Newdam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=pd.read_csv('~/Sardar_sarovar_dam_daily.csv')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>412002</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>412002.1</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th></th>\n",
       "      <th>Unnamed: 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>and</td>\n",
       "      <td>141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>151</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Date</td>\n",
       "      <td>Discharge (ML/d)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Discharge (ML)</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mean</td>\n",
       "      <td>Qual</td>\n",
       "      <td>Total</td>\n",
       "      <td>Qual</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:00:00 10/01/1954</td>\n",
       "      <td>979.351</td>\n",
       "      <td>38</td>\n",
       "      <td>979.351</td>\n",
       "      <td>38</td>\n",
       "      <td>Sites:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00:00:00 11/01/1954</td>\n",
       "      <td>1030.009</td>\n",
       "      <td>38</td>\n",
       "      <td>1030.009</td>\n",
       "      <td>38</td>\n",
       "      <td>412002 - LACHLAN RIVER AT COWRA Lat:-33.833 Lo...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00:00:00 12/01/1954</td>\n",
       "      <td>919.242</td>\n",
       "      <td>38</td>\n",
       "      <td>919.242</td>\n",
       "      <td>38</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00:00:00 13/01/1954</td>\n",
       "      <td>952.675</td>\n",
       "      <td>38</td>\n",
       "      <td>952.675</td>\n",
       "      <td>38</td>\n",
       "      <td>Variables:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00:00:00 14/01/1954</td>\n",
       "      <td>955.59</td>\n",
       "      <td>38</td>\n",
       "      <td>955.59</td>\n",
       "      <td>38</td>\n",
       "      <td>100 - Stream Water Level (Metres)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00:00:00 15/01/1954</td>\n",
       "      <td>969.969</td>\n",
       "      <td>38</td>\n",
       "      <td>969.969</td>\n",
       "      <td>38</td>\n",
       "      <td>141 - Discharge Rate (Megalitres/Day)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00:00:00 16/01/1954</td>\n",
       "      <td>991.797</td>\n",
       "      <td>38</td>\n",
       "      <td>991.796</td>\n",
       "      <td>38</td>\n",
       "      <td>151 - Discharge Volume (Megalitres)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>00:00:00 17/01/1954</td>\n",
       "      <td>908.621</td>\n",
       "      <td>38</td>\n",
       "      <td>908.621</td>\n",
       "      <td>38</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>00:00:00 18/01/1954</td>\n",
       "      <td>841.898</td>\n",
       "      <td>38</td>\n",
       "      <td>841.898</td>\n",
       "      <td>38</td>\n",
       "      <td>Qualities:</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>00:00:00 19/01/1954</td>\n",
       "      <td>837.74</td>\n",
       "      <td>38</td>\n",
       "      <td>837.74</td>\n",
       "      <td>38</td>\n",
       "      <td>5 - RATINGS/TS - No data in the period have be...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>00:00:00 20/01/1954</td>\n",
       "      <td>837.74</td>\n",
       "      <td>38</td>\n",
       "      <td>837.74</td>\n",
       "      <td>38</td>\n",
       "      <td>7 - RATINGS/TS - Large uncertainty - data in p...</td>\n",
       "      <td>Good Gauging</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>00:00:00 21/01/1954</td>\n",
       "      <td>811.396</td>\n",
       "      <td>38</td>\n",
       "      <td>811.395</td>\n",
       "      <td>38</td>\n",
       "      <td>9 - RATINGS/TS - Large uncertainty - data in p...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>00:00:00 22/01/1954</td>\n",
       "      <td>771.695</td>\n",
       "      <td>38</td>\n",
       "      <td>771.696</td>\n",
       "      <td>38</td>\n",
       "      <td>10 - TS - Data adjusted during processing - se...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>00:00:00 23/01/1954</td>\n",
       "      <td>888.125</td>\n",
       "      <td>38</td>\n",
       "      <td>888.125</td>\n",
       "      <td>38</td>\n",
       "      <td>34 - Pre 21 Aug 2004 - RATINGS - Good record w...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>00:00:00 24/01/1954</td>\n",
       "      <td>882.645</td>\n",
       "      <td>38</td>\n",
       "      <td>882.645</td>\n",
       "      <td>38</td>\n",
       "      <td>38 - Pre 1 Nov 1996 - RATINGS/TS - Good record...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>00:00:00 25/01/1954</td>\n",
       "      <td>827.639</td>\n",
       "      <td>38</td>\n",
       "      <td>827.639</td>\n",
       "      <td>38</td>\n",
       "      <td>40 - Pre 21 Aug 2004 - TS - Fair measured data</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>00:00:00 26/01/1954</td>\n",
       "      <td>785.958</td>\n",
       "      <td>38</td>\n",
       "      <td>785.958</td>\n",
       "      <td>38</td>\n",
       "      <td>41 - Pre 21 Aug 2004 - RATINGS/TS - Fair measu...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>00:00:00 27/01/1954</td>\n",
       "      <td>769.846</td>\n",
       "      <td>38</td>\n",
       "      <td>769.846</td>\n",
       "      <td>38</td>\n",
       "      <td>44 - Pre 21 Aug 2004 - RATINGS - Fair record w...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>00:00:00 28/01/1954</td>\n",
       "      <td>755.547</td>\n",
       "      <td>38</td>\n",
       "      <td>755.548</td>\n",
       "      <td>38</td>\n",
       "      <td>51 - TS - Data estimated - see comments</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>00:00:00 29/01/1954</td>\n",
       "      <td>888.815</td>\n",
       "      <td>38</td>\n",
       "      <td>888.815</td>\n",
       "      <td>38</td>\n",
       "      <td>58 - Pre 1 Nov 1996 - TS - Fair record process...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>00:00:00 30/01/1954</td>\n",
       "      <td>1129.708</td>\n",
       "      <td>38</td>\n",
       "      <td>1129.707</td>\n",
       "      <td>38</td>\n",
       "      <td>60 - Pre 21 Aug 2004 - TS - Poor measured data...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>00:00:00 31/01/1954</td>\n",
       "      <td>1400.324</td>\n",
       "      <td>38</td>\n",
       "      <td>1400.324</td>\n",
       "      <td>38</td>\n",
       "      <td>63 - Pre 21 Aug 2004 - RATINGS - Low segment R...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>00:00:00 01/02/1954</td>\n",
       "      <td>1517.684</td>\n",
       "      <td>38</td>\n",
       "      <td>1517.684</td>\n",
       "      <td>38</td>\n",
       "      <td>75 - Pre 21 Aug 2004 - RATINGS - Reasonable es...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>00:00:00 02/02/1954</td>\n",
       "      <td>1183.4</td>\n",
       "      <td>38</td>\n",
       "      <td>1183.4</td>\n",
       "      <td>38</td>\n",
       "      <td>100 - Quality unknown</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>00:00:00 03/02/1954</td>\n",
       "      <td>997.167</td>\n",
       "      <td>38</td>\n",
       "      <td>997.167</td>\n",
       "      <td>38</td>\n",
       "      <td>140 - RATINGS - Current rating - may be subjec...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>00:00:00 04/02/1954</td>\n",
       "      <td>902.372</td>\n",
       "      <td>38</td>\n",
       "      <td>902.372</td>\n",
       "      <td>38</td>\n",
       "      <td>201 - Data unavailable</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>00:00:00 05/02/1954</td>\n",
       "      <td>896.658</td>\n",
       "      <td>38</td>\n",
       "      <td>896.658</td>\n",
       "      <td>38</td>\n",
       "      <td>255 - No data exists</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22976</th>\n",
       "      <td>00:00:00 03/12/2016</td>\n",
       "      <td>943.23</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22977</th>\n",
       "      <td>00:00:00 04/12/2016</td>\n",
       "      <td>1112.347</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22978</th>\n",
       "      <td>00:00:00 05/12/2016</td>\n",
       "      <td>1089.857</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22979</th>\n",
       "      <td>00:00:00 06/12/2016</td>\n",
       "      <td>992.21</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22980</th>\n",
       "      <td>00:00:00 07/12/2016</td>\n",
       "      <td>817.307</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22981</th>\n",
       "      <td>00:00:00 08/12/2016</td>\n",
       "      <td>889.291</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22982</th>\n",
       "      <td>00:00:00 09/12/2016</td>\n",
       "      <td>877.835</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22983</th>\n",
       "      <td>00:00:00 10/12/2016</td>\n",
       "      <td>899.079</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22984</th>\n",
       "      <td>00:00:00 11/12/2016</td>\n",
       "      <td>863.327</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22985</th>\n",
       "      <td>00:00:00 12/12/2016</td>\n",
       "      <td>781.462</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22986</th>\n",
       "      <td>00:00:00 13/12/2016</td>\n",
       "      <td>821.476</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22987</th>\n",
       "      <td>00:00:00 14/12/2016</td>\n",
       "      <td>895.357</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22988</th>\n",
       "      <td>00:00:00 15/12/2016</td>\n",
       "      <td>1159.23</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22989</th>\n",
       "      <td>00:00:00 16/12/2016</td>\n",
       "      <td>1117.682</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22990</th>\n",
       "      <td>00:00:00 17/12/2016</td>\n",
       "      <td>952.201</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22991</th>\n",
       "      <td>00:00:00 18/12/2016</td>\n",
       "      <td>852.859</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22992</th>\n",
       "      <td>00:00:00 19/12/2016</td>\n",
       "      <td>775.405</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22993</th>\n",
       "      <td>00:00:00 20/12/2016</td>\n",
       "      <td>758.723</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22994</th>\n",
       "      <td>00:00:00 21/12/2016</td>\n",
       "      <td>744.589</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22995</th>\n",
       "      <td>00:00:00 22/12/2016</td>\n",
       "      <td>736.339</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22996</th>\n",
       "      <td>00:00:00 23/12/2016</td>\n",
       "      <td>732.869</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22997</th>\n",
       "      <td>00:00:00 24/12/2016</td>\n",
       "      <td>775.192</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22998</th>\n",
       "      <td>00:00:00 25/12/2016</td>\n",
       "      <td>675.152</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22999</th>\n",
       "      <td>00:00:00 26/12/2016</td>\n",
       "      <td>658.38</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23000</th>\n",
       "      <td>00:00:00 27/12/2016</td>\n",
       "      <td>539.519</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23001</th>\n",
       "      <td>00:00:00 28/12/2016</td>\n",
       "      <td>502.858</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23002</th>\n",
       "      <td>00:00:00 29/12/2016</td>\n",
       "      <td>554.372</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23003</th>\n",
       "      <td>00:00:00 30/12/2016</td>\n",
       "      <td>844.288</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23004</th>\n",
       "      <td>00:00:00 31/12/2016</td>\n",
       "      <td>967.899</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23005</th>\n",
       "      <td>00:00:00 01/01/2017</td>\n",
       "      <td>712.538</td>\n",
       "      <td>34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>255</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23006 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Time            412002 Unnamed: 2        412002.1  \\\n",
       "0                      and               141        NaN             151   \n",
       "1                     Date  Discharge (ML/d)        NaN  Discharge (ML)   \n",
       "2                      NaN              Mean       Qual           Total   \n",
       "3      00:00:00 10/01/1954           979.351         38         979.351   \n",
       "4      00:00:00 11/01/1954          1030.009         38        1030.009   \n",
       "5      00:00:00 12/01/1954           919.242         38         919.242   \n",
       "6      00:00:00 13/01/1954           952.675         38         952.675   \n",
       "7      00:00:00 14/01/1954            955.59         38          955.59   \n",
       "8      00:00:00 15/01/1954           969.969         38         969.969   \n",
       "9      00:00:00 16/01/1954           991.797         38         991.796   \n",
       "10     00:00:00 17/01/1954           908.621         38         908.621   \n",
       "11     00:00:00 18/01/1954           841.898         38         841.898   \n",
       "12     00:00:00 19/01/1954            837.74         38          837.74   \n",
       "13     00:00:00 20/01/1954            837.74         38          837.74   \n",
       "14     00:00:00 21/01/1954           811.396         38         811.395   \n",
       "15     00:00:00 22/01/1954           771.695         38         771.696   \n",
       "16     00:00:00 23/01/1954           888.125         38         888.125   \n",
       "17     00:00:00 24/01/1954           882.645         38         882.645   \n",
       "18     00:00:00 25/01/1954           827.639         38         827.639   \n",
       "19     00:00:00 26/01/1954           785.958         38         785.958   \n",
       "20     00:00:00 27/01/1954           769.846         38         769.846   \n",
       "21     00:00:00 28/01/1954           755.547         38         755.548   \n",
       "22     00:00:00 29/01/1954           888.815         38         888.815   \n",
       "23     00:00:00 30/01/1954          1129.708         38        1129.707   \n",
       "24     00:00:00 31/01/1954          1400.324         38        1400.324   \n",
       "25     00:00:00 01/02/1954          1517.684         38        1517.684   \n",
       "26     00:00:00 02/02/1954            1183.4         38          1183.4   \n",
       "27     00:00:00 03/02/1954           997.167         38         997.167   \n",
       "28     00:00:00 04/02/1954           902.372         38         902.372   \n",
       "29     00:00:00 05/02/1954           896.658         38         896.658   \n",
       "...                    ...               ...        ...             ...   \n",
       "22976  00:00:00 03/12/2016            943.23          5             NaN   \n",
       "22977  00:00:00 04/12/2016          1112.347          5             NaN   \n",
       "22978  00:00:00 05/12/2016          1089.857          5             NaN   \n",
       "22979  00:00:00 06/12/2016            992.21          5             NaN   \n",
       "22980  00:00:00 07/12/2016           817.307          5             NaN   \n",
       "22981  00:00:00 08/12/2016           889.291          5             NaN   \n",
       "22982  00:00:00 09/12/2016           877.835          5             NaN   \n",
       "22983  00:00:00 10/12/2016           899.079          5             NaN   \n",
       "22984  00:00:00 11/12/2016           863.327          5             NaN   \n",
       "22985  00:00:00 12/12/2016           781.462          5             NaN   \n",
       "22986  00:00:00 13/12/2016           821.476          5             NaN   \n",
       "22987  00:00:00 14/12/2016           895.357          5             NaN   \n",
       "22988  00:00:00 15/12/2016           1159.23          5             NaN   \n",
       "22989  00:00:00 16/12/2016          1117.682          5             NaN   \n",
       "22990  00:00:00 17/12/2016           952.201          5             NaN   \n",
       "22991  00:00:00 18/12/2016           852.859          5             NaN   \n",
       "22992  00:00:00 19/12/2016           775.405          5             NaN   \n",
       "22993  00:00:00 20/12/2016           758.723          5             NaN   \n",
       "22994  00:00:00 21/12/2016           744.589          5             NaN   \n",
       "22995  00:00:00 22/12/2016           736.339          5             NaN   \n",
       "22996  00:00:00 23/12/2016           732.869          5             NaN   \n",
       "22997  00:00:00 24/12/2016           775.192         34             NaN   \n",
       "22998  00:00:00 25/12/2016           675.152         34             NaN   \n",
       "22999  00:00:00 26/12/2016            658.38         34             NaN   \n",
       "23000  00:00:00 27/12/2016           539.519         34             NaN   \n",
       "23001  00:00:00 28/12/2016           502.858         34             NaN   \n",
       "23002  00:00:00 29/12/2016           554.372         34             NaN   \n",
       "23003  00:00:00 30/12/2016           844.288         34             NaN   \n",
       "23004  00:00:00 31/12/2016           967.899         34             NaN   \n",
       "23005  00:00:00 01/01/2017           712.538         34             NaN   \n",
       "\n",
       "      Unnamed: 4                                                     \\\n",
       "0            NaN                                                      \n",
       "1            NaN                                                      \n",
       "2           Qual                                                      \n",
       "3             38                                             Sites:   \n",
       "4             38  412002 - LACHLAN RIVER AT COWRA Lat:-33.833 Lo...   \n",
       "5             38                                                      \n",
       "6             38                                         Variables:   \n",
       "7             38                  100 - Stream Water Level (Metres)   \n",
       "8             38              141 - Discharge Rate (Megalitres/Day)   \n",
       "9             38                151 - Discharge Volume (Megalitres)   \n",
       "10            38                                                      \n",
       "11            38                                         Qualities:   \n",
       "12            38  5 - RATINGS/TS - No data in the period have be...   \n",
       "13            38  7 - RATINGS/TS - Large uncertainty - data in p...   \n",
       "14            38  9 - RATINGS/TS - Large uncertainty - data in p...   \n",
       "15            38  10 - TS - Data adjusted during processing - se...   \n",
       "16            38  34 - Pre 21 Aug 2004 - RATINGS - Good record w...   \n",
       "17            38  38 - Pre 1 Nov 1996 - RATINGS/TS - Good record...   \n",
       "18            38     40 - Pre 21 Aug 2004 - TS - Fair measured data   \n",
       "19            38  41 - Pre 21 Aug 2004 - RATINGS/TS - Fair measu...   \n",
       "20            38  44 - Pre 21 Aug 2004 - RATINGS - Fair record w...   \n",
       "21            38            51 - TS - Data estimated - see comments   \n",
       "22            38  58 - Pre 1 Nov 1996 - TS - Fair record process...   \n",
