{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scaling data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>month</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jan</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.7</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.8</td>\n",
       "      <td>24.4</td>\n",
       "      <td>29.1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>16.4</td>\n",
       "      <td>19.4</td>\n",
       "      <td>21.6</td>\n",
       "      <td>13.7</td>\n",
       "      <td>26.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>March</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>27.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>April</td>\n",
       "      <td>19.5</td>\n",
       "      <td>17.2</td>\n",
       "      <td>21.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>20.5</td>\n",
       "      <td>28.7</td>\n",
       "      <td>29.2</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.2</td>\n",
       "      <td>21.6</td>\n",
       "      <td>21.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>26.2</td>\n",
       "      <td>29.2</td>\n",
       "      <td>29.5</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>June</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>July</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.8</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>26.9</td>\n",
       "      <td>30.8</td>\n",
       "      <td>30.1</td>\n",
       "      <td>30.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aug</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.2</td>\n",
       "      <td>23.5</td>\n",
       "      <td>27.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sept</td>\n",
       "      <td>18.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>19.6</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.2</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oct</td>\n",
       "      <td>18.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>27.7</td>\n",
       "      <td>27.7</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nov</td>\n",
       "      <td>19.0</td>\n",
       "      <td>17.1</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.7</td>\n",
       "      <td>20.7</td>\n",
       "      <td>25.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>23.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Dec</td>\n",
       "      <td>19.4</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>30.6</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month  2001  2002  2003  2004  2005  2006  2007  2008  2009  2010\n",
       "0     jan  13.0  18.2  21.5  21.7  25.3  23.8  24.4  29.1  30.0  30.0\n",
       "1     Feb  16.4  19.4  21.6  13.7  26.7  23.0  24.8  28.8  29.0  29.0\n",
       "2   March  22.0  19.0  22.3  20.0  26.0  25.6  27.5  29.0  29.0  29.0\n",
       "3   April  19.5  17.2  21.1  21.5  25.0  24.5  20.5  28.7  29.2  29.2\n",
       "4     May  20.0  11.2  21.6  21.4  24.0  24.3  26.2  29.2  29.5  29.5\n",
       "5    June  20.0  18.5  21.1  22.4  22.0  24.0  26.0  30.2  29.0  29.0\n",
       "6    July  20.0  16.0  20.8  23.4  23.0  24.5  26.9  30.8  30.1  30.1\n",
       "7     Aug  18.0  19.1  21.5  21.8  22.2  23.5  27.1  29.0  27.6  27.6\n",
       "8    Sept  18.2  15.5  19.6  21.5  25.0  26.2  27.5  30.0  29.0  29.0\n",
       "9     Oct  18.2  20.5  20.2  22.8  26.0  27.3  27.7  27.7  26.2  26.2\n",
       "10    Nov  19.0  17.1  21.0  22.7  20.7  25.5  27.5  30.0  23.7  23.7\n",
       "11    Dec  19.4  21.5  21.9  24.8  22.0  25.5  28.2  30.6  26.7  26.7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/temperature.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean of 2001 = 18.64\n",
      "standard deviation of 2001 = 2.254 \n",
      "Maximum value of 2001 =  22.0\n",
      "Minumum value of 2001 =  13.0\n",
      "sum of 2001 =  223.7\n"
     ]
    }
   ],
   "source": [
    "print('mean of 2001 = %0.2f'%df['2001'].mean())\n",
    "print('standard deviation of 2001 = %0.3f ' %df['2001'].std())\n",
    "print('Maximum value of 2001 = ',df['2001'].max())\n",
    "print('Minumum value of 2001 = ',df['2001'].min())\n",
    "print('sum of 2001 = ',df['2001'].sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = [15,6])\n",
    "plt.plot(df['month'],df['2001'],color ='b')\n",
    "plt.xlabel('Month',fontsize =18,color='k')\n",
    "plt.ylabel('Temperature',fontsize =18,color='k')\n",
    "plt.title('Original data',fontsize =24,color='k')\n",
    "plt.tick_params(axis='x', rotation= 45)\n",
    "plt.savefig(\"image/normal1.png\", dpi = 600) # dpi dot per inch\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     13.0\n",
       "1     16.4\n",
       "2     22.0\n",
       "3     19.5\n",
       "4     20.0\n",
       "5     20.0\n",
       "6     20.0\n",
       "7     18.0\n",
       "8     18.2\n",
       "9     18.2\n",
       "10    19.0\n",
       "11    19.4\n",
       "Name: 2001, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['2001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13. ],\n",
       "       [16.4],\n",
       "       [22. ],\n",
       "       [19.5],\n",
       "       [20. ],\n",
       "       [20. ],\n",
       "       [20. ],\n",
       "       [18. ],\n",
       "       [18.2],\n",
       "       [18.2],\n",
       "       [19. ],\n",
       "       [19.4]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A1=df['2001'].values.reshape(-1,1)\n",
    "A2=df['2002'].values.reshape(-1,1)\n",
    "A1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example:1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.6137048 ],\n",
       "       [-1.03853263],\n",
       "       [ 1.55586859],\n",
       "       [ 0.39765376],\n",
       "       [ 0.62929673],\n",
       "       [ 0.62929673],\n",
       "       [ 0.62929673],\n",
       "       [-0.29727514],\n",
       "       [-0.20461795],\n",
       "       [-0.20461795],\n",
       "       [ 0.16601079],\n",
       "       [ 0.35132517]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_scaled1 = preprocessing.StandardScaler().fit(A1).transform(A1) # (A- mean A)/sd A\n",
    "f_scaled2 = preprocessing.StandardScaler().fit(A2).transform(A2) # (A- mean A)/sd A\n",
    "f_scaled1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1086244689504383e-15"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(f_scaled1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = [15,6])\n",
    "plt.plot(df['month'],f_scaled1,color ='brown',label='2001')\n",
    "plt.plot(df['month'],f_scaled2,color ='blue',label='2002')\n",
