Histograms of EDM Dataset.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GenderCode</th>\n",
       "      <th>IntBrothers</th>\n",
       "      <th>IntSisters</th>\n",
       "      <th>IntSchoolBrothers</th>\n",
       "      <th>IntSchoolSisters</th>\n",
       "      <th>ClassSchoolStatus</th>\n",
       "      <th>Disability01</th>\n",
       "      <th>Lang1</th>\n",
       "      <th>Lang2</th>\n",
       "      <th>Lang3</th>\n",
       "      <th>Lang4</th>\n",
       "      <th>Religion</th>\n",
       "      <th>RESULT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FAIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>FAIL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GenderCode  IntBrothers  IntSisters  IntSchoolBrothers  IntSchoolSisters  \\\n",
       "0           0            3           3                  3                 2   \n",
       "1           0            4           5                  1                 1   \n",
       "2           0            5           3                  0                 0   \n",
       "3           0            2           4                  1                 4   \n",
       "4           0            2           1                  0                 0   \n",
       "\n",
       "   ClassSchoolStatus  Disability01  Lang1  Lang2  Lang3  Lang4  Religion  \\\n",
       "0                  1             0      1      0      0      0         1   \n",
       "1                  1             0      1      0      0      0         1   \n",
       "2                  1             0      1      0      0      0         1   \n",
       "3                  1             0      1      0      0      0         1   \n",
       "4                  1             0      1      0      0      0         1   \n",
       "\n",
       "  RESULT  \n",
       "0   FAIL  \n",
       "1   PASS  \n",
       "2   PASS  \n",
       "3   PASS  \n",
       "4   FAIL  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here you begin working on the data\n",
    "df = pd.read_excel('Data.xlsx')\n",
    "\n",
    "# Deleting the two mentioned columns from the dataframe\n",
    "df.drop('VchMotherTounge', axis=1, inplace =True)\n",
    "df.drop('MotherTongueBin', axis=1, inplace =True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GenderCode</th>\n",
       "      <th>IntBrothers</th>\n",
       "      <th>IntSisters</th>\n",
       "      <th>IntSchoolBrothers</th>\n",
       "      <th>IntSchoolSisters</th>\n",
       "      <th>ClassSchoolStatus</th>\n",
       "      <th>Disability01</th>\n",
       "      <th>Lang1</th>\n",
       "      <th>Lang2</th>\n",
       "      <th>Lang3</th>\n",
       "      <th>Lang4</th>\n",
       "      <th>Religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GenderCode  IntBrothers  IntSisters  IntSchoolBrothers  IntSchoolSisters  \\\n",
       "0           0            3           3                  3                 2   \n",
       "1           0            4           5                  1                 1   \n",
       "2           0            5           3                  0                 0   \n",
       "3           0            2           4                  1                 4   \n",
       "4           0            2           1                  0                 0   \n",
       "\n",
       "   ClassSchoolStatus  Disability01  Lang1  Lang2  Lang3  Lang4  Religion  \n",
       "0                  1             0      1      0      0      0         1  \n",
