GDP1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF1=pd.read_csv(r'C:\\Users\\Vaibhav\\Desktop\\GDP\\DataI.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DF1.drop([5,6,7,8,9,10],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Items  Description</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>...</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal1</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "      <th>All_India GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>379402.00</td>\n",
       "      <td>11063.00</td>\n",
       "      <td>143175.00</td>\n",
       "      <td>247144.00</td>\n",
       "      <td>158074.00</td>\n",
       "      <td>42367.00</td>\n",
       "      <td>615606.00</td>\n",
       "      <td>297539.00</td>\n",
       "      <td>...</td>\n",
       "      <td>359433.00</td>\n",
       "      <td>19208.00</td>\n",
       "      <td>724049.00</td>\n",
       "      <td>115523.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3979.00</td>\n",
       "      <td>18768.00</td>\n",
       "      <td>343767.00</td>\n",
       "      <td>16818.00</td>\n",
       "      <td>8736039.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>411404.00</td>\n",
       "      <td>12547.00</td>\n",
       "      <td>156864.00</td>\n",
       "      <td>282368.00</td>\n",
       "      <td>177511.00</td>\n",
       "      <td>38120.00</td>\n",
       "      <td>724495.00</td>\n",
       "      <td>347032.00</td>\n",
       "      <td>...</td>\n",
       "      <td>401493.00</td>\n",
       "      <td>21663.00</td>\n",
       "      <td>822903.00</td>\n",
       "      <td>131835.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4421.00</td>\n",
       "      <td>21609.00</td>\n",
       "      <td>391238.00</td>\n",
       "      <td>18875.00</td>\n",
       "      <td>9946636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>464272.00</td>\n",
       "      <td>14602.00</td>\n",
       "      <td>177745.00</td>\n",
       "      <td>317101.00</td>\n",
       "      <td>206690.00</td>\n",
       "      <td>35921.00</td>\n",
       "      <td>807623.00</td>\n",
       "      <td>400662.00</td>\n",
       "      <td>...</td>\n",
       "      <td>452186.00</td>\n",
       "      <td>25593.00</td>\n",
       "      <td>944146.00</td>\n",
       "      <td>149817.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5159.00</td>\n",
       "      <td>24787.00</td>\n",
       "      <td>443783.00</td>\n",
       "      <td>21870.00</td>\n",
       "      <td>11236635.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>526468.00</td>\n",
       "      <td>16761.00</td>\n",
       "      <td>198098.00</td>\n",
       "      <td>373920.00</td>\n",
       "      <td>234982.00</td>\n",
       "      <td>40633.00</td>\n",
       "      <td>895027.00</td>\n",
       "      <td>437462.00</td>\n",
       "      <td>...</td>\n",
       "      <td>511178.00</td>\n",
       "      <td>29667.00</td>\n",
       "      <td>1043371.00</td>\n",
       "      <td>161985.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5721.00</td>\n",
       "      <td>27844.00</td>\n",
       "      <td>492424.00</td>\n",
       "      <td>24089.00</td>\n",
       "      <td>12433749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>609934.00</td>\n",
       "      <td>18784.00</td>\n",
       "      <td>224234.00</td>\n",
       "      <td>413503.00</td>\n",
       "      <td>260776.00</td>\n",
       "      <td>45002.00</td>\n",
       "      <td>994316.00</td>\n",
       "      <td>485184.00</td>\n",
       "      <td>...</td>\n",
       "      <td>575631.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1153795.00</td>\n",
       "      <td>184091.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30304.00</td>\n",
       "      <td>551963.00</td>\n",
       "      <td>26533.00</td>\n",
       "      <td>13675331.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>699307.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>290140.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>547396.00</td>\n",
       "      <td>...</td>\n",
       "      <td>654294.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>622385.00</td>\n",
       "      <td>29557.00</td>\n",
       "      <td>15251028.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>8.43</td>\n",
       "      <td>13.41</td>\n",
       "      <td>9.56</td>\n",
       "      <td>14.25</td>\n",
       "      <td>12.30</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>17.69</td>\n",
       "      <td>16.63</td>\n",
       "      <td>...</td>\n",
       "      <td>11.70</td>\n",
       "      <td>12.78</td>\n",
       "      <td>13.65</td>\n",
       "      <td>14.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.13</td>\n",
       "      <td>15.14</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.23</td>\n",
       "      <td>13.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.85</td>\n",
       "      <td>16.38</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.44</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>11.47</td>\n",
       "      <td>15.45</td>\n",
       "      <td>...</td>\n",
       "      <td>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.68</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.43</td>\n",
       "      <td>15.87</td>\n",
       "      <td>12.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>11.45</td>\n",
       "      <td>17.92</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.18</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.89</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.07</td>\n",
       "      <td>13.19</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.91</td>\n",
       "      <td>...</td>\n",
       "      <td>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.84</td>\n",
       "      <td>12.09</td>\n",
       "      <td>10.15</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>14.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.82</td>\n",
       "      <td>...</td>\n",
       "      <td>13.67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.76</td>\n",
       "      <td>11.40</td>\n",
       "      <td>11.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Items  Description Duration  Andhra Pradesh   \\\n",
       "0   GSDP - CURRENT PRICES (` in Crore)  2011-12        379402.00   \n",
       "1   GSDP - CURRENT PRICES (` in Crore)  2012-13        411404.00   \n",
       "2   GSDP - CURRENT PRICES (` in Crore)  2013-14        464272.00   \n",
       "3   GSDP - CURRENT PRICES (` in Crore)  2014-15        526468.00   \n",
       "4   GSDP - CURRENT PRICES (` in Crore)  2015-16        609934.00   \n",
       "5   GSDP - CURRENT PRICES (` in Crore)  2016-17        699307.00   \n",
       "6        (% Growth over previous year)  2012-13             8.43   \n",
       "7        (% Growth over previous year)  2013-14            12.85   \n",