    "plt.axhline(y=0,color='k')\n",
    "plt.xlabel('Month',fontsize =18,color='k')\n",
    "plt.ylabel('y',fontsize =18,color='k')\n",
    "plt.title('Scaled data',fontsize =24,color='g')\n",
    "plt.tick_params(axis='x', rotation= 45)\n",
    "plt.legend()\n",
    "plt.savefig(\"image/scaled1.png\", dpi = 600) # dpi dot per inch\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$f(x,\\mu,\\sigma) = \\frac{1}{\\sqrt{2\\pi\\sigma^{2}}} e ^{-\\frac{1}{2}(\\frac{x-\\mu}{\\sigma})^{2}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "const=np.sqrt(2*np.pi)*df['2001'].std()\n",
    "normal=np.exp(-0.5*(f_scaled1)**2)/const\n",
    "plt.subplots(figsize = [15,6])\n",
    "plt.plot(f_scaled1,normal,color ='m')\n",
    "plt.xlabel('f_scaled1',fontsize =18,color='k')\n",
    "plt.ylabel('y',fontsize =18,color='k')\n",
    "plt.title('Normalized data',fontsize =24,color='g')\n",
    "plt.tick_params(axis='x', rotation= 45)\n",
    "plt.savefig(\"image/normal.png\", dpi = 600) # dpi dot per inch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example:2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        ],\n",
       "       [0.37777778],\n",
       "       [1.        ],\n",
       "       [0.72222222],\n",
       "       [0.77777778],\n",
       "       [0.77777778],\n",
       "       [0.77777778],\n",
       "       [0.55555556],\n",
       "       [0.57777778],\n",
       "       [0.57777778],\n",
       "       [0.66666667],\n",
       "       [0.71111111]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_scaled3 = preprocessing.MinMaxScaler().fit(A1).transform(A1)# (A- min A)/( max A -min A)\n",
    "f_scaled3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize = [15,6])\n",
    "plt.plot(df['month'],f_scaled3,color ='orange')\n",
    "plt.xlabel('no.',fontsize =18,color='k')\n",
    "plt.ylabel('y',fontsize =18,color='k')\n",
    "plt.title('Scaled data',fontsize =24,color='g')\n",
    "plt.tight_layout() \n",
    "plt.savefig(\"image/scaled2.png\", dpi = 600) # dpi dot per inch\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example:3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19, 52, 88, 24, 65, 93, 23, 64, 92, 24],\n",
       "       [73, 38, 32, 71, 14, 82, 81, 36, 22, 30],\n",
       "       [51,  6, 17, 95, 63, 37, 81, 33, 72, 37],\n",
       "       [77, 94, 14, 64, 64, 56, 29, 40, 33, 42],\n",
       "       [58, 49, 80, 22, 51, 95,  5, 75, 26,  1],\n",
       "       [79, 65, 85, 42, 46, 74, 42, 38, 15, 81],\n",
       "       [63,  7, 84, 31, 79, 28, 92, 63, 96,  4],\n",
       "       [54, 77, 19, 32, 52, 71, 68, 87, 51,  7],\n",
       "       [49, 86, 23, 71, 96,  0,  3, 45, 80, 45],\n",
       "       [69, 67, 10, 21, 40,  4, 92, 73, 31, 12]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = (100*np.random.rand(10,10)).astype(int) # construction of 10 x 10 matrix\n",
    "A "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=np.arange(1,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(A, columns= ['Nepali','English','Math','Science','Health','History','Geography','Social','Moral','Computer'],\\\n",
    "                  index= [n])"
   ]
  },
  {
   "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>Nepali</th>\n",
       "      <th>English</th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>Health</th>\n",
       "      <th>History</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Social</th>\n",
       "      <th>Moral</th>\n",
       "      <th>Computer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>52</td>\n",
       "      <td>88</td>\n",
       "      <td>24</td>\n",
       "      <td>65</td>\n",
       "      <td>93</td>\n",
       "      <td>23</td>\n",
       "      <td>64</td>\n",
       "      <td>92</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>82</td>\n",
       "      <td>81</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>95</td>\n",
       "      <td>63</td>\n",
       "      <td>37</td>\n",
       "      <td>81</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77</td>\n",
       "      <td>94</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>56</td>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>80</td>\n",
       "      <td>22</td>\n",
       "      <td>51</td>\n",
       "      <td>95</td>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79</td>\n",
       "      <td>65</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>46</td>\n",
       "      <td>74</td>\n",
       "      <td>42</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>31</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>92</td>\n",
       "      <td>63</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>54</td>\n",
       "      <td>77</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>52</td>\n",
       "      <td>71</td>\n",
       "      <td>68</td>\n",
       "      <td>87</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>49</td>\n",
       "      <td>86</td>\n",
       "      <td>23</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>69</td>\n",
       "      <td>67</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>73</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Nepali  English  Math  Science  Health  History  Geography  Social  Moral  \\\n",
       "1       19       52    88       24      65       93         23      64     92   \n",
       "2       73       38    32       71      14       82         81      36     22   \n",
       "3       51        6    17       95      63       37         81      33     72   \n",
       "4       77       94    14       64      64       56         29      40     33   \n",
       "5       58       49    80       22      51       95          5      75     26   \n",
       "6       79       65    85       42      46       74         42      38     15   \n",
       "7       63        7    84       31      79       28         92      63     96   \n",
       "8       54       77    19       32      52       71         68      87     51   \n",
       "9       49       86    23       71      96        0          3      45     80   \n",
       "10      69       67    10       21      40        4         92      73     31   \n",
       "\n",
       "    Computer  \n",
       "1         24  \n",
       "2         30  \n",
       "3         37  \n",
       "4         42  \n",
       "5          1  \n",
       "6         81  \n",
       "7          4  \n",
       "8          7  \n",
       "9         45  \n",
       "10        12  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Nepali</th>\n",
       "      <th>English</th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>Health</th>\n",
