -1.2537      0.584     -2.147      0.038        -2.437    -0.070\n",
       " wd120          0.5455      0.255      2.135      0.039         0.028     1.063\n",
       " bv_kgMilkn     0.0378      0.007      5.283      0.000         0.023     0.052\n",
       "==============================================================================\n",
       "Omnibus:                        1.819   Durbin-Watson:                   1.972\n",
       "Prob(Omnibus):                  0.403   Jarque-Bera (JB):                1.179\n",
       "Skew:                           0.075   Prob(JB):                        0.555\n",
       "Kurtosis:                       2.183   Cond. No.                     6.49e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 6.49e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est4 = est4.fit()\n",
    "est4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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F6H03wvZKjWySqq2lMrDfFklJtQkd3a/ZkPsg/+o2BkOj8FSNsnVuXJFZmwh0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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11806a9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def two_scales(ax1, time, data1, data2, c1, c2):\n",
    "    \"\"\"\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax : axis\n",
    "        Axis to put two scales on\n",
    "\n",
    "    time : array-like\n",
    "        x-axis values for both datasets\n",
    "\n",
    "    data1: array-like\n",
    "        Data for left hand scale\n",
    "\n",
    "    data2 : array-like\n",
    "        Data for right hand scale\n",
    "\n",
    "    c1 : color\n",
    "        Color for line 1\n",
    "\n",
    "    c2 : color\n",
    "        Color for line 2\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ax : axis\n",
    "        Original axis\n",
    "    ax2 : axis\n",
    "        New twin axis\n",
    "    \"\"\"\n",
    "    ax2 = ax1.twinx()\n",
    "\n",
    "    ax1.plot(time, data1, color=c1)\n",
    "    ax1.set_xlabel('cow')\n",
    "    ax1.set_ylabel('protein')\n",
    "\n",
    "    ax2.plot(time, data2, color=c2)\n",
    "    ax2.set_ylabel('fat')\n",
    "    return ax1, ax2\n",
    "\n",
    "\n",
    "# Create some mock data\n",
    "t = cows.index\n",
    "s1 = cows[' total_kgProtein_305d']\n",
    "s2 = cows[' total_kgFat_305d']\n",
    "\n",
    "# Create axes\n",
    "fig, ax = plt.subplots()\n",
    "ax1, ax2 = two_scales(ax, t, s1, s2, 'r', 'b')\n",
    "\n",
    "\n",
    "# Change color of each axis\n",
    "def color_y_axis(ax, color):\n",
    "    \"\"\"Color your axes.\"\"\"\n",
    "    for t in ax.get_yticklabels():\n",
    "        t.set_color(color)\n",
    "    return None\n",
    "color_y_axis(ax1, 'r')\n",
    "color_y_axis(ax2, 'b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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