       "8        (% Growth over previous year)  2014-15            13.40   \n",
       "9        (% Growth over previous year)  2015-16            15.85   \n",
       "10       (% Growth over previous year)  2016-17            14.65   \n",
       "\n",
       "    Arunachal Pradesh      Assam      Bihar  Chhattisgarh       Goa  \\\n",
       "0            11063.00  143175.00  247144.00     158074.00  42367.00   \n",
       "1            12547.00  156864.00  282368.00     177511.00  38120.00   \n",
       "2            14602.00  177745.00  317101.00     206690.00  35921.00   \n",
       "3            16761.00  198098.00  373920.00     234982.00  40633.00   \n",
       "4            18784.00  224234.00  413503.00     260776.00  45002.00   \n",
       "5                 NaN        NaN        NaN     290140.00       NaN   \n",
       "6               13.41       9.56      14.25         12.30    -10.02   \n",
       "7               16.38      13.31      12.30         16.44     -5.77   \n",
       "8               14.79      11.45      17.92         13.69     13.12   \n",
       "9               12.07      13.19      10.59         10.98     10.75   \n",
       "10                NaN        NaN        NaN         11.26       NaN   \n",
       "\n",
       "      Gujarat    Haryana      ...        Telangana   Tripura  Uttar Pradesh  \\\n",
       "0   615606.00  297539.00      ...        359433.00  19208.00      724049.00   \n",
       "1   724495.00  347032.00      ...        401493.00  21663.00      822903.00   \n",
       "2   807623.00  400662.00      ...        452186.00  25593.00      944146.00   \n",
       "3   895027.00  437462.00      ...        511178.00  29667.00     1043371.00   \n",
       "4   994316.00  485184.00      ...        575631.00       NaN     1153795.00   \n",
       "5         NaN  547396.00      ...        654294.00       NaN            NaN   \n",
       "6       17.69      16.63      ...            11.70     12.78          13.65   \n",
       "7       11.47      15.45      ...            12.63     18.14          14.73   \n",
       "8       10.82       9.18      ...            13.05     15.92          10.51   \n",
       "9       11.09      10.91      ...            12.61       NaN          10.58   \n",
       "10        NaN      12.82      ...            13.67       NaN            NaN   \n",
       "\n",
       "    Uttarakhand  West Bengal1  Andaman & Nicobar Islands  Chandigarh  \\\n",
       "0     115523.00           NaN                    3979.00    18768.00   \n",
       "1     131835.00           NaN                    4421.00    21609.00   \n",
       "2     149817.00           NaN                    5159.00    24787.00   \n",
       "3     161985.00           NaN                    5721.00    27844.00   \n",
       "4     184091.00           NaN                        NaN    30304.00   \n",
       "5           NaN           NaN                        NaN         NaN   \n",
       "6         14.12           NaN                      11.13       15.14   \n",
       "7         13.64           NaN                      16.68       14.71   \n",
       "8          8.12           NaN                      10.89       12.33   \n",
       "9         13.65           NaN                        NaN        8.84   \n",
       "10          NaN           NaN                        NaN         NaN   \n",
       "\n",
       "        Delhi  Puducherry  All_India GDP  \n",
       "0   343767.00    16818.00     8736039.00  \n",
       "1   391238.00    18875.00     9946636.00  \n",
       "2   443783.00    21870.00    11236635.00  \n",
       "3   492424.00    24089.00    12433749.00  \n",
       "4   551963.00    26533.00    13675331.00  \n",
       "5   622385.00    29557.00    15251028.00  \n",
       "6       13.81       12.23          13.86  \n",
       "7       13.43       15.87          12.97  \n",
       "8       10.96       10.14          10.65  \n",
       "9       12.09       10.15           9.99  \n",
       "10      12.76       11.40          11.52  \n",
       "\n",
       "[11 rows x 36 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF1.drop([5,10],axis=0,inplace=True) # removing rows for year 2016-17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Items  Description</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>...</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal1</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "      <th>All_India GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>379402.00</td>\n",
       "      <td>11063.00</td>\n",
       "      <td>143175.00</td>\n",
       "      <td>247144.00</td>\n",
       "      <td>158074.00</td>\n",
       "      <td>42367.00</td>\n",
       "      <td>615606.00</td>\n",
       "      <td>297539.00</td>\n",
       "      <td>...</td>\n",
       "      <td>359433.00</td>\n",
       "      <td>19208.00</td>\n",
       "      <td>724049.00</td>\n",
       "      <td>115523.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3979.00</td>\n",
       "      <td>18768.00</td>\n",
       "      <td>343767.00</td>\n",
       "      <td>16818.00</td>\n",
       "      <td>8736039.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>411404.00</td>\n",
       "      <td>12547.00</td>\n",
       "      <td>156864.00</td>\n",
       "      <td>282368.00</td>\n",
       "      <td>177511.00</td>\n",
       "      <td>38120.00</td>\n",
       "      <td>724495.00</td>\n",
       "      <td>347032.00</td>\n",
       "      <td>...</td>\n",
       "      <td>401493.00</td>\n",
       "      <td>21663.00</td>\n",
       "      <td>822903.00</td>\n",
       "      <td>131835.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4421.00</td>\n",
       "      <td>21609.00</td>\n",
       "      <td>391238.00</td>\n",
       "      <td>18875.00</td>\n",
       "      <td>9946636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>464272.00</td>\n",
       "      <td>14602.00</td>\n",
       "      <td>177745.00</td>\n",
       "      <td>317101.00</td>\n",
       "      <td>206690.00</td>\n",
       "      <td>35921.00</td>\n",
       "      <td>807623.00</td>\n",
       "      <td>400662.00</td>\n",
       "      <td>...</td>\n",
       "      <td>452186.00</td>\n",
       "      <td>25593.00</td>\n",
       "      <td>944146.00</td>\n",
       "      <td>149817.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5159.00</td>\n",
       "      <td>24787.00</td>\n",
       "      <td>443783.00</td>\n",
       "      <td>21870.00</td>\n",
       "      <td>11236635.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>526468.00</td>\n",
       "      <td>16761.00</td>\n",
       "      <td>198098.00</td>\n",
       "      <td>373920.00</td>\n",
       "      <td>234982.00</td>\n",
       "      <td>40633.00</td>\n",
       "      <td>895027.00</td>\n",
       "      <td>437462.00</td>\n",
       "      <td>...</td>\n",
       "      <td>511178.00</td>\n",
       "      <td>29667.00</td>\n",
       "      <td>1043371.00</td>\n",