       "      <th>History</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Social</th>\n",
       "      <th>Moral</th>\n",
       "      <th>Computer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roll no</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>52</td>\n",
       "      <td>88</td>\n",
       "      <td>24</td>\n",
       "      <td>65</td>\n",
       "      <td>93</td>\n",
       "      <td>23</td>\n",
       "      <td>64</td>\n",
       "      <td>92</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>82</td>\n",
       "      <td>81</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>95</td>\n",
       "      <td>63</td>\n",
       "      <td>37</td>\n",
       "      <td>81</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77</td>\n",
       "      <td>94</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>56</td>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>80</td>\n",
       "      <td>22</td>\n",
       "      <td>51</td>\n",
       "      <td>95</td>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79</td>\n",
       "      <td>65</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>46</td>\n",
       "      <td>74</td>\n",
       "      <td>42</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>31</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>92</td>\n",
       "      <td>63</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>54</td>\n",
       "      <td>77</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>52</td>\n",
       "      <td>71</td>\n",
       "      <td>68</td>\n",
       "      <td>87</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>49</td>\n",
       "      <td>86</td>\n",
       "      <td>23</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>69</td>\n",
       "      <td>67</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>73</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Nepali  English  Math  Science  Health  History  Geography  Social  \\\n",
       "Roll no                                                                       \n",
       "1            19       52    88       24      65       93         23      64   \n",
       "2            73       38    32       71      14       82         81      36   \n",
       "3            51        6    17       95      63       37         81      33   \n",
       "4            77       94    14       64      64       56         29      40   \n",
       "5            58       49    80       22      51       95          5      75   \n",
       "6            79       65    85       42      46       74         42      38   \n",
       "7            63        7    84       31      79       28         92      63   \n",
       "8            54       77    19       32      52       71         68      87   \n",
       "9            49       86    23       71      96        0          3      45   \n",
       "10           69       67    10       21      40        4         92      73   \n",
       "\n",
       "         Moral  Computer  \n",
       "Roll no                   \n",
       "1           92        24  \n",
       "2           22        30  \n",
       "3           72        37  \n",
       "4           33        42  \n",
       "5           26         1  \n",
       "6           15        81  \n",
       "7           96         4  \n",
       "8           51         7  \n",
       "9           80        45  \n",
       "10          31        12  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.names = ['Roll no'] #naming index\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[5,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 10 entries, (1,) to (10,)\n",
      "Data columns (total 10 columns):\n",
      " #   Column     Non-Null Count  Dtype\n",
      "---  ------     --------------  -----\n",
      " 0   Nepali     10 non-null     int32\n",
      " 1   English    10 non-null     int32\n",
      " 2   Math       10 non-null     int32\n",
      " 3   Science    10 non-null     int32\n",
      " 4   Health     10 non-null     int32\n",
      " 5   History    10 non-null     int32\n",
      " 6   Geography  10 non-null     int32\n",
      " 7   Social     10 non-null     int32\n",
      " 8   Moral      10 non-null     int32\n",
      " 9   Computer   10 non-null     int32\n",
      "dtypes: int32(10)\n",
      "memory usage: 866.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Nepali</th>\n",
       "      <th>English</th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>Health</th>\n",
       "      <th>History</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Social</th>\n",
       "      <th>Moral</th>\n",
       "      <th>Computer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>59.200000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>45.200000</td>\n",
       "      <td>47.300000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>51.600000</td>\n",
       "      <td>55.400000</td>\n",
       "      <td>51.800000</td>\n",
       "      <td>28.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.718791</td>\n",
       "      <td>30.259801</td>\n",
       "      <td>34.146091</td>\n",
       "      <td>25.991665</td>\n",
       "      <td>22.295989</td>\n",
       "      <td>35.055512</td>\n",
       "      <td>35.302502</td>\n",
       "      <td>19.294213</td>\n",
       "      <td>30.673188</td>\n",
       "      <td>24.48605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.750000</td>\n",
       "      <td>40.750000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>47.250000</td>\n",
       "      <td>30.250000</td>\n",
       "      <td>24.500000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>27.250000</td>\n",
       "      <td>8.25000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.500000</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>63.500000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>27.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>69.250000</td>\n",
       "      <td>64.750000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>70.750000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>40.75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>81.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Nepali    English       Math    Science     Health    History  \\\n",
       "count  10.000000  10.000000  10.000000  10.000000  10.000000  10.000000   \n",
       "mean   59.200000  54.100000  45.200000  47.300000  57.000000  54.000000   \n",
       "std    17.718791  30.259801  34.146091  25.991665  22.295989  35.055512   \n",