       "23            38  60 - Pre 21 Aug 2004 - TS - Poor measured data...   \n",
       "24            38  63 - Pre 21 Aug 2004 - RATINGS - Low segment R...   \n",
       "25            38  75 - Pre 21 Aug 2004 - RATINGS - Reasonable es...   \n",
       "26            38                              100 - Quality unknown   \n",
       "27            38  140 - RATINGS - Current rating - may be subjec...   \n",
       "28            38                             201 - Data unavailable   \n",
       "29            38                               255 - No data exists   \n",
       "...          ...                                                ...   \n",
       "22976        255                                                      \n",
       "22977        255                                                      \n",
       "22978        255                                                      \n",
       "22979        255                                                      \n",
       "22980        255                                                      \n",
       "22981        255                                                      \n",
       "22982        255                                                      \n",
       "22983        255                                                      \n",
       "22984        255                                                      \n",
       "22985        255                                                      \n",
       "22986        255                                                      \n",
       "22987        255                                                      \n",
       "22988        255                                                      \n",
       "22989        255                                                      \n",
       "22990        255                                                      \n",
       "22991        255                                                      \n",
       "22992        255                                                      \n",
       "22993        255                                                      \n",
       "22994        255                                                      \n",
       "22995        255                                                      \n",
       "22996        255                                                      \n",
       "22997        255                                                      \n",
       "22998        255                                                      \n",
       "22999        255                                                      \n",
       "23000        255                                                      \n",
       "23001        255                                                      \n",
       "23002        255                                                      \n",
       "23003        255                                                      \n",
       "23004        255                                                      \n",
       "23005        255                                                      \n",
       "\n",
       "          Unnamed: 6  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "5                NaN  \n",
       "6                NaN  \n",
       "7                NaN  \n",
       "8                NaN  \n",
       "9                NaN  \n",
       "10               NaN  \n",
       "11               NaN  \n",
       "12               NaN  \n",
       "13      Good Gauging  \n",
       "14               NaN  \n",
       "15               NaN  \n",
       "16               NaN  \n",
       "17               NaN  \n",
       "18               NaN  \n",
       "19               NaN  \n",
       "20               NaN  \n",
       "21               NaN  \n",
       "22               NaN  \n",
       "23               NaN  \n",
       "24               NaN  \n",
       "25               NaN  \n",
       "26               NaN  \n",
       "27               NaN  \n",
       "28               NaN  \n",
       "29               NaN  \n",
       "...              ...  \n",
       "22976            NaN  \n",
       "22977            NaN  \n",
       "22978            NaN  \n",
       "22979            NaN  \n",
       "22980            NaN  \n",
       "22981            NaN  \n",
       "22982            NaN  \n",
       "22983            NaN  \n",
       "22984            NaN  \n",
       "22985            NaN  \n",
       "22986            NaN  \n",
       "22987            NaN  \n",
       "22988            NaN  \n",
       "22989            NaN  \n",
       "22990            NaN  \n",
       "22991            NaN  \n",
       "22992            NaN  \n",
       "22993            NaN  \n",
       "22994            NaN  \n",
       "22995            NaN  \n",
       "22996            NaN  \n",
       "22997            NaN  \n",
       "22998            NaN  \n",
       "22999            NaN  \n",
       "23000            NaN  \n",
       "23001            NaN  \n",
       "23002            NaN  \n",
       "23003            NaN  \n",
       "23004            NaN  \n",
       "23005            NaN  \n",
       "\n",
       "[23006 rows x 7 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=data.drop(data.index[[0,1,2]])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.iloc[:,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>412002</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00:00:00 10/01/1954</td>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00:00:00 11/01/1954</td>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>00:00:00 12/01/1954</td>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>00:00:00 13/01/1954</td>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00:00:00 14/01/1954</td>\n",
       "      <td>955.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00:00:00 15/01/1954</td>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>00:00:00 16/01/1954</td>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>00:00:00 17/01/1954</td>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>00:00:00 18/01/1954</td>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>00:00:00 19/01/1954</td>\n",
       "      <td>837.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>00:00:00 20/01/1954</td>\n",
       "      <td>837.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>00:00:00 21/01/1954</td>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>00:00:00 22/01/1954</td>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>00:00:00 23/01/1954</td>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>00:00:00 24/01/1954</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>00:00:00 25/01/1954</td>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>00:00:00 26/01/1954</td>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>00:00:00 27/01/1954</td>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>00:00:00 28/01/1954</td>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>00:00:00 29/01/1954</td>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>00:00:00 30/01/1954</td>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>00:00:00 31/01/1954</td>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>00:00:00 01/02/1954</td>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>00:00:00 02/02/1954</td>\n",
       "      <td>1183.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>00:00:00 03/02/1954</td>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>00:00:00 04/02/1954</td>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>00:00:00 05/02/1954</td>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>00:00:00 06/02/1954</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>00:00:00 07/02/1954</td>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>00:00:00 08/02/1954</td>\n",
       "      <td>837.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22976</th>\n",
       "      <td>00:00:00 03/12/2016</td>\n",
       "      <td>943.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22977</th>\n",
       "      <td>00:00:00 04/12/2016</td>\n",
       "      <td>1112.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22978</th>\n",
       "      <td>00:00:00 05/12/2016</td>\n",
       "      <td>1089.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22979</th>\n",
       "      <td>00:00:00 06/12/2016</td>\n",
       "      <td>992.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22980</th>\n",
       "      <td>00:00:00 07/12/2016</td>\n",
       "      <td>817.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22981</th>\n",
       "      <td>00:00:00 08/12/2016</td>\n",
       "      <td>889.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22982</th>\n",
       "      <td>00:00:00 09/12/2016</td>\n",
       "      <td>877.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22983</th>\n",
       "      <td>00:00:00 10/12/2016</td>\n",
       "      <td>899.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22984</th>\n",
       "      <td>00:00:00 11/12/2016</td>\n",
       "      <td>863.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22985</th>\n",
       "      <td>00:00:00 12/12/2016</td>\n",
       "      <td>781.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22986</th>\n",
       "      <td>00:00:00 13/12/2016</td>\n",
       "      <td>821.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22987</th>\n",
       "      <td>00:00:00 14/12/2016</td>\n",
       "      <td>895.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22988</th>\n",
       "      <td>00:00:00 15/12/2016</td>\n",
       "      <td>1159.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22989</th>\n",
       "      <td>00:00:00 16/12/2016</td>\n",
       "      <td>1117.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22990</th>\n",
       "      <td>00:00:00 17/12/2016</td>\n",
       "      <td>952.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22991</th>\n",
       "      <td>00:00:00 18/12/2016</td>\n",
       "      <td>852.859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22992</th>\n",
       "      <td>00:00:00 19/12/2016</td>\n",
       "      <td>775.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22993</th>\n",
       "      <td>00:00:00 20/12/2016</td>\n",
       "      <td>758.723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22994</th>\n",
       "      <td>00:00:00 21/12/2016</td>\n",
       "      <td>744.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22995</th>\n",
       "      <td>00:00:00 22/12/2016</td>\n",
       "      <td>736.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22996</th>\n",
       "      <td>00:00:00 23/12/2016</td>\n",
       "      <td>732.869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22997</th>\n",
       "      <td>00:00:00 24/12/2016</td>\n",
       "      <td>775.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22998</th>\n",
       "      <td>00:00:00 25/12/2016</td>\n",
       "      <td>675.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22999</th>\n",
       "      <td>00:00:00 26/12/2016</td>\n",
       "      <td>658.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23000</th>\n",
       "      <td>00:00:00 27/12/2016</td>\n",
       "      <td>539.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23001</th>\n",
       "      <td>00:00:00 28/12/2016</td>\n",
       "      <td>502.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23002</th>\n",
       "      <td>00:00:00 29/12/2016</td>\n",
       "      <td>554.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23003</th>\n",
       "      <td>00:00:00 30/12/2016</td>\n",
       "      <td>844.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23004</th>\n",
       "      <td>00:00:00 31/12/2016</td>\n",
       "      <td>967.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23005</th>\n",
       "      <td>00:00:00 01/01/2017</td>\n",
       "      <td>712.538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23003 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Time    412002\n",
       "3      00:00:00 10/01/1954   979.351\n",
       "4      00:00:00 11/01/1954  1030.009\n",
       "5      00:00:00 12/01/1954   919.242\n",
       "6      00:00:00 13/01/1954   952.675\n",
       "7      00:00:00 14/01/1954    955.59\n",
       "8      00:00:00 15/01/1954   969.969\n",
       "9      00:00:00 16/01/1954   991.797\n",
       "10     00:00:00 17/01/1954   908.621\n",
       "11     00:00:00 18/01/1954   841.898\n",
       "12     00:00:00 19/01/1954    837.74\n",
       "13     00:00:00 20/01/1954    837.74\n",
       "14     00:00:00 21/01/1954   811.396\n",
       "15     00:00:00 22/01/1954   771.695\n",
       "16     00:00:00 23/01/1954   888.125\n",
       "17     00:00:00 24/01/1954   882.645\n",
       "18     00:00:00 25/01/1954   827.639\n",
       "19     00:00:00 26/01/1954   785.958\n",
       "20     00:00:00 27/01/1954   769.846\n",
       "21     00:00:00 28/01/1954   755.547\n",
       "22     00:00:00 29/01/1954   888.815\n",
       "23     00:00:00 30/01/1954  1129.708\n",
       "24     00:00:00 31/01/1954  1400.324\n",
       "25     00:00:00 01/02/1954  1517.684\n",
       "26     00:00:00 02/02/1954    1183.4\n",
       "27     00:00:00 03/02/1954   997.167\n",
       "28     00:00:00 04/02/1954   902.372\n",
       "29     00:00:00 05/02/1954   896.658\n",
       "30     00:00:00 06/02/1954   882.645\n",
       "31     00:00:00 07/02/1954   840.576\n",
       "32     00:00:00 08/02/1954    837.74\n",
       "...                    ...       ...\n",
       "22976  00:00:00 03/12/2016    943.23\n",
       "22977  00:00:00 04/12/2016  1112.347\n",
       "22978  00:00:00 05/12/2016  1089.857\n",
       "22979  00:00:00 06/12/2016    992.21\n",
       "22980  00:00:00 07/12/2016   817.307\n",
       "22981  00:00:00 08/12/2016   889.291\n",
       "22982  00:00:00 09/12/2016   877.835\n",
       "22983  00:00:00 10/12/2016   899.079\n",
       "22984  00:00:00 11/12/2016   863.327\n",
       "22985  00:00:00 12/12/2016   781.462\n",
       "22986  00:00:00 13/12/2016   821.476\n",
       "22987  00:00:00 14/12/2016   895.357\n",
       "22988  00:00:00 15/12/2016   1159.23\n",
       "22989  00:00:00 16/12/2016  1117.682\n",
       "22990  00:00:00 17/12/2016   952.201\n",
       "22991  00:00:00 18/12/2016   852.859\n",
       "22992  00:00:00 19/12/2016   775.405\n",
       "22993  00:00:00 20/12/2016   758.723\n",
       "22994  00:00:00 21/12/2016   744.589\n",
       "22995  00:00:00 22/12/2016   736.339\n",
       "22996  00:00:00 23/12/2016   732.869\n",
       "22997  00:00:00 24/12/2016   775.192\n",
       "22998  00:00:00 25/12/2016   675.152\n",
       "22999  00:00:00 26/12/2016    658.38\n",
       "23000  00:00:00 27/12/2016   539.519\n",
       "23001  00:00:00 28/12/2016   502.858\n",
       "23002  00:00:00 29/12/2016   554.372\n",
       "23003  00:00:00 30/12/2016   844.288\n",
       "23004  00:00:00 31/12/2016   967.899\n",
       "23005  00:00:00 01/01/2017   712.538\n",
       "\n",
       "[23003 rows x 2 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>412002</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10-01-1954</td>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11-01-1954</td>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12-01-1954</td>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13-01-1954</td>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14-01-1954</td>\n",
       "      <td>955.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15-01-1954</td>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16-01-1954</td>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>17-01-1954</td>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18-01-1954</td>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19-01-1954</td>\n",
       "      <td>837.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20-01-1954</td>\n",
       "      <td>837.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21-01-1954</td>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22-01-1954</td>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23-01-1954</td>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24-01-1954</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>25-01-1954</td>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26-01-1954</td>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>27-01-1954</td>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>28-01-1954</td>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>29-01-1954</td>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30-01-1954</td>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>31-01-1954</td>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>01-02-1954</td>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>02-02-1954</td>\n",
       "      <td>1183.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>03-02-1954</td>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>04-02-1954</td>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>05-02-1954</td>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>06-02-1954</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>07-02-1954</td>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>08-02-1954</td>\n",
       "      <td>837.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22976</th>\n",
       "      <td>03-12-2016</td>\n",
       "      <td>943.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22977</th>\n",
       "      <td>04-12-2016</td>\n",
       "      <td>1112.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22978</th>\n",
       "      <td>05-12-2016</td>\n",
       "      <td>1089.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22979</th>\n",
       "      <td>06-12-2016</td>\n",
       "      <td>992.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22980</th>\n",
       "      <td>07-12-2016</td>\n",
       "      <td>817.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22981</th>\n",
       "      <td>08-12-2016</td>\n",
       "      <td>889.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22982</th>\n",
       "      <td>09-12-2016</td>\n",
       "      <td>877.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22983</th>\n",
       "      <td>10-12-2016</td>\n",
       "      <td>899.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22984</th>\n",
       "      <td>11-12-2016</td>\n",
       "      <td>863.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22985</th>\n",
       "      <td>12-12-2016</td>\n",
       "      <td>781.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22986</th>\n",
       "      <td>13-12-2016</td>\n",
       "      <td>821.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22987</th>\n",
       "      <td>14-12-2016</td>\n",
       "      <td>895.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22988</th>\n",
       "      <td>15-12-2016</td>\n",
       "      <td>1159.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22989</th>\n",
       "      <td>16-12-2016</td>\n",
       "      <td>1117.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22990</th>\n",
       "      <td>17-12-2016</td>\n",
       "      <td>952.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22991</th>\n",
       "      <td>18-12-2016</td>\n",
       "      <td>852.859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22992</th>\n",
       "      <td>19-12-2016</td>\n",
       "      <td>775.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22993</th>\n",
       "      <td>20-12-2016</td>\n",
       "      <td>758.723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22994</th>\n",
       "      <td>21-12-2016</td>\n",
