crime_analysis.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE/UT</th>\n",
       "      <th>CRIME HEAD</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>INFANTICIDE</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",
       "      <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>2</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>3</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHHATTISGARH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            STATE/UT   CRIME HEAD  2001  2002  2003  2004  2005  2006  2007  \\\n",
       "0     ANDHRA PRADESH  INFANTICIDE     1     1     3     0     0     0     1   \n",
       "1  ARUNACHAL PRADESH  INFANTICIDE     0     0     0     0     0     0     0   \n",
       "2              ASSAM  INFANTICIDE     0     5     0     0     1     0     0   \n",
       "3              BIHAR  INFANTICIDE     0     0     0     0     2     0     2   \n",
       "4       CHHATTISGARH  INFANTICIDE     7    29     5    12     0    15    11   \n",
       "\n",
       "   2008  2009  2010  2011  2012  \n",
       "0     0     5     6     1     6  \n",
       "1     0     0     0     0     0  \n",
       "2     0     0     0     0     0  \n",
       "3     2     0     2     5     2  \n",
       "4     6     2     0    10     5  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('https://query.data.world/s/2flixt3ifiq4j43dr5is3u362jnugw')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE/UT</th>\n",
       "      <th>CRIME HEAD</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHHATTISGARH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            STATE/UT   CRIME HEAD  2001  2002  2003  2004  2005  2006  2007  \\\n",
       "0     ANDHRA PRADESH  INFANTICIDE     1     1     3     0     0     0     1   \n",
       "1  ARUNACHAL PRADESH  INFANTICIDE     0     0     0     0     0     0     0   \n",
       "2              ASSAM  INFANTICIDE     0     5     0     0     1     0     0   \n",
       "3              BIHAR  INFANTICIDE     0     0     0     0     2     0     2   \n",
       "4       CHHATTISGARH  INFANTICIDE     7    29     5    12     0    15    11   \n",
       "\n",
       "   2008  2009  2010  2011  2012  total  \n",
       "0     0     5     6     1     6     24  \n",
       "1     0     0     0     0     0      0  \n",
       "2     0     0     0     0     0      6  \n",
       "3     2     0     2     5     2     15  \n",
       "4     6     2     0    10     5    102  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['total']=df.iloc[:,-12:].sum(axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE/UT</th>\n",
       "      <th>CRIME HEAD</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHHATTISGARH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            STATE/UT   CRIME HEAD  total\n",
       "0     ANDHRA PRADESH  INFANTICIDE     24\n",
       "1  ARUNACHAL PRADESH  INFANTICIDE      0\n",
       "2              ASSAM  INFANTICIDE      6\n",
       "3              BIHAR  INFANTICIDE     15\n",
       "4       CHHATTISGARH  INFANTICIDE    102"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df.columns[[2,3,4,5,6,7,8,9,10,11,12,13]], axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 494 entries, 0 to 493\n",
      "Data columns (total 3 columns):\n",
      "STATE/UT      494 non-null object\n",
      "CRIME HEAD    494 non-null object\n",
      "total         494 non-null int64\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 11.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=df[:-38] #to drop total crime rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 456 artists>"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.bar(x=\"CRIME HEAD\", height=\"total\", width=0.8, bottom=None, align='center', data=d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>State/UT</th>\n",
       "      <th>crime_list</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHHATTISGARH</td>\n",
       "      <td>INFANTICIDE</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State/UT   crime_list  count\n",
       "0     ANDHRA PRADESH  INFANTICIDE     24\n",
       "1  ARUNACHAL PRADESH  INFANTICIDE      0\n",
       "2              ASSAM  INFANTICIDE      6\n",
       "3              BIHAR  INFANTICIDE     15\n",
       "4       CHHATTISGARH  INFANTICIDE    102"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.columns = ['State/UT', 'crime_list','count']\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>State/UT</th>\n",
       "      <th>crime_list</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>5023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>CHHATTISGARH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             State/UT        crime_list  count\n",
       "76     ANDHRA PRADESH  RAPE OF CHILDREN   5023\n",
       "77  ARUNACHAL PRADESH  RAPE OF CHILDREN     96\n",
       "78              ASSAM  RAPE OF CHILDREN    443\n",
       "79              BIHAR  RAPE OF CHILDREN    692\n",
       "80       CHHATTISGARH  RAPE OF CHILDREN   4391"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analyzing a specific crime-rape\n",
    "rape = d[d.crime_list.str.contains('RAPE OF CHILDREN')]\n",
    "rape.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 3)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rape.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>State/UT</th>\n",