       "min    19.000000   6.000000  10.000000  21.000000  14.000000   0.000000   \n",
       "25%    51.750000  40.750000  17.500000  25.750000  47.250000  30.250000   \n",
       "50%    60.500000  58.500000  27.500000  37.000000  57.500000  63.500000   \n",
       "75%    72.000000  74.500000  83.000000  69.250000  64.750000  80.000000   \n",
       "max    79.000000  94.000000  88.000000  95.000000  96.000000  95.000000   \n",
       "\n",
       "       Geography     Social      Moral  Computer  \n",
       "count  10.000000  10.000000  10.000000  10.00000  \n",
       "mean   51.600000  55.400000  51.800000  28.30000  \n",
       "std    35.302502  19.294213  30.673188  24.48605  \n",
       "min     3.000000  33.000000  15.000000   1.00000  \n",
       "25%    24.500000  38.500000  27.250000   8.25000  \n",
       "50%    55.000000  54.000000  42.000000  27.00000  \n",
       "75%    81.000000  70.750000  78.000000  40.75000  \n",
       "max    92.000000  87.000000  96.000000  81.00000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nepali       59.2\n",
       "English      54.1\n",
       "Math         45.2\n",
       "Science      47.3\n",
       "Health       57.0\n",
       "History      54.0\n",
       "Geography    51.6\n",
       "Social       55.4\n",
       "Moral        51.8\n",
       "Computer     28.3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df,axis =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,8])\n",
    "plt.pie(np.mean(df,axis =0),startangle=90, autopct='%1.0f%%', shadow = True) \n",
    "plt.legend(df.columns, loc = 'upper right') \n",
    "plt.title(\"subject wise mark distribution\",fontsize =24,color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y+XOo+TM9lPU9Kp6QUnqYQ00eY2Qmcj/gtLIrg/dK5Fg9U4Pbl3pEiZnEUj9gnoVtvIXt3NIfbha25uZPFfI+vuAnHDi+7CX8B3su8Jj5075gYWsVFNq8nX1fkhXZDFxqYftNJFEKus0exXcROSAcxWO6G1iEH9Q92cJ2QekvSAtbJwvbj/DFdc8os+81+BaiY4CJFrbhpfZrbWE7z8L23zL7zA4u+1rYDosizvJEakxFil+WTbwmV7G+KrOC5/nQyJd1kKCOwFfKBz/Ls3R30Y347rr2wEcWtjPMn4aGYP8e5iuUz8UnJIngzeDyFAvbzcGPCyxsuUGtsZuA9bG+06DEx/H4Qd9HAK9G7rua+y/Fn8YJ4O8Wth9EfiBY2Abga9xV1m1XkZoc98jr+uhgJmdt2wT808L2fQtbShDjQHzZiFz8jNe/ltnnQ/xEiVTgSQtb12C/dAvbZcA/2b/rs4Tzp+OKTLK4KNpgg3pwkTIZ/7awnW1BgWUL2zDgDXx38Wrqflyu1DElZhJLRfguszOAp4ECC9t2C9tGfLfZi/hp4cXALS7kxpfeOWjFirRqnQOstrBtwH+R34k/rUxVXZCf42v53ASst7AV4j8wzw/WX+9C7oPqPqDgA3csPlHIxbegbbKwrbewbcePqXkQX7Hbldn3w+B+d+G/YD8Nno8t+NasJylTnd+F3AJ8N2QK8ElwP0uCv2i7y0onWguCVpiK1kdmakajDf55ngpst7Ctxz/WKfjZnuvwRXdLBEV5T8Ifk274Vp2tFrZ1FrZIqYv78eVVKppdWadcyL2BjxN8gdytwetqNb7W2L+p4tywB3Hfi/FFkQvwCf6LFbQmV+Rq/PFIx/842RK8H2fgf0j9pAZhRX3c8a21C/FnfJhnYVtT6nVd6amsauhv+Bawx/HHaxO+XMlw/OmjzimTOEaK2P4U/14YBSwK9tuEryH2BP4zrCKRcXW/s7BtLfX4qnsqpB/gP9+y8K15Wy1sm/FDAAbik8IzXMjF/EeAJBYlZhIzLuRex5cS+AW+oGykiGcL/PiN6fikaZALuQOKwAa38Uf8efw+wX+AJuF/yZ7hQu6OasZxNXAhvhUrBZ8UvgOMc9U/PUrp2/san3hdHtxOIb6kwx78F9yf8B/kj5ez71P4cXF/BiInTt6LT/T+hf8wLutM/K/5xfjnLjI4P9rWjckV/B/xKftOYj3DlXMS7iqchi/mGmlpaIEfNzgD39LYz4XcjLI7uZD7FH8qphvwhVm34Lt/duC/hO4BDnUhV5MaW7Xl2/gfDXPxP0AM/7gvcCF3cW3ecfD6G41PBEcDz5duZaxi3634MYS3se/1txP/w2kENasXFvVxDwosj8a/R1bgk4/I67o2qgPswie0d+CLzabifww9BQx1IVfu7GMXcs8DJ+Lf51vwPzS/AC6pxnG+A/+anoF/fUQeX7W6Np0/G8gRwLX490FREPcC/OdmPxdyB1vfTeoBcxWW/BFpHIKujkhl/g4u5CqqcyUiIlKr1GImsm9qfTG+NUxERCQulJhJoxa0ll0dXP0yKJ4pIiISF6r8L42Whe1h/BT4SCmL++MYjoiIiFrMpFHLws/8mwlc7ELuP3GOR0REGjkN/hcRERFJEA2iK7N169auS5cu8Q5DREREpEqfffbZOudcbnnrGkRi1qVLF6ZNmxbvMERERESqZGZLK1qnMWYiIiIiCUKJmYiIiEiCUGImIiIikiCUmImIiIgkCCVmIiIiIglCiZmIiIhIglBiJiIiIpIglJiJiIiIJAglZiIiIiIJQomZiIiISIJQYiYiIiKSIJSYiYiIiCQIJWYiIiIiCSIl3gGIiDRW9785nwcmLahw/ZWje3L1mF51GJGIxJs55+Idw0EbPny4mzZtWrzDEBGpsW//42MAnr7siDhHIiK1zcw+c84NL2+dujJFREREEoQSMxEREZEEocRMREREJEEoMRMRERFJEErMRERERBKEymWIiEitUDkQkegpMRMRkVpx9ZheJYmXyoGIVI+6MkVEREQShBIzERERkQShxExEREQkQSgxExEREUkQSsxEREREEoQSMxEREZEEocRMREREJEGojlkjpuKPIiIiiUWJWSOm4o8iIiKJRV2ZIiIiIglCiZmIiIhIglBiJiIiIpIglJiJiIiIJIi4Dv43s6uBSwAHzAQuAtoDTwHZwHTgfOfc7rgFKSIi0khp9n7di1tiZmZ5wM+Bvs65HWb2DHAecDJwv3PuKTP7O3Ax8Ld4xSkiItJYafZ+3Yt3V2YKkGZmKUA6sBI4Hng2WP8ocHqcYhMRERGpU3FLzJxzK4DfAsvwCdkm4DNgo3NuT7DZciAvPhGKiIiI1K24JWZmlgWcBnQFOgDNgXHlbOoq2P9SM5tmZtPWrl1be4GKiIiI1JF4dmWeACx2zq11zhUB44EjgcygaxOgI1BQ3s7OuQedc8Odc8Nzc3PrJmIRERGRWhTPxGwZcLiZpZuZAaOBOcA7wNnBNhcAL8QpPhEREZE6Fc8xZlPwg/yn40tlJAEPAjcA15jZ10AO8FC8YhQRERGpS3GtY+acCwGhMosXASPiEI6IiIhIXMW7XIaIiIiIBOLaYiYiUhlVHReRxkaJmYgkLFUdF5HGRomZNFhqbRERkfpGiZk0WGptERGR+kaD/0VEREQShBIzERERkQShxExEREQkQSgxExEREUkQGvxfAc3oExERkbqmxKwCmtEnIiIidU1dmSIiIiIJQomZiIiISIJQYiYiIiKSIJSYiYiIiCQIJWYiIiIiCUKJmYiIiEiCULkMERGRGlLNS4k1JWYiIiI1pJqXEmvqyhQRERFJEErMRERERBKEEjMRERGRBKHETERERCRBKDETERERSRCalSlST2mavohIw6PETKSe0jR9EZGGR12ZIiIiIglCiZmIiIhIglBiJiIiIpIglJiJiIiIJAglZiIiIiIJQomZiIiISIJQYiYiIiKSIJSYiYiIiCQIJWYiIiIiCUKJmYiIiEiCUGImIiIikiCUmImIiIgkCCVmIiIiIglCiZmIiIhIglBiJiIiIpIglJiJiIiIJAglZiIiIiIJQomZiIiISIJQYiYiIiKSIJSYiYiIiCQIJWYiIiIiCUKJmYiIiEiCUGImIiIikiCUmImIiIgkCCVmIiIiIglCiZmIiIhIglBiJiIiIpIglJiJiIiIJAglZiIiIiIJotqJmZk1qcY27Q8uHBEREZHGK5oWs8crW2lmbYG3Dy4cERERkcYrmsTsLDP7fXkrzKw1PilrG5OoRERERBqhaBKzHwNXmdm1pReaWQ4+KcsDxsYwNhEREZFGJaW6GzrnHjKzDsA9ZlbgnHvSzLKAt4AuwFjn3Ke1FKeIiIhIg1ftxAzAOfcrM+sIPGxmRcANQE/gZOfcx7URoIiIiEhjEVViFvgJ0A54GtgBfMs5915MoxIRERFphCpMzMzsu5Xs9xJwLDAe6FB6W+fcEzGLTkRERKQRqazF7D+AA6ycdZHlFwR/pZdXOzEzs0zgX0D/YN8fAvPwrXFdgCXAuc65DdW9TREREZH6qrLEbEwd3P8DwETn3NlmlgqkAzcDk5xzd5vZjcCN+LFsIiIiIg1ahYmZc25Sbd6xmbUERgIXBve3G9htZqfhu0kBHgXeRYmZiIiINALVqmNmZi3MbJeZ3RLD++4GrMXP8PzczP5lZs2Bts65lQDBZZsY3qeIiIhIwqpWYuac2wpsBdbF8L5TgKHA35xzQ4Bt+G7LajGzS81smplNW7t2bQzD2t+Ez1fw+bKNTFlcyFF3v82Ez1fU2n2JiIhI4xZN5f/JwDExvO/lwHLn3JTg+rP4RG115GToweWa8nZ2zj3onBvunBuem5sbw7D2mfD5Cm4aP5PdxXsBWLFxBzeNn6nkTERERGpFNInZL4BjzeyXQZfjQXHOrQK+MbPewaLRwBzgRfbN9LwAeOFg76um7nt9HjuKivdbtqOomHsnfhWniERERKQhi6bA7MRg+9uBkJmtAraX2cY553qX3bESPwP+G8zIXARchE8WnzGzi4FlwDlR3F5MFWzcUf7yTTv56RPTGde/Pcf1ySU9tSZ1ekVERET2F01GsQZYDSyI1Z07574AhpezanSs7uNgdMhMY0U5yVl6ajIfL1zPyzNW0qxJEsf2asO4Ae04vk8bMpo1iUOkIiIi0hBEcxLzo2szkER03dje3DR+5n7dmWlNkvnNGQM4dVAHpi4uZOKslbw2axUTZ68iNTmJY3q2ZtyA9ow5pC2t0pWkiYiISPWpD64Spw/JA+D6Z2ewu3gveZlpXDe2d8nyI7rncET3HEKn9uPzbzbw6sxVvDZzJZO+WkNKknFkj9aM69+OE/u2JadF03g+FBEREakHlJhV4fQheTw5dRkAT192RLnbJCUZw/KzGZafza2nHMKM5Zt4ddZKJs5axU3jZ3LL8zM5vFsO4/q3Y2y/drRp2awuH4KIiIjUE1ElZmZ2OL7W2GFAFgfO6nTOuUbdNGRmDOqUyaBOmdx4Uh/mrNzMxFmreHXmSn75wmxue3E2w/OzGNe/PSf1b0eHzLR4hyyS8CL1BHcX7+Wou9/er+VaRKQhqXZiZmZHA5PwhWanAmPxtc1aAMOAWcCXtRBjvWVm9OvQin4dWnHtib1ZsHqL7+6ctZI7Xp7DHS/PYXCnTMb1b8e4/u3pnJMe75BFEk5F9QQBJWci0uBE02J2K35W5qFAMX6W5q+cc2+b2TjgaeCy2IfYcPRsm8GVbTO48oSeLFq71U8amLWKu177irte+4r+eS0Z17894/q3o1tui3iHK5IQ7p34Vbn1BMMvzeaQ9i3pntuclORoSjKKiCSuaBKzEcAfnHOrzSw7WJYE4Jx7zcz+C9xJgpS6SHTdcltwxXE9uOK4HnxTuN13d85