       "      <td>744.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22995</th>\n",
       "      <td>22-12-2016</td>\n",
       "      <td>736.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22996</th>\n",
       "      <td>23-12-2016</td>\n",
       "      <td>732.869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22997</th>\n",
       "      <td>24-12-2016</td>\n",
       "      <td>775.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22998</th>\n",
       "      <td>25-12-2016</td>\n",
       "      <td>675.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22999</th>\n",
       "      <td>26-12-2016</td>\n",
       "      <td>658.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23000</th>\n",
       "      <td>27-12-2016</td>\n",
       "      <td>539.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23001</th>\n",
       "      <td>28-12-2016</td>\n",
       "      <td>502.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23002</th>\n",
       "      <td>29-12-2016</td>\n",
       "      <td>554.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23003</th>\n",
       "      <td>30-12-2016</td>\n",
       "      <td>844.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23004</th>\n",
       "      <td>31-12-2016</td>\n",
       "      <td>967.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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     "execution_count": 60,
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    "data=data.replace({'00:00:00 ': ''}, regex=True)\n",
    "data=data.replace({'/': '-'}, regex=True)\n",
    "data\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "data.columns = ['Time','Discharge']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "             Time Discharge\n",
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       "4      11-01-1954  1030.009\n",
       "5      12-01-1954   919.242\n",
       "6      13-01-1954   952.675\n",
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       "12     19-01-1954    837.74\n",
       "13     20-01-1954    837.74\n",
       "14     21-01-1954   811.396\n",
       "15     22-01-1954   771.695\n",
       "16     23-01-1954   888.125\n",
       "17     24-01-1954   882.645\n",
       "18     25-01-1954   827.639\n",
       "19     26-01-1954   785.958\n",
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       "21     28-01-1954   755.547\n",
       "22     29-01-1954   888.815\n",
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       "24     31-01-1954  1400.324\n",
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       "27     03-02-1954   997.167\n",
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       "30     06-02-1954   882.645\n",
       "31     07-02-1954   840.576\n",
       "32     08-02-1954    837.74\n",
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       "\n",
       "[23003 rows x 2 columns]"
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     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data\n",
    "\n",
    "\n",
    "\n",
    "\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\HP\\\\New folder (9)\\\\SIH\\\\DAY 3'"
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     },
     "execution_count": 63,
     "metadata": {},
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   "source": [
    "pwd"
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "\n"
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  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.to_csv(\"~/damkc1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "data2=pd.read_csv('~/damkc1.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
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       "      <td>3</td>\n",
       "      <td>10-01-1954</td>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>11-01-1954</td>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>12-01-1954</td>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>13-01-1954</td>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>14-01-1954</td>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>15-01-1954</td>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>16-01-1954</td>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>17-01-1954</td>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>18-01-1954</td>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>19-01-1954</td>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13</td>\n",
       "      <td>20-01-1954</td>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14</td>\n",
       "      <td>21-01-1954</td>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15</td>\n",
       "      <td>22-01-1954</td>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>23-01-1954</td>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17</td>\n",
       "      <td>24-01-1954</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18</td>\n",
       "      <td>25-01-1954</td>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>19</td>\n",
       "      <td>26-01-1954</td>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>27-01-1954</td>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21</td>\n",
       "      <td>28-01-1954</td>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22</td>\n",
       "      <td>29-01-1954</td>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>30-01-1954</td>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24</td>\n",
       "      <td>31-01-1954</td>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25</td>\n",
       "      <td>01-02-1954</td>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26</td>\n",
       "      <td>02-02-1954</td>\n",
       "      <td>1183.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>27</td>\n",
       "      <td>03-02-1954</td>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>28</td>\n",
       "      <td>04-02-1954</td>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>29</td>\n",
       "      <td>05-02-1954</td>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30</td>\n",
       "      <td>06-02-1954</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31</td>\n",
       "      <td>07-02-1954</td>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>32</td>\n",
       "      <td>08-02-1954</td>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22973</th>\n",
       "      <td>22976</td>\n",
       "      <td>03-12-2016</td>\n",
       "      <td>943.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22974</th>\n",
       "      <td>22977</td>\n",
       "      <td>04-12-2016</td>\n",
       "      <td>1112.347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22975</th>\n",
       "      <td>22978</td>\n",
       "      <td>05-12-2016</td>\n",
       "      <td>1089.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22976</th>\n",
       "      <td>22979</td>\n",
       "      <td>06-12-2016</td>\n",
       "      <td>992.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22977</th>\n",
       "      <td>22980</td>\n",
       "      <td>07-12-2016</td>\n",
       "      <td>817.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22978</th>\n",
       "      <td>22981</td>\n",
       "      <td>08-12-2016</td>\n",
       "      <td>889.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22979</th>\n",
       "      <td>22982</td>\n",
       "      <td>09-12-2016</td>\n",
       "      <td>877.835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22980</th>\n",
       "      <td>22983</td>\n",
       "      <td>10-12-2016</td>\n",
       "      <td>899.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22981</th>\n",
       "      <td>22984</td>\n",
       "      <td>11-12-2016</td>\n",
       "      <td>863.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22982</th>\n",
       "      <td>22985</td>\n",
       "      <td>12-12-2016</td>\n",
       "      <td>781.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22983</th>\n",
       "      <td>22986</td>\n",
       "      <td>13-12-2016</td>\n",
       "      <td>821.476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22984</th>\n",
       "      <td>22987</td>\n",
       "      <td>14-12-2016</td>\n",
       "      <td>895.357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22985</th>\n",
       "      <td>22988</td>\n",
       "      <td>15-12-2016</td>\n",
       "      <td>1159.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22986</th>\n",
       "      <td>22989</td>\n",
       "      <td>16-12-2016</td>\n",
       "      <td>1117.682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22987</th>\n",
       "      <td>22990</td>\n",
       "      <td>17-12-2016</td>\n",
       "      <td>952.201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22988</th>\n",
       "      <td>22991</td>\n",
       "      <td>18-12-2016</td>\n",
       "      <td>852.859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22989</th>\n",
       "      <td>22992</td>\n",
       "      <td>19-12-2016</td>\n",
       "      <td>775.405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22990</th>\n",
       "      <td>22993</td>\n",
       "      <td>20-12-2016</td>\n",
       "      <td>758.723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22991</th>\n",
       "      <td>22994</td>\n",
       "      <td>21-12-2016</td>\n",
       "      <td>744.589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22992</th>\n",
       "      <td>22995</td>\n",
       "      <td>22-12-2016</td>\n",
       "      <td>736.339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22993</th>\n",
       "      <td>22996</td>\n",
       "      <td>23-12-2016</td>\n",
       "      <td>732.869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22994</th>\n",
       "      <td>22997</td>\n",
       "      <td>24-12-2016</td>\n",
       "      <td>775.192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22995</th>\n",
       "      <td>22998</td>\n",
       "      <td>25-12-2016</td>\n",
       "      <td>675.152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22996</th>\n",
       "      <td>22999</td>\n",
       "      <td>26-12-2016</td>\n",
       "      <td>658.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22997</th>\n",
       "      <td>23000</td>\n",
       "      <td>27-12-2016</td>\n",
       "      <td>539.519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22998</th>\n",
       "      <td>23001</td>\n",
       "      <td>28-12-2016</td>\n",
       "      <td>502.858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22999</th>\n",
       "      <td>23002</td>\n",
       "      <td>29-12-2016</td>\n",
       "      <td>554.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23000</th>\n",
       "      <td>23003</td>\n",
       "      <td>30-12-2016</td>\n",
       "      <td>844.288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23001</th>\n",
       "      <td>23004</td>\n",
       "      <td>31-12-2016</td>\n",
       "      <td>967.899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23002</th>\n",
       "      <td>23005</td>\n",
       "      <td>01-01-2017</td>\n",
       "      <td>712.538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23003 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0        Time  Discharge\n",
       "0               3  10-01-1954    979.351\n",
       "1               4  11-01-1954   1030.009\n",
       "2               5  12-01-1954    919.242\n",
       "3               6  13-01-1954    952.675\n",
       "4               7  14-01-1954    955.590\n",
       "5               8  15-01-1954    969.969\n",
       "6               9  16-01-1954    991.797\n",
       "7              10  17-01-1954    908.621\n",
       "8              11  18-01-1954    841.898\n",
       "9              12  19-01-1954    837.740\n",
       "10             13  20-01-1954    837.740\n",
       "11             14  21-01-1954    811.396\n",
       "12             15  22-01-1954    771.695\n",
       "13             16  23-01-1954    888.125\n",
       "14             17  24-01-1954    882.645\n",
       "15             18  25-01-1954    827.639\n",
       "16             19  26-01-1954    785.958\n",
       "17             20  27-01-1954    769.846\n",
       "18             21  28-01-1954    755.547\n",
       "19             22  29-01-1954    888.815\n",
       "20             23  30-01-1954   1129.708\n",
       "21             24  31-01-1954   1400.324\n",
       "22             25  01-02-1954   1517.684\n",
       "23             26  02-02-1954   1183.400\n",
       "24             27  03-02-1954    997.167\n",
       "25             28  04-02-1954    902.372\n",
       "26             29  05-02-1954    896.658\n",
       "27             30  06-02-1954    882.645\n",
       "28             31  07-02-1954    840.576\n",
       "29             32  08-02-1954    837.740\n",
       "...           ...         ...        ...\n",
       "22973       22976  03-12-2016    943.230\n",
       "22974       22977  04-12-2016   1112.347\n",
       "22975       22978  05-12-2016   1089.857\n",
       "22976       22979  06-12-2016    992.210\n",
       "22977       22980  07-12-2016    817.307\n",
       "22978       22981  08-12-2016    889.291\n",
       "22979       22982  09-12-2016    877.835\n",
       "22980       22983  10-12-2016    899.079\n",
       "22981       22984  11-12-2016    863.327\n",
       "22982       22985  12-12-2016    781.462\n",
       "22983       22986  13-12-2016    821.476\n",
       "22984       22987  14-12-2016    895.357\n",
       "22985       22988  15-12-2016   1159.230\n",
       "22986       22989  16-12-2016   1117.682\n",
       "22987       22990  17-12-2016    952.201\n",
       "22988       22991  18-12-2016    852.859\n",
       "22989       22992  19-12-2016    775.405\n",
       "22990       22993  20-12-2016    758.723\n",
       "22991       22994  21-12-2016    744.589\n",
       "22992       22995  22-12-2016    736.339\n",
       "22993       22996  23-12-2016    732.869\n",
       "22994       22997  24-12-2016    775.192\n",
       "22995       22998  25-12-2016    675.152\n",
       "22996       22999  26-12-2016    658.380\n",
       "22997       23000  27-12-2016    539.519\n",
       "22998       23001  28-12-2016    502.858\n",
       "22999       23002  29-12-2016    554.372\n",
       "23000       23003  30-12-2016    844.288\n",
       "23001       23004  31-12-2016    967.899\n",
       "23002       23005  01-01-2017    712.538\n",
       "\n",
       "[23003 rows x 3 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10-01-1954</td>\n",
       "      <td>979.351</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11-01-1954</td>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12-01-1954</td>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13-01-1954</td>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14-01-1954</td>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time  Discharge\n",
       "0  10-01-1954    979.351\n",
       "1  11-01-1954   1030.009\n",
       "2  12-01-1954    919.242\n",
       "3  13-01-1954    952.675\n",
       "4  14-01-1954    955.590"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data2.drop(data2.columns[[0]],axis = 1)\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data3=data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Discharge</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>10-01-1954</td>\n",
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       "      <th>1</th>\n",
       "      <td>11-01-1954</td>\n",
       "      <td>1030.009</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12-01-1954</td>\n",
       "      <td>919.242</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13-01-1954</td>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14-01-1954</td>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15-01-1954</td>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16-01-1954</td>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17-01-1954</td>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18-01-1954</td>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19-01-1954</td>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20-01-1954</td>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21-01-1954</td>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22-01-1954</td>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23-01-1954</td>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24-01-1954</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25-01-1954</td>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>26-01-1954</td>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>27-01-1954</td>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28-01-1954</td>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>29-01-1954</td>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>30-01-1954</td>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31-01-1954</td>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>01-02-1954</td>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>02-02-1954</td>\n",
       "      <td>1183.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>03-02-1954</td>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>04-02-1954</td>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>05-02-1954</td>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>06-02-1954</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>07-02-1954</td>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>08-02-1954</td>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22639</th>\n",
       "      <td>04-01-2016</td>\n",
       "      <td>1229.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22640</th>\n",
       "      <td>05-01-2016</td>\n",
       "      <td>1226.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22641</th>\n",
       "      <td>06-01-2016</td>\n",
       "      <td>1249.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22642</th>\n",
       "      <td>07-01-2016</td>\n",
       "      <td>1316.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22643</th>\n",
       "      <td>08-01-2016</td>\n",
       "      <td>1411.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22644</th>\n",
       "      <td>09-01-2016</td>\n",
       "      <td>1535.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22645</th>\n",
       "      <td>10-01-2016</td>\n",
       "      <td>1738.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22646</th>\n",
       "      <td>11-01-2016</td>\n",
       "      <td>1766.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22647</th>\n",
       "      <td>12-01-2016</td>\n",
       "      <td>1930.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22648</th>\n",
       "      <td>13-01-2016</td>\n",
       "      <td>2031.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22649</th>\n",
       "      <td>14-01-2016</td>\n",
       "      <td>2046.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22650</th>\n",
       "      <td>15-01-2016</td>\n",
       "      <td>2301.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22651</th>\n",
       "      <td>16-01-2016</td>\n",
       "      <td>2531.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22652</th>\n",
       "      <td>17-01-2016</td>\n",
       "      <td>2272.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22653</th>\n",
       "      <td>18-01-2016</td>\n",
       "      <td>2152.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22654</th>\n",
       "      <td>19-01-2016</td>\n",
       "      <td>1940.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22655</th>\n",
       "      <td>20-01-2016</td>\n",
       "      <td>1789.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22656</th>\n",
