MTT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('crimes_against_women_2001-2014.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>sr_no</th>\n",
       "      <th>state_ut</th>\n",
       "      <th>district</th>\n",
       "      <th>year</th>\n",
       "      <th>rape</th>\n",
       "      <th>kidnapping_and_abduction</th>\n",
       "      <th>dowry_deaths</th>\n",
       "      <th>assault_on_women</th>\n",
       "      <th>insult_to_modesty</th>\n",
       "      <th>cruelty_by_husband_or_relatives</th>\n",
       "      <th>importation_of_girls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ADILABAD</td>\n",
       "      <td>2001</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>149</td>\n",
       "      <td>34</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>2001</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>118</td>\n",
       "      <td>24</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CHITTOOR</td>\n",
       "      <td>2001</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>14</td>\n",
       "      <td>112</td>\n",
       "      <td>83</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CUDDAPAH</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>126</td>\n",
       "      <td>38</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI</td>\n",
       "      <td>2001</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>109</td>\n",
       "      <td>58</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10181</th>\n",
       "      <td>829</td>\n",
       "      <td>DEHLI UT</td>\n",
       "      <td>VIGILANCE</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10182</th>\n",
       "      <td>830</td>\n",
       "      <td>DEHLI UT</td>\n",
       "      <td>WEST</td>\n",
       "      <td>2014</td>\n",
       "      <td>251</td>\n",
       "      <td>546</td>\n",
       "      <td>13</td>\n",
       "      <td>484</td>\n",
       "      <td>63</td>\n",
       "      <td>416</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10183</th>\n",
       "      <td>832</td>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10184</th>\n",
       "      <td>834</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>KARAIKAL</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10185</th>\n",
       "      <td>835</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10186 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sr_no        state_ut       district  year  rape  \\\n",
       "0          0  ANDHRA PRADESH       ADILABAD  2001    50   \n",
       "1          1  ANDHRA PRADESH      ANANTAPUR  2001    23   \n",
       "2          2  ANDHRA PRADESH       CHITTOOR  2001    27   \n",
       "3          3  ANDHRA PRADESH       CUDDAPAH  2001    20   \n",
       "4          4  ANDHRA PRADESH  EAST GODAVARI  2001    23   \n",
       "...      ...             ...            ...   ...   ...   \n",
       "10181    829        DEHLI UT      VIGILANCE  2014     0   \n",
       "10182    830        DEHLI UT           WEST  2014   251   \n",
       "10183    832     LAKSHADWEEP    LAKSHADWEEP  2014     1   \n",
       "10184    834      PUDUCHERRY       KARAIKAL  2014     3   \n",
       "10185    835      PUDUCHERRY     PUDUCHERRY  2014     7   \n",
       "\n",
       "       kidnapping_and_abduction  dowry_deaths  assault_on_women  \\\n",
       "0                            30            16               149   \n",
       "1                            30             7               118   \n",
       "2                            34            14               112   \n",
       "3                            20            17               126   \n",
       "4                            26            12               109   \n",
       "...                         ...           ...               ...   \n",
       "10181                         0             0                 0   \n",
       "10182                       546            13               484   \n",
       "10183                         0             0                 1   \n",
       "10184                         1             0                12   \n",
       "10185                         6             1                20   \n",
       "\n",
       "       insult_to_modesty  cruelty_by_husband_or_relatives  \\\n",
       "0                     34                              175   \n",
       "1                     24                              154   \n",
       "2                     83                              186   \n",
       "3                     38                               57   \n",
       "4                     58                              247   \n",
       "...                  ...                              ...   \n",
       "10181                  0                                0   \n",
       "10182                 63                              416   \n",
       "10183                  2                                0   \n",
       "10184                  1                                1   \n",
       "10185                  7                                3   \n",
       "\n",
       "       importation_of_girls  \n",
       "0                         0  \n",
       "1                         0  \n",
       "2                         0  \n",
       "3                         0  \n",
       "4                         0  \n",