       "      <td>161985.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5721.00</td>\n",
       "      <td>27844.00</td>\n",
       "      <td>492424.00</td>\n",
       "      <td>24089.00</td>\n",
       "      <td>12433749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>609934.00</td>\n",
       "      <td>18784.00</td>\n",
       "      <td>224234.00</td>\n",
       "      <td>413503.00</td>\n",
       "      <td>260776.00</td>\n",
       "      <td>45002.00</td>\n",
       "      <td>994316.00</td>\n",
       "      <td>485184.00</td>\n",
       "      <td>...</td>\n",
       "      <td>575631.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1153795.00</td>\n",
       "      <td>184091.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30304.00</td>\n",
       "      <td>551963.00</td>\n",
       "      <td>26533.00</td>\n",
       "      <td>13675331.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>8.43</td>\n",
       "      <td>13.41</td>\n",
       "      <td>9.56</td>\n",
       "      <td>14.25</td>\n",
       "      <td>12.30</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>17.69</td>\n",
       "      <td>16.63</td>\n",
       "      <td>...</td>\n",
       "      <td>11.70</td>\n",
       "      <td>12.78</td>\n",
       "      <td>13.65</td>\n",
       "      <td>14.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.13</td>\n",
       "      <td>15.14</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.23</td>\n",
       "      <td>13.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.85</td>\n",
       "      <td>16.38</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.44</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>11.47</td>\n",
       "      <td>15.45</td>\n",
       "      <td>...</td>\n",
       "      <td>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.68</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.43</td>\n",
       "      <td>15.87</td>\n",
       "      <td>12.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>11.45</td>\n",
       "      <td>17.92</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.18</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.89</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.07</td>\n",
       "      <td>13.19</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.91</td>\n",
       "      <td>...</td>\n",
       "      <td>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.84</td>\n",
       "      <td>12.09</td>\n",
       "      <td>10.15</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Items  Description Duration  Andhra Pradesh   \\\n",
       "0  GSDP - CURRENT PRICES (` in Crore)  2011-12        379402.00   \n",
       "1  GSDP - CURRENT PRICES (` in Crore)  2012-13        411404.00   \n",
       "2  GSDP - CURRENT PRICES (` in Crore)  2013-14        464272.00   \n",
       "3  GSDP - CURRENT PRICES (` in Crore)  2014-15        526468.00   \n",
       "4  GSDP - CURRENT PRICES (` in Crore)  2015-16        609934.00   \n",
       "6       (% Growth over previous year)  2012-13             8.43   \n",
       "7       (% Growth over previous year)  2013-14            12.85   \n",
       "8       (% Growth over previous year)  2014-15            13.40   \n",
       "9       (% Growth over previous year)  2015-16            15.85   \n",
       "\n",
       "   Arunachal Pradesh      Assam      Bihar  Chhattisgarh       Goa    Gujarat  \\\n",
       "0           11063.00  143175.00  247144.00     158074.00  42367.00  615606.00   \n",
       "1           12547.00  156864.00  282368.00     177511.00  38120.00  724495.00   \n",
       "2           14602.00  177745.00  317101.00     206690.00  35921.00  807623.00   \n",
       "3           16761.00  198098.00  373920.00     234982.00  40633.00  895027.00   \n",
       "4           18784.00  224234.00  413503.00     260776.00  45002.00  994316.00   \n",
       "6              13.41       9.56      14.25         12.30    -10.02      17.69   \n",
       "7              16.38      13.31      12.30         16.44     -5.77      11.47   \n",
       "8              14.79      11.45      17.92         13.69     13.12      10.82   \n",
       "9              12.07      13.19      10.59         10.98     10.75      11.09   \n",
       "\n",
       "     Haryana      ...        Telangana   Tripura  Uttar Pradesh  Uttarakhand  \\\n",
       "0  297539.00      ...        359433.00  19208.00      724049.00    115523.00   \n",
       "1  347032.00      ...        401493.00  21663.00      822903.00    131835.00   \n",
       "2  400662.00      ...        452186.00  25593.00      944146.00    149817.00   \n",
       "3  437462.00      ...        511178.00  29667.00     1043371.00    161985.00   \n",
       "4  485184.00      ...        575631.00       NaN     1153795.00    184091.00   \n",
       "6      16.63      ...            11.70     12.78          13.65        14.12   \n",
       "7      15.45      ...            12.63     18.14          14.73        13.64   \n",
       "8       9.18      ...            13.05     15.92          10.51         8.12   \n",
       "9      10.91      ...            12.61       NaN          10.58        13.65   \n",
       "\n",
       "   West Bengal1  Andaman & Nicobar Islands  Chandigarh      Delhi  Puducherry  \\\n",
       "0           NaN                    3979.00    18768.00  343767.00    16818.00   \n",
       "1           NaN                    4421.00    21609.00  391238.00    18875.00   \n",
       "2           NaN                    5159.00    24787.00  443783.00    21870.00   \n",
       "3           NaN                    5721.00    27844.00  492424.00    24089.00   \n",
       "4           NaN                        NaN    30304.00  551963.00    26533.00   \n",
       "6           NaN                      11.13       15.14      13.81       12.23   \n",
       "7           NaN                      16.68       14.71      13.43       15.87   \n",
       "8           NaN                      10.89       12.33      10.96       10.14   \n",
       "9           NaN                        NaN        8.84      12.09       10.15   \n",
       "\n",
       "   All_India GDP  \n",
       "0     8736039.00  \n",
       "1     9946636.00  \n",
       "2    11236635.00  \n",
       "3    12433749.00  \n",
       "4    13675331.00  \n",
       "6          13.86  \n",
       "7          12.97  \n",
       "8          10.65  \n",
       "9           9.99  \n",
       "\n",
       "[9 rows x 36 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF2=DF1.iloc[6:10,:-1]   # selecting rows '(% Growth over previous year)' over the duration 2013-14, 2014-15 and 2015-16 \n",
    "                         # for all States"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Items  Description</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>...</th>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal1</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.85</td>\n",