       "1                  1             0      1      0      0      0         1  \n",
       "2                  1             0      1      0      0      0         1  \n",
       "3                  1             0      1      0      0      0         1  \n",
       "4                  1             0      1      0      0      0         1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: RESULT, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Slicing from main dataframe to Input Data(X) and output Data(y)\n",
    "y = df.iloc[:, 12]\n",
    "X = df.iloc[:,:12]\n",
    "# Replacng PASS and FAIL with integers 1 and 0 respectively\n",
    "y[y == 'PASS'] = 1\n",
    "y[y == 'FAIL'] = 0\n",
    "display(X.head())\n",
    "display(y.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x76b9d83550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = X['GenderCode'].value_counts()\n",
    "x =  np.array(['Male', 'Female'])\n",
    "y = np.array([3552, 2057])\n",
    "plt.bar(x, y, label = 'Gender Distribution')\n",
    "plt.legend()\n",
    "#plt.hist(b, bins, histtype = 'bar', rwidth = 0.8, label = )\n",
    "plt.savefig('GenderDistribution.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DICJ+B+yWdF5qmgk8B6wDFqe2xcB9aXod8Nl0NtBU4PXOoSIzM+t/tV4H8GXgLkmnAy8C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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x76b6b6f630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = X['IntBrothers'].value_counts()\n",
    "x = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "y = np.array([630, 1147, 1339, 1124, 706, 394, 180, 89])\n",
    "\n",
    "plt.bar(x, y, label = 'IntBrothers')\n",
    "plt.legend()\n",
    "plt.savefig('IntBrothers.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0gEaOAKYDV0bEVuB+6kM/S4ExETGi6tMBbK+ma8AEgGr5GcCuI1eamcszsyszu9rb2xso\nT5J0LIMOgMy8KTM7MrMTmA88npkfA9YAV1fdFgAPVdOrq3mq5Y9nZp8jAElSc5yI+wAWAQsjYjP1\nMf67q/a7gbFV+0Jg8QnYtiTpOI0YuMvAMvMJ4Ilq+nlgaj999gHzhmJ7kqTGeSewJBXKAJCkQhkA\nklQoA0CSCmUASFKhDABJKpQBIEmFGpL7ACS9tXQu/m5Tt7f11subuj3VeQQgSYUyACSpUAaAJBXK\nAJCkQhkAklQoA0CSCmUASFKhDABJKpQBIEmFMgAkqVAGgCQVygCQpEIZAJJUKANAkgr1ln4cdDMf\naevjbCW92XgEIEmFGnQARMSEiFgTEZsiYmNEfLFqf3tEPBoRz1WvZ1btERHLImJzRPw0Ii4aqg8h\nSXrjGjkC2A/8WWaeD0wDrouIycBi4LHMnAQ8Vs0DzAYmVV/dwB0NbFuS1KBBB0Bm7sjM9dX0y8Am\nYDwwF7i36nYvcFU1PRf4RtY9BYyJiLMHXbkkqSFDcg4gIjqBC4G1wFmZuQPqIQG8o+o2HtjW6221\nqu3IdXVHxLqIWNfT0zMU5UmS+tFwAETEacADwJ9m5n8dq2s/bdmnIXN5ZnZlZld7e3uj5UmSjqKh\nAIiIk6j/8L8vMx+smn99YGinet1ZtdeACb3e3gFsb2T7kqTBa+QqoADuBjZl5m29Fq0GFlTTC4CH\nerV/sroaaBqw58BQkSSp+Rq5EWw68AngZxGxoWr7C+BW4FsRcS3wAjCvWvYwcBmwGfgNcE0D25Yk\nNWjQAZCZ/0r/4/oAM/vpn8B1g92eJGloeSewJBXKAJCkQhkAklQoA0CSCvWWfhy0pDc/H+t+4ngE\nIEmFMgAkqVAGgCQVygCQpEIZAJJUKANAkgplAEhSoQwASSqUASBJhTIAJKlQBoAkFcpnAUnScWjm\nM4mgOc8l8ghAkgplAEhSoQwASSqUASBJhTIAJKlQBoAkFarpARARsyLi3yJic0Qsbvb2JUl1TQ2A\niGgDbgdmA5OBj0bE5GbWIEmqa/YRwFRgc2Y+n5mvAPcDc5tcgySJ5gfAeGBbr/la1SZJarLIzOZt\nLGIe8EeZ+elq/hPA1Mz8Qq8+3UB3Nftu4N+aVuAh44D/bMF2hzP3SV/uk77cJ321Yp+8KzPbB+rU\n7GcB1YAJveY7gO29O2TmcmB5M4s6UkSsy8yuVtYw3LhP+nKf9OU+6Ws475NmDwH9GJgUERMj4mRg\nPrC6yTVIkmjyEUBm7o+IPwG+B7QBKzJzYzNrkCTVNf1x0Jn5MPBws7f7BrV0CGqYcp/05T7py33S\n17DdJ009CSxJGj58FIQkFcoA6MXHVBwuIiZExJqI2BQRGyPii62uabiIiLaIeCYi/qnVtQwXETEm\nIlZGxLPV/5k/aHVNrRYRN1TfOz+PiG9GxMhW19SbAVDxMRX92g/8WWaeD0wDrnOfHPRFYFOrixhm\nvg48kpnnARdQ+P6JiPHA9UBXZr6H+oUv81tb1eEMgEN8TMURMnNHZq6vpl+m/g1d/J3bEdEBXA7c\n1epahouIOB24GLgbIDNfyczdra1qWBgB/E5EjABGccR9T61mABziYyqOISI6gQuBta2tZFhYCvw5\n8HqrCxlGfhfoAf6+Ghq7KyJObXVRrZSZ/w78LfACsAPYk5n/3NqqDmcAHBL9tHmJFBARpwEPAH+a\nmf/V6npaKSLmADsz8+lW1zLMjAAuAu7IzAuB/waKPo8WEWdSH0WYCJwDnBoRH29tVYczAA4Z8DEV\nJYqIk6j/8L8vMx9sdT3DwHTgyojYSn2Y8IMR8Q+tLWlYqAG1zDxwhLiSeiCU7FLgV5nZk5mvAg8C\nf9jimg5jABziYyqOEBFBfUx3U2be1up6hoPMvCkzOzKzk/r/kcczc1j9VtcKmfkfwLaIeHfVNBP4\nRQtLGg5eAKZFxKjqe2kmw+zEeNPvBB6ufExFv6YDnwB+FhEbqra/qO7mlo70BeC+6heo54FrWlxP\nS2Xm2ohYCaynfkXdMwyzu4K9E1iSCuUQkCQVygCQpEIZAJJUKANAkgplAEhSoQwASSqUASBJhTIA\nJKlQ/wujK3ZKnG1oxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x76b965b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = X['IntSisters'].value_counts()\n",