       "...                     ...  \n",
       "10181                     0  \n",
       "10182                     0  \n",
       "10183                     0  \n",
       "10184                     0  \n",
       "10185                     0  \n",
       "\n",
       "[10186 rows x 11 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10186 entries, 0 to 10185\n",
      "Data columns (total 11 columns):\n",
      " #   Column                           Non-Null Count  Dtype \n",
      "---  ------                           --------------  ----- \n",
      " 0   sr_no                            10186 non-null  int64 \n",
      " 1   state_ut                         10186 non-null  object\n",
      " 2   district                         10186 non-null  object\n",
      " 3   year                             10186 non-null  int64 \n",
      " 4   rape                             10186 non-null  int64 \n",
      " 5   kidnapping_and_abduction         10186 non-null  int64 \n",
      " 6   dowry_deaths                     10186 non-null  int64 \n",
      " 7   assault_on_women                 10186 non-null  int64 \n",
      " 8   insult_to_modesty                10186 non-null  int64 \n",
      " 9   cruelty_by_husband_or_relatives  10186 non-null  int64 \n",
      " 10  importation_of_girls             10186 non-null  int64 \n",
      "dtypes: int64(9), object(2)\n",
      "memory usage: 875.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df.sort_values([\"year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_kar = df[df[\"state_ut\"]==\"KARNATAKA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bangalore = df_kar[df_kar[\"district\"]==\"BANGALORE COMMR.\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sr_no</th>\n",
       "      <th>state_ut</th>\n",
       "      <th>district</th>\n",
       "      <th>year</th>\n",
       "      <th>rape</th>\n",
       "      <th>kidnapping_and_abduction</th>\n",
       "      <th>dowry_deaths</th>\n",
       "      <th>assault_on_women</th>\n",
       "      <th>insult_to_modesty</th>\n",
       "      <th>cruelty_by_husband_or_relatives</th>\n",
       "      <th>importation_of_girls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>255</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2001</td>\n",
       "      <td>40</td>\n",
       "      <td>56</td>\n",
       "      <td>40</td>\n",
       "      <td>223</td>\n",
       "      <td>51</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>972</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2002</td>\n",
       "      <td>32</td>\n",
       "      <td>58</td>\n",
       "      <td>33</td>\n",
       "      <td>237</td>\n",
       "      <td>62</td>\n",
       "      <td>242</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>1695</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2003</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>213</td>\n",
       "      <td>39</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2307</th>\n",
       "      <td>2423</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2004</td>\n",
       "      <td>32</td>\n",
       "      <td>61</td>\n",
       "      <td>44</td>\n",
       "      <td>151</td>\n",
       "      <td>35</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3006</th>\n",
       "      <td>3157</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2005</td>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
       "      <td>39</td>\n",
       "      <td>167</td>\n",
       "      <td>39</td>\n",
       "      <td>287</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3705</th>\n",
       "      <td>3891</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2006</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>50</td>\n",
       "      <td>143</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4412</th>\n",
       "      <td>4633</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2007</td>\n",
       "      <td>62</td>\n",
       "      <td>35</td>\n",
       "      <td>54</td>\n",
       "      <td>187</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5124</th>\n",
       "      <td>5380</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2008</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5854</th>\n",
       "      <td>6145</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2009</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>251</td>\n",
       "      <td>35</td>\n",
       "      <td>367</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6593</th>\n",
       "      <td>6919</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2010</td>\n",
       "      <td>65</td>\n",
       "      <td>29</td>\n",
       "      <td>52</td>\n",
       "      <td>308</td>\n",
       "      <td>50</td>\n",
       "      <td>398</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7340</th>\n",
       "      <td>7701</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2011</td>\n",
       "      <td>97</td>\n",
       "      <td>206</td>\n",
       "      <td>53</td>\n",
       "      <td>250</td>\n",
       "      <td>40</td>\n",
       "      <td>458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8106</th>\n",
       "      <td>8502</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2012</td>\n",
       "      <td>90</td>\n",
       "      <td>433</td>\n",
       "      <td>51</td>\n",