       "      <td>16.38</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.44</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>11.47</td>\n",
       "      <td>15.45</td>\n",
       "      <td>...</td>\n",
       "      <td>13.51</td>\n",
       "      <td>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.68</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.43</td>\n",
       "      <td>15.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>11.45</td>\n",
       "      <td>17.92</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.18</td>\n",
       "      <td>...</td>\n",
       "      <td>12.51</td>\n",
       "      <td>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.89</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.07</td>\n",
       "      <td>13.19</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.91</td>\n",
       "      <td>...</td>\n",
       "      <td>10.99</td>\n",
       "      <td>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.84</td>\n",
       "      <td>12.09</td>\n",
       "      <td>10.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Items  Description Duration  Andhra Pradesh   Arunachal Pradesh  \\\n",
       "7  (% Growth over previous year)  2013-14            12.85              16.38   \n",
       "8  (% Growth over previous year)  2014-15            13.40              14.79   \n",
       "9  (% Growth over previous year)  2015-16            15.85              12.07   \n",
       "\n",
       "   Assam  Bihar  Chhattisgarh    Goa  Gujarat  Haryana     ...      \\\n",
       "7  13.31  12.30         16.44  -5.77    11.47    15.45     ...       \n",
       "8  11.45  17.92         13.69  13.12    10.82     9.18     ...       \n",
       "9  13.19  10.59         10.98  10.75    11.09    10.91     ...       \n",
       "\n",
       "   Tamil Nadu  Telangana  Tripura  Uttar Pradesh  Uttarakhand  West Bengal1  \\\n",
       "7       13.51      12.63    18.14          14.73        13.64           NaN   \n",
       "8       12.51      13.05    15.92          10.51         8.12           NaN   \n",
       "9       10.99      12.61      NaN          10.58        13.65           NaN   \n",
       "\n",
       "   Andaman & Nicobar Islands  Chandigarh  Delhi  Puducherry  \n",
       "7                      16.68       14.71  13.43       15.87  \n",
       "8                      10.89       12.33  10.96       10.14  \n",
       "9                        NaN        8.84  12.09       10.15  \n",
       "\n",
       "[3 rows x 35 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF2.set_index('Duration',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Items  Description</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>Himachal Pradesh</th>\n",
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       "      <th>Tamil Nadu</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
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       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal1</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-14</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>12.85</td>\n",
       "      <td>16.38</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.44</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>11.47</td>\n",
       "      <td>15.45</td>\n",
       "      <td>14.42</td>\n",
       "      <td>...</td>\n",
       "      <td>13.51</td>\n",
       "      <td>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.68</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.43</td>\n",
       "      <td>15.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-15</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>11.45</td>\n",
       "      <td>17.92</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.18</td>\n",
       "      <td>10.14</td>\n",
       "      <td>...</td>\n",
       "      <td>12.51</td>\n",
       "      <td>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.89</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-16</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.07</td>\n",
       "      <td>13.19</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>10.99</td>\n",
       "      <td>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.84</td>\n",
       "      <td>12.09</td>\n",
       "      <td>10.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Items  Description  Andhra Pradesh   Arunachal Pradesh  \\\n",
       "Duration                                                                      \n",
       "2013-14   (% Growth over previous year)            12.85              16.38   \n",
       "2014-15   (% Growth over previous year)            13.40              14.79   \n",
       "2015-16   (% Growth over previous year)            15.85              12.07   \n",
       "\n",
       "          Assam  Bihar  Chhattisgarh    Goa  Gujarat  Haryana  \\\n",
       "Duration                                                        \n",
       "2013-14   13.31  12.30         16.44  -5.77    11.47    15.45   \n",
       "2014-15   11.45  17.92         13.69  13.12    10.82     9.18   \n",
       "2015-16   13.19  10.59         10.98  10.75    11.09    10.91   \n",
       "\n",
       "          Himachal Pradesh     ...      Tamil Nadu  Telangana  Tripura  \\\n",
       "Duration                       ...                                       \n",
       "2013-14              14.42     ...           13.51      12.63    18.14   \n",
       "2014-15              10.14     ...           12.51      13.05    15.92   \n",
       "2015-16                NaN     ...           10.99      12.61      NaN   \n",
       "\n",
       "          Uttar Pradesh  Uttarakhand  West Bengal1  Andaman & Nicobar Islands  \\\n",
       "Duration                                                                        \n",
       "2013-14           14.73        13.64           NaN                      16.68   \n",
       "2014-15           10.51         8.12           NaN                      10.89   \n",
       "2015-16           10.58        13.65           NaN                        NaN   \n",
       "\n",
       "          Chandigarh  Delhi  Puducherry  \n",
       "Duration                                 \n",
       "2013-14        14.71  13.43       15.87  \n",
       "2014-15        12.33  10.96       10.14  \n",
       "2015-16         8.84  12.09       10.15  \n",
       "\n",
       "[3 rows x 34 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF2.drop(\"Items  Description\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>Himachal Pradesh</th>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <th>...</th>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal1</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-14</th>\n",
       "      <td>12.85</td>\n",
       "      <td>16.38</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.44</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>11.47</td>\n",