       "      <td>21-01-2016</td>\n",
       "      <td>1776.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22657</th>\n",
       "      <td>22-01-2016</td>\n",
       "      <td>1981.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22658</th>\n",
       "      <td>23-01-2016</td>\n",
       "      <td>2098.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22659</th>\n",
       "      <td>24-01-2016</td>\n",
       "      <td>1818.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22660</th>\n",
       "      <td>25-01-2016</td>\n",
       "      <td>1415.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22661</th>\n",
       "      <td>26-01-2016</td>\n",
       "      <td>1431.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22662</th>\n",
       "      <td>27-01-2016</td>\n",
       "      <td>1399.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22663</th>\n",
       "      <td>28-01-2016</td>\n",
       "      <td>1176.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22664</th>\n",
       "      <td>29-01-2016</td>\n",
       "      <td>1051.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22665</th>\n",
       "      <td>30-01-2016</td>\n",
       "      <td>854.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22666</th>\n",
       "      <td>31-01-2016</td>\n",
       "      <td>726.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22667</th>\n",
       "      <td>01-02-2016</td>\n",
       "      <td>730.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22668</th>\n",
       "      <td>02-02-2016</td>\n",
       "      <td>662.152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22669 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Time  Discharge\n",
       "0      10-01-1954    979.351\n",
       "1      11-01-1954   1030.009\n",
       "2      12-01-1954    919.242\n",
       "3      13-01-1954    952.675\n",
       "4      14-01-1954    955.590\n",
       "5      15-01-1954    969.969\n",
       "6      16-01-1954    991.797\n",
       "7      17-01-1954    908.621\n",
       "8      18-01-1954    841.898\n",
       "9      19-01-1954    837.740\n",
       "10     20-01-1954    837.740\n",
       "11     21-01-1954    811.396\n",
       "12     22-01-1954    771.695\n",
       "13     23-01-1954    888.125\n",
       "14     24-01-1954    882.645\n",
       "15     25-01-1954    827.639\n",
       "16     26-01-1954    785.958\n",
       "17     27-01-1954    769.846\n",
       "18     28-01-1954    755.547\n",
       "19     29-01-1954    888.815\n",
       "20     30-01-1954   1129.708\n",
       "21     31-01-1954   1400.324\n",
       "22     01-02-1954   1517.684\n",
       "23     02-02-1954   1183.400\n",
       "24     03-02-1954    997.167\n",
       "25     04-02-1954    902.372\n",
       "26     05-02-1954    896.658\n",
       "27     06-02-1954    882.645\n",
       "28     07-02-1954    840.576\n",
       "29     08-02-1954    837.740\n",
       "...           ...        ...\n",
       "22639  04-01-2016   1229.611\n",
       "22640  05-01-2016   1226.988\n",
       "22641  06-01-2016   1249.116\n",
       "22642  07-01-2016   1316.805\n",
       "22643  08-01-2016   1411.780\n",
       "22644  09-01-2016   1535.792\n",
       "22645  10-01-2016   1738.196\n",
       "22646  11-01-2016   1766.817\n",
       "22647  12-01-2016   1930.445\n",
       "22648  13-01-2016   2031.766\n",
       "22649  14-01-2016   2046.541\n",
       "22650  15-01-2016   2301.107\n",
       "22651  16-01-2016   2531.314\n",
       "22652  17-01-2016   2272.333\n",
       "22653  18-01-2016   2152.691\n",
       "22654  19-01-2016   1940.654\n",
       "22655  20-01-2016   1789.787\n",
       "22656  21-01-2016   1776.703\n",
       "22657  22-01-2016   1981.752\n",
       "22658  23-01-2016   2098.387\n",
       "22659  24-01-2016   1818.521\n",
       "22660  25-01-2016   1415.694\n",
       "22661  26-01-2016   1431.905\n",
       "22662  27-01-2016   1399.450\n",
       "22663  28-01-2016   1176.481\n",
       "22664  29-01-2016   1051.522\n",
       "22665  30-01-2016    854.108\n",
       "22666  31-01-2016    726.150\n",
       "22667  01-02-2016    730.435\n",
       "22668  02-02-2016    662.152\n",
       "\n",
       "[22669 rows x 2 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindata=data3.iloc[0: 22669]\n",
    "traindata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testdata=data3.iloc[22639:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10-01-1954</td>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11-01-1954</td>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12-01-1954</td>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13-01-1954</td>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14-01-1954</td>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time  Discharge\n",
       "0  10-01-1954    979.351\n",
       "1  11-01-1954   1030.009\n",
       "2  12-01-1954    919.242\n",
       "3  13-01-1954    952.675\n",
       "4  14-01-1954    955.590"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testdata.head()\n",
    "traindata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "traindata.to_csv(\"~/trainDam.csv\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testdata.to_csv(\"~/testDam.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dateparse = lambda dates: pd.datetime.strptime(dates, '%d-%m-%Y')\n",
    "traindata = pd.read_csv('~/trainDam.csv', parse_dates=['Time'], index_col='Time',date_parser=dateparse)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1954-01-10</th>\n",
       "      <td>0</td>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-11</th>\n",
       "      <td>1</td>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-12</th>\n",
       "      <td>2</td>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-13</th>\n",
       "      <td>3</td>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-14</th>\n",
       "      <td>4</td>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-15</th>\n",
       "      <td>5</td>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-16</th>\n",
       "      <td>6</td>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-17</th>\n",
       "      <td>7</td>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-18</th>\n",
       "      <td>8</td>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-19</th>\n",
       "      <td>9</td>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-20</th>\n",
       "      <td>10</td>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-21</th>\n",
       "      <td>11</td>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-22</th>\n",
       "      <td>12</td>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-23</th>\n",
       "      <td>13</td>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-24</th>\n",
       "      <td>14</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-25</th>\n",
       "      <td>15</td>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-26</th>\n",
       "      <td>16</td>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-27</th>\n",
       "      <td>17</td>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-28</th>\n",
       "      <td>18</td>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-29</th>\n",
       "      <td>19</td>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-30</th>\n",
       "      <td>20</td>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-31</th>\n",
       "      <td>21</td>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-01</th>\n",
       "      <td>22</td>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-02</th>\n",
       "      <td>23</td>\n",
       "      <td>1183.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-03</th>\n",
       "      <td>24</td>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-04</th>\n",
       "      <td>25</td>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-05</th>\n",
       "      <td>26</td>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-06</th>\n",
       "      <td>27</td>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-07</th>\n",
       "      <td>28</td>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-08</th>\n",
       "      <td>29</td>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>22639</td>\n",
       "      <td>1229.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>22640</td>\n",
       "      <td>1226.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>22641</td>\n",
       "      <td>1249.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>22642</td>\n",
       "      <td>1316.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>22643</td>\n",
       "      <td>1411.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>22644</td>\n",
       "      <td>1535.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>22645</td>\n",
       "      <td>1738.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>22646</td>\n",
       "      <td>1766.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>22647</td>\n",
       "      <td>1930.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>22648</td>\n",
       "      <td>2031.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>22649</td>\n",
       "      <td>2046.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>22650</td>\n",
       "      <td>2301.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-16</th>\n",
       "      <td>22651</td>\n",
       "      <td>2531.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>22652</td>\n",
       "      <td>2272.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>22653</td>\n",
       "      <td>2152.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>22654</td>\n",
       "      <td>1940.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>22655</td>\n",
       "      <td>1789.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21</th>\n",
       "      <td>22656</td>\n",
       "      <td>1776.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22</th>\n",
       "      <td>22657</td>\n",
       "      <td>1981.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-23</th>\n",
       "      <td>22658</td>\n",
       "      <td>2098.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-24</th>\n",
       "      <td>22659</td>\n",
       "      <td>1818.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-25</th>\n",
       "      <td>22660</td>\n",
       "      <td>1415.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-26</th>\n",
       "      <td>22661</td>\n",
       "      <td>1431.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27</th>\n",
       "      <td>22662</td>\n",
       "      <td>1399.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-28</th>\n",
       "      <td>22663</td>\n",
       "      <td>1176.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-29</th>\n",
       "      <td>22664</td>\n",
       "      <td>1051.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-30</th>\n",
       "      <td>22665</td>\n",
       "      <td>854.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>22666</td>\n",
       "      <td>726.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>22667</td>\n",
       "      <td>730.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>22668</td>\n",
       "      <td>662.152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22669 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0  Discharge\n",
       "Time                             \n",
       "1954-01-10           0    979.351\n",
       "1954-01-11           1   1030.009\n",
       "1954-01-12           2    919.242\n",
       "1954-01-13           3    952.675\n",
       "1954-01-14           4    955.590\n",
       "1954-01-15           5    969.969\n",
       "1954-01-16           6    991.797\n",
       "1954-01-17           7    908.621\n",
       "1954-01-18           8    841.898\n",
       "1954-01-19           9    837.740\n",
       "1954-01-20          10    837.740\n",
       "1954-01-21          11    811.396\n",
       "1954-01-22          12    771.695\n",
       "1954-01-23          13    888.125\n",
       "1954-01-24          14    882.645\n",
       "1954-01-25          15    827.639\n",
       "1954-01-26          16    785.958\n",
       "1954-01-27          17    769.846\n",
       "1954-01-28          18    755.547\n",
       "1954-01-29          19    888.815\n",
       "1954-01-30          20   1129.708\n",
       "1954-01-31          21   1400.324\n",
       "1954-02-01          22   1517.684\n",
       "1954-02-02          23   1183.400\n",
       "1954-02-03          24    997.167\n",
       "1954-02-04          25    902.372\n",
       "1954-02-05          26    896.658\n",
       "1954-02-06          27    882.645\n",
       "1954-02-07          28    840.576\n",
       "1954-02-08          29    837.740\n",
       "...                ...        ...\n",
       "2016-01-04       22639   1229.611\n",
       "2016-01-05       22640   1226.988\n",
       "2016-01-06       22641   1249.116\n",
       "2016-01-07       22642   1316.805\n",
       "2016-01-08       22643   1411.780\n",
       "2016-01-09       22644   1535.792\n",
       "2016-01-10       22645   1738.196\n",
       "2016-01-11       22646   1766.817\n",
       "2016-01-12       22647   1930.445\n",
       "2016-01-13       22648   2031.766\n",
       "2016-01-14       22649   2046.541\n",
       "2016-01-15       22650   2301.107\n",
       "2016-01-16       22651   2531.314\n",
       "2016-01-17       22652   2272.333\n",
       "2016-01-18       22653   2152.691\n",
       "2016-01-19       22654   1940.654\n",
       "2016-01-20       22655   1789.787\n",
       "2016-01-21       22656   1776.703\n",
       "2016-01-22       22657   1981.752\n",
       "2016-01-23       22658   2098.387\n",
       "2016-01-24       22659   1818.521\n",
       "2016-01-25       22660   1415.694\n",
       "2016-01-26       22661   1431.905\n",
       "2016-01-27       22662   1399.450\n",
       "2016-01-28       22663   1176.481\n",
       "2016-01-29       22664   1051.522\n",
       "2016-01-30       22665    854.108\n",
       "2016-01-31       22666    726.150\n",
       "2016-02-01       22667    730.435\n",
       "2016-02-02       22668    662.152\n",
       "\n",
       "[22669 rows x 2 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindata\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1954-01-10</th>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-11</th>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-12</th>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-13</th>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-14</th>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-15</th>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-16</th>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-17</th>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-18</th>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-19</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-20</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-21</th>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-22</th>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-23</th>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-24</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-25</th>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-26</th>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-27</th>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-28</th>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-29</th>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-30</th>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-31</th>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-01</th>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-02</th>\n",
       "      <td>1183.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-03</th>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-04</th>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-05</th>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-06</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-07</th>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-08</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1229.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>1226.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1249.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1316.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1411.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>1535.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>1738.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>1766.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>1930.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>2031.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>2046.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>2301.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-16</th>\n",
       "      <td>2531.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>2272.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>2152.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>1940.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>1789.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21</th>\n",
       "      <td>1776.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22</th>\n",
       "      <td>1981.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-23</th>\n",
       "      <td>2098.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-24</th>\n",
       "      <td>1818.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-25</th>\n",
       "      <td>1415.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-26</th>\n",
       "      <td>1431.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27</th>\n",
       "      <td>1399.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-28</th>\n",
       "      <td>1176.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-29</th>\n",
       "      <td>1051.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-30</th>\n",
       "      <td>854.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>726.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>730.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>662.152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22669 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Discharge\n",
       "Time                 \n",
       "1954-01-10    979.351\n",
       "1954-01-11   1030.009\n",
       "1954-01-12    919.242\n",
       "1954-01-13    952.675\n",
       "1954-01-14    955.590\n",
       "1954-01-15    969.969\n",
       "1954-01-16    991.797\n",
       "1954-01-17    908.621\n",
       "1954-01-18    841.898\n",
       "1954-01-19    837.740\n",
       "1954-01-20    837.740\n",
       "1954-01-21    811.396\n",
       "1954-01-22    771.695\n",
       "1954-01-23    888.125\n",
       "1954-01-24    882.645\n",
       "1954-01-25    827.639\n",
       "1954-01-26    785.958\n",
       "1954-01-27    769.846\n",
       "1954-01-28    755.547\n",
       "1954-01-29    888.815\n",
       "1954-01-30   1129.708\n",
       "1954-01-31   1400.324\n",
       "1954-02-01   1517.684\n",
       "1954-02-02   1183.400\n",
       "1954-02-03    997.167\n",
       "1954-02-04    902.372\n",
       "1954-02-05    896.658\n",
       "1954-02-06    882.645\n",
       "1954-02-07    840.576\n",
       "1954-02-08    837.740\n",
       "...               ...\n",
       "2016-01-04   1229.611\n",
       "2016-01-05   1226.988\n",
       "2016-01-06   1249.116\n",
       "2016-01-07   1316.805\n",
       "2016-01-08   1411.780\n",
       "2016-01-09   1535.792\n",
       "2016-01-10   1738.196\n",
       "2016-01-11   1766.817\n",
       "2016-01-12   1930.445\n",
       "2016-01-13   2031.766\n",
       "2016-01-14   2046.541\n",
       "2016-01-15   2301.107\n",
       "2016-01-16   2531.314\n",
       "2016-01-17   2272.333\n",
       "2016-01-18   2152.691\n",
       "2016-01-19   1940.654\n",
       "2016-01-20   1789.787\n",
       "2016-01-21   1776.703\n",
       "2016-01-22   1981.752\n",
       "2016-01-23   2098.387\n",
       "2016-01-24   1818.521\n",
       "2016-01-25   1415.694\n",
       "2016-01-26   1431.905\n",
       "2016-01-27   1399.450\n",
       "2016-01-28   1176.481\n",
       "2016-01-29   1051.522\n",
       "2016-01-30    854.108\n",
       "2016-01-31    726.150\n",
       "2016-02-01    730.435\n",
       "2016-02-02    662.152\n",
       "\n",
       "[22669 rows x 1 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindata= traindata.drop(traindata.columns[[0]],axis = 1)\n",
    "traindata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1954-01-10', '1954-01-11', '1954-01-12', '1954-01-13',\n",
       "               '1954-01-14', '1954-01-15', '1954-01-16', '1954-01-17',\n",
       "               '1954-01-18', '1954-01-19',\n",
       "               ...\n",
       "               '2016-01-24', '2016-01-25', '2016-01-26', '2016-01-27',\n",
       "               '2016-01-28', '2016-01-29', '2016-01-30', '2016-01-31',\n",
       "               '2016-02-01', '2016-02-02'],\n",
       "              dtype='datetime64[ns]', name='Time', length=22669, freq=None)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindata.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6205,  6206,  6207,  6208,  6209,  6210,  6211,  6212,  6213,\n",
       "         6214,  6215,  6216,  6217,  6218,  6219,  6220,  6221,  6222,\n",
       "         6223,  6224,  6225,  6226,  6227,  6228,  6229,  6230,  6231,\n",
       "         6232,  6233,  6234,  6235,  6236,  6237,  6238,  6239,  6240,\n",
       "         6241,  6242,  6243,  6244,  6245,  6246,  6247,  6248,  6249,\n",
       "         6250,  6251,  6252,  6253,  6254,  6255,  6256,  6257,  6258,\n",
       "         6259,  6260,  6261,  6262,  6263,  6264,  6265,  6266,  6267,\n",
       "         6268,  6269,  6270,  6271,  6272,  6273,  6274,  6275,  6276,\n",
       "         6277,  6278,  6279,  6280,  6281,  6282,  6283,  6284,  6285,\n",
       "         6286,  6287,  6682,  6683,  6684,  6685,  6686,  6687,  6688,\n",
       "         6689,  6690,  6691,  6692,  6693,  6694,  6695,  6696,  6697,\n",
       "         6698,  6699,  6700,  6701,  6702,  6703,  6704,  6705,  6706,\n",
       "         6707,  6708,  6709,  6710,  6711,  6712,  6713,  6714,  6715,\n",
       "         6716,  6717,  6718,  6719,  6720,  6721,  6722,  6723,  6724,\n",
       "         6725,  6726,  6727,  6728,  6729,  6730,  6731,  6732,  6733,\n",
       "         6734,  6735,  6736,  6737,  6738,  6739,  6740,  6741,  6742,\n",
       "         6743,  6744,  6745,  6746,  6747,  6748,  6749,  6750,  6751,\n",
       "         6752,  6753, 15904, 15905, 15906, 15907, 15908, 15909, 15910,\n",
       "        15911, 15912, 15913, 15914, 15915, 15916, 15917, 15918, 15919,\n",
       "        15920, 15921, 15922, 15923, 15924, 15925, 15926, 15927, 15928,\n",
       "        15929, 15930, 15931, 15932, 15933, 15934, 15935, 15936, 15937,\n",
       "        15938, 15939, 15940, 15941, 15942, 15943, 15944, 15945, 15946,\n",
       "        15947, 15948, 15949, 15950, 15951, 16021, 16022, 16023, 16024,\n",
       "        16025, 16026, 16027, 16028, 16029, 16030, 16031, 16032, 16033,\n",
       "        16034, 16035, 16036, 16037, 16038, 16039, 16040, 16041, 16042,\n",
       "        16043, 16044, 16045, 16046, 16047, 16048, 16049, 16050, 16051,\n",
       "        16052, 16053, 16054, 16055, 16056, 16057, 16058, 16059, 16060,\n",
       "        16061, 16062, 16063, 16064, 16065, 16066, 16067, 16068, 16069,\n",
       "        17070, 17071, 17072, 17073, 17074, 17075, 17076, 17124, 17125,\n",
       "        17126, 17127, 17128, 17129, 17130, 17131, 17132, 17133, 17134,\n",
       "        17135, 17136, 17137, 17138], dtype=int64),\n",
       " array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(pd.isnull(traindata))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Discharge\n",
      "count   22395.000000\n",
      "mean     2376.156403\n",
      "std      5690.099848\n",
      "min         0.000000\n",
      "25%       492.740000\n",
      "50%      1091.953000\n",
      "75%      1967.172000\n",
      "max    210881.461000\n"
     ]
    }
   ],
   "source": [
    "print(traindata.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discharge    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(traindata).any()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindata.isnull().values.any()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testdata.to_csv(\"~/testDam2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1954-01-10</th>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-11</th>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-12</th>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-13</th>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-14</th>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-15</th>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-16</th>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-17</th>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-18</th>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-19</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-20</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-21</th>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-22</th>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-23</th>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-24</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-25</th>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-26</th>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-27</th>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-28</th>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-29</th>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-30</th>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-31</th>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-01</th>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-02</th>\n",
       "      <td>1183.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-03</th>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-04</th>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-05</th>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-06</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-07</th>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-08</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1229.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>1226.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1249.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1316.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1411.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>1535.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>1738.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>1766.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>1930.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>2031.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>2046.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>2301.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-16</th>\n",
       "      <td>2531.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>2272.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>2152.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>1940.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>1789.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21</th>\n",
       "      <td>1776.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22</th>\n",
       "      <td>1981.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-23</th>\n",
       "      <td>2098.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-24</th>\n",
       "      <td>1818.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-25</th>\n",
       "      <td>1415.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-26</th>\n",
       "      <td>1431.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27</th>\n",
       "      <td>1399.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-28</th>\n",
       "      <td>1176.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-29</th>\n",
       "      <td>1051.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-30</th>\n",
       "      <td>854.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>726.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>730.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>662.152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22669 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Discharge\n",
       "Time                 \n",
       "1954-01-10    979.351\n",
       "1954-01-11   1030.009\n",
       "1954-01-12    919.242\n",
       "1954-01-13    952.675\n",
       "1954-01-14    955.590\n",
       "1954-01-15    969.969\n",
       "1954-01-16    991.797\n",
       "1954-01-17    908.621\n",
       "1954-01-18    841.898\n",
       "1954-01-19    837.740\n",
       "1954-01-20    837.740\n",
       "1954-01-21    811.396\n",
       "1954-01-22    771.695\n",
       "1954-01-23    888.125\n",
       "1954-01-24    882.645\n",
       "1954-01-25    827.639\n",
       "1954-01-26    785.958\n",
       "1954-01-27    769.846\n",
       "1954-01-28    755.547\n",
       "1954-01-29    888.815\n",
       "1954-01-30   1129.708\n",
       "1954-01-31   1400.324\n",
       "1954-02-01   1517.684\n",
       "1954-02-02   1183.400\n",
       "1954-02-03    997.167\n",
       "1954-02-04    902.372\n",
       "1954-02-05    896.658\n",
       "1954-02-06    882.645\n",
       "1954-02-07    840.576\n",
       "1954-02-08    837.740\n",
       "...               ...\n",
       "2016-01-04   1229.611\n",
       "2016-01-05   1226.988\n",
       "2016-01-06   1249.116\n",
       "2016-01-07   1316.805\n",
       "2016-01-08   1411.780\n",
       "2016-01-09   1535.792\n",
       "2016-01-10   1738.196\n",
       "2016-01-11   1766.817\n",
       "2016-01-12   1930.445\n",
       "2016-01-13   2031.766\n",
       "2016-01-14   2046.541\n",
       "2016-01-15   2301.107\n",
       "2016-01-16   2531.314\n",
       "2016-01-17   2272.333\n",
       "2016-01-18   2152.691\n",
       "2016-01-19   1940.654\n",
       "2016-01-20   1789.787\n",
       "2016-01-21   1776.703\n",
       "2016-01-22   1981.752\n",
       "2016-01-23   2098.387\n",
       "2016-01-24   1818.521\n",
       "2016-01-25   1415.694\n",
       "2016-01-26   1431.905\n",
       "2016-01-27   1399.450\n",
       "2016-01-28   1176.481\n",
       "2016-01-29   1051.522\n",
       "2016-01-30    854.108\n",
       "2016-01-31    726.150\n",
       "2016-02-01    730.435\n",
       "2016-02-02    662.152\n",
       "\n",
       "[22669 rows x 1 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newTrainData= traindata.interpolate(method='cubic')\n",
    "newTrainData\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Discharge    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(traindata).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64), array([], dtype=int64))"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(pd.isnull(newTrainData))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b30a88668>]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PAFDKeMwByLekwaC19rtx3jo3xrpW0o1x9vO0pKdjLJ8l6egYy3fE+gwAAAAAQOayPYAM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\n348xBcuDMUa+DL+Mv/fkDJ374Mf6zWu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/7ksFXl9q38vJZ30XAm+l7cwBngTu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\nOeRi0MzMzMzMLIf+D/59S4/kKZCkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b34346518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(newTrainData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23b30c62a20>]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AgGzGaw5AqnUZDFprvxviTxcGWddKujXEfl6R9EqQ5QslHRdkeXmwzwAAAAAAxC/RA8gA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\nwj2f26tb9INHZAPvABghjm0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UpHxJ/7TWPpLmJCVdS5tL7/qU+FTUNWtg72667c3FEW2/\n9v7R3gEuQjnhvs/07k/P0KA+3TU4oE9d1OkNeCCdecS+Qdf7YHHoESsl/9o5D2utmttcOqh/T63c\nsVfnHDVYM3xKer72zCy98ePTVF3fou6Fedqvb49OwUbJXv8XyZS1pfpk2U6dNGQfHbJv+4AJvbt3\n3Aou29HE6qq/tw9vHC6w8Hh80np9/aSDvE2+PIdl9sboSsJj9dnq3Zqwqv1fpDwZxqqGFr8agSMG\n99H/XXyU37qvfrGl0/Z7G1t8XvCRlep61q9tatXcjeW66Nj9/f6+eU+ddlc3qk+P9nOyp7ZJox6Y\nrEX3XBx2vyPvn9Spz9/y4ipNW1umX154pDeAK69t0gth5u2bvXGPBvbupsMH9Qm5TiQl7n17FGr/\nfu33VmV9iwb37a66pla9NX+brjllqPqHGCW3MN9ENIJifkBAWlxZr+qGZv3m3WXaWl6v578/UqOG\ndczr5mn++Pv/LA85EMzQgT017fbzVNPYqjsDmgPXNbf5tVyQpEn/d46G799Xk1aXaMm2St0xeoSa\nW11hM1/tJaztaX93wXbd8f5y3XDGobpj9Ait2bVXb83bpu6F+Xr4quNVVFqrQX38mwnvrGrQQfv0\n1HX/nKfFPv1Lrh45RA9ddZyKKxu0b+9ufi0CRv9tpt6+5XSd+tAUSdKFx+yvwX2766MlO/Trd5Zq\n4d0XaVCf7nonTI1dU4tvTU3I1bz3+xkPT1VBnlGry2rK7efqiMF99L2XOgdCHoETVm955Ap97dlZ\nWrmjc8DrmxlbcNdF2lZRp1MObT/XnmaVtY2tqmtq9c4H5mv1rr06ceg+ob9EhF79YrOuP+NQfe+l\neV2O+Bos497Q3KZj/tjx3AnWCuCB/61WU6vLW3AlSRc9MUNbHrkiqrRe9OT09lqEMBnoCx+f7v35\n7iuO0WerS/Svm0/VRU9M7zIwDsb3o9aV1HgH4/KYu6lCU9aU6IjBnZ81m/fU6a8T1+qNIP3nKuua\nNcBdWOZbUBasq8Wv3vavSXznltN17Ysdo3oXldbo33O3aVCfblpWXN0eWIY4thvL6vyuvdV/vlS9\nukWejRxxzwSNOnSAFm6t1GmHDdQ/rjsl6HqPXHW8tzvCXyas0/R1ZXrnJ2doT22Trvr7bO2savA7\nj+ceNVhLtlVq6m/P06A+/vmZf87c7J2a58lrT9SXhw3UwN7dokp3KMGupCPv+lT/d1H7u/POD5Zr\n2m/PS/iUBK+7C61CzemcaoGFW+FsLKvVj15fqG75eVoXUFAxZEBPFVcGv89aXdb7fD7j4ama/Jtz\ntcdnsKqSvY06dN/eamlzadHWSt33344RiSetLtFfJqzVXVf4F3Ke/vAUb8udn/67I39990crdemX\nDvDLGwfeh2c+PMVbk/36nK365Lav6IQh8T9T08XRwaAxJl/Sc5IullQsaYEx5hNrbXJHlEiw8tom\nnfvXz3XTV4bpW6cMUd8ehRrQq9DvAeGbuVxeXKV7Pu64kC96YrqiEW5UxP/94ix99ZlZqmlq1WVP\ndZTY/+rC4frVhcO9tQ3WWp39l2nar293vXXL6TIyeuyzdTrryEE65yj/ZpaBtR6zN5Zr2Jhx+tqJ\nB/kt/1MXA4Gc/ZdpmnPnBfpoyU7N3FCm2RvL9YfLR0iSThy6j64/41CdNHQfHX9fR6ZpxY5qnXCf\nfyZq7A9PDfs5kvRLd6n/n77+Jd145jC/QWq2Bcm0BCv5eumGUbpgxH464g/jvcvO/evn3p9fnrVZ\n3x41VAV57QHXQ988XucdPVi3vrnY20l6zsZy3fH+MpXsbVJzq0uXH3+AfnnhcI04oJ8aW9q0vaJe\nlfUtWr2zWkMG9FJFXbM+X1+qb5x0sF6YsUlPf/fkTqO5BjNvU7nWldTo8c/Wa9ShA/T0d09W7+4F\n3sF/xrkHOvB4asoGvbNgu+bceYF3WeBAQZI6HXtfBXlGhfl5nZpJPvzpWj3s06T56pFD9Pi320c9\nvPm19pHEBvft7nfMy+uaVd/cqv8t26U73m9/8Tz//ZHq26NQR+zX2zskdSDP6H5PTl6vDQ9epilr\nSoMOU/2Tcw73BojhMuzhzL3zQp3+8BS/ZQN7t79M7vxguSav6aiNC1cjWdccuoalpc2lwvy89uYx\nARHJD171H4XtZ28ELzyqa27TM1ODF270KixQQX6eBvTupsm/OVfPTSvSh0FqIjzu+Xil3r7lDP3Y\n3RT5759vDLrebecfqT49CvTIp2u1eFulN3DxnMuxc7Zq7Bz/7zxjfVnQDPiZj0wNmml4f3Gx3g9R\n4LSupEYn+/QHPePhKfro1q/osc/amxJv2VOnwrzwBWgXHbO/t4ninDB9hHyDN0+G9dMVu/S90w6N\nahS6LXvqggaCgb784GRJ0oc/P1NDB/bSdS+3X78NLZ2D939cN1I//fdi3fnBCu1NQBPCB8atibjv\npG+LjMCaBY9gtXae2qPA7giXPTVT435xVpfdHkJ9Vlc83+uzVSUqS/DogSv/dKl2VTXo4idn6PU5\nW/0KOH09N63jfhrQq1WCh3IAABhhSURBVFBPfedk3fDKfK3ZtVdnugdg8wwUNOKAvnrom8fruWlF\n+tfcrXry2hP1f+8s6xQgBvZPvMhn2h+PhuY2v37PoZz96DR9MeaCLguffS10FxrM21wR8tx5CgI9\n5m2u0MayWj3x2fqg72dPU8BRD0zW2vtH++WBfLs2/PHjVappbNVB/XvosWtO9B7DcJ6eskEvzdik\ne756rO54f7kev+ZEXX3KEEmhB095dXZ7K6It5fU67M7xOnXYQL1605fVszBfeXlGNY0tuuM/yzVn\nU7nuv/I4jT7uAEntBXa9uxeoX4+OgsKaxhb17VEol8tqZ3WDX7AbOFJ6MFPWlMgY6YIR+2v8il2a\nu6lcf77yuC63C/Tx0h268qSDu1xvybZK7d+vhwb27uZ3HqasKdHNXfQ9jLS2WerIF1818mB9sHiH\nZhXt0eC+3XXsHycGXf+lmZv9gsGbXp0ftgvHlx+crHUPjFb3guD3QWCT5n/P3aq/fCtzg0GT7g7J\n4RhjzpB0n7X2Uvfvd0qStfbhUNuMGjXKLlyYms6ukbDW6rA7xwf924Uj9tMvLxyucSt26cUwNRWB\nfnLu4brq5CHKz2vPuPoOiPLo1cfr2i8fEnb7xpY2/fyNxZoapKnYmz8+TaV7m1RR19zl6IEbH7pc\n7y7c7q1BWH7fJaqqa9E5f+3cXObuK47xvlyPGNxb4355ti56YrrOPGJfXXzsAd7MZCi/veQo3XbB\ncEntmcQbXpkfdv1gfnbeETp7+CD9ZOwiv+HL/3btSfp1kH4YXfG8dMJlNgb37a77rzxOP/33In34\n8zN18iEDtL2iXlc8PVN7G0OPkrfqT5fqrEen+vUXDaYw32jd/ZcpL8/o46U7OpUCh3LzWYfpjtFH\n6y8T1ulln9FVzx4+yK9/wOPXnKjb32tvytSne0FE89n5fofC/DxNXLW7U5O7QCcN3UdLfZpa/OrC\n4SqrbfIbVW79A5fphlfmae6m2JrXLvvjJTrxz52D11vOOVzXn35o0GZe0djyyBXea8FTsj5nY7nf\n3JpdGXnIPn61XcEcuV8fVdY1q9wd/F49ckjIICgWvrUCFXXNQQfV8fXo1ccHbepzwpD+OmFIf509\nfLAu/dIB2l5Rr7P/Mk1XjxyiG844VFcG1JKk24BehV3eb/G49fwj/DL3qfbLC4frutMO8daOxmL4\nfn30wDeO86tZ6sot5xyuP1x+jC5+Yro2lAZvhjn5N+fq5Vmb9VaYJryRuuiY/UOOaBrM4YN7B+0X\nHOi8owfr2lFDNWrYQD0zdYNf4cWVJx2kj5dGNnprQZ5R0UOXS5IuePzziD67b48CzbrjArW4XBr1\nwGTvcr8atKtP0Le/PNRvu9MfmtKpb/3mhy8PmSfx9cltX+k0VUYo7//sDF39fPB+m5E47+jB3sDq\n6e+erHOGD9JJf/Z/7pw4pH/Q/qLB7N+vu7eFT69u+SGvO0n63mmH6P1Fxbr7q8fq6ycepP49C/XE\nZ+v09NQivfmj04I2a37x+lOUn2e6DG6C+WLMBTr/sc/DNq382XlH6PLjDtSe2ibd9NoC/e7So/XX\nEH3gf3PxUWpzWfXslq+zhw/S4D7d1at7gYyk61+eF/R98sOvHKaLjt1PJw3dx1tD+vKszdpV1aCK\numa5rNVHAdfztaOG6oYzD9XUNaV6fFJkTdcfu+ZEFeYbHXtgP7+mzrF66JvH69TDBvpVkPz9+yP1\n8yAFn9/58lBdfcoQLd5a6Vf4fM5Rg3XJsft3GrMilD9+9VgNHdiryzzqDWccGlOQnWzGmEXW2lFd\nrufwYPBbkkZba3/k/v16SadZa28LtY3TgkGpvX9SLKNjrbjvEvXtUaiq+mZd9fxsbSqr08w7ztfQ\ngb0Skq7WNpdO+vMk/f6yEbonwhsjHE8pSpvL6qrnZ3vbUX/31EP08FXH66Hxa/TijE367qlD9fBV\nJ/htW17bpFN8XnJXHH+gpq4t9ZbcvHj9KbrkSwd4/17d0KIPFxfrxjOHSVLQl9vKP12qX7y52Dt4\nxaaHLvfWfNY0tvjVMHqMOKBv2JEL//T1L2nh1kpV1DXp3zefJmOM1u7e69cv6qqTD9aB+/TQ+pJa\n7/DakjT19nN1uLs5kKfp4/79uusn5xyhb40aohXF1UH7pwUa+8NTtW53jf42eb23T0y3grxOL5fL\njz9A41e0Nxn1ZHj+ecOosAPKbHnkCtU2tWrKmhLd/u6ykE2qRhzQV7v3NqrKnXkObL77l2+doG+P\n6siYTFy1W2PeX64zjxykcct36ZyjBuuRq47X7I3l+q072PQ4fFBvTf3teSrZ26hfvLVE28rr9dR3\nTtJph++r5laXdlc36pZ/LYx4hMl5f7hQ+/fr4T3nZx6xrw7dt5duPf9I77xqza0uHXV3e1+ei4/d\nXzM3lEU9BcaWR67Q8fdO1JlH7qsXrm9/9lY3tHRq/nf7xUfpypMO1jNTN2jaujJdcfwBem9RseaM\nuVD9exV6A8rDB/fWU9eerB6Fee19koYP1itfdO63WvTgZXp/cbEmrynVzWcdpu+4M+q/uODITjWA\nGx+6XPnukumNZXXeZmsH79NTO6oadPcVx+hHZx/e6TOstXpxxiYt31GtZ797sowx+vKDk/2m2/D1\n8o2jdOEx+3fax9F3T+hUU/HC9ado2tpSfbpyt64aebBOOXSAbnszfOGBr80PXy5jTNBCmdMPH6gb\nzximB8atiamZXySm/+48PTphrfdek9ozfKt2VGviqpKIAvVXfjBK5x+9n7e1SLDv8sA3jtO3Thmi\nNbv26uRDBmhTWa2enLxBPznncP1j+kbvNAYeL90wSqU1jbrmlKF+tUGjHpjk17zK4x/XjdSSbVU6\n7+j9dOyB/bzNmEv3Nmrcil36039Xa8bvztch+/bS2Dlb9MePV6lvjwId1L+n3v3pGeqWn6d7P1mp\n348eodW79ur6l+frpRtG6WJ3M/Ci0hr9eOwib1/TB795nD5YvEPv/uQM5ecZfVG0p9Pzb8Kvz9aN\nr8z3NvU//fCBmrupolPhkceM352vg/bpoQfHr9Hgvt31lwkdGei194/WpX+b4e1TffNZh+m60w/V\nYYN6q7iyXj3c8yAO7N1NxZUNKsw3uvujlZrt7vfl+0xzuazW7N6rg/r31N0frdST156kuqZWv9rn\nYPp2L9C033U0ZTzr0alBm8V5Ap+7Lj9GPz7H/370vTYO7N/D2zz/g5+fqZEBzWwDz/VXTzhQz35v\npDaW1XqbxN51+TF6aeamoKNsB+rfs1DVDS362XlH6PkQrQAC/fvm0zRq2ACt3V2jF6ZvDDos/8s3\njtJrs7fokmP31/VnDPP7nj8994hOAx09+M3jtH53jf505XFqbGnTQ+PXdGpZEKtYp0AJpVt+Xpdz\nFx+8T0/VNLaELSBOlh6FeeqWn5fSz77xjEPVo1u+TjlkgIYO7KXLnpqpn5xzuN6Yt81b4Hzb+Uf6\ndc857+jB+ucNo1SQn6dfvLVE/122U8cd3E//+uFpne67t358us7w6a707sLt3gGognnqOyfpSwf1\n10VPTNdR+/fRNacM9TYt7srcOy/U/eNW6/BBvXX7JUdHcxhSIluCwWskXRoQDJ5qrf1FwHq3SLpF\nkg455JBTtm5NzEMhWay1+nDJDk1aXaJzjhqsirpm9eleoOOH9Nfy7VXaWlGvm886zJtRTVWaPB3W\nN5TU6tiD+inPGJ06bKCa21zaXlGvplaXyuuaNOKAfpqwcpdqm9o0b3O5NpXV6Y7RR+vn5x0Z9jNa\n2lyatLpEow4doP36BR9uf/OeOg3bt5dfE9qymqaI+zXurGrQlDUlOvmQATru4PCT9rpcVlvK67S+\npFY7qhp0/MH9deph7c3Xahpb5HJJ/XsVqqm1Tda29xMK1XSmzWVVWtOoHZUN3n5alXXN+tfcrSqv\nbdKW8nq9dtOXu+w7sL2iXi/N3KSWNquehfk6oH93tbqsLj5mf1n38bnUHRRX1DXruWlFcrkHvCnM\ny9OHS3boSwf108EDeur3o0eoR2G+t0/J3oZW9e9VqKXbq/Tx0h0q3dukcSt26f5vHKevHLGvBvft\nrr4+zVP2Nrbo3QXbNWPDHj1y1fF6ccYmGSP95JwjdECI6RK27KnTvn26+e2nK8WV9Xrk07UqrmzQ\nVSMP1vdPOzSi5i9/mbBWS7ZVqaqhRfv17a67rzhGxhi1ulw6dGBvNbe6/PrlWWu1t7FVfbsXdBp8\nRWqf2PzSv83QlNvP1b69u2lHVYMe+XStCvPzdNXIg/XmvG0a2Lubjtq/rz5eukPfP+1QHTygp3p3\nK9BZw9ubG1XWNatbQZ5fH9TKumZV1Ddr6bYqXXjMftqnV/ipMkr3NmrTnjqdfnjwvrcVdc1auKVC\n2yrqNWzf3p36XMbCWqsVO6p13EH9gx6bUNvM2VSu7700T/16FGjeHy7qsmnZ+pIaPTlpvXp3L9Dk\nNSV68tqTdP7R+4X9jG/8fbaKSmr0+LdP0leO3Ferd+7VSYfsoylrSnXZcQd476nNe+q0eudeXXHC\ngdpeUa/tlfU684jOzcCq61u0dvdebSyrU2lNoxZvq1Lf7gXaWFarljaXDhvUR+tK9mp3daNOPWyg\