       "      <td>321</td>\n",
       "      <td>51</td>\n",
       "      <td>524</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8885</th>\n",
       "      <td>299</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2013</td>\n",
       "      <td>80</td>\n",
       "      <td>538</td>\n",
       "      <td>52</td>\n",
       "      <td>378</td>\n",
       "      <td>73</td>\n",
       "      <td>482</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sr_no   state_ut          district  year  rape  \\\n",
       "244     255  KARNATAKA  BANGALORE COMMR.  2001    40   \n",
       "926     972  KARNATAKA  BANGALORE COMMR.  2002    32   \n",
       "1614   1695  KARNATAKA  BANGALORE COMMR.  2003    34   \n",
       "2307   2423  KARNATAKA  BANGALORE COMMR.  2004    32   \n",
       "3006   3157  KARNATAKA  BANGALORE COMMR.  2005    38   \n",
       "3705   3891  KARNATAKA  BANGALORE COMMR.  2006    33   \n",
       "4412   4633  KARNATAKA  BANGALORE COMMR.  2007    62   \n",
       "5124   5380  KARNATAKA  BANGALORE COMMR.  2008    54   \n",
       "5854   6145  KARNATAKA  BANGALORE COMMR.  2009    65   \n",
       "6593   6919  KARNATAKA  BANGALORE COMMR.  2010    65   \n",
       "7340   7701  KARNATAKA  BANGALORE COMMR.  2011    97   \n",
       "8106   8502  KARNATAKA  BANGALORE COMMR.  2012    90   \n",
       "8885    299  KARNATAKA  BANGALORE COMMR.  2013    80   \n",
       "\n",
       "      kidnapping_and_abduction  dowry_deaths  assault_on_women  \\\n",
       "244                         56            40               223   \n",
       "926                         58            33               237   \n",
       "1614                        11            34               213   \n",
       "2307                        61            44               151   \n",
       "3006                        25            39               167   \n",
       "3705                        29            50               143   \n",
       "4412                        35            54               187   \n",
       "5124                         2            52               179   \n",
       "5854                         2            50               251   \n",
       "6593                        29            52               308   \n",
       "7340                       206            53               250   \n",
       "8106                       433            51               321   \n",
       "8885                       538            52               378   \n",
       "\n",
       "      insult_to_modesty  cruelty_by_husband_or_relatives  importation_of_girls  \n",
       "244                  51                              229                     0  \n",
       "926                  62                              242                     0  \n",
       "1614                 39                              213                     0  \n",
       "2307                 35                              227                     0  \n",
       "3006                 39                              287                     0  \n",
       "3705                  2                              290                     0  \n",
       "4412                  2                              290                     0  \n",
       "5124                  1                              307                     0  \n",
       "5854                 35                              367                     0  \n",
       "6593                 50                              398                     0  \n",
       "7340                 40                              458                     0  \n",
       "8106                 51                              524                     0  \n",
       "8885                 73                              482                     0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bangalore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-33-e5de1c3c6535>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_bangalore[\"t\"] = np.arange(1,14)\n",
      "<ipython-input-33-e5de1c3c6535>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_bangalore[\"t_square\"] = df_bangalore[\"t\"] * df_bangalore[\"t\"]\n",
      "<ipython-input-33-e5de1c3c6535>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_bangalore[\"log_rape\"] = np.log(df_bangalore[\"rape\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['sr_no', 'state_ut', 'district', 'year', 'rape',\n",
       "       'kidnapping_and_abduction', 'dowry_deaths', 'assault_on_women',\n",
       "       'insult_to_modesty', 'cruelty_by_husband_or_relatives',\n",
       "       'importation_of_girls', 't', 't_square', 'log_rape'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df_bangalore[\"t\"] = np.arange(1,14)\n",
    "\n",
    "df_bangalore[\"t_square\"] = df_bangalore[\"t\"] * df_bangalore[\"t\"]\n",
    "df_bangalore[\"log_rape\"] = np.log(df_bangalore[\"rape\"])\n",
    "df_bangalore.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Partition\n",
    "Train = df_bangalore.head(10)\n",
    "Test = df_bangalore.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>sr_no</th>\n",
       "      <th>state_ut</th>\n",
       "      <th>district</th>\n",
       "      <th>year</th>\n",
       "      <th>rape</th>\n",
       "      <th>kidnapping_and_abduction</th>\n",
       "      <th>dowry_deaths</th>\n",
       "      <th>assault_on_women</th>\n",