       "      <td>15.45</td>\n",
       "      <td>14.42</td>\n",
       "      <td>10.09</td>\n",
       "      <td>...</td>\n",
       "      <td>13.51</td>\n",
       "      <td>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.68</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.43</td>\n",
       "      <td>15.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-15</th>\n",
       "      <td>13.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>11.45</td>\n",
       "      <td>17.92</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.18</td>\n",
       "      <td>10.14</td>\n",
       "      <td>4.70</td>\n",
       "      <td>...</td>\n",
       "      <td>12.51</td>\n",
       "      <td>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.89</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-16</th>\n",
       "      <td>15.85</td>\n",
       "      <td>12.07</td>\n",
       "      <td>13.19</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.91</td>\n",
       "      <td>...</td>\n",
       "      <td>10.99</td>\n",
       "      <td>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.84</td>\n",
       "      <td>12.09</td>\n",
       "      <td>10.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Andhra Pradesh   Arunachal Pradesh  Assam  Bihar  Chhattisgarh  \\\n",
       "Duration                                                                   \n",
       "2013-14             12.85              16.38  13.31  12.30         16.44   \n",
       "2014-15             13.40              14.79  11.45  17.92         13.69   \n",
       "2015-16             15.85              12.07  13.19  10.59         10.98   \n",
       "\n",
       "            Goa  Gujarat  Haryana  Himachal Pradesh  Jammu & Kashmir  \\\n",
       "Duration                                                               \n",
       "2013-14   -5.77    11.47    15.45             14.42            10.09   \n",
       "2014-15   13.12    10.82     9.18             10.14             4.70   \n",
       "2015-16   10.75    11.09    10.91               NaN            17.91   \n",
       "\n",
       "             ...      Tamil Nadu  Telangana  Tripura  Uttar Pradesh  \\\n",
       "Duration     ...                                                      \n",
       "2013-14      ...           13.51      12.63    18.14          14.73   \n",
       "2014-15      ...           12.51      13.05    15.92          10.51   \n",
       "2015-16      ...           10.99      12.61      NaN          10.58   \n",
       "\n",
       "          Uttarakhand  West Bengal1  Andaman & Nicobar Islands  Chandigarh  \\\n",
       "Duration                                                                     \n",
       "2013-14         13.64           NaN                      16.68       14.71   \n",
       "2014-15          8.12           NaN                      10.89       12.33   \n",
       "2015-16         13.65           NaN                        NaN        8.84   \n",
       "\n",
       "          Delhi  Puducherry  \n",
       "Duration                     \n",
       "2013-14   13.43       15.87  \n",
       "2014-15   10.96       10.14  \n",
       "2015-16   12.09       10.15  \n",
       "\n",
       "[3 rows x 33 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF3=DF2.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Andhra Pradesh               14.033333\n",
       "Arunachal Pradesh            14.413333\n",
       "Assam                        12.650000\n",
       "Bihar                        13.603333\n",
       "Chhattisgarh                 13.703333\n",
       "Goa                           6.033333\n",
       "Gujarat                      11.126667\n",
       "Haryana                      11.846667\n",
       "Himachal Pradesh             12.280000\n",
       "Jammu & Kashmir              10.900000\n",
       "Jharkhand                    11.500000\n",
       "Karnataka                    14.120000\n",
       "Kerala                       12.583333\n",
       "Madhya Pradesh               12.626667\n",
       "Maharashtra                  11.260000\n",
       "Manipur                      14.610000\n",
       "Meghalaya                     6.953333\n",
       "Mizoram                      17.700000\n",
       "Nagaland                     16.415000\n",
       "Odisha                        9.836667\n",
       "Punjab                       11.185000\n",
       "Rajasthan                    11.320000\n",
       "Sikkim                       10.486667\n",
       "Tamil Nadu                   12.336667\n",
       "Telangana                    12.763333\n",
       "Tripura                      17.030000\n",
       "Uttar Pradesh                11.940000\n",
       "Uttarakhand                  11.803333\n",
       "West Bengal1                       NaN\n",
       "Andaman & Nicobar Islands    13.785000\n",
       "Chandigarh                   11.960000\n",
       "Delhi                        12.160000\n",
       "Puducherry                   12.053333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt=DF3.sort_values().plot(kind='bar',grid=True,title='Average % Growth over previous year for all States',figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF4=DF1.iloc[6:10,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Items  Description</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>...</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal1</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "      <th>All_India GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.85</td>\n",
       "      <td>16.38</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.44</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>11.47</td>\n",
       "      <td>15.45</td>\n",
       "      <td>...</td>\n",
       "      <td>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.68</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.43</td>\n",
       "      <td>15.87</td>\n",
       "      <td>12.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>11.45</td>\n",
       "      <td>17.92</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.18</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.89</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.07</td>\n",
       "      <td>13.19</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.91</td>\n",
       "      <td>...</td>\n",
       "      <td>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.84</td>\n",
       "      <td>12.09</td>\n",
       "      <td>10.15</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Items  Description Duration  Andhra Pradesh   Arunachal Pradesh  \\\n",
       "7  (% Growth over previous year)  2013-14            12.85              16.38   \n",
       "8  (% Growth over previous year)  2014-15            13.40              14.79   \n",
       "9  (% Growth over previous year)  2015-16            15.85              12.07   \n",
       "\n",
       "   Assam  Bihar  Chhattisgarh    Goa  Gujarat  Haryana      ...        \\\n",