       "      <th>crime_list</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>5023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>CHHATTISGARH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>4391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>GOA</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>GUJARAT</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>1514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>HARYANA</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>1785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>HIMACHAL PRADESH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>JAMMU &amp; KASHMIR</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>JHARKHAND</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>1019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>KERALA</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>3060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>MADHYA PRADESH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>13688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>9757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>MANIPUR</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>MEGHALAYA</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>MIZORAM</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>NAGALAND</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>ODISHA</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>1597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>RAJASTHAN</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>2940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>SIKKIM</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>1841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>TRIPURA</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>9934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>UTTARAKHAND</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>TOTAL (STATES)</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>62598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>A &amp; N ISLANDS</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>CHANDIGARH</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>D &amp; N HAVELI</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>DAMAN &amp; DIU</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>DELHI</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>3796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>TOTAL (UTs)</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>4179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>TOTAL (ALL-INDIA)</td>\n",
       "      <td>RAPE OF CHILDREN</td>\n",
       "      <td>66777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              State/UT        crime_list  count\n",
       "76      ANDHRA PRADESH  RAPE OF CHILDREN   5023\n",
       "77   ARUNACHAL PRADESH  RAPE OF CHILDREN     96\n",
       "78               ASSAM  RAPE OF CHILDREN    443\n",
       "79               BIHAR  RAPE OF CHILDREN    692\n",
       "80        CHHATTISGARH  RAPE OF CHILDREN   4391\n",
       "81                 GOA  RAPE OF CHILDREN    309\n",
       "82             GUJARAT  RAPE OF CHILDREN   1514\n",
       "83             HARYANA  RAPE OF CHILDREN   1785\n",
       "84    HIMACHAL PRADESH  RAPE OF CHILDREN    819\n",
       "85     JAMMU & KASHMIR  RAPE OF CHILDREN     79\n",
       "86           JHARKHAND  RAPE OF CHILDREN    313\n",
       "87           KARNATAKA  RAPE OF CHILDREN   1019\n",
       "88              KERALA  RAPE OF CHILDREN   3060\n",
       "89      MADHYA PRADESH  RAPE OF CHILDREN  13688\n",
       "90         MAHARASHTRA  RAPE OF CHILDREN   9757\n",
       "91             MANIPUR  RAPE OF CHILDREN     47\n",
       "92           MEGHALAYA  RAPE OF CHILDREN    346\n",
       "93             MIZORAM  RAPE OF CHILDREN    281\n",
       "94            NAGALAND  RAPE OF CHILDREN     35\n",
       "95              ODISHA  RAPE OF CHILDREN    964\n",
       "96              PUNJAB  RAPE OF CHILDREN   1597\n",
       "97           RAJASTHAN  RAPE OF CHILDREN   2940\n",
       "98              SIKKIM  RAPE OF CHILDREN    121\n",
       "99          TAMIL NADU  RAPE OF CHILDREN   1841\n",
       "100            TRIPURA  RAPE OF CHILDREN    474\n",
       "101      UTTAR PRADESH  RAPE OF CHILDREN   9934\n",
       "102        UTTARAKHAND  RAPE OF CHILDREN    236\n",
       "103        WEST BENGAL  RAPE OF CHILDREN    794\n",
       "104     TOTAL (STATES)  RAPE OF CHILDREN  62598\n",
       "105      A & N ISLANDS  RAPE OF CHILDREN    110\n",
       "106         CHANDIGARH  RAPE OF CHILDREN    170\n",
       "107       D & N HAVELI  RAPE OF CHILDREN     14\n",
       "108        DAMAN & DIU  RAPE OF CHILDREN     18\n",
       "109              DELHI  RAPE OF CHILDREN   3796\n",
       "110        LAKSHADWEEP  RAPE OF CHILDREN      0\n",
       "111         PUDUCHERRY  RAPE OF CHILDREN     71\n",
       "112        TOTAL (UTs)  RAPE OF CHILDREN   4179\n",
       "113  TOTAL (ALL-INDIA)  RAPE OF CHILDREN  66777"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "r=rape.drop(rape.index[[-1,-2,-10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25ba6b588d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r.plot(kind='bar',x='State/UT',y='count',color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>State/UT</th>\n",