       "      <th>insult_to_modesty</th>\n",
       "      <th>cruelty_by_husband_or_relatives</th>\n",
       "      <th>importation_of_girls</th>\n",
       "      <th>t</th>\n",
       "      <th>t_square</th>\n",
       "      <th>log_rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>255</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2001</td>\n",
       "      <td>40</td>\n",
       "      <td>56</td>\n",
       "      <td>40</td>\n",
       "      <td>223</td>\n",
       "      <td>51</td>\n",
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       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>972</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2002</td>\n",
       "      <td>32</td>\n",
       "      <td>58</td>\n",
       "      <td>33</td>\n",
       "      <td>237</td>\n",
       "      <td>62</td>\n",
       "      <td>242</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3.465736</td>\n",
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       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>1695</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2003</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>213</td>\n",
       "      <td>39</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3.526361</td>\n",
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       "    <tr>\n",
       "      <th>2307</th>\n",
       "      <td>2423</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2004</td>\n",
       "      <td>32</td>\n",
       "      <td>61</td>\n",
       "      <td>44</td>\n",
       "      <td>151</td>\n",
       "      <td>35</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>3.465736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3006</th>\n",
       "      <td>3157</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2005</td>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
       "      <td>39</td>\n",
       "      <td>167</td>\n",
       "      <td>39</td>\n",
       "      <td>287</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>3.637586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3705</th>\n",
       "      <td>3891</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2006</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>50</td>\n",
       "      <td>143</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>3.496508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4412</th>\n",
       "      <td>4633</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2007</td>\n",
       "      <td>62</td>\n",
       "      <td>35</td>\n",
       "      <td>54</td>\n",
       "      <td>187</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>49</td>\n",
       "      <td>4.127134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5124</th>\n",
       "      <td>5380</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2008</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>307</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>3.988984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5854</th>\n",
       "      <td>6145</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2009</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>251</td>\n",
       "      <td>35</td>\n",
       "      <td>367</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>81</td>\n",
       "      <td>4.174387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6593</th>\n",
       "      <td>6919</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2010</td>\n",
       "      <td>65</td>\n",
       "      <td>29</td>\n",
       "      <td>52</td>\n",
       "      <td>308</td>\n",
       "      <td>50</td>\n",
       "      <td>398</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>4.174387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sr_no   state_ut          district  year  rape  \\\n",
       "244     255  KARNATAKA  BANGALORE COMMR.  2001    40   \n",
       "926     972  KARNATAKA  BANGALORE COMMR.  2002    32   \n",
       "1614   1695  KARNATAKA  BANGALORE COMMR.  2003    34   \n",
       "2307   2423  KARNATAKA  BANGALORE COMMR.  2004    32   \n",
       "3006   3157  KARNATAKA  BANGALORE COMMR.  2005    38   \n",
       "3705   3891  KARNATAKA  BANGALORE COMMR.  2006    33   \n",
       "4412   4633  KARNATAKA  BANGALORE COMMR.  2007    62   \n",
       "5124   5380  KARNATAKA  BANGALORE COMMR.  2008    54   \n",
       "5854   6145  KARNATAKA  BANGALORE COMMR.  2009    65   \n",
       "6593   6919  KARNATAKA  BANGALORE COMMR.  2010    65   \n",
       "\n",
       "      kidnapping_and_abduction  dowry_deaths  assault_on_women  \\\n",
       "244                         56            40               223   \n",
       "926                         58            33               237   \n",
       "1614                        11            34               213   \n",
       "2307                        61            44               151   \n",
       "3006                        25            39               167   \n",
       "3705                        29            50               143   \n",
       "4412                        35            54               187   \n",
       "5124                         2            52               179   \n",
       "5854                         2            50               251   \n",
       "6593                        29            52               308   \n",