       "7  13.31  12.30         16.44  -5.77    11.47    15.45      ...         \n",
       "8  11.45  17.92         13.69  13.12    10.82     9.18      ...         \n",
       "9  13.19  10.59         10.98  10.75    11.09    10.91      ...         \n",
       "\n",
       "   Telangana  Tripura  Uttar Pradesh  Uttarakhand  West Bengal1  \\\n",
       "7      12.63    18.14          14.73        13.64           NaN   \n",
       "8      13.05    15.92          10.51         8.12           NaN   \n",
       "9      12.61      NaN          10.58        13.65           NaN   \n",
       "\n",
       "   Andaman & Nicobar Islands  Chandigarh  Delhi  Puducherry  All_India GDP  \n",
       "7                      16.68       14.71  13.43       15.87          12.97  \n",
       "8                      10.89       12.33  10.96       10.14          10.65  \n",
       "9                        NaN        8.84  12.09       10.15           9.99  \n",
       "\n",
       "[3 rows x 36 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.603333333333333"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF3['Bihar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.203333333333333"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF4['All_India GDP'].mean() # when compared to national average, my home town is growing faster :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Items  Description</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <th>Assam</th>\n",
       "      <th>Bihar</th>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <th>Goa</th>\n",
       "      <th>Gujarat</th>\n",
       "      <th>Haryana</th>\n",
       "      <th>...</th>\n",
       "      <th>Telangana</th>\n",
       "      <th>Tripura</th>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <th>Uttarakhand</th>\n",
       "      <th>West Bengal1</th>\n",
       "      <th>Andaman &amp; Nicobar Islands</th>\n",
       "      <th>Chandigarh</th>\n",
       "      <th>Delhi</th>\n",
       "      <th>Puducherry</th>\n",
       "      <th>All_India GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2011-12</td>\n",
       "      <td>379402.00</td>\n",
       "      <td>11063.00</td>\n",
       "      <td>143175.00</td>\n",
       "      <td>247144.00</td>\n",
       "      <td>158074.00</td>\n",
       "      <td>42367.00</td>\n",
       "      <td>615606.00</td>\n",
       "      <td>297539.00</td>\n",
       "      <td>...</td>\n",
       "      <td>359433.00</td>\n",
       "      <td>19208.00</td>\n",
       "      <td>724049.00</td>\n",
       "      <td>115523.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3979.00</td>\n",
       "      <td>18768.00</td>\n",
       "      <td>343767.00</td>\n",
       "      <td>16818.00</td>\n",
       "      <td>8736039.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>411404.00</td>\n",
       "      <td>12547.00</td>\n",
       "      <td>156864.00</td>\n",
       "      <td>282368.00</td>\n",
       "      <td>177511.00</td>\n",
       "      <td>38120.00</td>\n",
       "      <td>724495.00</td>\n",
       "      <td>347032.00</td>\n",
       "      <td>...</td>\n",
       "      <td>401493.00</td>\n",
       "      <td>21663.00</td>\n",
       "      <td>822903.00</td>\n",
       "      <td>131835.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4421.00</td>\n",
       "      <td>21609.00</td>\n",
       "      <td>391238.00</td>\n",
       "      <td>18875.00</td>\n",
       "      <td>9946636.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>464272.00</td>\n",
       "      <td>14602.00</td>\n",
       "      <td>177745.00</td>\n",
       "      <td>317101.00</td>\n",
       "      <td>206690.00</td>\n",
       "      <td>35921.00</td>\n",
       "      <td>807623.00</td>\n",
       "      <td>400662.00</td>\n",
       "      <td>...</td>\n",
       "      <td>452186.00</td>\n",
       "      <td>25593.00</td>\n",
       "      <td>944146.00</td>\n",
       "      <td>149817.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5159.00</td>\n",
       "      <td>24787.00</td>\n",
       "      <td>443783.00</td>\n",
       "      <td>21870.00</td>\n",
       "      <td>11236635.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>526468.00</td>\n",
       "      <td>16761.00</td>\n",
       "      <td>198098.00</td>\n",
       "      <td>373920.00</td>\n",
       "      <td>234982.00</td>\n",
       "      <td>40633.00</td>\n",
       "      <td>895027.00</td>\n",
       "      <td>437462.00</td>\n",
       "      <td>...</td>\n",
       "      <td>511178.00</td>\n",
       "      <td>29667.00</td>\n",
       "      <td>1043371.00</td>\n",
       "      <td>161985.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5721.00</td>\n",
       "      <td>27844.00</td>\n",
       "      <td>492424.00</td>\n",
       "      <td>24089.00</td>\n",
       "      <td>12433749.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GSDP - CURRENT PRICES (` in Crore)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>609934.00</td>\n",
       "      <td>18784.00</td>\n",
       "      <td>224234.00</td>\n",
       "      <td>413503.00</td>\n",
       "      <td>260776.00</td>\n",
       "      <td>45002.00</td>\n",
       "      <td>994316.00</td>\n",
       "      <td>485184.00</td>\n",
       "      <td>...</td>\n",
       "      <td>575631.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1153795.00</td>\n",
       "      <td>184091.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30304.00</td>\n",
       "      <td>551963.00</td>\n",
       "      <td>26533.00</td>\n",
       "      <td>13675331.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2012-13</td>\n",
       "      <td>8.43</td>\n",
       "      <td>13.41</td>\n",
       "      <td>9.56</td>\n",
       "      <td>14.25</td>\n",
       "      <td>12.30</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>17.69</td>\n",
       "      <td>16.63</td>\n",
       "      <td>...</td>\n",
       "      <td>11.70</td>\n",
       "      <td>12.78</td>\n",
       "      <td>13.65</td>\n",
       "      <td>14.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.13</td>\n",
       "      <td>15.14</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.23</td>\n",
       "      <td>13.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2013-14</td>\n",
       "      <td>12.85</td>\n",
       "      <td>16.38</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.30</td>\n",
       "      <td>16.44</td>\n",
       "      <td>-5.77</td>\n",
       "      <td>11.47</td>\n",
       "      <td>15.45</td>\n",
       "      <td>...</td>\n",
       "      <td>12.63</td>\n",
       "      <td>18.14</td>\n",
       "      <td>14.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.68</td>\n",
       "      <td>14.71</td>\n",
       "      <td>13.43</td>\n",
       "      <td>15.87</td>\n",
       "      <td>12.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2014-15</td>\n",