nfnbukRrQu1Dbyut17tGDvZm77gXtmcY+Pufatz+krxXF1ZqzaY/Ka5s1fP++Oqh/DxUW5GnkIQOC\nXuslexv14ZIdGtirm3p2y9fA3t10xuH7hjw3rW0uTVi1WxV1zdpT26wvDxugs4d3HjXZ10fud9CR\n+/VRn+4F/9/enQdHWd9xHH9/JaYQEAg3yJFgwYIV5RC1HlBbnBat6DiMVjtevaZjO2rrOOrY2qnt\njD3Gcay2HaeFotPqtGqLjihDW4utonIfEcqNAjExIuQg5+bbP55fcEnYhA0ku5vn85p5Jk9+2efh\n9/DZ37P73efYNkVH6xwq65oY0Ofo8QTp33G0PU+t2I17VLQMDgVTqv9TgO3l1byyMSqCvz3rjGN+\nRUOyyrpG/rW5nD75vZg1cWi7l1S0eG71Xkb0731kjLdnV0UNG/YepDHhXDpxCHUNzQzrH+3Dk58n\nLcoq61jw311cOWUU9U2JI68diWZn075DTBk9oM22by6t5I5n1zJ2UF+euHEquysOU1ZZ1+byDYie\nF0+/tYfyqnq+OGk408d9Uiy2/n+tqmtk8br9zD17JEs2lnKotpHq+iYuHD+Yc8cOpF/+0fvNiup6\nFq/bT37eKSQSzRT2zWf6uEIGFuQfc1uT/12Irj9c9/5BEs3NzJ8+ps1zO/n5tf79g8x74g3mTB7O\nj68665iXRhyqbeTpFbuB6MPJf7xbRnlVPWbwzDvRtWZjBvXhg0N13HZRMcP692blrgMcrG3ggSsm\nc+Wvo68sueVzRazYEV1WMW5wAdPHFTJtbCEziwfxo8WbOGvUAK6ZejqNiWb2HaylsraJpSUfMGNc\nIWeOOI2lJWXs+LCaBbecR+9TT6EgP+/ItaqPv7adiup6SvYd4juzz2DO5BFtxv+uihq2l1ezce9B\nGhLO82v2MrN4EL3MuH/uJEYM6M3a9z7mF6/+j7vmTKS2MUFTopnGhFPb2MSO8hr698mjtqGZ3y7f\nzjcuHs+tFxXxQWUdZ40aQKLZ+fvafazac4Axgwr4qLqBmvomtpZVcdOFRZw/fhDPrdpLeVU9y7d+\nyPcu+zQvbSileHABm0ureGf3AYoGF3DJhKHcP3cSi1bs5pFl0eUns88cyoGaRq6ddjpV9U0se7eM\n7eXV0VepzJlIwafyuHzy8DbjruXyh8MNTby18yOmjS1s93Wypr6Jjw83HHmPXF5Zx2/+vYOdFTV8\n85LiNvu+RLMz6YeffBB5yYQhnDG0H5W1jTx09WePvF43hGuTW8ZFdX0Tf3xjF8+v2UezO9+6dDyr\n93zM9eeNZca4Qsyi52ei2Y/rPUsm9JRiMOdPExUREREREelOx1sMZvtXS6wEJphZsZnlA9cDL2a4\nTyIiIiIiIjkvq+8m6u5NZvZdYCnRV0sscPeSDhYTERERERGRDmR1MQjg7kuAjm99JSIiIiIiIsct\n208TFRERERERkS6gYlBERERERCSGVAyKiIiIiIjEkIpBERERERGRGFIxKCIiIiIiEkMqBkVERERE\nRGJIxaCIiIiIiEgMqRgUERERERGJIRWDIiIiIiIiMaRiUEREREREJIbM3TPdh5PKzD4E9qS52BCg\nogu6I9lHWceDco4PZR0fyjoelHN8KOuuNc7dh3b0oB5XDHaGma1y9xmZ7od0PWUdD8o5PpR1fCjr\neFDO8aGss4NOExUREREREYkhFYMiIiIiIiIxpGIw8mSmOyDdRlnHg3KOD2UdH8o6HpRzfCjrLKBr\nBkVERERERGJIRwZFRERERERiqMcWg2a2wMzKzWxTUts5ZrbCzDaa2Utm1j+0F5lZrZmtC9PvQnuB\nmb1sZlvMrMTMHs7U9sixpZNz+NuU8LeS8Pfeyjk3pDmmb0waz+vMrNnMzlXW2S/NnE81s0WhfbOZ\n3Rfax5jZa6GtxMzuyNT2SGppZp1vZgtD+3ozmx3aNaazXKrxaGaDzGyZmW0LPwtDu5nZY2a23cw2\nmNm00H5u0uv3BjO7LpPbJW11IuvPhEzrzezujtYjXcTde+QEXApMAzYlta0EZoX524CHwnxR8uOS\nHl8AfD7M5wP/Ab6c6W3T1Omc84ANwDnh98FAL+WcG1M6Wbda7mxgZ5hX1lk+pTmmbwCeTcp2d9if\njwSmhfbTgK3A5Exvm6YTyvp2YGGYHwasJvpAW2M6y6dU4xH4BXBvaL8X+HmYnwu8AhhwAfB2aJ8I\nTAjzo4BSYGCmt0/TCWU9DDgP+Blwd0fryfT29dSpxx4ZdPfXgQOtms8EXg/zy4BrO1jHYXd/Lcw3\nAGuA0Se5q3IC0sz5cmCDu68Py37k7gnlnBtOYEx/FXgmrENZZ7k0c3agr5nlAX2ABqDS3UvdfU1Y\nXxWwGTi9q/su6Ukz68nAP8Ny5cBBYIbGdPZrZzzOAxaFhy0Crg7z84CnPPIWMNDMRrr7VnffFtaz\nHygHOvxCbek+6Wbt7uXuvhJoPM71SBfoscVgCpuAq8L8fGBM0t+KzWytmS03s0taL2hmA4GvEF6M\nJKulynki4Ga21MzWmNk9rRdUzjmnvTHd4jpCMZhMWeeUVDk/B9QQHSF4D/iVux9VXJhZETAVeLs7\nOionLFXW64F5ZpZnZsXAdFqNd43p7NdqPA5391KI3vwTHSWC6E3/+0mL7aVVIWBmM4mOBO/o2h5L\nZx1n1umuR7pA3IrB24DbzWw10WHnhtBeCox196nA94E/29HXmeURvZl8zN13dnOfJX2pcs4DLgZu\nDD+vMbMvtCyknHNSqqwBMLPzgcPuvqlVu7LOLalyngkkiE4ZKwZ+YGbjWxYys37A88Cd7l7ZvV2W\nTkqV9QKiomAV8CjwJtDUspDGdPZLYzzaMdqO3PrezEYCTwO3unvzye2lnAwna9+rfXj3yMt0B7qT\nu28hOlUQM5sIXBHa64H6ML/azHYQHUVaFRZ9Etjm7o92e6clbalyJnojsdzdK8LflhBdr9LyKbJy\nzjHtZN3ieo5xVBBlnVPayfkG4FV3bwTKzewNYAaw08xOJXoT8Sd3fyED3ZZOaOd1ugm4q+VxZvYm\nsC1pUY3pLJZiPJaF0z9LQ4FXHtr3cvRR39HA/rCe/sDLwAPhFFLJMmlmne56pAvE6sigmQ0LP08B\nHgBa7ho61Mx6hfnxwARgZ/j9p8AA4M5M9FnSlypnYCkwJdx9Lg+YBbwbHqucc1A7Wbe0zQeebbWM\nss4x7eT8HnBZuPtgX6KbTWwxMwP+AGx290cy0WfpnHZepwtCxpjZHKDJ3bX/zgHtjMcXgZvD/M3A\n4qT2m8K4vgA4FIqIfOBvRNcT/rWbui9p6ETW6a5HukCP/dJ5M3sGmA0MAcqAB4F+RHckA3gBuM/d\n3cyuBX5CdMpJAnjQ3V8ys9FE561vIRw5BB53999324ZIu9LJOTz+a8B9RKecLHH3e5RzbuhE1rOB\nh939gqR1KOssl+a+ux+wkOjmIkZ0t8lfmtnFRHeV3Ai0nEZ2v7sv6bYNkQ6lmXUR0Qd6zcA+4Ovu\nvkdjOvulGo9E14D9BRhL9MHOfHc/EAqBx4EvAYeJTgddFV6/FwIlSau/xd3Xdc+WSEc6kfUIorPw\n+ofHVxPtz6ccaz3ah3eNHlsMioiIiIiISGqxOk1UREREREREIioGRUREREREYkjFoIiIiIiISAyp\nGBQREREREYkhFYMiIiIiIiIxpGJQREREREQkhlQMioiIiIiIxJCKQRERERERkRj6PwzjP/fGXI2m\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b30a840b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(traindata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23b30c8a710>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAADwIIJBAAAAAPAggkEkrW7/Z5r77AQF/H63swIAAAAgQwSDSNrSJ29SxZpJWvTeC25n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\nBCiYspisy+8GjEKCfTs2kDp2KuGRvglEe2szIb/t3DcvP/7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Q6CI29gBnm6MDRmAMBrEZ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\nzKAQQgghhBBCDEL/D00fK2K2g6IOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b34949da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = newTrainData.plot()\n",
    "traindata.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "newTrainData.to_csv(\"~/trainDam3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "def test_stationarity(timeseries):\n",
    "    \n",
    "    #Determing rolling statistics\n",
    "    rolmean = pd.rolling_mean(timeseries, window=12)\n",
    "    rolstd = pd.rolling_std(timeseries, window=12)\n",
    "\n",
    "    #Plot rolling statistics:\n",
    "    orig = plt.plot(timeseries, color='blue',label='Original')\n",
    "    mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    #Perform Dickey-Fuller test:\n",
    "    print ('Results of Dickey-Fuller Test:')\n",
    "    dftest = adfuller(timeseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print (dfoutput)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).mean()\n",
      "  \"\"\"\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: pd.rolling_std is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).std()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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C9Llz5w5ikyRJUrnYV1CS6lupweDlwHuB8cCbwIXp9Fw1d7GE6fnSWntijFfE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\nFAqFQqFQtED+Hxh7HTn7b9iDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b2f9c3978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-93-094794e8f3fd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtest_stationarity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnewTrainData\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-92-5a32db4c458e>\u001b[0m in \u001b[0;36mtest_stationarity\u001b[1;34m(timeseries)\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[1;31m#Perform Dickey-Fuller test:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'Results of Dickey-Fuller Test:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m     \u001b[0mdftest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madfuller\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mautolag\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'AIC'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m     \u001b[0mdfoutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdftest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Test Statistic'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'p-value'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'#Lags Used'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Number of Observations Used'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdftest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py\u001b[0m in \u001b[0;36madfuller\u001b[1;34m(x, maxlag, regression, autolag, store, regresults)\u001b[0m\n\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    219\u001b[0m     \u001b[0mxdiff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 220\u001b[1;33m     \u001b[0mxdall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlagmat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxdiff\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxlag\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'both'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moriginal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'in'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    221\u001b[0m     \u001b[0mnobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxdall\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# pylint: disable=E1103\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py\u001b[0m in \u001b[0;36mlagmat\u001b[1;34m(x, maxlag, trim, original, use_pandas)\u001b[0m\n\u001b[0;32m    373\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mxa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    374\u001b[0m         \u001b[0mxa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxa\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m     \u001b[0mnobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnvar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    376\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0moriginal\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'ex'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sep'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[0mdropidx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnvar\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "test_stationarity(newTrainData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainset=newTrainData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1954-01-10</th>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-11</th>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-12</th>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-13</th>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-14</th>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-15</th>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-16</th>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-17</th>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-18</th>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-19</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-20</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-21</th>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-22</th>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-23</th>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-24</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-25</th>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-26</th>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-27</th>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-28</th>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-29</th>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-30</th>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-31</th>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-01</th>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-02</th>\n",
       "      <td>1183.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-03</th>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-04</th>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-05</th>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-06</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-07</th>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-08</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1229.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>1226.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1249.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1316.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1411.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>1535.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>1738.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>1766.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>1930.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>2031.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>2046.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>2301.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-16</th>\n",
       "      <td>2531.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>2272.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>2152.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>1940.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>1789.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21</th>\n",
       "      <td>1776.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22</th>\n",
       "      <td>1981.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-23</th>\n",
       "      <td>2098.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-24</th>\n",
       "      <td>1818.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-25</th>\n",
       "      <td>1415.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-26</th>\n",
       "      <td>1431.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27</th>\n",
       "      <td>1399.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-28</th>\n",
       "      <td>1176.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-29</th>\n",
       "      <td>1051.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-30</th>\n",
       "      <td>854.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>726.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>730.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>662.152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22669 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Discharge\n",
       "Time                 \n",
       "1954-01-10    979.351\n",
       "1954-01-11   1030.009\n",
       "1954-01-12    919.242\n",
       "1954-01-13    952.675\n",
       "1954-01-14    955.590\n",
       "1954-01-15    969.969\n",
       "1954-01-16    991.797\n",
       "1954-01-17    908.621\n",
       "1954-01-18    841.898\n",
       "1954-01-19    837.740\n",
       "1954-01-20    837.740\n",
       "1954-01-21    811.396\n",
       "1954-01-22    771.695\n",
       "1954-01-23    888.125\n",
       "1954-01-24    882.645\n",
       "1954-01-25    827.639\n",
       "1954-01-26    785.958\n",
       "1954-01-27    769.846\n",
       "1954-01-28    755.547\n",
       "1954-01-29    888.815\n",
       "1954-01-30   1129.708\n",
       "1954-01-31   1400.324\n",
       "1954-02-01   1517.684\n",
       "1954-02-02   1183.400\n",
       "1954-02-03    997.167\n",
       "1954-02-04    902.372\n",
       "1954-02-05    896.658\n",
       "1954-02-06    882.645\n",
       "1954-02-07    840.576\n",
       "1954-02-08    837.740\n",
       "...               ...\n",
       "2016-01-04   1229.611\n",
       "2016-01-05   1226.988\n",
       "2016-01-06   1249.116\n",
       "2016-01-07   1316.805\n",
       "2016-01-08   1411.780\n",
       "2016-01-09   1535.792\n",
       "2016-01-10   1738.196\n",
       "2016-01-11   1766.817\n",
       "2016-01-12   1930.445\n",
       "2016-01-13   2031.766\n",
       "2016-01-14   2046.541\n",
       "2016-01-15   2301.107\n",
       "2016-01-16   2531.314\n",
       "2016-01-17   2272.333\n",
       "2016-01-18   2152.691\n",
       "2016-01-19   1940.654\n",
       "2016-01-20   1789.787\n",
       "2016-01-21   1776.703\n",
       "2016-01-22   1981.752\n",
       "2016-01-23   2098.387\n",
       "2016-01-24   1818.521\n",
       "2016-01-25   1415.694\n",
       "2016-01-26   1431.905\n",
       "2016-01-27   1399.450\n",
       "2016-01-28   1176.481\n",
       "2016-01-29   1051.522\n",
       "2016-01-30    854.108\n",
       "2016-01-31    726.150\n",
       "2016-02-01    730.435\n",
       "2016-02-02    662.152\n",
       "\n",
       "[22669 rows x 1 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "trainset = trainset.iloc[:,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  979.351,  1030.009,   919.242, ...,   726.15 ,   730.435,\n",
       "         662.152])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: pd.rolling_mean is deprecated for ndarrays and will be removed in a future version\n",
      "  \"\"\"\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: pd.rolling_std is deprecated for ndarrays and will be removed in a future version\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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C9Llz5w5ikyRJUrnYV1CS6lupweDlwHuB8cCbwIXp9Fw1d7GE6fnSWntijFfE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\nsnfkl59y6oWlH5ocyxqvmxriZcZ5H4mAHHhvnjfuNpvIEk4TwV8xzaS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b2fd55c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                -1.201587e+01\n",
      "p-value                        3.103461e-22\n",
      "#Lags Used                     4.700000e+01\n",
      "Number of Observations Used    2.262100e+04\n",
      "Critical Value (1%)           -3.430639e+00\n",
      "Critical Value (5%)           -2.861668e+00\n",
      "Critical Value (10%)          -2.566838e+00\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1954-01-10</th>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-11</th>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-12</th>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-13</th>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-14</th>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-15</th>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-16</th>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-17</th>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-18</th>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-19</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-20</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-21</th>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-22</th>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-23</th>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-24</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-25</th>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-26</th>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-27</th>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-28</th>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-29</th>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-30</th>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-31</th>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-01</th>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-02</th>\n",
       "      <td>1183.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-03</th>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-04</th>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-05</th>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-06</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-07</th>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-08</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1229.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>1226.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1249.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1316.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1411.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>1535.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>1738.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>1766.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>1930.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>2031.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>2046.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>2301.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-16</th>\n",
       "      <td>2531.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>2272.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>2152.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>1940.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>1789.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21</th>\n",
       "      <td>1776.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22</th>\n",
       "      <td>1981.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-23</th>\n",
       "      <td>2098.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-24</th>\n",
       "      <td>1818.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-25</th>\n",
       "      <td>1415.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-26</th>\n",