       "      <th>crime_list</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>6093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>ASSAM</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>8579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>CHHATTISGARH</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>GOA</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>GUJARAT</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>5558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>HARYANA</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>1609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>HIMACHAL PRADESH</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>JAMMU &amp; KASHMIR</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>JHARKHAND</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>KERALA</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>MADHYA PRADESH</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>4517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>7549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>MANIPUR</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>MEGHALAYA</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>MIZORAM</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>NAGALAND</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>ODISHA</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>2355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>RAJASTHAN</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>2952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>SIKKIM</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>2506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>TRIPURA</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>30625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>UTTARAKHAND</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>2626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>TOTAL (STATES)</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>80963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>A &amp; N ISLANDS</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>CHANDIGARH</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>D &amp; N HAVELI</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>DAMAN &amp; DIU</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>DELHI</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>3464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>TOTAL (UTs)</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>4006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>TOTAL (ALL-INDIA)</td>\n",
       "      <td>KIDNAPPING and ABDUCTION OF CHILDREN</td>\n",
       "      <td>84969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              State/UT                            crime_list  count\n",
       "114     ANDHRA PRADESH  KIDNAPPING and ABDUCTION OF CHILDREN   6093\n",
       "115  ARUNACHAL PRADESH  KIDNAPPING and ABDUCTION OF CHILDREN    105\n",
       "116              ASSAM  KIDNAPPING and ABDUCTION OF CHILDREN    204\n",
       "117              BIHAR  KIDNAPPING and ABDUCTION OF CHILDREN   8579\n",
       "118       CHHATTISGARH  KIDNAPPING and ABDUCTION OF CHILDREN   1333\n",
       "119                GOA  KIDNAPPING and ABDUCTION OF CHILDREN    160\n",
       "120            GUJARAT  KIDNAPPING and ABDUCTION OF CHILDREN   5558\n",
       "121            HARYANA  KIDNAPPING and ABDUCTION OF CHILDREN   1609\n",
       "122   HIMACHAL PRADESH  KIDNAPPING and ABDUCTION OF CHILDREN    591\n",
       "123    JAMMU & KASHMIR  KIDNAPPING and ABDUCTION OF CHILDREN    219\n",
       "124          JHARKHAND  KIDNAPPING and ABDUCTION OF CHILDREN    245\n",
       "125          KARNATAKA  KIDNAPPING and ABDUCTION OF CHILDREN    881\n",
       "126             KERALA  KIDNAPPING and ABDUCTION OF CHILDREN   1012\n",
       "127     MADHYA PRADESH  KIDNAPPING and ABDUCTION OF CHILDREN   4517\n",
       "128        MAHARASHTRA  KIDNAPPING and ABDUCTION OF CHILDREN   7549\n",
       "129            MANIPUR  KIDNAPPING and ABDUCTION OF CHILDREN    281\n",
       "130          MEGHALAYA  KIDNAPPING and ABDUCTION OF CHILDREN     47\n",
       "131            MIZORAM  KIDNAPPING and ABDUCTION OF CHILDREN      7\n",
       "132           NAGALAND  KIDNAPPING and ABDUCTION OF CHILDREN     16\n",
       "133             ODISHA  KIDNAPPING and ABDUCTION OF CHILDREN    402\n",
       "134             PUNJAB  KIDNAPPING and ABDUCTION OF CHILDREN   2355\n",
       "135          RAJASTHAN  KIDNAPPING and ABDUCTION OF CHILDREN   2952\n",
       "136             SIKKIM  KIDNAPPING and ABDUCTION OF CHILDREN     32\n",
       "137         TAMIL NADU  KIDNAPPING and ABDUCTION OF CHILDREN   2506\n",
       "138            TRIPURA  KIDNAPPING and ABDUCTION OF CHILDREN    148\n",
       "139      UTTAR PRADESH  KIDNAPPING and ABDUCTION OF CHILDREN  30625\n",
       "140        UTTARAKHAND  KIDNAPPING and ABDUCTION OF CHILDREN    311\n",
       "141        WEST BENGAL  KIDNAPPING and ABDUCTION OF CHILDREN   2626\n",
       "142     TOTAL (STATES)  KIDNAPPING and ABDUCTION OF CHILDREN  80963\n",
       "143      A & N ISLANDS  KIDNAPPING and ABDUCTION OF CHILDREN     55\n",
       "144         CHANDIGARH  KIDNAPPING and ABDUCTION OF CHILDREN    292\n",
       "145       D & N HAVELI  KIDNAPPING and ABDUCTION OF CHILDREN     78\n",
       "146        DAMAN & DIU  KIDNAPPING and ABDUCTION OF CHILDREN      6\n",
       "147              DELHI  KIDNAPPING and ABDUCTION OF CHILDREN   3464\n",
       "148        LAKSHADWEEP  KIDNAPPING and ABDUCTION OF CHILDREN      0\n",
       "149         PUDUCHERRY  KIDNAPPING and ABDUCTION OF CHILDREN    111\n",
       "150        TOTAL (UTs)  KIDNAPPING and ABDUCTION OF CHILDREN   4006\n",
       "151  TOTAL (ALL-INDIA)  KIDNAPPING and ABDUCTION OF CHILDREN  84969"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Analyzing a specific crime-kidnapping\n",
    "kidnap = d[d.crime_list.str.contains('KIDNAPPING and ABDUCTION OF CHILDREN')]\n",
    "kidnap.shape\n",
    "kidnap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "k=kidnap.drop(kidnap.index[[-1,-2,-10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25ba72c50b8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k.plot(kind='bar',x='State/UT',y='count',color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}