       "\n",
       "      insult_to_modesty  cruelty_by_husband_or_relatives  \\\n",
       "244                  51                              229   \n",
       "926                  62                              242   \n",
       "1614                 39                              213   \n",
       "2307                 35                              227   \n",
       "3006                 39                              287   \n",
       "3705                  2                              290   \n",
       "4412                  2                              290   \n",
       "5124                  1                              307   \n",
       "5854                 35                              367   \n",
       "6593                 50                              398   \n",
       "\n",
       "      importation_of_girls   t  t_square  log_rape  \n",
       "244                      0   1         1  3.688879  \n",
       "926                      0   2         4  3.465736  \n",
       "1614                     0   3         9  3.526361  \n",
       "2307                     0   4        16  3.465736  \n",
       "3006                     0   5        25  3.637586  \n",
       "3705                     0   6        36  3.496508  \n",
       "4412                     0   7        49  4.127134  \n",
       "5124                     0   8        64  3.988984  \n",
       "5854                     0   9        81  4.174387  \n",
       "6593                     0  10       100  4.174387  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.874949435319593"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "linear_model = smf.ols('rape ~ t', data = Train).fit()\n",
    "pred_linear =  pd.Series(linear_model.predict(pd.DataFrame(Test['t'])))\n",
    "rmse_linear = np.sqrt(np.mean((np.array(Test['rape']) - np.array(pred_linear))**2))\n",
    "rmse_linear\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7340    66.866667\n",
       "8106    70.751515\n",
       "8885    74.636364\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exponential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.928533729978376"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exp = smf.ols('log_rape ~ t', data = Train).fit()\n",
    "pred_Exp = pd.Series(Exp.predict(pd.DataFrame(Test['t'])))\n",
    "rmse_Exp = np.sqrt(np.mean((np.array(Test['rape']) - np.array(np.exp(pred_Exp)))**2))\n",
    "rmse_Exp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quadratic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.459506695306924"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Quad = smf.ols(str('rape')+ '~ t+ t_square', data = Train).fit()\n",
    "pred_Quad = pd.Series(Quad.predict(Test[[\"t\", \"t_square\"]]))\n",
    "rmse_Quad = np.sqrt(np.mean((np.array(Test['rape']) - np.array(pred_Quad))**2))\n",
    "rmse_Quad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>MODEL</th>\n",
       "      <th>RMSE_Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rmse_linear</td>\n",
       "      <td>20.874949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rmse_Exp</td>\n",
       "      <td>18.928534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rmse_Quad</td>\n",
       "      <td>17.459507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MODEL  RMSE_Values\n",
       "0  rmse_linear    20.874949\n",
       "1     rmse_Exp    18.928534\n",
       "2    rmse_Quad    17.459507"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"MODEL\":pd.Series([\"rmse_linear\",\"rmse_Exp\",\"rmse_Quad\"]),\"RMSE_Values\":pd.Series([rmse_linear,rmse_Exp,rmse_Quad])}\n",
    "table_rmse = pd.DataFrame(data)\n",
    "table_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i'll be using sklearns polynomial features \n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "lin_mod = LinearRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d526f15490>]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Train[['t']], Train[['rape']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the polynomial model i'll try a degree 2-4 model\n",
    "\n",
    "def polynomial_reg(degree, x_train, y_train, x_test, y_test, return_metrics = False):\n",
    "    \n",
    "    poly_feat = PolynomialFeatures(degree)\n",
    "    X_poly = poly_feat.fit_transform(x_train)\n",
    "    x_test = poly_feat.fit_transform(x_test)\n",
    "\n",
    "# Make and fit the polynomial regression model\n",
    "# TODO: Create a LinearRegression object and fit it to the polynomial predictor\n",
    "# features\n",
    "    model = LinearRegression()\n",
    "    poly_model = model.fit(X_poly, y_train)\n",
    "    pred = poly_model.predict(x_test)\n",
    "    if return_metrics == True:\n",
    "        rmse = np.sqrt(mean_squared_error(y_test, pred))\n",
    "        mae = mean_absolute_error(y_test, pred)\n",
    "        r2 = r2_score(y_test, pred)\n",
    "        print('Rmse: {}, Mae: {}, r2score: {}'.format(rmse, mae, r2),'\\n', pred)\n",
    "    else:\n",
    "        return pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, y_train, x_test, y_test = Train[['t']], Train[['rape']], Test[['t']], Test['rape']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[86.29090909]])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "polynomial_reg(1, x_train, y_train, np.array([[16]]), y_test, return_metrics= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "degree 1\n",