       "      <td>13.40</td>\n",
       "      <td>14.79</td>\n",
       "      <td>11.45</td>\n",
       "      <td>17.92</td>\n",
       "      <td>13.69</td>\n",
       "      <td>13.12</td>\n",
       "      <td>10.82</td>\n",
       "      <td>9.18</td>\n",
       "      <td>...</td>\n",
       "      <td>13.05</td>\n",
       "      <td>15.92</td>\n",
       "      <td>10.51</td>\n",
       "      <td>8.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.89</td>\n",
       "      <td>12.33</td>\n",
       "      <td>10.96</td>\n",
       "      <td>10.14</td>\n",
       "      <td>10.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(% Growth over previous year)</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.07</td>\n",
       "      <td>13.19</td>\n",
       "      <td>10.59</td>\n",
       "      <td>10.98</td>\n",
       "      <td>10.75</td>\n",
       "      <td>11.09</td>\n",
       "      <td>10.91</td>\n",
       "      <td>...</td>\n",
       "      <td>12.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.58</td>\n",
       "      <td>13.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.84</td>\n",
       "      <td>12.09</td>\n",
       "      <td>10.15</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Items  Description Duration  Andhra Pradesh   \\\n",
       "0  GSDP - CURRENT PRICES (` in Crore)  2011-12        379402.00   \n",
       "1  GSDP - CURRENT PRICES (` in Crore)  2012-13        411404.00   \n",
       "2  GSDP - CURRENT PRICES (` in Crore)  2013-14        464272.00   \n",
       "3  GSDP - CURRENT PRICES (` in Crore)  2014-15        526468.00   \n",
       "4  GSDP - CURRENT PRICES (` in Crore)  2015-16        609934.00   \n",
       "6       (% Growth over previous year)  2012-13             8.43   \n",
       "7       (% Growth over previous year)  2013-14            12.85   \n",
       "8       (% Growth over previous year)  2014-15            13.40   \n",
       "9       (% Growth over previous year)  2015-16            15.85   \n",
       "\n",
       "   Arunachal Pradesh      Assam      Bihar  Chhattisgarh       Goa    Gujarat  \\\n",
       "0           11063.00  143175.00  247144.00     158074.00  42367.00  615606.00   \n",
       "1           12547.00  156864.00  282368.00     177511.00  38120.00  724495.00   \n",
       "2           14602.00  177745.00  317101.00     206690.00  35921.00  807623.00   \n",
       "3           16761.00  198098.00  373920.00     234982.00  40633.00  895027.00   \n",
       "4           18784.00  224234.00  413503.00     260776.00  45002.00  994316.00   \n",
       "6              13.41       9.56      14.25         12.30    -10.02      17.69   \n",
       "7              16.38      13.31      12.30         16.44     -5.77      11.47   \n",
       "8              14.79      11.45      17.92         13.69     13.12      10.82   \n",
       "9              12.07      13.19      10.59         10.98     10.75      11.09   \n",
       "\n",
       "     Haryana      ...        Telangana   Tripura  Uttar Pradesh  Uttarakhand  \\\n",
       "0  297539.00      ...        359433.00  19208.00      724049.00    115523.00   \n",
       "1  347032.00      ...        401493.00  21663.00      822903.00    131835.00   \n",
       "2  400662.00      ...        452186.00  25593.00      944146.00    149817.00   \n",
       "3  437462.00      ...        511178.00  29667.00     1043371.00    161985.00   \n",
       "4  485184.00      ...        575631.00       NaN     1153795.00    184091.00   \n",
       "6      16.63      ...            11.70     12.78          13.65        14.12   \n",
       "7      15.45      ...            12.63     18.14          14.73        13.64   \n",
       "8       9.18      ...            13.05     15.92          10.51         8.12   \n",
       "9      10.91      ...            12.61       NaN          10.58        13.65   \n",
       "\n",
       "   West Bengal1  Andaman & Nicobar Islands  Chandigarh      Delhi  Puducherry  \\\n",
       "0           NaN                    3979.00    18768.00  343767.00    16818.00   \n",
       "1           NaN                    4421.00    21609.00  391238.00    18875.00   \n",
       "2           NaN                    5159.00    24787.00  443783.00    21870.00   \n",
       "3           NaN                    5721.00    27844.00  492424.00    24089.00   \n",
       "4           NaN                        NaN    30304.00  551963.00    26533.00   \n",
       "6           NaN                      11.13       15.14      13.81       12.23   \n",
       "7           NaN                      16.68       14.71      13.43       15.87   \n",
       "8           NaN                      10.89       12.33      10.96       10.14   \n",
       "9           NaN                        NaN        8.84      12.09       10.15   \n",
       "\n",
       "   All_India GDP  \n",
       "0     8736039.00  \n",
       "1     9946636.00  \n",
       "2    11236635.00  \n",
       "3    12433749.00  \n",
       "4    13675331.00  \n",
       "6          13.86  \n",
       "7          12.97  \n",
       "8          10.65  \n",
       "9           9.99  \n",
       "\n",
       "[9 rows x 36 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF5=DF1.iloc[4:5,1:-1]  # selecting data for year 2015-16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF5=DF5.dropna(axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF5.set_index('Duration',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF5.sort_values(by='2015-16',ascending=False,inplace=True)\n",
    "#DF5=DF5.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Duration</th>\n",
       "      <th>2015-16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>1212668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>1153795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>1027068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>994316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>609934.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerala</th>\n",
       "      <td>588337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telangana</th>\n",
       "      <td>575631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>551963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madhya Pradesh</th>\n",
       "      <td>543975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>485184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>413503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odisha</th>\n",
       "      <td>341887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>260776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>241955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>224234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttarakhand</th>\n",
       "      <td>184091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <td>118387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goa</th>\n",
       "      <td>45002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chandigarh</th>\n",
       "      <td>30304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>26745.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>26533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>18784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>16637.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Duration             2015-16\n",
       "Tamil Nadu         1212668.0\n",
       "Uttar Pradesh      1153795.0\n",
       "Karnataka          1027068.0\n",
       "Gujarat             994316.0\n",
       "Andhra Pradesh      609934.0\n",
       "Kerala              588337.0\n",
       "Telangana           575631.0\n",
       "Delhi               551963.0\n",
       "Madhya Pradesh      543975.0\n",
       "Haryana             485184.0\n",
       "Bihar               413503.0\n",
       "Odisha              341887.0\n",
       "Chhattisgarh        260776.0\n",
       "Jharkhand           241955.0\n",
       "Assam               224234.0\n",
       "Uttarakhand         184091.0\n",
       "Jammu & Kashmir     118387.0\n",
       "Goa                  45002.0\n",
       "Chandigarh           30304.0\n",
       "Meghalaya            26745.0\n",
       "Puducherry           26533.0\n",
       "Arunachal Pradesh    18784.0\n",
       "Sikkim               16637.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize']=(20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d2f4fc8278>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "DF5.plot(kind='bar',sort_columns =True,title='Total GDP of the states for the year 2015-16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Duration</th>\n",
       "      <th>2015-16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>1212668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>1153795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>1027068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>994316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>609934.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kerala</th>\n",
       "      <td>588337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telangana</th>\n",
       "      <td>575631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <td>551963.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madhya Pradesh</th>\n",
       "      <td>543975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haryana</th>\n",
       "      <td>485184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bihar</th>\n",
       "      <td>413503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Odisha</th>\n",
       "      <td>341887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chhattisgarh</th>\n",
       "      <td>260776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jharkhand</th>\n",
       "      <td>241955.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Assam</th>\n",
       "      <td>224234.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttarakhand</th>\n",
       "      <td>184091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jammu &amp; Kashmir</th>\n",
       "      <td>118387.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goa</th>\n",
       "      <td>45002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chandigarh</th>\n",
       "      <td>30304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>26745.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>26533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>18784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>16637.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Duration             2015-16\n",
       "Tamil Nadu         1212668.0\n",
       "Uttar Pradesh      1153795.0\n",
       "Karnataka          1027068.0\n",
       "Gujarat             994316.0\n",
       "Andhra Pradesh      609934.0\n",
       "Kerala              588337.0\n",
       "Telangana           575631.0\n",
       "Delhi               551963.0\n",
       "Madhya Pradesh      543975.0\n",
       "Haryana             485184.0\n",
       "Bihar               413503.0\n",
       "Odisha              341887.0\n",
       "Chhattisgarh        260776.0\n",
       "Jharkhand           241955.0\n",
       "Assam               224234.0\n",
       "Uttarakhand         184091.0\n",
       "Jammu & Kashmir     118387.0\n",
       "Goa                  45002.0\n",
       "Chandigarh           30304.0\n",
       "Meghalaya            26745.0\n",
       "Puducherry           26533.0\n",
       "Arunachal Pradesh    18784.0\n",
       "Sikkim               16637.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DF5.set_index('Duration',inplace=True)\n",
    "DF5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DF5.sort_values(by = '2015-16', ascending = False)\n",
    "Top5 = DF5.iloc[:5, ]\n",
    "Bottom5=DF5.iloc[-5:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Duration</th>\n",
       "      <th>2015-16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tamil Nadu</th>\n",
       "      <td>1212668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uttar Pradesh</th>\n",
       "      <td>1153795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karnataka</th>\n",
       "      <td>1027068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gujarat</th>\n",
       "      <td>994316.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andhra Pradesh</th>\n",
       "      <td>609934.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "Duration           2015-16\n",
       "Tamil Nadu       1212668.0\n",
       "Uttar Pradesh    1153795.0\n",
       "Karnataka        1027068.0\n",
       "Gujarat           994316.0\n",
       "Andhra Pradesh    609934.0"
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     "execution_count": 124,
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   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>2015-16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Chandigarh</th>\n",
       "      <td>30304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meghalaya</th>\n",
       "      <td>26745.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puducherry</th>\n",
       "      <td>26533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arunachal Pradesh</th>\n",
       "      <td>18784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sikkim</th>\n",
       "      <td>16637.0</td>\n",
       "    </tr>\n",
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       "Duration           2015-16\n",
       "Chandigarh         30304.0\n",
       "Meghalaya          26745.0\n",
       "Puducherry         26533.0\n",
       "Arunachal Pradesh  18784.0\n",
       "Sikkim             16637.0"
      ]
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     "execution_count": 125,
     "metadata": {},
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    "Bottom5"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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