    "x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])\n",
    "y = np.array([232, 745, 1274, 1223, 944, 664, 324, 139, 64])\n",
    "\n",
    "plt.bar(x, y, label = 'IntSisters')\n",
    "plt.legend()\n",
    "plt.savefig('IntSisters.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/m2cm8ICkq4HXgQlZ88eAC4FG4EPgKoCIeFvSPwBLsnZ/HxH7Hlg2M7NO1G4ARMRlrUw6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x76b9d1af28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = X['IntSchoolBrothers'].value_counts()\n",
    "x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])\n",
    "y = np.array([1020, 1497, 1575, 911, 416, 133, 45, 8, 4])\n",
    "\n",
    "plt.bar(x, y, label = 'IntSchoolBrothers')\n",
    "plt.legend()\n",
    "plt.savefig('IntSchoolBrothers.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zM7MlM1urx23AmsycAqypHgMsAKZUX0uBG0Zg3pKkIToUm4AWArdW928F3tuj/rXs8mNg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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x76b9350588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = X['IntSchoolSisters'].value_counts()\n",
    "x = np.array([0,    1,   2,    3,   4,   5,  6,  7, 8])\n",
    "y = np.array([2177 ,1417,1089, 571, 222, 96, 27, 8, 2])\n",
    "\n",
    "plt.bar(x, y, label = 'IntSchoolSisters')\n",
    "plt.legend()\n",
    "plt.savefig('IntSchoolSisters.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KZQBIUqEMAEkqlAEgSYUyACSpUAaAJBXKAJCkQhkAklQoA0CSCmUASFKhDABJKpQBIEmF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x76b9b8f240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = X['ClassSchoolStatus'].value_counts()\n",
    "x =  np.array(['School1', 'School2'])\n",
    "y = np.array([1182, 4427])\n",
    "plt.bar(x, y, label = 'ClassSchoolStatus')\n",
    "plt.legend()\n",
    "plt.savefig('ClassSchoolStatus Distribution.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BRcS7wHxgCHCYpI5pUW/g9TRdC/QBSMs/CWworjeyTvE2pkZEVURUde/evTnDMzOzZijl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x76b9c23f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = X['Disability01'].value_counts()\n",
    "x =  np.array(['Disabled', 'Healthy'])\n",
    "y = np.array([61, 5548])\n",
    "plt.bar(x, y, label = 'Disabled and Healthy')\n",
    "plt.legend()\n",
    "plt.savefig('Disability Distribution.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x76b9c667b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x =  np.array(['Lang1', 'Lang2', 'Lang3', 'Lang4'])\n",
    "y = np.array([3011, 1795, 513, 290])\n",
    "plt.bar(x, y, label = 'Language Frequency')\n",
    "plt.legend()\n",
    "plt.savefig('Language Distribution.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x76b965ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x =  np.array(['Muslim', 'Non-muslim'])\n",
    "y = np.array([5076, 533])\n",
    "plt.bar(x, y, label = 'Religion Frequency')\n",
    "plt.legend()\n",
    "plt.savefig('Religion Distribution.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}