      "Rmse: 20.874949435319607, Mae: 18.248484848484853, r2score: -7.954044806722269 \n",
      " [[66.86666667]\n",
      " [70.75151515]\n",
      " [74.63636364]]\n",
      "degree 2\n",
      "Rmse: 17.45950669530686, Mae: 14.719191919191928, r2score: -5.263720014591749 \n",
      " [[ 81.03333333]\n",
      " [ 92.64545455]\n",
      " [105.54545455]]\n",
      "degree 3\n",
      "Rmse: 32.64155441121477, Mae: 32.62626262626461, r2score: -20.893241254389615 \n",
      " [[64.66666667]\n",
      " [58.42424242]\n",
      " [46.03030303]]\n",
      "degree 4\n",
      "Rmse: 65.03641700764615, Mae: 62.080808080825335, r2score: -85.91237405189955 \n",
      " [[55.66666667]\n",
      " [33.06060606]\n",
      " [-7.96969697]]\n",
      "degree 5\n",
      "Rmse: 56.83485245077442, Mae: 55.05858585923456, r2score: -65.3739819130404 \n",
      " [[57.13333333]\n",
      " [38.52727273]\n",
      " [ 6.16363636]]\n",
      "degree 6\n",
      "Rmse: 439.42412681839113, Mae: 317.81818139345955, r2score: -3966.6759567829895 \n",
      " [[110.86666663]\n",
      " [297.42424214]\n",
      " [812.16363541]]\n",
      "degree 7\n",
      "Rmse: 883.8482452197486, Mae: 649.9697564211023, r2score: -16050.802477630728 \n",
      " [[   13.19999419]\n",
      " [ -297.4545817 ]\n",
      " [-1398.65468176]]\n",
      "degree 8\n",
      "Rmse: 15986.086800007719, Mae: 11337.705413631722, r2score: -5251128.544740706 \n",
      " [[  -755.19923833]\n",
      " [ -6095.37644858]\n",
      " [-26895.54055399]]\n",
      "degree 9\n",
      "Rmse: 159006.18827836044, Mae: 109812.60268585454, r2score: -519513038.2632893 \n",
      " [[  -5888.97154377]\n",
      " [ -53232.67429011]\n",
      " [-270049.16222368]]\n"
     ]
    }
   ],
   "source": [
    "for a in range(1, 10):\n",
    "    print('degree {}'.format(a))\n",
    "    polynomial_reg(a, x_train, y_train, x_test, y_test, return_metrics= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i want a function that sums all states and just leaves year\n",
    "unique_states = df['state_ut'].unique()\n",
    "def state_reg(df, state, crime, year):\n",
    "    state_df = df[df['state_ut']== state]\n",
    "    state_df = state_df.groupby('year', as_index= False)['rape',\n",
    "       'kidnapping_and_abduction', 'dowry_deaths', 'assault_on_women',\n",
    "       'insult_to_modesty', 'cruelty_by_husband_or_relatives',\n",
    "       'importation_of_girls'].sum()\n",
    "    state_df.fillna(0, inplace= True)\n",
    "    \n",
    "    state_df[\"t\"] = np.arange(1,len(state_df)+1)\n",
    "\n",
    "    state_df[\"t_square\"] = state_df[\"t\"] * state_df[\"t\"]\n",
    "    state_df['log_crime'] = np.log(state_df[crime])\n",
    "    x_train = state_df[['t', 't_square']]\n",
    "    y_train = state_df[crime]\n",
    "    if year <= 2014:\n",
    "        yeartopred = state_df[['t', 't_square']][state_df['year'] == year]\n",
    "    elif year== 2015:\n",
    "        yeartopred1 = 15\n",
    "        yeartopred2 = 15**2\n",
    "        yeartopred= np.array([[yeartopred1, yeartopred2]])\n",
    "    elif year == 2016:\n",
    "        yeartopred3 = 16\n",
    "        yeartopred4 = 16**2\n",
    "        yeartopred= np.array([[yeartopred3, yeartopred4]])\n",
    "        \n",
    "    \n",
    "    #Quad = smf.ols(str(crime) + '~ t+ t_square', data = state_df).fit()\n",
    "    pred_Quad = polynomial_reg(2, x_train, y_train, yeartopred, y_test, return_metrics= False)\n",
    "    return pred_Quad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-188-3f2f70702179>:5: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  state_df = state_df.groupby('year', as_index= False)['rape',\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2615.97952048])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_reg(df, 'KARNATAKA', 'rape', 2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-150-f144869b77d5>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df[df.state_ut =='KARNATAKA'].groupby('year')['rape',\n"
     ]
    },
    {
     "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>rape</th>\n",
       "      <th>kidnapping_and_abduction</th>\n",
       "      <th>dowry_deaths</th>\n",
       "      <th>assault_on_women</th>\n",
       "      <th>insult_to_modesty</th>\n",
       "      <th>cruelty_by_husband_or_relatives</th>\n",
       "      <th>importation_of_girls</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</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>2001</th>\n",
       "      <td>293</td>\n",
       "      <td>271</td>\n",
       "      <td>220</td>\n",
       "      <td>1665</td>\n",
       "      <td>81</td>\n",
       "      <td>1755</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>292</td>\n",
       "      <td>300</td>\n",
       "      <td>233</td>\n",
       "      <td>1648</td>\n",
       "      <td>100</td>\n",
       "      <td>1826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>321</td>\n",
       "      <td>244</td>\n",
       "      <td>194</td>\n",
       "      <td>1585</td>\n",
       "      <td>84</td>\n",
       "      <td>1704</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>291</td>\n",
       "      <td>286</td>\n",
       "      <td>259</td>\n",
       "      <td>1435</td>\n",
       "      <td>57</td>\n",