       "      <td>1431.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27</th>\n",
       "      <td>1399.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-28</th>\n",
       "      <td>1176.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-29</th>\n",
       "      <td>1051.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-30</th>\n",
       "      <td>854.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>726.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>730.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>662.152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22669 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Discharge\n",
       "Time                 \n",
       "1954-01-10    979.351\n",
       "1954-01-11   1030.009\n",
       "1954-01-12    919.242\n",
       "1954-01-13    952.675\n",
       "1954-01-14    955.590\n",
       "1954-01-15    969.969\n",
       "1954-01-16    991.797\n",
       "1954-01-17    908.621\n",
       "1954-01-18    841.898\n",
       "1954-01-19    837.740\n",
       "1954-01-20    837.740\n",
       "1954-01-21    811.396\n",
       "1954-01-22    771.695\n",
       "1954-01-23    888.125\n",
       "1954-01-24    882.645\n",
       "1954-01-25    827.639\n",
       "1954-01-26    785.958\n",
       "1954-01-27    769.846\n",
       "1954-01-28    755.547\n",
       "1954-01-29    888.815\n",
       "1954-01-30   1129.708\n",
       "1954-01-31   1400.324\n",
       "1954-02-01   1517.684\n",
       "1954-02-02   1183.400\n",
       "1954-02-03    997.167\n",
       "1954-02-04    902.372\n",
       "1954-02-05    896.658\n",
       "1954-02-06    882.645\n",
       "1954-02-07    840.576\n",
       "1954-02-08    837.740\n",
       "...               ...\n",
       "2016-01-04   1229.611\n",
       "2016-01-05   1226.988\n",
       "2016-01-06   1249.116\n",
       "2016-01-07   1316.805\n",
       "2016-01-08   1411.780\n",
       "2016-01-09   1535.792\n",
       "2016-01-10   1738.196\n",
       "2016-01-11   1766.817\n",
       "2016-01-12   1930.445\n",
       "2016-01-13   2031.766\n",
       "2016-01-14   2046.541\n",
       "2016-01-15   2301.107\n",
       "2016-01-16   2531.314\n",
       "2016-01-17   2272.333\n",
       "2016-01-18   2152.691\n",
       "2016-01-19   1940.654\n",
       "2016-01-20   1789.787\n",
       "2016-01-21   1776.703\n",
       "2016-01-22   1981.752\n",
       "2016-01-23   2098.387\n",
       "2016-01-24   1818.521\n",
       "2016-01-25   1415.694\n",
       "2016-01-26   1431.905\n",
       "2016-01-27   1399.450\n",
       "2016-01-28   1176.481\n",
       "2016-01-29   1051.522\n",
       "2016-01-30    854.108\n",
       "2016-01-31    726.150\n",
       "2016-02-01    730.435\n",
       "2016-02-02    662.152\n",
       "\n",
       "[22669 rows x 1 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_stationarity(trainset)\n",
    "newTrainData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PuJG3GA0otpp8N+L/Om003tt2AjY18H7lhHJcfdYQvLn5uO9za4qt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\nQxypvz/6+9Ykl0YRTXvqeUsOoHL2orjW39MZUfQ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G57ReLwfgxaV7evx/wX9hu3BrGZX1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p3lNS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\npgGML0XaaPcFT1x3HA2BUKzXa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b2fd16eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "decomposition = seasonal_decompose(newTrainData)\n",
    "\n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "plt.subplot(411)\n",
    "plt.plot(newTrainData, label='Original')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label='Trend')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal,label='Seasonality')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label='Residuals')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).mean()\n",
      "  \"\"\"\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: pd.rolling_std is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).std()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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d3sRLA8Is64CfNLGeZ4BnwqTPBHqFSS8Ltw0RERHJHRoGICKSnVI50YyIiIiI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\nabDGwZsOdg9Zf6WDbxz80/1/e/cd50Zx/3/8NZK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GljKQArYRooJt7EoAmMCzjGc7PfCO\n9dMo5iXOAFZwJVPZ6q/reziHKn9fOJXXOJM1lDSYnxevnDEs4SUOZBN96EolP2IyI6g/p6zGayn+\nGOOYw96czBt8yGH0YB0OmE9foJBiijmIeYzmHbbThy1EWEIfKtiNnYSYwOsM9KfVF3iDIg6iinV4\nx4j/cSQz6EM1pTgiFOE9WdOPjUzgBXYBdvW3u2XAT5hIF4rpwnLKqGYpQ+hBFREq6MYKFrEvAUJc\nxsNsoAdPMJ7dmU9/CpnFIEJACZs4npc4kMYv+ZkHfEApMzmWtXTDqOEynuABJrCVEKfwPHuxnQrg\nRH873elvQ1sZwfMMZzthNtOFiTxOH2AU3rHmU7z20V8An7M3Y5nHPLqxkH5U0Z91dGMrJVzC4xyL\nV0u9ENjMQN5gEOfxJjuB5zmMTXSjll5EqKYn1WymCyUU0p3tdGch69iNMirpz2pCbOBDjmIzXXFU\nEaSGi3iGHRRRQynVFLMrq/grZ9KNKmoIsoMighjgiBAkAHRjAZUMJkKIIcwiQhFLGMohfMBUDsWI\n0IVaruEpduKdF4b429JU9uVYZrMb8DPOppIIAUIYYcAIEiAU89v7gHdEMIwIDkcACBKhhO1spg9B\nglRTzZeYwVdZzIfAw0zECGBAkJA/XvRKLkAXtuAIUU0RRoTTeIvJHIZhBAkSIEKQMI4wQSIEMQow\n+rCRg3mXgL8fLmMwUxlLX9ayL+s5kDmU+fm2FRgM9PC3qX9yCWFqiBAmgHcsdcCu7OQLurI7S1hD\nX4aygA3syjm8xiHAJ8CDlHMR0xmL1990L7yOqjYCLzGKRZSzG0v5ggC9WMNSBnM6r7OSoXzE/v5S\nB6jFex4kQhdGUsN61rCeUgpwhAgRJMAI3uEk5vMsx7MJ6MpmttKDEqoYzka+oIAP2ZdCjCKMrzKJ\nErzz6H2cynn8l4XAenryf1zLf6z+ibls0dlqCg8FXor5fT1wfVPjZGNNYUvh31SNttN59FGzXXap\n/3/rVrMQ1XE1gNHPHT+vsO+PfTvhfw0/n51+XavTmKhpwNVXe5N+iglmYHfyI9u+3ftvF76om++2\nR54xiNT93p+P64bHMCPh89mRiCVchnN4zPoR37YoHK5PoyNsJ/O87ckSe5ET48a9nl/ZJ5947Ymi\nzZPOH/2pGdgiBtuf/lTf9qiiwou3YoXZP/7hhQWpscOYGnMHfH8DswULvHYE11xTPzszsx07Ei/D\nWKY3Wt5Nm/z4LRRd5ui6uuces2d+PiNuPmPGJB8/+kx9NK2JRP+//PLGixG1erXZnDlmPdlow5lr\n3+GeukiHMdWL6/+e/483m1ymXXYxW08ve41jzGtXa3Htapty1VX1aYu2G0ukstJrH/KTn3j5NmlS\n4/0vuuz77292BpPtEN6pi9OdzfYzbraebLRPGdnsPrdjh9eebseO+OkHqUkY/wPG1Q1voZuB2csv\nxy/DccdZ3DgbN3rh27aZ/fKXZtXV5rdNjs/n3//ei7dunVmAWoOIhaiuW9fPc3Kj9ET/i7bFCYfj\nl6O6unG7jEjEu3G6fbvX/hDM5sY3c2tk507vOBfdrrdt88LA7Bwes+N4pW6+4bAlbGtUWVl/w3bV\nKm/+0XEiEbNZs7xl37zZ+++rX60/rt19d/1ijxpltoMi+y+nWAFVBt7xdwgL7bd3RuwXB/2n0Xrq\nzmZ75x2z5cu96S1c2Hg9NczuIDUWojpuX46qqPC2zYYjbbv+V2bmtY9q6PPPvWgnnFAfVlvrhQ0b\n5u3H77zjhY8a5YXHHiNjt5Vrromf91imG0RsAMutN+usoiJ+3uvXm330UeP0xm5Hs2Ylyvl6XlTv\nPFFNyBYu9MJj24Y1XFfXXWdWQJXdwo11kQaw3MrYYJGId3yK2rYtcfr6sLbphPmiTY6nTzd7+mmv\nrVF82us/J5xQP/zOO9ZofUVt3Gg2jHlWyrZGB9j1683Ky722osksXRq/jTds67sfs+xNDm+UwEmc\nHRdv5UqvHWvs8jjCNo4PzMBGMdNefdVbh0uWxB+Xg9SYI2y78IWFqLZiKs3APmS0hcPJ0w9mezPH\ngtQY1O+7zZ2XGgqHG28XZt7x43vf87afDRta3g46kYbjvvNOfTq7scUGs8guvND777vfjd8H29NN\nN8Xsn2ny7rvePLp2NSssrN/uzczuu8/7b1dWWwFVdtVVVr8i/vznFk1/0ybvGBx7ffQUE+omU8q2\nuM31+OO9vIxE6o/7L73k5em8eYnzPirZf5s3e8vZEuvXm118sdnBvGvFVNqmTfXni9/9rmXTyARa\nWFPYbIRs+ODdyFuMdxMi2tHMvk2Nk8uFwq1b409eiSS77jQze/22d5NGOIbX7BlOMwOrue7Gdk33\nihXebErYbiOYHXcgB7MyNlh/vMbiRDc/sJ49zeYxzF7kxCYP/s8xPsHFRdMnjNjosRe5yca75x7v\nxAn1R/1EJ4/bb49Z5/7Ah4y2NWvq41RWehdb0QJnpL4cbF/jETOw03gmpRNeKt56cFHcCmjqYBm9\nWH/kkeRx3vbvNWzc6B0Y58/3fh90UOO4DTc9R9jA7Gc/q/+zZsv2JtNfE1NOGjjQ4tZtc15/PfWL\niahHHzV75pn4sO3bvYufZPvdLbeYzWK/5BHAepOgNxAfmF3M/TaUBXEF6a5srRv+HT+0m29uPO5F\nF1njg0ATpkzxCxgN5h/7OeMM7+LmS7xlD3Jh3R/vv++th0yZOrX1+Zqq4cPN7r/f6o5XiT633Wb2\nwhXP1QV0Z7N1Y4uBd3OkJYYMqZ9eUxd3tbXWKAHVi5envFyJCgtm3gXTj7jTDOxwvBs2y5db3U2G\nRMfdpvIi2Y08g7oCXlOix5sQ1d5NkxhN9FdiYDaBp1p0jriY+208z1klxWZgX+ef7bJtNVzk6DZ0\n2WVNj1dR0fi80p4be8N09WK9DWZRs7NIlI0tjXvzzS1bjGTTnzLF7LHHWr6MmbJzZ/zxqSXbeHvN\nN5Ni8+zVV2MCHnig1RN7uceZ9tBDibe7tnQ21F7mzu2481B76VSFQm95OAWYj/d03g3Nxc/lQmFL\nJLtIMTN74zfv1QUUUBUXAcx6s85e4CSrWZnCVXYLVFebHXBA/eyKihKnt7LS7LPPvMDfclXcXVTn\nkk//WU5ttMBg1lRWe72CeZ//ckrceKFQ4/jRO18t2dmnTYs/eW8f2fw2F5v8ISw0MPvxj5ufV2u8\n+qrZaTxj1/FrK6TpM0ckYvbee6nfPV20KPFBukePxNvnrFlmT3CmV/BpRmwhOlWRiNlf/9q+J5Dl\ny+vT88orZldeWf/71lvNPqJ+47+H7yTcVpM588zYqBEbygI7htesf3+r++PAceGE+RNb47F8n+Na\ntWyxSS0urq+VA7M7+ZEZ2K+4vk1319tLR5+Mt2+PXz9PP212xRXe8IIFZpNunR+Xx9FPS3pYjRo6\n1Btn/vxmIvoTP56XbR8+a9NyJeII2xhmJFy/sTVMLS0gGNhSBtprHNPCEeL961+p53c0/ne4p65W\n/8QTm467jVIzvJsw0Rqetpg82auJnzPHq8Uw82oymuulMHa/+xvfaPeNPdl1Q3OzOLxx5WKL5/HH\nP7ZsHtHzKVjCm1+5IPYckS9ib2RMmWL1P1r6WE+M/+N0M7BP7vmfmXn7UWlp/SRvat8O81ttwYLc\ny+dOVyhM9dPZC4XDhiU/sE+5vb5QGFtoaXgyT9cFXnT6d9/dOCx6rNixo/539DEnsLrHTRN5mq/E\nTejp3pcaeLUap/tCjQAAGp9JREFUyVR6T65YMBhfqASzgoLG8e+/vxU7uz/C6qtub2nUuM8tt6Qw\nrxTE1pZ19MGroiL+wAlW1410KunJpgNvtCY8Nj3R3889V3/RfByv2HDmNsroppZjw4bE28all5rt\nw2d2EQ/EPZ7WiD/Cg/e0ru/z6Py++12zjz+ODythu13J783RRFVzB+robSL2WBX76FTU3/9ucXnc\nmvR5r8RI/lhh1EG8V1ezk4510FT6b+BWM7AzecJmzvTSeuCBZm+9lXx6ezHferLRqyVv5cpJdZSD\nD46f1cKFyV8JEI3zTe6zWgJ2x68z27d8JOLdbHrvPbNr+U27b+y9eyc+zjQ3i5oar8AafVxxxIjk\ncR991OyNN+KvA7LpOJ5uGzem79oqG0UiZuee692EXrXKbBQzbQSzWzWt6HbyySfx4e++61ckZAnv\n1VW5tU23tFCYMy+vl3ifNvGO8eg72qfhden/K37aZLx08V4b4Pnvf+PDY+c9MKbVe5cuyacXpn6C\njgj/OPTvAIxI1JOBr7gYTjkFnngCIv7m/q7fJUT8y8jj03zhhcmn2dAt3MQ8hrPxG6l16x/V/MuM\nWyfd+duU0lLYay8YN64+LF3L2VH694dbb4WFC+vD+vb1vsvL4Vwm8TN+wWscx3z25nDeavG0o9td\naWl8+FlnwVxG8CAXx+1PyViX0uYjNeGee2BUg95EdtCFP/AD8vV0EbveCwoa/+8cjOYjjmJKXdhd\ndzWO15SLL/YuMRrmf0PTOJgltPxl8e3p1/yUQSxhxylnccABXlqnTYPDD08+zkKGsZkyNtCH6YxL\nHrEdvfcefO979b+HDoWuXZse5+9cRogwBSWhpiOmmXNw991w8MH156v2NH9+68YLhaCoqH77P/PM\n5HG/9jU4+uj634WFrZtnriory+y5t6M5B489BscdB/36wSwOYA4j2zTNUIPd8JBDmr7O62h77gnj\nx8NbLT/F54z8PMt3Ak0daLcOGsUHlHMlfwDgRn5FLzYwgs86KHWeQMzW9eUvx4e35qAZf5J0HHKo\nY8oU+MUvko/jHDz3HEyYUD/+HVwLwG9+0zh+9AIwlQLMz7mFfZiHC7R8ocrK6of32afl80pFNpyY\n7r/fO5iXl8dvA7nIObjxRu8iM+rFF+Gyy6BPH1jFAG7lZ3j9q8HbHE4tLSjJEb+vNDX/ZJ7gLACC\nodZn+qBBrR61wzUsuKZT9JhwxBGJ/3cOPmY0b3JUXdgPf9gBCetgRoDPGdSG8b1t8yRebKcUJdfa\nY182FWCq/P6Gk93UbY1evRKHv/JKy8aPHqfC4ZbPs0cP7/uEE1o+juS3ltwAzaRAAJ5/vukbYrkq\ns7fFJC2suISD+CAubBO92ESSM0KaJDsxO9e6k3a4wQW2c3DUUUkiJxC9KAkQARrfjYoNS+WkF9WS\nC/uo2IPeuY3fedsuUklPuowaBZ+14V5EdXV2nyDGjoX77kv+v3cjovmNKbo/mEHPnrB5c/3vljif\nR/gu9/KHVq6riorE+0M2WrGi/kKzIwQC8OGHXs13Itlw86W9lJTAjh1Nx2nt8kaPv5vjXrySHq09\n9mVTofBvXMZAlnEb13NDO0536dLGN4COP75l40aPxZFI83E/+ADWrIEDDvB+X599vfRLlsrmc35n\nlyOXAdIUM7jggvqTebZcpDSVjrbUFC5mcKumEb0YqcF7Bmb48MZxxo+HAw9suvYxmZamZ+xYiH2F\nZrryK1u2g7ZI9LheLoleCEPT+VHkv4Tw0ku9R3ESaWr8GgpZT99Wn0wTPbbYowds2dK66aXTgAEd\nP88xY5L/1zBfjj02vWlJp3RejEX3BUcL73S0QWsLhdH9MBvspIRruLPdp7vnnq0f99RTvcLdWWc1\nH7c85o1oLb25JQIqFGaSCoWdxMMP1w83dfE4fjy88EL60wONHxcZOBCWLfOGW1NgWc4eAHWPxabq\nKu5iIXvxycBT2T4ncfvF7t29djKt0ZILkejJsU+f1s0jFdlQUyietfSNazvbUEGBV1tXUgKTJtWH\nx+4nLdln2rO2b889Ydas9pteZ9UwX3L5ZszJJ8OTT8Lvf9/+086FQuH48e2bjmw3ezZs2NDy+Pvt\npwKepJ+uXTJHqz7Hxd6Ni2rqouSmm9KXlqi1a+GNN2BIg/4QFi6EqqrWT/cmbuUC/sVzeI3TUr34\n2koPbuOnEAg02aFNa6VyIOuIE2tsek4/Pf3zk8aiF8KH8m6zeV5a6uVZsk4xerbgqbv2LBS++CI8\n9FDuPFaaKZ2pUBitmY92oNSecqFQ2L17+6YjW919t3fDZ+TI5G1lRTJFhcLM0arPYVu2wNSpjcOb\nuigpKUlfeqL69o3vfSyqoKC+zUZrLpyqKeIRLiDakUcuX3x1hNj1c9VVmUtHPrsNryHNEga3+EQX\n264p9sZKU48wRrVnAa5fP68XXu1nqenfP9MpaL3oNtpUm7GWtCdL5EPGArCxA9q2p3JROTKmo8R8\n2davvBL23z/TqRBJTIXCzNGqz2HduyduA5ELJ7b2SGO2LWcq6Ym2WUznXdpsWz/56BZ+jsMwAi3O\nj9gT4t57pza/dNTq6XGxpsWunwce8F7rkat+/Ws47TQ444z2n/aP+B2H8g7zSL275VQ7gGnpvrZ+\nvdchSqrjiUj6qFCYOXowqBPKhRNbZywUpiLa+1s6H1dqS+FC2l9LC1dt2a71qGfHiy2wXHxxxpLR\nLgYOhGefbTpOa28SVFPEexya8nibN6fe8UT02Ndcj5e9e6ecHBFJMxUKM0eXEJ1QcxeVCxbEv4A7\nV7W6a/Q8qfnYd1/vHYh/+QvstlumU1OvrAwuvzzTqeh4HVEoHNcx7weXGOopL71a8/qR6EVlqm3H\nc/lGo0hnoUJh5qhQmGfMvPdtJXvnVqZMnZrbbXGgdRcU6SyglpTAnDnpm35rbdyY6RRkt7acENPx\n/r5hw7ztqKXvMpPOLRduqp10EvzqV6m/HkSFQpHMU6Ewc1QozDNDh2Y6BYl96Uupj9PaE7hO/JIJ\nHVFTmA577eUVCr///UynJDtlW36lWy4UCo84wusQJ9/yRqQzUKEwc7TqO6HYE+EfGrzSL1+63M6E\nVC5AojU6mXgRt6THsGGJh1OlC9nckm/51ZpCYSaOc63Jl2zKy4kTM50CkcxQoTBztOo7seOOgyuu\nyHQqJJEjjoBHHknPS6Ils+bNg7/9rXF4rtYUZlt6sk10/eyTeqeabVJW1rHzywfZtK13xOujRLKR\nCoWZo1XfCWXTia05V1/d8fPMlsefzjsv9Y4QJPuZJd4HW7rd/fKX8b/Hj297miT9RozouHlVVsKq\nVR03v7aKfbfhuedmLh0ikv1UKMwcrXrJGDO4885Mp6L95FJhXNpfbP4n2ha6dm3ZdE49FZYtg9df\n934//TRs2ND29El6deTNppISKC7uuPm11b//XT+cjTWcnek8JJLrVCjMHK16yVlHHdW68VR4k3RK\nVDjYf3944YWWT2OPPeCYY7zhwkLo1at90ibtL3o86YhC4c6dsGNH+ufTlNYcP484on44W57UiHX1\n1dmZLpF8pEJh5qj30U6s4Ulu+PDMpCNdWvtC9nSd/HUgy2+xhYOGF87XXAODBnV4ktqVLpoT68hC\nYVFR+ufRnFSW86GHvPelioi0lN79mjm6jO2EGl6kTJjgfX/725lJT3srKIBnn23543gdRT2J5rdo\nu6lAQLXR+STf8jqVgumFF8LYselLS2eWb9uVSLRzJW37maOawk6o4Q41eXJm0pEuffvCaae1fvx0\n1ejpQJbfwmHvOxjUtpBPDj7Y++7sPT2PGOG9r/K66zKdEhHJZq19Km3aNK+Zhc6fmaNCYSemx70S\na+/18pe/wIwZ7TtNyT3XXuvVxvfr17m6k9cJumm77ZYfx9poDWFbe0zOh3Ulkq8qKiDUypLFfvt5\nH8kcFQol57T1oqK9L3K/9a32nZ7kpm99q35bKC2FzZuhZ0/vd2coWOliPr+1V9tJbUcinVdpaaZT\nIG2hNoWdUPSkq45PEusMF+iS/Xr0yHQKRNpPR3aoIzpPiUjHU7GhE4qetDvrSUV3qiXXdNZ9UfLH\n0Ud73337ZjQZIiKSJioUdkKdvVDYWZdLOq9cvhGh/U0Abr8d5s2DgQPbNp1c3hc6UnQ9H3ZYZtMh\nIvlDhcJOqLMXCttK60VEJDWhUOd71202Kyjwvo85JrPpEJH8oUJhJ1RW5n0PG5bZdKRL9H1wraVC\noUjLXXyx9633zUl7UE1harS+RKSjqPfRTujAA+H55+HYYzOdkuwyYACsXAlf+1qmUyL5JpdvRJx+\nui5MRTpaLh8zRCQ3qaawkxo/vv69Up1Nay9Q997b+452mCAiIh1LNxhSo/UlIh1FhULJOTpJSq7R\nNivi0b4gIpKdVCgUEclx55yT6RSISDroMVIR6ShtKhQ65/6fc26uc26Wc+7/nHM9/fBBzrkdzrmZ\n/ucvMeOMc8594pxb6Jz7g3PeIc8518s594pzboH/XeaHOz/eQn8+6u5AWkV3qCVT0n1h969/wcaN\n6Z2HSHtQISc1Om+JSEdpa03hK8B+ZjYKmA9cH/PfIjMb7X++HRP+Z+ByYJj/OdkPvw54zcyGAa/5\nvwHGx8S93B9f8phOkiLxCgrqex0WkdynwrOIdLQ2FQrN7GUzq/V/vgfs3lR851w/oLuZvWtmBjwE\nnOH/fTrwoD/8YIPwh8zzHtDTn45ISnSSFRHJLN3UExHJTu3ZpvBS4IWY34Odcx855/7nnDvCDxsA\nrIiJs8IPA9jVzFYD+N+7xIyzPMk4cZxzlzvnpjvnpq9bt65tSyNZSxcVIiIiIiLtp9n3FDrnXgV2\nS/DXDWb2jB/nBqAWeMT/bzUw0Mw2OOfGAU875/YFEtXVNHeJ3+JxzOw+4D6A8vJyFR06KRUKRURy\nk47fIiLZqdlCoZkd39T/zrmLgFOB4/xHQjGzKqDKH57hnFsEDMer5Yt9xHR3YJU/vMY518/MVvuP\nh671w1cAeyQZR/LI8OEwfz5ccEGmUyIiIpJ+KkSLSEdpa++jJwM/Ab5iZpUx4X2dc0F/eAheJzGL\n/cdCtznnDvF7Hf068Iw/2rPARf7wRQ3Cv+73QnoIsCX6mKnkl6FDve+TTspsOkREpHV0U69l1AZe\nRDpaszWFzfgTUAS84r9Z4j2/p9EjgVucc7VAGPi2mUU7TP8O8E+gBK8NYrQd4m+Ax51z3wCWARP9\n8OeBU4CFQCVwSRvTLDlOd04l1+gCT/LdnnvC55/DkCGZTkluOOww7/vIIzObDhHJH20qFJrZXknC\nnwKeSvLfdGC/BOEbgOMShBvwvbakUzoHXVhLrtK2KyKpOOoo2LwZevTIdEpEJF+0Z++jIiKSgGq3\nRTzaF1pOBUIR6UgqFErOae1FRfQEW1DQfmkREZHmqbZcRCS7tbVNoUjO+Nvf4JBD4Igjmo8r0p50\nQSwiIiLZTIVCyRu9e8O112Y6FSIi+UuPj4qIZCc9Pio5Q7UtIiK5ScdvEZHspkKh5BzdaZZcM2pU\nplMgkh10/BYRyU4qFErO0J1myVUjRmQ6BSIiIiLJqVAoOaO42PsOBjObDhERERGRzkQdzUjOuPde\n2GsvOOmkTKdERERSoSc9RESymwqFkjP69oXbbst0KkREpLXUplBEJDvp8VERERFJK9UUiohkNxUK\nRUREpEOoplBEJDupUCgiIiJppZpCEZHspkKhiIiIdAjVFIqIZCcVCkVERCStVFMoIpLdVCgUERGR\nDqGaQhGR7KRCoYhImqmWRPKd9gERkeymQqGIiIh0CNUUiohkJxUKRUREJK1UUygikt1UKBQREZEO\noZpCEZHspEKhiIiIpJVqCkVEspsKhSIiIpJWd90Fe+wBgwdnOiUiIpJIKNMJEBERkc7t5JNh2bJM\np0JERJJRTaGIiIiIiEgeU6FQREREREQkj6lQKCIiIiIiksdUKBQREREREcljKhSKiIiIiIjkMRUK\nRURERERE8pgKhSIiIiIiInmsTYVC59zNzrmVzrmZ/ueUmP+ud84tdM7Nc86dFBN+sh+20Dl3XUz4\nYOfc+865Bc65fzvnCv3wIv/3Qv//QW1Js4iIiIiIiNRrj5rCu8xstP95HsA5NxI4F9gXOBm41zkX\ndM4FgXuA8cBI4Gt+XIDb/WkNAzYB3/DDvwFsMrO9gLv8eCIiIiIiItIO0vX46OnAJDOrMrMlwELg\nIP+z0MwWm1k1MAk43TnngGOBJ/3xHwTOiJnWg/7wk8BxfnwRkawWCnnfZplNh4iIiEhT2qNQ+H3n\n3Czn3P3OuTI/bACwPCbOCj8sWXhvYLOZ1TYIj5uW//8WP76ISFabNg1uugkCar0tIiIiWSzUXATn\n3KvAbgn+ugH4M3ArYP73b4FLgUQ1eUbiQqg1EZ9m/muY1suBywEGDhyYKIqISIcZM8b7iIiIiGSz\nZguFZnZ8SybknPsb8F//5wpgj5i/dwdW+cOJwtcDPZ1zIb82MDZ+dFornHMhoAewMUla7wPuAygv\nL9cDWyIiIiIiIs1oa++j/WJ+TgA+9YefBc71ew4dDAwDpgEfAMP8nkYL8TqjedbMDHgDOMsf/yLg\nmZhpXeQPnwW87scXERERERGRNmq2prAZdzjnRuM9zrkU+BaAmc12zj0OfAbUAt8zszCAc+77wEtA\nELjfzGb70/oJMMk590vgI+Affvg/gH855xbi1RCe28Y0i4iIiIiIiM911kq38vJymz59eqaTISIi\nIiIikhHOuRlmVt5cPPWJJyIiIiIiksdUKBQREREREcljKhSKiIiIiIjkMRUKRURERERE8pgKhSIi\nIiIiInlMhUIREREREZE8pkKhiIiIiIhIHlOhUEREREREJI+pUCgiIiIiIpLHnJllOg1p4ZxbB3ye\n4mh9gPVpSI5kH+V1flA+5w/ldf5QXucH5XP+UF6n155m1re5SJ22UNgazrnpZlae6XRI+imv84Py\nOX8or/OH8jo/KJ/zh/I6O+jxURERERERkTymQqGIiIiIiEgeU6Ew3n2ZToB0GOV1flA+5w/ldf5Q\nXucH5XP+UF5nAbUpFBERERERyWOqKRQREREREcljnb5Q6Jy73zm31jn3aUzYAc65d51znzjn/uOc\n6+6HD3LO7XDOzfQ/f/HDuzjnnnPOzXXOzXbO/SZTyyOJpZLP/n+j/P9m+/8XK59zQ4r79Pkx+/NM\n51zEOTdaeZ0bUszrAufcg374HOfc9X74Hs65N/yw2c65H2RqeSSxFPO50Dn3gB/+sXPuaD9c+3QO\nSLY/Oud6Oedecc4t8L/L/HDnnPuDc26hc26Wc26sHz465hw+yzl3TiaXS+K1Ip/38fOzyjn34+am\nI2liZp36AxwJjAU+jQn7ADjKH74UuNUfHhQbLyZ+F+AYf7gQeAsYn+ll06fV+RwCZgEH+L97A0Hl\nc258UsnrBuPtDyz2h5XXOfBJcb8+D5gUk79L/WN6P2CsH94NmA+MzPSy6dPqfP4e8IA/vAswA+8G\nt/bpHPgk2x+BO4Dr/PDrgNv94VOAFwAHHAK874cPB4b5w/2B1UDPTC+fPq3O512AA4FfAT9ubjqZ\nXr7O+un0NYVm9iawsUHw3sCb/vArwJnNTKPSzN7wh6uBD4Hd2zmp0gYp5vOJwCwz+9gfd4OZhZXP\nuaEN+/TXgMf8aSivc0CKeW1AqXMuBJQA1cBWM1ttZh/609sGzAEGpDvt0nIp5vNI4DV/vLXAZqBc\n+3RuaGJ/PB140I/2IHCGP3w68JB53gN6Ouf6mdl8M1vgT2cVsBZo9uXc0jFSzWczW2tmHwA1LZyO\npEGnLxQm8SnwFX94IrBHzH+DnXMfOef+55w7ouGIzrmewGn4JyXJasnyeThgzrmXnHMfOueubTii\n8jnnNLVPR52DXyiMpbzOOcny+klgO16NwTLgTjOLK2g45wYBY4D3OyKh0ibJ8vlj4HTnXMg5NxgY\nR4P9Xft0bmiwP+5qZqvBKwjg1RyBVwBYHjPaChoUCpxzB+HVDi9Kb4qlNVqYz6lOR9IgXwuFlwLf\nc87NwKuOrvbDVwMDzWwM8CPgURffDi2Ed1H5BzNb3MFpltQly+cQcDhwvv89wTl3XHQk5XNOSpbX\nADjnDgYqzezTBuHK69yTLK8PAsJ4j5INBq52zg2JjuSc6wo8BfzQzLZ2bJKlFZLl8/14BYPpwO+B\nd4Da6Ejap3NDCvujSxBW122+c64f8C/gEjOLtG8qpa3a67ir43fHCGU6AZlgZnPxHiHEOTcc+LIf\nXgVU+cMznHOL8GqVpvuj3gcsMLPfd3iiJWXJ8hnvguJ/Zrbe/+95vPYs0bvKyucc00ReR51LglpC\nlNc5p4m8Pg940cxqgLXOubeBcmCxc64A74LiETObnIFkS4qaOE/XAldF4znn3gEWxIyqfTrLJdkf\n1/iPha72C3pr/fAVxNcE7w6s8qfTHXgOuNF/tFSySIr5nOp0JA3ysqbQObeL/x0AbgSivYz2dc4F\n/eEhwDBgsf/7l0AP4IeZSLOkLlk+Ay8Bo/ze6kLAUcBnflzlcw5qIq+jYROBSQ3GUV7noCbyehlw\nrN9bYSlepxRznXMO+Acwx8x+l4k0S+qaOE938fMX59wJQK2Z6fidI5rYH58FLvKHLwKeiQn/ur9f\nHwJs8QsUhcD/4bU3fKKDki8t1Ip8TnU6kgad/uX1zrnHgKOBPsAa4OdAV7wezAAmA9ebmTnnzgRu\nwXsUJQz83Mz+45zbHe+Z9rn4NYnAn8zs7x22INKkVPLZj38BcD3eYyjPm9m1yufc0Iq8Phr4jZkd\nEjMN5XUOSPH43RV4AK8jEofXQ+X/c84djtcT5SdA9PGyn5rZ8x22INKkFPN5EN6NvQiwEviGmX2u\nfTo3JNsf8dqJPQ4MxLvBM9HMNvqFgj8BJwOVeI+JTvfP4Q8As2Mmf7GZzeyYJZGmtCKfd8N7Kq+7\nH78C71g+KtF0dPxOj05fKBQREREREZHk8vLxUREREREREfGoUCgiIiIiIpLHVCgUERERERHJYyoU\nioiIiIiI5DEVCkVERERERPKYCoUiIiIiIiJ5TIVCERERERGRPKZCoYiIiIiISB77/1+JH6ytS7Mr\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b2fc2aa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-9b46f337041f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mts_log_decompose\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresidual\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mts_log_decompose\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mtest_stationarity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mts_log_decompose\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-92-5a32db4c458e>\u001b[0m in \u001b[0;36mtest_stationarity\u001b[1;34m(timeseries)\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[1;31m#Perform Dickey-Fuller test:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'Results of Dickey-Fuller Test:'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m     \u001b[0mdftest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0madfuller\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeseries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mautolag\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'AIC'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m     \u001b[0mdfoutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdftest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Test Statistic'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'p-value'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'#Lags Used'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Number of Observations Used'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdftest\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py\u001b[0m in \u001b[0;36madfuller\u001b[1;34m(x, maxlag, regression, autolag, store, regresults)\u001b[0m\n\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    219\u001b[0m     \u001b[0mxdiff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 220\u001b[1;33m     \u001b[0mxdall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlagmat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxdiff\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxlag\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'both'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moriginal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'in'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    221\u001b[0m     \u001b[0mnobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxdall\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# pylint: disable=E1103\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    222\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py\u001b[0m in \u001b[0;36mlagmat\u001b[1;34m(x, maxlag, trim, original, use_pandas)\u001b[0m\n\u001b[0;32m    373\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mxa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    374\u001b[0m         \u001b[0mxa\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxa\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m     \u001b[0mnobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnvar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxa\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    376\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0moriginal\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'ex'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sep'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[0mdropidx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnvar\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 2)"