       "      <td>1588</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>343</td>\n",
       "      <td>312</td>\n",
       "      <td>261</td>\n",
       "      <td>1585</td>\n",
       "      <td>71</td>\n",
       "      <td>1883</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>400</td>\n",
       "      <td>328</td>\n",
       "      <td>244</td>\n",
       "      <td>1683</td>\n",
       "      <td>38</td>\n",
       "      <td>2129</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>436</td>\n",
       "      <td>390</td>\n",
       "      <td>251</td>\n",
       "      <td>1828</td>\n",
       "      <td>28</td>\n",
       "      <td>2507</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>446</td>\n",
       "      <td>405</td>\n",
       "      <td>259</td>\n",
       "      <td>1954</td>\n",
       "      <td>44</td>\n",
       "      <td>2638</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>509</td>\n",
       "      <td>408</td>\n",
       "      <td>264</td>\n",
       "      <td>2186</td>\n",
       "      <td>64</td>\n",
       "      <td>3185</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>586</td>\n",
       "      <td>586</td>\n",
       "      <td>248</td>\n",
       "      <td>2544</td>\n",
       "      <td>83</td>\n",
       "      <td>3441</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>636</td>\n",
       "      <td>715</td>\n",
       "      <td>267</td>\n",
       "      <td>2608</td>\n",
       "      <td>81</td>\n",
       "      <td>3712</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>621</td>\n",
       "      <td>1070</td>\n",
       "      <td>218</td>\n",
       "      <td>2978</td>\n",
       "      <td>100</td>\n",
       "      <td>3684</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1030</td>\n",
       "      <td>1359</td>\n",
       "      <td>277</td>\n",
       "      <td>3913</td>\n",
       "      <td>137</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1324</td>\n",
       "      <td>1457</td>\n",
       "      <td>313</td>\n",
       "      <td>5263</td>\n",
       "      <td>170</td>\n",
       "      <td>3025</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      rape  kidnapping_and_abduction  dowry_deaths  assault_on_women  \\\n",
       "year                                                                   \n",
       "2001   293                       271           220              1665   \n",
       "2002   292                       300           233              1648   \n",
       "2003   321                       244           194              1585   \n",
       "2004   291                       286           259              1435   \n",
       "2005   343                       312           261              1585   \n",
       "2006   400                       328           244              1683   \n",
       "2007   436                       390           251              1828   \n",
       "2008   446                       405           259              1954   \n",
       "2009   509                       408           264              2186   \n",
       "2010   586                       586           248              2544   \n",
       "2011   636                       715           267              2608   \n",
       "2012   621                      1070           218              2978   \n",
       "2013  1030                      1359           277              3913   \n",
       "2014  1324                      1457           313              5263   \n",
       "\n",
       "      insult_to_modesty  cruelty_by_husband_or_relatives  importation_of_girls  \n",
       "year                                                                            \n",
       "2001                 81                             1755                     0  \n",
       "2002                100                             1826                     0  \n",
       "2003                 84                             1704                     0  \n",
       "2004                 57                             1588                     0  \n",
       "2005                 71                             1883                     0  \n",
       "2006                 38                             2129                     0  \n",
       "2007                 28                             2507                     0  \n",
       "2008                 44                             2638                     1  \n",
       "2009                 64                             3185                     2  \n",
       "2010                 83                             3441                     0  \n",
       "2011                 81                             3712                    12  \n",
       "2012                100                             3684                    32  \n",
       "2013                137                             3276                     0  \n",
       "2014                170                             3025                     0  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.state_ut =='KARNATAKA'].groupby('year')['rape',\n",
    "       'kidnapping_and_abduction', 'dowry_deaths', 'assault_on_women',\n",
    "       'insult_to_modesty', 'cruelty_by_husband_or_relatives',\n",
    "       'importation_of_girls'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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