     ]
    }
   ],
   "source": [
    "ts_log_decompose = residual\n",
    "ts_log_decompose.dropna(inplace=True)\n",
    "test_stationarity(ts_log_decompose)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mkc=ts_log_decompose.iloc[:,0].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "mkc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_stationarity(mkc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "lag_acf = acf(newTrainData, nlags=20)\n",
    "lag_pacf = pacf(newTrainData, nlags=20, method='ols')\n",
    "#Plot ACF: \n",
    "plt.subplot(121) \n",
    "plt.plot(lag_acf)\n",
    "plt.axhline(y=0,linestyle='--',color='gray')\n",
    "plt.axhline(y=-1.96/np.sqrt(len(newTrainData)),linestyle='--',color='gray')\n",
    "plt.axhline(y=1.96/np.sqrt(len(newTrainData)),linestyle='--',color='gray')\n",
    "plt.title('Autocorrelation Function')\n",
    "#Plot PACF:\n",
    "plt.subplot(122)\n",
    "plt.plot(lag_pacf)\n",
    "plt.axhline(y=0,linestyle='--',color='gray')\n",
    "plt.axhline(y=-1.96/np.sqrt(len(newTrainData)),linestyle='--',color='gray')\n",
    "plt.axhline(y=1.96/np.sqrt(len(newTrainData)),linestyle='--',color='gray')\n",
    "plt.title('Partial Autocorrelation Function')\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1954-01-13</th>\n",
       "      <td>78.620740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-14</th>\n",
       "      <td>79.906469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-15</th>\n",
       "      <td>-86.520687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-16</th>\n",
       "      <td>-62.168084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-17</th>\n",
       "      <td>-2.931279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-18</th>\n",
       "      <td>17.548112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-19</th>\n",
       "      <td>-4.453985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-20</th>\n",
       "      <td>92.459740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-21</th>\n",
       "      <td>58.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-22</th>\n",
       "      <td>-187.250116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-23</th>\n",
       "      <td>-72.541227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-24</th>\n",
       "      <td>57.671435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-25</th>\n",
       "      <td>77.247112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-26</th>\n",
       "      <td>-27.334271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-27</th>\n",
       "      <td>4.145312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-28</th>\n",
       "      <td>-95.841388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-29</th>\n",
       "      <td>-268.830402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-30</th>\n",
       "      <td>-93.833369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-31</th>\n",
       "      <td>270.301721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-01</th>\n",
       "      <td>433.289969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-02</th>\n",
       "      <td>51.716586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-03</th>\n",
       "      <td>-17.118260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-04</th>\n",
       "      <td>-43.682817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-05</th>\n",
       "      <td>-159.941116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-06</th>\n",
       "      <td>-135.669084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-07</th>\n",
       "      <td>-34.967565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-08</th>\n",
       "      <td>35.651112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-09</th>\n",
       "      <td>3.733872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-10</th>\n",
       "      <td>110.010740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-11</th>\n",
       "      <td>54.892898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>-106.831687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02</th>\n",
       "      <td>-108.025084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03</th>\n",
       "      <td>-43.290422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>-41.427031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-75.535985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>17.306454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>15.352183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-174.095687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>-159.696084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>56.894293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>47.870540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>38.282872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>79.489883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>6.226040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>-2.011973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-16</th>\n",
       "      <td>217.618059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>119.199007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>104.708540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>-107.875271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>-114.792117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21</th>\n",
       "      <td>-74.779388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22</th>\n",
       "      <td>27.875741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-23</th>\n",
       "      <td>208.070631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-24</th>\n",
       "      <td>109.960578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-25</th>\n",
       "      <td>-140.517174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-26</th>\n",
       "      <td>-37.585128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27</th>\n",
       "      <td>189.817454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-28</th>\n",
       "      <td>111.167898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-29</th>\n",
       "      <td>-123.576116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-30</th>\n",
       "      <td>-220.143941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22663 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Discharge\n",
       "Time                  \n",
       "1954-01-13   78.620740\n",
       "1954-01-14   79.906469\n",
       "1954-01-15  -86.520687\n",
       "1954-01-16  -62.168084\n",
       "1954-01-17   -2.931279\n",
       "1954-01-18   17.548112\n",
       "1954-01-19   -4.453985\n",
       "1954-01-20   92.459740\n",
       "1954-01-21   58.093040\n",
       "1954-01-22 -187.250116\n",
       "1954-01-23  -72.541227\n",
       "1954-01-24   57.671435\n",
       "1954-01-25   77.247112\n",
       "1954-01-26  -27.334271\n",
       "1954-01-27    4.145312\n",
       "1954-01-28  -95.841388\n",
       "1954-01-29 -268.830402\n",
       "1954-01-30  -93.833369\n",
       "1954-01-31  270.301721\n",
       "1954-02-01  433.289969\n",
       "1954-02-02   51.716586\n",
       "1954-02-03  -17.118260\n",
       "1954-02-04  -43.682817\n",
       "1954-02-05 -159.941116\n",
       "1954-02-06 -135.669084\n",
       "1954-02-07  -34.967565\n",
       "1954-02-08   35.651112\n",
       "1954-02-09    3.733872\n",
       "1954-02-10  110.010740\n",
       "1954-02-11   54.892898\n",
       "...                ...\n",
       "2016-01-01 -106.831687\n",
       "2016-01-02 -108.025084\n",
       "2016-01-03  -43.290422\n",
       "2016-01-04  -41.427031\n",
       "2016-01-05  -75.535985\n",
       "2016-01-06   17.306454\n",
       "2016-01-07   15.352183\n",
       "2016-01-08 -174.095687\n",
       "2016-01-09 -159.696084\n",
       "2016-01-10   56.894293\n",
       "2016-01-11   47.870540\n",
       "2016-01-12   38.282872\n",
       "2016-01-13   79.489883\n",
       "2016-01-14    6.226040\n",
       "2016-01-15   -2.011973\n",
       "2016-01-16  217.618059\n",
       "2016-01-17  119.199007\n",
       "2016-01-18  104.708540\n",
       "2016-01-19 -107.875271\n",
       "2016-01-20 -114.792117\n",
       "2016-01-21  -74.779388\n",
       "2016-01-22   27.875741\n",
       "2016-01-23  208.070631\n",
       "2016-01-24  109.960578\n",
       "2016-01-25 -140.517174\n",
       "2016-01-26  -37.585128\n",
       "2016-01-27  189.817454\n",
       "2016-01-28  111.167898\n",
       "2016-01-29 -123.576116\n",
       "2016-01-30 -220.143941\n",
       "\n",
       "[22663 rows x 1 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newTrainData.head(-15)\n",
    "trend\n",
    "residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Water Discharge')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ITZDpTfpwzq2XdFlIsQDVR/UQANQ2bhQCQCIFaToL1C3KKQBQmzh/A0AyBa7RNLNdJB0q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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b2fd16be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "model = ARIMA(newTrainData, order=(2,0,0) ) \n",
    "results_ARIMA = model.fit()  \n",
    "\n",
    "plt.plot(newTrainData)\n",
    "plt.plot(results_ARIMA.fittedvalues, color='red')\n",
    "#plt.title('RSS: %.4f'% sum((results_ARIMA.fittedvalues-ts)**2))\n",
    "plt.xlabel('DATE', fontsize = 20)\n",
    "plt.ylabel('Water Discharge', fontsize = 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-01-01    1712.330362\n",
       "2016-01-02    1658.560583\n",
       "2016-01-03    1613.264886\n",
       "2016-01-04    1501.197027\n",
       "2016-01-05    1420.712161\n",
       "2016-01-06    1436.694167\n",
       "2016-01-07    1459.180146\n",
       "2016-01-08    1522.533274\n",
       "2016-01-09    1604.824800\n",
       "2016-01-10    1711.088355\n",
       "2016-01-11    1890.108221\n",
       "2016-01-12    1882.124165\n",
       "2016-01-13    2039.750450\n",
       "2016-01-14    2111.074622\n",
       "2016-01-15    2107.523257\n",
       "2016-01-16    2358.111028\n",
       "2016-01-17    2541.274685\n",
       "2016-01-18    2242.168619\n",
       "2016-01-19    2169.793201\n",
       "2016-01-20    1980.403047\n",
       "2016-01-21    1868.508618\n",
       "2016-01-22    1882.666455\n",
       "2016-01-23    2089.011356\n",
       "2016-01-24    2168.109557\n",
       "2016-01-25    1868.683114\n",
       "2016-01-26    1518.349513\n",
       "2016-01-27    1607.033809\n",
       "2016-01-28    1571.826786\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jSJIkSZLWcgZHSZIkSVIng6MkSZIkqZPBUZIkSZLUyeAoSZIkSepkcJQkSZIk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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23b2fae45f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_index = '2016-01-01'\n",
    "end_index = '2016-01-28'\n",
    "forecast = results_ARIMA.predict(start=start_index, end=end_index)\n",
    "plt.bar(forecast.index,forecast,width=10)\n",
    "plt.title('NEXT YEAR PREDICTION',fontsize = 20)\n",
    "plt.xlabel('DATE', fontsize = 20)\n",
    "plt.ylabel('Water Discharge', fontsize = 20)\n",
    "forecast\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1954-01-10</th>\n",
       "      <td>979.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-11</th>\n",
       "      <td>1030.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-12</th>\n",
       "      <td>919.242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-13</th>\n",
       "      <td>952.675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-14</th>\n",
       "      <td>955.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-15</th>\n",
       "      <td>969.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-16</th>\n",
       "      <td>991.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-17</th>\n",
       "      <td>908.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-18</th>\n",
       "      <td>841.898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-19</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-20</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-21</th>\n",
       "      <td>811.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-22</th>\n",
       "      <td>771.695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-23</th>\n",
       "      <td>888.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-24</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-25</th>\n",
       "      <td>827.639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-26</th>\n",
       "      <td>785.958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-27</th>\n",
       "      <td>769.846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-28</th>\n",
       "      <td>755.547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-29</th>\n",
       "      <td>888.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-30</th>\n",
       "      <td>1129.708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-01-31</th>\n",
       "      <td>1400.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-01</th>\n",
       "      <td>1517.684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-02</th>\n",
       "      <td>1183.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-03</th>\n",
       "      <td>997.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-04</th>\n",
       "      <td>902.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-05</th>\n",
       "      <td>896.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-06</th>\n",
       "      <td>882.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-07</th>\n",
       "      <td>840.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954-02-08</th>\n",
       "      <td>837.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>1229.611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>1226.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>1249.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>1316.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>1411.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-09</th>\n",
       "      <td>1535.792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-10</th>\n",
       "      <td>1738.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>1766.817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>1930.445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>2031.766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>2046.541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>2301.107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-16</th>\n",
       "      <td>2531.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-17</th>\n",
       "      <td>2272.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>2152.691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19</th>\n",
       "      <td>1940.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>1789.787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21</th>\n",
       "      <td>1776.703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22</th>\n",
       "      <td>1981.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-23</th>\n",
       "      <td>2098.387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-24</th>\n",
       "      <td>1818.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-25</th>\n",
       "      <td>1415.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-26</th>\n",
       "      <td>1431.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27</th>\n",
       "      <td>1399.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-28</th>\n",
       "      <td>1176.481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-29</th>\n",
       "      <td>1051.522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-30</th>\n",
       "      <td>854.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>726.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>730.435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-02</th>\n",
       "      <td>662.152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22669 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Discharge\n",
       "Time                 \n",
       "1954-01-10    979.351\n",
       "1954-01-11   1030.009\n",
       "1954-01-12    919.242\n",
       "1954-01-13    952.675\n",
       "1954-01-14    955.590\n",
       "1954-01-15    969.969\n",
       "1954-01-16    991.797\n",
       "1954-01-17    908.621\n",
       "1954-01-18    841.898\n",
       "1954-01-19    837.740\n",
       "1954-01-20    837.740\n",
       "1954-01-21    811.396\n",
       "1954-01-22    771.695\n",
       "1954-01-23    888.125\n",
       "1954-01-24    882.645\n",
       "1954-01-25    827.639\n",
       "1954-01-26    785.958\n",
       "1954-01-27    769.846\n",
       "1954-01-28    755.547\n",
       "1954-01-29    888.815\n",
       "1954-01-30   1129.708\n",
       "1954-01-31   1400.324\n",
       "1954-02-01   1517.684\n",
       "1954-02-02   1183.400\n",
       "1954-02-03    997.167\n",
       "1954-02-04    902.372\n",
       "1954-02-05    896.658\n",
       "1954-02-06    882.645\n",
       "1954-02-07    840.576\n",
       "1954-02-08    837.740\n",
       "...               ...\n",
       "2016-01-04   1229.611\n",
       "2016-01-05   1226.988\n",
       "2016-01-06   1249.116\n",
       "2016-01-07   1316.805\n",
       "2016-01-08   1411.780\n",
       "2016-01-09   1535.792\n",
       "2016-01-10   1738.196\n",
       "2016-01-11   1766.817\n",
       "2016-01-12   1930.445\n",
       "2016-01-13   2031.766\n",
       "2016-01-14   2046.541\n",
       "2016-01-15   2301.107\n",
       "2016-01-16   2531.314\n",
       "2016-01-17   2272.333\n",
       "2016-01-18   2152.691\n",
       "2016-01-19   1940.654\n",
       "2016-01-20   1789.787\n",
       "2016-01-21   1776.703\n",
       "2016-01-22   1981.752\n",
       "2016-01-23   2098.387\n",
       "2016-01-24   1818.521\n",
       "2016-01-25   1415.694\n",
       "2016-01-26   1431.905\n",
       "2016-01-27   1399.450\n",
       "2016-01-28   1176.481\n",
       "2016-01-29   1051.522\n",
       "2016-01-30    854.108\n",
       "2016-01-31    726.150\n",
       "2016-02-01    730.435\n",
       "2016-02-02    662.152\n",
       "\n",
       "[22669 rows x 1 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newTrainData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Jandata = newTrainData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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