ayX2vz+O+1+fRu20G4wb4lrRebVtgZvEOWaTOTZ6/loJNO8tdt2F7EWP/8B5NU5Lo0y6Dvh1a0T+vJf06tKJPuwyaNUmu42hFRA5eNIlZGv40SgC7gsuMUuunA9+NRVCNTafsdH40shs/GtmNlZt2MHHWKl6btYoHJi3gD28toFtu85Luzn4dWipJkwZvzead3PHyHF6esZKUJGPPXnfANm0ymnLTyX2YvWIzsws288qMgpKJOslJRvfc5sFQAp+s9e3QklZpKmEjIoktmsRsJZAH4JzbZmabgH7A88H6PHwXpxyE9q3SuOiorlx0VFfWbNnJ67NXM3HWSv4+eRF/eWchnbPTGde/HSf1b8fgTplK0qRBKd7reGLKUu6dOI9de/Zy1Qk96ZiZxi9fmH1APcGbTz6E04fkccYQv8w5x/INO5hdsInZBT5Z+2jhOp4vdW7bTtlp9GsfJGt5LenfoZVmSUtMaIKKxEo0idmnwFGlrr8BXG1mi/Bdmj/FTwqQGGmT0YzzD8/n/MPzKdy2mzfnrOLVmat46IPF/OO9RXRo1YyT+rdn3IB2DOucRVKSkjSpv2YXbOLm52fx5TcbOapHDr86rX/JWMuU5KQK6wlGmBmdstPplJ3OSf3blyxft3UXsws2M2vFJuYUbGZ2wSYmzl5Vsr51i6ZBq1rLkha2ztnpej9JtWmCisRSNInZv4Efmlmac24HcDMwEngcMPxkgOtjH6IAZDdP5duHdubbh3Zm0/Yi3pq7mtdmreI/U5by7w8X0yajKWP7tWPcgHaM6JKtwdBSb2zbtYf735zPwx8tISu9CX/49mBOG9xhv9bg6tQTrEjrFk0Z1SuXUb1yS5Zt2VnE3JVbmF2wiVkrfLL24dfrSrpMM5qmcEiZZK1HmxY00ftKynHf6/PKnaBy3+vzlJhJ1KI5JdPrwOulri80s17AGHwX5nvOuQ2xD1HKapXehLOGdeSsYR3ZsrOIt79aw8RZq/jfZ9/w+CdLyW6eyth+bTmpf3uO7J6jLxNJWG/MXsXtL86mYNNOvjOiMzee1KdOTmWW0awJI7pmM6JrdsmynUXFLFi9taQrdFbBJp6cuoydRb4VJDUlid5tM+if15K+QbJ2SLuWpKVqkkFjtGHbbj5dUsjUxYXlnlMZfMvZlEXrGdQpU5NRpNoOqvK/c24LMD5GsUgNZDRrwmPI9v4AACAASURBVGmD8zhtcB7bd+9h8ry1vDprFS9+UcCTU7+hVVoTxvRty7j+7Ti6Z2uapujDQeKvYOMOQi/O5s05q+ndNoPnvjuEYfnZVe9Yi5o1SWZAx1YM6NiqZFnxXsfidVtLxqzNWrGJV2eu4smp3wCQZH6GdaQrtH8wySAzPTVeD0NqyZotO5m62CdiUxYVMm/1FsAn7KnJSSXdmGV9+8FPaJLsa1oOz89ieJcshuVnk5vRqGuxSyV0SqYGJD01hXED2jNuQHt2FhXz/oJ1vDZzJa/PXsWzny0no2kKxx/ShnH923Ns79ySX3AatCp1ZU/xXh75aAm/f3M+e53jxnF9uPjorgnbqpucZPRok0GPNhmcNti/J5xzrNi4oyRZm1OwiamLC3nhi4KS/fIy0/brBu2f14q2LZtWOFlH78HEs3zD9pJEbOriQhat2wZAemoyw/KzOHVQew7rlsPAjq14baY//V7ZCSq3nHIIbVs2Y9rSQj5bsoHHPlnKvz5YDEB+TjrD8rMYnp/N8C5Z9MhtoXGNAlSRmJnZG1HennPOjT2IeCRGmjVJZkzftozp25bde/by4cJ1TJy5ijfmrOKFLwpIT03muN5tyGmRyjPTvtGgVal1X3yzkZvHz2TOys0c36cN4f/rR6fs+ne2CzOjY1Y6HbPSGduvXcny9cEkg9nBBIM5BZt5c+5qXFDpI6d5Kn1LJWv9OrSkS05zXvyyQAPH48w5x+J12/a1iJXqnmzZLIURXbM5b0QnDuuaQ78OLQ8Ywxs5ThVNUBnTty0Au/YUM2vFJqYt2cC0pRt4d95axk/3s4ZbpTVhaOdMhnfJZlh+FoM6ZqqbvJGqqsXsBGAP1S+DcWCxIYm71JQkjuvdhuN6t+HXxf2ZsriQV2eu5PXZq1m3ddcB22vQqsTS5p1F3DdxHv+ZspQ2GU352/eGclL/dg2u1EtOi6aM7JXLyFKTDLbu2sPclZuZvWJfCY+HPlhEUbH/qGyemkxRsTugG0zvwdq1d69j/potJUnY1MWFrN3iPwtbt0hlRNdsfnRMV0Z0zaFPu4xqtWRVZ4JK05RkhuVnMyw/m8vYlxBOW7qBz5ZsYNrSQt6ZtxaAlCSjX17Q/ZmfxbAuWbTJUGmXxqCqxGwvPtl6GXgYeM05p+SrHktJTuKoHq05qkdr7jitPz1ufrXcbLqggsGsItXlnOPlGf68sOu37uKCI7pw7Ym9yGjWeIq8tmiawqFdsjm0y77xc7v27D/J4LGPl5a774qNO7h1wky6tW5B9zYt6Na6OXmZaeruqoE9xXuZs3JzSSL26ZJCNm4vAqB9q2Yc1T2HEV1zGNE1m+65zevsR4OZ0S23Bd1yW3Du8E6An1Tw2VLfovbZ0kIe/2QpDwXdn52z00uStOH52fRso+7PhqiqxCwPuBC4AHgJWGlmjwIPO+e+ruXYpJYlJxkdMtPKnVHUolkKO3YXqyldamTZ+u3c+sIs3pu/lgF5rfj3BYfuN6i+MWuakkz/vFb0z/PPx6S5a8p9DzZJNl74ooAtO/eU2jeJrq2b0z23Bd1y9112y21Bi6YaMhyxe89eZizfWNIa9tnSDWzd5Z/H/Jx0TuzblhFdczisazYds9ISqvU2q3kqJ/Rtywn7dX9u5rOlhUxbsoHJ89cyPiia3LJZCkMjLWr52QzupO7PhqDSd7JzbjVwD3CPmR0J/BBfSPZGM/sIeAh4xjm3vdYjlVpx3djeBwxaTTLYsnMPx//uXW4c14f/G9QhoT64JHHt3rOXf76/iD9OWkCT5CRCp/blB0d0IVm/6itU3nswrUkyd505gNMGd2Dd1t0sWruVhWu3sWjtVhat28asgk28Nmslpc9U1Saj6X6JWvcgceuQmdbgn/8du4v5fNmGkkRs+rIN7Nrju4d7tmnB6UM6+BaxLtm0a1W/ugN992cWw/KzuHSkb4lesn4705YUlrSsvVu6+7NDS4YFEwqG52fpzBb1UDR1zD4CPjKznwPnABfhE7MHzOwnzrknailGqUUVDVpt36oZv3plDlc+9QWPfLSEX36rL0M7Z8U5WklkUxat55YJs/h6zVbG9W9H6NR+9e5LMB6qGjiem9GU3IymHNYtZ7/9du0pZtn67SwsSdq2sWjdVl76soDNpVrZUlOS6Na6uU/YWregext/2S23eb3tVt6ys4hpSzeUDNafsXwjRcUOM+jbviXfOyyfEV2zObRLFjktGlZZCjOja+vmdG3dnHOC7s+N20t1fy7ZwH+DwuPgT0M2PN9PKBjeJYtebao3Zk7iJ+q276B17FEz+wr4FX6CQI9YByZ1p6JBqy9ecTTPTV/Ova/P48y/fsTpgztw/Ul96JCZFq9QJQFt2Lab37w6l/99tpy8zDT+feFwju/TNt5h1Ss1ObNB05RkerbNoGfbjP2WO+dYv203i9ZuY+Harb6Vbe025hRsZuKsVQe0su1rYQu6R1u3IC8rsVrZNmzbzdQl+0pXzC7YxF7nW4gGdGzFxUd347Cu2QzNz2qUJ6rPTE9l9CFtGX2If9/t3rOX2QWbfLK2ZAPvL9h3ztiMZikM7bxvQsHgTpmkp6obPJFEdTTMrC1wPr61rA+wGrgPeDT2oUm8JSUZ5wzvxMkD2vO3dxfy4PuLmDh7FZeO7M6PR3XTm7mRc87x3PQV/ObVuWzeUcRlo7px5eieel3EmZnRukVTWrdout+ZDcB/YS8r3MbXa3zrWiR5e2XGSjbtKCrZLjUlia45zQ8Yx9Yttzkt66CVbc3mnSXdklMX71/MdUinTH56XA8O65bDkM5KKsqTmpLEkM5ZDOmcxSXH+PfqssLtJWU6PltayO/e9N2fySXdn/tqqrUtp/tTtfbqTpWvaDNLBk7FJ2Pj8LM0XwFuBF51zlW3lIbUU82bpvCLsb05b0Qn7n7tK/44aQFPf7qMG07qw+mD89Qs3gh9vWYrtzw/kymLCxnaOZPfnDmAPu1axjssqUJqSlJJwdzSnHMUbtu93zi2hWu28tWqLbwxZzXFpZrZcjOa0q1185KZot2D1rbKWtmq+lJfvmE7UxYFidiSQhZXUsxVZy+JnpmRn9Oc/JzmnDWsIwCbthcxfZkv0TFtyQaenLqMhz9cAkDHrLSgRS2b4flZzC3YzC0TZqnWXh2pqsDs74HvAa2BOfhk7HHn3No6iE0STMesdP783aFceGQhv3p5Dtc88yWPfrSE207tG/fT6Ujd2FlUzF/e+Zq/T15YMkD928M7KTmv58yMnBZNyamklS0yji3SPVpeK1uXnPQDxrHNXbWZX700d78v9Ruem8FHC9dRVOz2O9dkpJjrdyop5iqx0Sq9Ccf1acNxfdoA/jjPWbm5ZFLBhwvXMyE4m4VxYJFS1dqrPVW1mF0F7ACeBKYGy75T2Qw959wfYxOaJKrhXbJ5/vKjmPDFCu6Z+BVn/e1jTh3UgRtO6k3HrPpXyV2q5/0Fa7l1wiyWrt/OGUPyuOWUQ2jdwAZWy4GqamVbtG7bfrNG56/ewptz929lK2vXnr08M215jYu5SuylpiQxuFMmgztllnR/flO4g2lLC7nmmS/L3Uf1LmtHdTrn04DvAt/BJ86VcYASs0YgKck4c2hHTurfjr9PXsSD7y3kjdmr+NEx3fjJsd1prppKDcaaLTu58+W5vPhlAV1bN+c/Fx/G0T1bxzssibPSrWylC+hCpJVtO4vWbuXSxz8rf3/g01tOUCmeBGVmdM5Jp3NOOr97Y365tfYawkSw+9+czwOTFlS4/srRPbl6TK86jKjqxGxMnUQh9VZ6agrXjOnFeYd24t6JX/Hnd77mmWnfcN3Y3pw1tKN+/dZje/c6npi6jHsmfsWuor38fHRPLj+2O82aaIyPVM63srWgR5sW5FVQxLpDZmIVdpWKlVdrr0mycd3Y3nGMKjauHtOrJPH69j8+Bqo/M7q2VFVgdlJdBSL1W4fMNP5w3hB+cGQX7nhpDtc9O4NHP17Cbd/qd8CYFUl8c1du5ubnZ/L5so0c0S2HO8/oT/fcFvEOS+qhigroNoQv9caibK291OQkzByHddNne23QqEqJqaGdsxj/kyN54LzBrN+6m3P/8TFX/Hc63xTq5BD1wfbde/jNq3P51p8+YOn67fz+3EE88aPDlJRJjZ0+JI+7zhxAajCIPy8zjbvOHKBB4/XM6UPyGNI5k8O6ZvPG1SMxM24ePxOdPjv2NBBIYi4pyThtcB4n9m3Hg+8t4u+TF/Lm3NVcfHRXLj+2e72tNt7QvTVnNaEXZ7Ni4w7OO7QTN47rQ2Z6arzDkgagJgV0JXF1ad2cG07qQ/ilOTw3fQVnByU4JDbUYia1Ji01mStP6Mk7vziWbw30RWqP++1knv50WaUztqRurdy0g8sen8Ylj02jedNk/vfjI7j7rIFKykSkQhcc0YURXbIJvzSbVZt2xjucBkWJmdS6dq2a8ftzBzPhiqPIz0nnhudmcuqfPuDjhevjHVqjtqd4Lw99sJgTfjeZyfPXcv1JvXn5Z8ccMMNORKSspCTjnrMHUlS8l5ufV5dmLCkxkzozuFMmz/74CP70nSFs2lHEd/75CZc9Po2l67fFO7RG58tvNnLaXz7kVy/P4dCu2bx59SguP7YHqSn6SBCR6unaujnXje3D21+tYfz0FfEOp8HQp7DUKTPj1EEdmHTtKH5xYi/eX7COMb9/j7tencvmnUVV34AclM07iwi9MIvT//oha7fs4i/fHcrDFx5Kp2wVBhaR6F14ZBeG52cRfmk2qzerSzMWapSYmVkXMzvMzDKq3lrkQM2aJPPT4/34s9MGd+DB9xdx3H3v8t8pSzX+rBY453hlxkpO+N1kHvtkKT84PJ+3rh3FKQPbq5aUiNRYcpJx3zmD2LVnr2ZpxkhUiZmZjTOzecBC4CPg0GB5GzP7yszOqIUYpQFr27IZ950ziBevOJruuS245flZnPLH9/nw63XxDq3B+KZwOxc98ilXPDGd3IymTLj8KMKn9aelZseKSAz4Ls3eTPpqDRO+UJfmwap2YmZmI4EXgW3Aryl1eibn3BrgG/xpm0SiNqBjK56+7HD++r2hbN21h+/9awqXPDqNxes0/qymior38td3v2bM/ZP5dHEhv/xWX1644igGdcqMd2gi0sBcdFRXhuVncfuLc1ijLs2DEk2L2W3ATHwrWXnnw/wQGBaLoKRxMjNOHtCet64ZxfUn9ebjhes48f7J3PnyHDbt0PizaHy6pJBT/vg+906cx6heubx17SguProrKckaVioisZecZNx79kB2FhVrluZBiqbA7AjgdudcsZmV94wvB9rFJixpzJo1SebyY3tw9rCO/P6N+Tz04WKem76ca07szXcO7aTkoowJn6/g82Ub2V28lyPumkR+djqfLC4kLzONf/1gOCf0bRvvEEWkEeie24JfnNibX786lxe+KNDZHWoomm+4ZODAM9Hu0xpQs4bETJuMZtx91kBe/tnR9GqbwS8nzOLkP77Pe/PXxju0hDHh8xXcNH4mu4v3ArBy004+WVzIcb1zefOakUrKRKRO/fDorgztnEnoxdms2aIuzZqIJjH7Cji6kvUnAzMOLhyRA/Xr0IqnLj2cv39/GDuL9vKDf0/lh498ytdrtsY7tDq1ZWcRsws28drMlfxj8kJufn4mNzw3Y7+TQ0fMX72V9FSdcU1E6lZkluaOomJueX6WujRrIJpP7oeB+83sDeCVYJkzs2bAb4CjgAtjG56IZ2ac1L8dx/XJ5dGPlvCnSV9z0h/e4/wj8rlydM8GcfqgvXsdqzbvZOn67XxTuJ1lhdtZGlwuW7+NDdv3b5DOSm/Crj17y72tgo2VNW6LiNQe36XZi9+8+hUvflnAaYPVpRmNaBKzv+CTr4eBDYAD/oPvwmwCPOacezzmEYqU0jQlmUtHdufMoR35/ZvzefSjJTz/+QquGt2T7x2eT5MEH3+2ffcevincwdL123zCVepveeGOki5J8L888zLTyM9JZ9yA9nTOTic/O51O2el0zkmnZbMmHHX326woJwnrkJlWlw9LRGQ/Fx/djddmrSL04myO6J5Dm4xm8Q6p3qh2YuZ8e+R3zGw88D3gEHzJjC/wSdnTtROiyIFat2jKb84YwPmH53PnK3O4/aU5PP7JUm79Vl+O690mbnE551i7ZZdv6Vq/f+K1dP121m3dtd/2GU1T6JyTTp92GYzp2zZIvprTOTudDpnNqpzocN3Y3tw0fuZ+3ZlpTZK5bmzvWnl8IiLVkZxk3Hf2IE7+4/vc+vws/nH+MBWzrqaoB6E45/4H/K8WYhGJ2iHtW/Kfiw/jrblr+PUrc7jo4U8Z1SuXW085hJ5ta+fEFDuLilm+YQfLCrexbL3vbvymVAK2s2hfq5cZdGiVRqfsNEb3aUPnnHQ6Z+/7y0xvclAfVpFZT9c/O4PdxXvJy0zjurG9NRtKROKuR5sWXDumF3e99hUvzVjJ/w3qEO+Q6gWNDpZ6z8wY07cto3rl8tjHS3hg0gJOeuB9vn9YZ646oReT568tKSdx1N1vV5m4OOco3LZ7X8K1vvRYr+2sKlM8MT01mc7Z6XTJac7Inrn7JV95WWk0TUmu1cd/+pA8npy6DICnLzuiVu9LRCQalxwTdGm+MIsjuuWQm9E03iElvGonZmZ2cxWbOHw5jWXAZOfc+oMJTCRaqSlJXHJMN84Ykscf3lrA458s5elPl1HsoKjYzwxasXEHN42fyZ7ivQzvkr3f4Hrf4rWDbwq3s3XXnv1uu23LpnTOTueoHq19d2NOMNYrO53WLVLVRC8iUo7kJOO35wzk5D9+wC8nzOJv3x+qz8sqRNNidic++YJSp2MKlF2+28zucc6FDiY4kZrIadGUX53en+8fns///fkDior3n7m4o6iYXzy7f2WX1JSkksH1h3XNLkm+OgeD7Zs1qd1WLxGRhqpHmwyuPqEX90z8ipdnrORUdWlWKprEbBDwb2AP/pRM84LlfYCf45Oyq4DOwNXArWa23Dn3z9iFK1J9vdtlsLuCchIAvz1nUEmXY5uMpiQl6VeciEht+NExXZk4exW3vTCLI7rn0LqFujQrEk1tgYuA3cDRzrknnXPTg78n8IVni4GzgtmZI4HZwI9jHrFIFCoqG5GXmcbZwzoyoms27Vo1U1ImIlKLUpKT+O3ZA9m2q5jbXpgV73ASWjSJ2XnA0865A8qMO+f2AE8B3w2u7w6u94lFkCI1dd3Y3qSV6YZUOQkRkbrXs20GV43pyaszV/HKjJXxDidhRZOYZQKV1R9oFWwTsY59Y89E4uL0IXncdeYAUoN6YHmZadx15gCVkxARiYNLj+nGwI6t+OULs1hfpq6jeNEkZjOAy82sY9kVZtYJ3235ZanFvQClxBJ3pw/JY0jnTA7rms2HNx6vpExEJE5SkpO47+xBbN25h9temB3vcBJSNInZzfjTL80zs8fM7Nbg73H8Cc5bA7cAmFkq/uwA78U6YBEREam/erfL4MoTevLKzJXq0ixHNKdketvMxgK/B75fZvUXwLXOuXeC60VAD0DtlCIiIrKfy0Z2Y+IsP0vz8G7Z5GiWZomozvjsnHvXOTcU6Agcg5992ck5N7RUUobztgWTAkRERERKpCQn8dtzBrF5ZxGhF9WlWVpUiVmEc67AOfehc+4D59yKWAclIiIiDVvvdhlcObonL89YyWsz1aUZUaPEzMzSzKydmXUo+xfrAEVERKRhumxUd/rnteSXL8yicNvueIeTEKI6ibmZnQ3cCvTnwNMyRTSIc9fc/+Z8Hpi0YL9lXW58peT/K0f35Ooxveo6LBERkQajSdCleeqfPiD04mz+9J0h8Q4p7qI5ifmpwDPA1/hTM10CPA00AU7Fl9OYWAsxxsXVY3op8RIREallfdq15OfH9+R3b87nlAHtOKl/+3iHFFfRdGVehy+LMQhfOgPgn865s4ERQG9gSmzDExERkYbux8d2p1+Hltw6YRYbGnmXZjSJ2WDgUefcDiByZuhkAOfcl8A/CeqYiYiIiFRXpEtz4/Yibn+pcc/SjCYxS8afZglgR3DZqtT6ucCAWAQlIiIijcsh7Vvys+N78sIXBbw+e1W8w4mbaBKzFUBngKDVbC0wtNT6XsC22IUmIiIijcnlx3Wnb/uW3PJ84+3SjCYx+wg4odT1l4CrzOxmM7sVuIIanILJzJLN7HMzezm43tXMppjZAjN7Oji9k4iIiDRw+7o0dxNupF2a0SRmfwM+NLO04PotwELgTuAOYCnwixrEcCW+GzTiHuB+51xPYANwcQ1uU0REROqhvh1a8tPjezDhiwLeaIRdmtVOzJxzU5xz1wfdmDjnVgMDgeHAEGCgc25pNHduZh2BU4B/BdcNOB54NtjkUeD0aG5TRERE6rfLj+3BIe1bcsuEWWzc3ri6NKuVmJlZ86DLckzp5cE5Mac7576s4Xkx/wBcz75ZnjnAxlK3tRzIqyCmS81smplNW7t2bQ3uWkRERBJRakoSvz1nIBu27eaOl+bEO5w6Va3EzDm3DQgB+bG6YzP7FrDGOfdZ6cXl3X0FMT3onBvunBuem5sbq7BEREQkAfTr0IrLj+vB+M9X8Nac1fEOp85EM8ZsIdAuhvd9FPB/ZrYEeArfhfkHINPMImck6AgUxPA+RUREpJ746XE96NMug5uen9loujSjHfx/sZllxeKOnXM3Oec6Oue6AOcBbzvnvge8A5wdbHYB8EIs7k9ERETqF9+lOYjCbbu54+XG0aUZzUnMC4GNwDwzexhYAGwvu5Fz7omDjOkG4CkzuxP4HHjoIG9PRERE6qn+ea244tju/PHtrzllQHtGH9I23iHVqmgSs8dL/X9dBds4IOrEzDn3LvBu8P8i/Lk3RURERPjp8T15Y85qbn5+Jm/kZ9MqvUm8Q6o10SRmY6reRERERCS2UlOSuO/sQZz+1w+54+U5/O7cQfEOqdZUOzFzzk2qzUBEREREKjKgYysuP7Y7f3r7a04Z2I7j+zTMLs1oBv+XMLMmZtbWzBpuW6KIiIgklJ8e34PebTO4afxMNu0oinc4tSKqxMzMBpnZG8BWfBmLY4LlbczsdTM7vhZiFBEREaFpSjL3nTOQdVt3c2cDnaVZ7a5MMxsIfIg/f+WTwPmRdc65NWbWEl/e4u1YBykiIpKI7n9zPg9MWrDfsi43vlLy/5Wje3L1mF51HVaDNrBjJj8e1Y2/vLOQkwe257jebeIdUkxFM/j/V8AqYCiQCvygzPpJwDkxiktERCThXT2mlxKvOPj56J68OWc1Nz03kzeuGUnLZg1nZFU0XZnHAP90zm2m/NMkLQM6xCQqERERkQo0TUnmt+cMYu3WXQ2uSzOaxCwN341ZkYyDjEVERESkWgZ2zOSykd14Ztpy3p23Jt7hxEw0idkiYFgl648F5h5UNCIiIiLVdOUJPenZpgU3jZ/J5p0NY5ZmNGPMngRuMbOngBnBMgdgZlcCJwNXxzY8EWnMNLBaRCrjZ2kO4sy/fshvXpnL3WcNjHdIBy2axOw+4ETgTWA2Pin7rZnlAnn42Zh/jnmEItJoaWC1iFRlcKdMLh3Znb9PXsjJA9ozslduvEM6KNXuynTO7QJGAzfhk7IiYACwBbgZONk5t7c2ghQRERGpyFUn9KRHmxbc+NyMet+lGVWBWedckXPuPufcYOdcM+dcqnOun3PuHudc/X4mREREpF5q1iSZ+84eyKrNO7nr1fo93L3aiZmZnWxmNTqFk4iIiEhtGtI5ix+N7MaTU7/hvflr4x1OjUWTaL0MrDCz+8xsQG0FJCIiIlITV5/Qi+65zblp/Ey21NMuzWgSs5/hi8heC3xhZp+b2ZXB4H8RERGRuGrWxM/SXLlpB3e99lW8w6mRaAb//8U5dxjQB7gbyATuB5ab2QtmdqaZNZxzIoiIiEi9M7RzFpcc040npizjgwXr4h1O1KIeM+acm++cu8U51xU/S/MJfHHZ/wErYxueiIiISHSuGdOLbrnNueG5GWzdtSfe4UTloAbzO+feAX4C/AJfNiMrFkGJiIiI1JSfpTmIgk076t0szRonZmZ2rJn9G1gN/B0oBv4Rq8BEREREampYfhaXHN2V/05Zxodf158uzagSMzPrZWZ3mtkSYBLwfeA94NtAe+fc5bEPUURERCR6157Ym26tm3P9s/WnSzOaOmaf4E9SfjOwAd992dE5d6pz7lnn3O5ailFEREQkas2aJHPv2QMp2LSDe+rJLM1oWszygT8Ag51zQ5xz9zvn1tRSXCIiIiIHbXiXbC4+qiuPf7KUj+pBl2Y0iVlH59y1zrkZFW1gZk1jEJOIiIhIzFx7Ym+6tm7O9c/NYFuCd2lGU8esuKJ1ZjbMzP4KFMQkKhEREZEYSUv1XZorNu7gnomJ3aV5MLMys83s52b2JTAV+DFQf09OJSIiIg3WoV2yuejIrjz28VI+Xrg+3uFUKOrEzMzGmtnTwAp85f9UIAwMcM71iXF8IiIiIjFx3djedMlJ5/rnvmT77sTs0qxWYmZmXc3sDjNbCrwKjAKeDVbf4py7wzk3u7aCFBERETlYvktzEMs3JO4szUoTMzP7rplNAhYA1wPTgDOAPHwrmdV6hCIiIiIxMqJrNhce2YVHP17KJ4sSr0uzqhaz/+DLZFwFdHDOneWcezGYCOBqPToRERGRGLtubG/yc9K5/tkZCdelWVVithvoApwGjDOztFqPSERERKQWpaemcO9ZA1lWuJ17J86Ldzj7qSoxa4dvLcsBHgdWm9lDZjYSdWOKiIhIPXVYtxwuPLILj3y0hCkJ1KVZaWLmnNvonPuzc24oMByfnJ0OvAN8gO/ObFXrUYqIiIjE2PUn9aZzdjrXPzeD4r2JMUIrmgKz051zVwAdgPOByCzMf5nZF2Z2q5n1q40gRURERGItPTWFe88eyNL125m+CW+ewAAAIABJREFUbANTFhdy1N1vM+HzFXGLKeo6Zs65Xc65J5xzo4HuwK+BLOAO4MsYxyciIiJSa1Zt2klykhFpMFuxcQc3jZ8Zt+SsxpX/AZxzS5xzt+EnCJwMjI9FUCIiIiJ14b7X5x3QjbmjqJj7Xo/PpICUWNyIc84BE4M/ERERkXqhYOOOqJbXtoNqMRMRERGpzzpkll8JrKLltU2JmYiIiDRa143tTVqT5P2WpTVJ5rqxveMST0y6MkVERETqo9OH5AFw/bMz2F28l7zMNK4b27tkeV1TYiYiIiKN2ulD8nhy6jIAnr7siLjGoq5MERERkQShxExEREQkQSgxExEREUkQSsxEREREEoQSMxEREZEEocRMREREJEGoXEYjdv+b83lg0oL9lnW58ZWS/68c3ZOrx/Sq67BEREQaLSVmjdjVY3op8RIREUkg6soUERERSRBKzEREREQShBIzERERkQShxExEREQkQSgxExEREUkQSsxEREREEoQSMxEREZEEoTpmIiIiUi4VIq97SsxE6il9YIpIbVMh8rqnxEykntIHpohIw6MxZiIiIiIJQomZiIiISIJQYiYiIiKSIJSYiYiIiCSIuA3+N7NOwGNAO2Av8KBz7gEzywaeBroAS4BznXMb4hWniEht0cxaESkrnrMy9wDXOuemm1kG8JmZvQlcCExyzt1tZjcCNwI3xDFOEZFaoZm1IlJW3LoynXMrnXPTg/+3AHOBPOA04NFgs0eB0+MToYiIiEjdSogxZmbWBRgCTAHaOudWgk/egDYV7HOpmU0zs2lr166tq1BFRP6/vTuPt2s6/zj++RKCmomxrTGUohWhCKoiZhU11qy0NQQxq2qjpVS1UjUrfooQNRdVYghR1YqiiLaommel5inP749nXY7bm0hyh73Pud/363Vfueecfe99ds4+ez97rWetZWbWbSpPzCTNClwGDI+I/07pz0XEmRExMCIG9uvXr/sCNDMzM+shlSZmkmYgk7JREXF5efp5SQuW1xcEXqgqPjMzM7OeVFliJknA2cBDEXFCw0u/A3Yu3+8MXNXTsZmZmZlVocpRmYOAHYH7Jd1bnjsc+CnwW0m7AU8AW1UUn5mZmVmPqiwxi4jbAU3i5cE9GYuZmZlZHVRe/G9mZmZmqcquTDMza2Fe2cBs6jkxMzOzbuGVDcymnrsyzczMzGrCiZmZmZlZTTgxMzMzM6sJJ2ZmZmZmNeHEzMzMzKwmnJiZmZmZ1YQTMzMzM7OacGJmZmZmVhNOzMzMzMxqwomZmZmZWU04MTMzMzOrCSdmZmZmZjXhxMzMzMysJpyYmZmZmdWEEzMzMzOzmnBiZmZmZlYTTszMzMzMaqJP1QGYdZeRY/7JiTc9/InnFj3s2o++329wf/YfslRPh2VmZjZJTsysZe0/ZCknXmZm1lTclWlmZmZWE07MzMzMzGrCiZmZmZlZTTgxMzMzM6sJJ2ZmZmZmNeHEzMzMzKwmnJiZmZmZ1YQTMzMzM7OacGJmZmZmVhNOzMzMzMxqwomZmZmZWU04MTMzMzOrCSdmZmZmZjXhxMzMzMysJpyYmZmZmdWEEzMzMzOzmnBiZmZmZlYTTszMzMzMasKJmZmZmVlNODEzMzMzqwknZmZmZmY14cTMzMzMrCacmJmZmZnVhBMzMzMzs5pwYmZmZmZWE07MzMzMzGqiT9UBmJmZmVVh5Jh/cuJND3/iuUUPu/aj7/cb3J/9hyzVozEpInr0D3aHgQMHxvjx46sOw8zMzOxTSbo7IgZ29Jq7Ms3MzMxqwomZmZmZWU04MTMzMzOrCSdmZmZmZjXhxMzMzMysJpyYmZmZmdWEEzMzMzOzmnBiZmZmZlYTTszMzMzMasKJmZmZmVlNODEzMzMzqwknZmZmZmY14cTMzMzMrCYUEVXH0GmSXgQe7+Y/My/wUjf/jaq1+j56/5pfq++j96/5tfo+ev+6xiIR0a+jF1oiMesJksZHxMCq4+hOrb6P3r/m1+r76P1rfq2+j96/7ueuTDMzM7OacGJmZmZmVhNOzKbcmVUH0ANafR+9f82v1ffR+9f8Wn0fvX/dzDVmZmZmZjXhFjMzMzOzmnBiZmZm1o0kqeoYrHk4Meskf+DMzHpWE553ZwKQ5GuufSofJJ0gaQHgNEnTVx1Ld2o7CTbhydCKjt67Vnk/fbHrPST1l9Qnmqg4WtLiwChJ/SNiYqsdr5IGSdqk6jiqJmkFSQt3xe9qqQOkp0XEc8CvgM9LmrPqeHrA/NCaF8JW3Kc2ktR2IZO0uqSvSpq5mS5uk1Iu0hPL95+TtFDVMXU3SctXHUMVJM0B7AusUB43y43Fu8AE4GhJS7RgcrYwcJKkjaoOpKc1NFp8ATgNmLErfm8rHRw9plwARgNExATgYGBMOXG0DEmLSNoyIqJ86G6SdBawg6RZq45vWkiar+H7LSTt13ayrDKu7tSQlO0LHA9sDdwnaYlKA+skSV8EhpTvhwNXAFdL2qvSwLrfiZJ+UnUQFXgXmA3YGD4+rusuIp4mL9oPAse1WnIWEb8FDiL3beOq4+lJ5dq4GnAxcHJEPCZphs7+3pY4MHpaRDwJLCvpd+XxXsAdwBUtlpwtCfxS0kHkxXwYMB74EvCtZkvOJC0KHC5pY0m7AD8G+gNjJa1ZYWjdTtLKwHrAGsDfgSeBxxpeb5bWh0YbA9tL2g34GrA+8F3gKEl7VxpZF+rgvTkQmFXS3E36vk0VSYtJ+kJEvAP8CFhT0qpVxzU57d+XkpydSIskZx3s32XAT4Cf9rbkDLgXmBXYHSAi3u/s+9qUB0WV2urJImIFYC5Jvy+P9yObqy9plW7NiLgJ2BXYDpgxIm4BzgL+DCwC7NFkydl/gReANcmL+iYRMQw4GviVpLWqDK4rdXDBfgq4ETgB2BTYoFwYdmzs6mwGkvoCRMTPgL+RrWZvAK9FxHgyAf2hpAOri7LrlLvyVSUtKWlu4CHyhmKlZnrfplTjsVtudHcDLpe0EzAPMAaYvbxeu2tYu9KBPSQdLukI8hj9GZmcHSNpqWZsqW+3f+tIWkPSwhExGjiGTM5atluzoftyBUlfIfOoZYCFJf0aoJxbp7n2vHYHdZ2VA/JDSX0AImJNYA5J15XHw8iWiKs686ZUreHAmz8ixgA/ADaQtENEfABcAtwDfB6Yt7pIp4ykOSTNGRGvANcDr5B1EUPLe3oGcCpwnqRBVcbaFdqdOL8laW1gemBDYGVgw3JXtz2wH6V2sBmUm55BkhYoJ//by9dngMGSZouIu4GvA8MkzdXsrUqltfM4skXiV8BawEXA/pJmrzK27tBw7M4HvBkRR5CtEcsDuwCHkYnN/HVMbBriHw5sC9wKbAmMBt4DfkpeJ77fFd1ePa1h/4aR+7I2cKukxSLiIvI4PVPSetVF2X3KjdJQ4Ndko8VFwBeAFYFVJI0q2304rX+jT1cE2hu0XewkDQaWlPR+RJwTEYMkjZN0dURsGhG7SfpiZ96UKjXs56Zkt9+2EXGtpB2AYyURERdIuhC4KSKerTjkyZI0IzAQWKm07vUFfkmeID8LbA5cHhG/lvQ+UOv9mRLtasp2AHaPiKckHUO2mB1dWiJWB3Yog1hqr7x/rwNfBr4PLAosGxF3SJqFvPhNJ+mPEfHn0v31bnURd56y0P8Y8gL/Lpmc/BB4FBgA9AP+K2m6OiYpU0PS/ORNw7nlov5jICSNiIgbJI0HZiZbvZchu+Uvq8u+lwR6pogYpxyEshzZMr8P8C/gA+Ay4Bvkze5nIuL9quKdWu1u+NYgzy1fA/YGZgDulbRyRIyW9B7wSHXRdh9JCwJ7AesAOwJfAV6IiLdLF/u95XP7wDS3aEeEv6bwC9iI7K7ciGyW/knDa38Fbqg6xk7sW5+G778K3A8MLI9nI1tXv0p+2HaqOt6p3Lf+wJ/I7rw1y3P9yILVnwPbVR1jN+zzvMANwAKN7y+wErAVsCewRNVxTsX+LAJcAyxNXgz+Q9bsfL5hmwOAC8muTVGWnGvWL+CL5Lp9J7R7fh7yDv1K4Kqq4+yifRUwFBgFHA78gUxsvgv8jkxmZmvYfm9gZNVxN8TT1iK9ADB/eW7ecs68ozz+MplU/rbqeKfl/Wn4fn3ypqgfsBNwY3n+fOB9YNGq4+3ife/b8P3s5M3BmWRvw+3AkuW1we3/r6b1y12ZU0BpXvLEvxV55/MwsJmk0wAiYgAworoop52kfsBZbbU7ZNfWucDMkr5D1iYdCzxOHoyPdfR76qSx+yoiHibr4q4F1i+1HS+SI6VeB5aSNFs1kXaNDrrr+pItgu3rHZ+LiEsi4rSIeLRnousSbwN/JI/DN4BVyS7p70haqWwzirxBuj+KSiLtOu+S7+NSapgiIyJejoi/R8RQ4PVW6M4s79XNZLK5GDAxIh6ILDO4lGyZ2LBhX98AVpM0a9Vd1W0lLmSZxAxkK97XI+Il8loxvmy6DHkjeHA1kU67ts+SpK2A4cDb5Ry6CDkaGmAs+X/QMj1xpYZxJ0nbS1oROJ38TL5DtoQOi4hHJK0DnFCuLZ0+73gR86mgnFC2H3BuRKyknDjwEeDoiPhhtdF1jqSlgIlkF98cwKFk68Tp5ElwHeD0iLinbF/bgvF2Te4DyHqOV8i72eHk3e0R5AVgSeD2iHi5onA7rd3+LgI8E1lD9kNgLrJl4QnlSNSdyfqrN+r6/jVqt2/zA9uTNS37k3fn+wBvkQno3MCeEfFGNdF2TkMZwQDyAv8a8G+yruwZ4OKIeKhh+yFkbeSqLXT8zkS2PA0HLomIk8vzuwFbALtGxPNl35+NiAeqirvE1f74fJk8Rr8BnEEmZaPJc9DXyFaVhysKt1OU00IcAJwTEdcpa633Jlt23yFbBLeOJimNmFLlZuA54E1gUET8U9KG5CCjz5E1hHsCh0TENV3xN91iNgltd2GSBkjarBQ2PgcE8FjJpGcju07GVBjqNJO0kKQbACLin+Top2vIOqudgI0i4myy0H9VMqGhbF/bi3rDiXIf8sJ1GHAB0NZK9gFwHXAVcE8LXdSGkxeBsZJWIY/Ll4EbJR1H3qkPi4jX6/z+tWmf/EfE88ApZPfBSPLO9UTgefLO/efNmpTBR0XFGwDnkZ+/y4Bv8/EAjZ0kLdvwI88C6zbz8Qsf7fe6ymLy7cjP5qnAMpL2KNucDXy7JGWKiDFVJ2UlrsZ6zvPJCUavJAdIDSOPy03J2s61mikp66AlciHypn0L5cCLD8j5u8aRN+97tVJS1rD/b5DX+SDLmIiI64CTyGP1Q3Lfr+my1tvO9oW28hfZSvQo+YG7B/gWOdXCqeWN+if5YYMmrWcBbiJbjNoeH03WYy1SHm9CTkmwWdWxTsG+zNXw/ZbkBXx2svvgMeAW8mI+XfmALVl1zF2475sAV5MtR0eU43MwMAtZc7UesHjVcU7F/jTWtOxNjvQ6lrwwTAccAlwOfKlsM2PVMXd2f8m5kG4G1ivPfRZ4gmyBWQg4p5WO2YZ9XxX4B5mA3g8cRbbWb1HOvXu3Pybq9EUmk3+hoWaznHe2I7vf1686xmnYp8bP3wpk78L0wGpkQrIPMF/VcfbA/8MAspdhTrL34VngyIb/l245p1a+43X9KieGi8luAoDNyIK/tcm7oA3bkrJm/OKTxf5XAn9peHxUSWo+Vz6QK5fna3liLLGtR3YbtF3UBgALksXD15eE7BZyIuBZqo63i/d9abJA+vyG5/YHflsS0OmrjrET+7ZfSVYGknWCNwLzlNdGlAR0pjofm5PZt77AQuX7BcrF/GxypGnbNuuTJQSQo/gqj7uL/w+WJ7v8diqP5yzn3ePK422A5aqO81P2YU9g2/L9LA3Pz1Li/1zVMXZi34YB95F1fn8mW27XJbvXD2n7LLbqF9n9fDOwb3m8GFkWcwY5Ufca3fF33ZXZTin0n5GsEfgyOaqGiLgKeIAcpv9sRFwXEbdVF2nnRMQHkpYp3w8FnpH0l/L4B2Sr2WXA0xFxV3m+zt1fS5OjuA6StGlE/JXs4hoAHBs5bcI4cjRfv+rC7LwOmsufJrtlF5S0I0BEjCQL4b9JJgBNpwxKWZysiVuHHFX7D3Ky0bnJ6RSGRcQ7NT82/0d5D1cCNpZ0CPAbssbzbfIGsM1MwHzlnPR2jwfaTRqO4ZXIlURWk7RARLxKtpCuo5zS5dKoQZdlm0l0VfUl55SbISLeKtvtSLamXBy5UkxTkDRXw/cDydaiDSNiS7K4/0ry5vZGsqaz8mlKuoOkJSR9JnJS9RHAEEn7RcRjZEvZo8B3IuL2bvn7TXY+6zYNhbdzAv+NnLl3OHlhuCkiripFuSPI6RXerDTgadSwnysAvyeHcm9dXruSbJ5evTxeKrL2rPaUo2a/TxbZrkEWSl8s6UwycXmX7IbeJXI0UVNqV1O2E9m8/jz5Xm4ODAJujYhRZZu5IuI/VcU7NToaUFI+j8uS9WOrK9f3vJmc/X6jqMH8VdNK0mfJloe1gRERcVJ5/lKyBW0sOYXEoRFxbUVhdqmG88+CUeZAVC7hsx1Z33oLecG/CBgSES9UF+2kSdqc7FF4lFySZzey9e8g8v08BNi8Wc6fAMq5444BvhcRY8rxeQywT0S8VrYZBYyPiJHKyZxfrzDkbiFpHnLpr9fIKbHeUq4Kcw7Ztf6ziOjWm6SWGdbaWeVksSk56uQ/ku4jJyLdk7wb2pKcLX5ksyZl8NF+bgjsQRZO7ytpVERsHxFDJd0g6a8RMaDuJ5WSXBIRfyObl98jL+KnAftIeoE8sexOjhw6vJmTskbKaUx2Iud8Gku28F5L3sFuKumDcrfedEmZpJ3JImoiJ/59m5y0sQ+wCvn+jmrWpKxhX18kB6W8Sa59uXZEjI2ILSVtS07lcn3khKW1HQU9Ncr5ZyPge5JuJ2t2TiZbnb5Lzuz/X+D7dUrKJM3S0Bo2nGyJvpU8r+xCLrU0K5loz0SOTqz1+bMDbb0OB0v6DDmX3ELkJLkXlm0eIIvgaaWkrN3n6xWyt+jrZA/MyIi4TdLlZN3uWeTNfvfF0wKf9WnW7mKwKjnSaytyRuNdI2Lp0mXyLfKgvSMi/q+ygDupNMPPSNYLjI6IUcolQe4EHomIbcp2q0TEXyoM9VOVu5oXye6tA8g51u4hk83fkS1J25P1OddKmj6adDUGAEnLAf+MiPfKMTmSTMrWI1sbNizd0zOTrSxjo+arMjRqaEnZg3zfDiO7nrcC7iK7954naz6GRMQ/Kgu2Exr2cwOgLbmenzzHTE8mau+RRf6/ry7S7iFpdXIpm83J1qUBZLfYEeRFb3fg2og4t6oY2ystekOAX5ATxJ5F3uQ9qZwF/tvA+xFxrHK6j4gmXHGiXa/DWuQ55kVyfsAbyQEqg8keo4cm9XuaVWmwWL08/Dk50GEwudzbNWQjzdER8efujqXX1piV+pXd9fEi3DOSo75WIwv9NyjPz06OwnwY+Ipy/pymFOldcvWCd8tz75N1BBtJ+nl57i9dNuy3m0ROEbAuOXJtBfL9Oo+c06pf5IK6l5NTDMxGE9dClLvXjYHZlcvPvELOb3U8OXJto5KU/RBYISIuapakTLkw9xwlWZmH7G4eSnYLjQGujogngK3JEcODmjEpU1k7t+znuuTIthMj4o3IiX5/SQ67P5gssn6vsmC7iaSFydKQrcmZ4weQ7+lyZMv2LWQN06aShqoG6w1L2oSMbWypFfuA3IdvAJTP2YTyHJH1jk2TlCkX4l6hPGzf63AwOQp6Y7K79mVgmxZNylYi1/18iFxP+Odkgno++X9yMHBGTyRlQO8dlUme/M8iR53MSt6JPwjcBsxZthlCzmrcl2zSHU6TDRHm41bRL5BJzEzkifGPfLyUxApkE/zfgS2qjnkq928w2Vo2D9kVcit5hzcjOc/cbFXH2EX7OQM5GGUUWYKwV9nvRcvrW5Anz/5VxzoV+zQXeVd+XMNn7pfkiKcrgJnLcwfTTaOfemg/5ydnru9bHo8gW4w+Q7bOjyNbzmYu7/HKVcfchfvedv5Zi7zBXaicg84FlimvjSJHYradj7anjFatOPYFyGSxbVT6TOXfTcmbhs0a4r2ivJ9NMzq4nDMnklOybEkmJH3IuQLXJ9dnvRoYWnWs3fz/sAI5GvrAhudOBK5seNx2fuqR97fX1Zg1dGldTXYdrA3sGBGnlT7kb5Cj29YnFws+NPIO6BlJJ0UTdYeV1pWJpajzPHLtxOnJLpT5gDMkPU6OPN2I7Dd/p6p4p0VE3CRpT7LOarWIOEM5GfB7NHmrQ2NXe+RM/m+TI/OOi4gDJS0KjCyNmwuQx3HtJ7Bs2K9XyWNydbKO80hyhvujgHkjFwXemqznubSqeLvAfGR37Gylhf5espD4LvJG8Ddkt964iLi37Ydaoa4sIqKUiawNXBYRz5Tu9sWBDUopxfzkdASPlJ8ZVVnAn/QuubrEO6WL8rBSBP4meR49T9Joct++EU1WexwRL5fW2xvJ5GQZcpqdp8lehwvKe7WtpJtoktVCpkE/ciDHZyQtERGPRsR+ksbq4wFwr0HPzUzQq2rMJC1N1jDcANwWEe+WfuUNgQkRcXq5OCxIzqdzTkRc32wnyHaFql8mW1OuJyfE3Zu8K9+RbLH4PPlBXJRsrdgymq9olVJQ/Auyq+uV8lxTvW+TImk/ck6k48kF2fcjLw4HkQlZP+DFaJ7uyz6RXa9tNw7bkKPYLo6In0k6ibxQPEkumfXtiLi/ypinRSmX2IWsc3xd0inkVDtHK9e+/E9EPCVpIbLYeOtooqkVppSk88neh20i4tby3Mpk0f/bwEkRcVmFIXaolHMcQNZxfpFMYG4nu7u+Th6ffwDebJbPXkckDSZvFAaQLWfbkbW7u1Km2okWLPQvXbivkwNQFgN+QE4F8ieygeJKsp713z0eYwtct6aYpK+STdMPk5NvLk5e7IaQXV/PkOtghqSZIqKpWo/go+Rzb3KU0MtkYf97ZIvYS+RQ9GFkK9luEfGYpMXIvvS9Ikc4NiVJm5HdRAMpJXUVhzRN2iXWe5LdXLtFxITy3DLkezw7OZdO0xynpcB4PLBKRLxQkpJLyEks3yCTlWPLcTwLmXA+VV3E007SmuR79xRZvzqI7MJ8ArgoIp6W9E2y8P2IiLhikr+sCSmnNukbERMknUt2nW1RWrMprYd9IuLVut5ElRiXJ1tUriq9J5T9uSEiLpzMjzeNcmN7HNnr8EbpdXis6ri6i6SvkTnA78kykcPJeroRZAvuI+QAueuqODZ7VfF/uVtbi/yPv4CsKduerE0aQnah7KZcB7PpusGUC5GPBu6NiKci51rZuLy8Z6SXyRqCccC8AOUDuFEzJ2Xw0STAa0XExDqe5KdEOUEeI+nz5Y79S2TCPKF0p0C2fJ5KjpiaaxK/qpYi4iVyOZeblSNNzwcujIi9yJPkfJJ+BjwXEfc0a1JW3AmcTt4MDSc/c6PJVuqtlYteP0vWtlxR9wE3U6JtH5QLXp9BdrUvGhG7kK0Qo9uO48iBD6+W72v5eS0x/ikiftuQlG1FJms9UwjeAyJHAB8K3CVp7rakrBWOyTYNx+acwFJkq+c+5I3ir8hpWg4lW0LvJltHqzk2Oyo8a/UvsuvyfkphONl6NIxcE3Jw1fFN4z4tS04XMbQ8np6cGBCyZfBBcm6gtu1nqDpmf/3Pe7gJ2XLU9h6KXMv0wHbbrVFe69PTMXbhvm5AFh4f1vDc9GTR8dFkjUvlcU7Dfi0GzNHwuA9ZSzaGvBufkRz5/Wuy+3a6qmPuhv+DdcnuoF3Ii9vJwNLltWvJqQeapki+Yb8WJBPsB6n5MlGd2MfNyBVDpmvG92gK9m9Dst76TmCd8ty8ZG3djcAS5MjwK8kpbCo5x/aqrsxGpWXiJHLEzSdqkurarD45ktYg6+amK4/HkC1nB5fHi5MX+fMj4ofVRWodkbQAOdv5IRFxV1tXeqmBPAQ4ISKulrQ92fX1tYh4rsqYO0s59cxJwFeizCxenv+oK7fZlGLqS4G5yrnkSuBf5Hu7LTklwfHkRLn/iYgHKwu2i5UWienJFvl7IwdUzUMunbUwsF9EPC5ppYi4u8pYp0UphF8H+EeUgQqtSNKsEfFG1XF0tVLXeBR5fO5BTjn0k8gBKW31oDdHxN2S1ibnjXymklibLP/oUspJHs8j7+aaYob0ySkX8VPIC8FtEfHjdq8vSA5JH1dFfDZpyjXqLgYOJGsgDyNbct8kR/X1J2uxViEneJxQUahdqhyzvyRrW16pOp6uUM4rbXMf3hkRI8rzg8kJc58DftRsN39TStK+5KCNn0TE8+WidzuZsB4ZOcK46W5+rXmVa9+FwH0RMVw5N+SZ5NrJx0VOFjxD5LyelevViRl8NKvzmxExtupYukI5+V8PzBhlyRrlbNv7kHVmtS207c0mMwJsAtm98DA5xQsR8XxFYXaLVhm00ajhczhDWyt8eWkd4JlokUk6G3oZliNrdx8mSyeGkiPcbibnEzwBWAQ4JSLOripe652Uk4zvSa7SsE9E/KG0gF5Iruawb9RoYuBen5i1aaVkpXTT/ioilpTUnxyKf1i04BIvreRTRoCNifrM79TlWrH7pHwOTyRbA1+qOp7u0lAWcjtZ63o8Of3OZ8lZ/fuRNxfrAtNHxGnVRGq9RcMNw4pkbeAT5Wso2Wp9UkTcUJKzpaNh/sA66HUTzE5KqyRlkCNsJE2U9BbwGHBQRFxXdVw2eSUx+VP5Aj4xAuyoquLqCa2WlMFHn8MPgQclfaEVyiUalVbAOcgJq3eMiDskbU62+o4mu6iXICfnXJ4snN+ionCtF5A0I7luadt6tKeRo73XIkde/oUc2PC9krxdT074XCtOzFpUaardFJjdSVnzKTUR25BN79tErqdoTSZygupvkdOejK04nC5VbmZflfQmOWv8HZHTfiwKfA+pfNj6AAAHXUlEQVTYNCIekjQfOYClJddZtHoocx8eCFwh6e/kqMrdIuJmSeuQE+e+RE7R05ec57OWetU8Zr1NRNwULTI/Ui/0Klmvs1lEPFB1MDbtIuLaiBjbCp/DhrmgFpG0THl8B7mM3cCy2W3A85TrS0S8AGwSTbh6gzUHScuSg0seAO6OnIftFWA55TKMN5M3RsPJ4/KsiBhfVbyfxi1mvUArddP2FpGTA19bdRzWdVrhc1i6iDYjZ0p/kmx1uJScH2p/SR8AKwE/aFdM3TSrU1hzkTQ7WeM4MiLOabgBGk/OK7gq8EdyntIXyLnJ3q4k2Cnk4n8zM5ukMqJthoh4pXQXnUoWUW9LjnBbQdLc5IoGywAPR8T4VhpQZfUlaQbgbPJYfK1t2osyBdEB5ACUt8u/R0bE5RWGO0WcmJmZWYdKInY8ufzQKHLFiW+Siz9vB+wQEY8266Sx1vzKEkvjyJkHri3PtSVnnyOXW3wFeDwi7mmGGwbXmJmZ2f8odTsXAFcBp0bEv8kJj/sDOwPfKknZusBZkharLFjrtSLXWz0J2ELSl8vTE8u/XwYGAddFxD1l+1onZeDEzMzM2indlycBp0fE2Q1TfaxFThR7J7CxpH3Iudp+UAquzapwBfAssEcZgTlR0iDgOOCyOk0eOyXclWlmZp8gqQ9Zt7NvlHVMJe0GDCNv6N8mFyf/Ijn58c3N0EVkrUvS/MDWwF7kQuxLAMdGxFXNdmw6MTMzs480TBw7Dji0TJQr8oJ3MRBkC8VmrTZprjW/kqBNBPpGxFPNlpSBp8swM7MGDRPHngxsKem5iPirpNMj4kNJqwH/BWaoNlKz/9V+LeFmS8rANWZmZtaxy8m6ne+Uuh1JWoPswjy5TBxrZl3MXZlmZtahhrqdPYH7yAk7fxoRV1YamFkLc2JmZmaTVRK0D8m6naebsW7HrFk4MTMzMzOrCdeYmZmZmdWEEzMzMzOzmnBiZmZmZlYTTszMzMzMasKJmZmZmVlNODEzs15F0rmSpng4uqSQdG43hmRm9hEnZmZWW5IWl3SmpL9LekvSfyRNkPQbSV+rOr6uJOlISUOrjsPMquW1Ms2sliQNBG4F3gfOAx4EZgaWAjYFXgdu6YFQZiYnV+1uI4DfAJ5V36wXc2JmZnU1ApgFWDEi7m18QdIwYIGeCCIi3umJv2NmBu7KNLP66g+83D4pA4iIiRHxDICkRUsd2JHttyvdgyFp0Q5e6yfpPEkvS3pT0k2SVuxguw5rzCStK+kGSa9KekfS3yTt0dGOSFpR0iWSnpf0rqQnJV0kaYm2+MumO5e/F1NTB2dmrcOJmZnV1aPAPJK+0U2//w/AgsCRwC+BgcBtkpb7tB+U9B3gBmBW4CfAASXe0yQd327bTYA7gcFkV+U+wK+BzwPLAS8CO5bNx5Xvd2x4zsx6EXdlmlldHQ0MAS6T9DBwO3AXMDYiHuqC3/84sEXbYtySLi+//+fABpP6IUkLAr8CRkfEdg0vnSrpROAASadHxKOSZgH+D3iN7JJ9umH7H0uaLiImAhdIOh/4V0Rc0AX7ZmZNyi1mZlZLEfEnYCWylWkOYFfgVGCCpHGSFu/kn/hZW1JW/t7dwBhgXUmzTubntgT6AmdLmrfxC7iaPK8OLtuuD8wL/KJdUtb2Nyd2ch/MrMW4xczMaisi7gd2AZC0CPBVYHdgTeAqSSt14td31Oo2AVgPWIQcBdqRZcq/N07md89f/u1f/r1nqqMzs17JiZmZNYWIeBw4r3T5jQMGAasAT07mx6b2HKep2GYn4NlJbPOvdtu6kN/MpogTMzNrKhERkv5MJmYLA/eVl+buYPPJdXcuQxblt3/uQ7L+bFIeLv++FBGTazUD+Ef5d0Wym9TMbLJcY2ZmtSRpiKT/uXmUNDPZ3QgwISJeB54D1pGkhu0WByY3k/4h7bYfAKwL3BQRb0zm534LvAv8qMTSPr45JPUtD28AXgIOLIMG2m/b2EL3Bh0nl2bWi7jFzMzqaiQ5XcbvgPuBt4DPAduRs/+fV2rQAE4mR3FeJ+lKYCFgD+ABYOVJ/P5FgOvL718QGAa8DRw8uaAi4ilJewJnAQ+VrtXHgX7A8mQyuCzw74h4S9JuwKXAA5LOAh4p264PnABcVX71neTAg0OBJ/JPxegp+p8ys5ahhkFJZma1IWk9YDNgDbLLck5y2om/AecD57aNaiwta8eQc3/NRRbxjyBHdY4AFouIf5dtzwV2BuYjE6ONyGWX7gQOLqMzG+MI4DcRsUu75wcBB5FdqnOSLWP/AK4BTmlcMUDSKsDhZV9mA54np/84IiL+VbbpD5wCrFq2ISKmpObNzFqIEzMzs0mQND3wAXB2ROxedTxm1vpcY2ZmNmkLlX9fqDQKM+s1XGNmZtYBSbsCW5WH11cZi5n1Hu7KNDPrgKQPgceAkRFxStXxmFnv4MTMzMzMrCZcY2ZmZmZWE07MzMzMzGrCiZmZmZlZTTgxMzMzM6sJJ2ZmZmZmNeHEzMzMzKwm/h8jeuF861sDDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,6])\n",
    "plt.errorbar(df.columns, np.mean(df,axis =0),yerr=np.std(df,axis =0),fmt='-o',capsize=4) #SE\n",
    "plt.xlabel('Subject',fontsize =18,color='k')\n",
    "plt.ylabel('Average Mark',fontsize =18,color='k')\n",
    "plt.title(\"Subject wise mark distribution\",fontsize =24,color='g')\n",
    "plt.tick_params(axis='x', rotation= 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Roll no\n",
       "1          54.4\n",
       "2          47.9\n",
       "3          49.2\n",
       "4          51.3\n",
       "5          46.2\n",
       "6          56.7\n",
       "7          54.7\n",
       "8          51.8\n",
       "9          49.8\n",
       "10         41.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(df,axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10,8])\n",
    "plt.bar(n,np.mean(df,axis =1),yerr=0.1*np.std(df,axis =1),align='center', color=\"pink\",capsize=4)\n",
    "plt.xlabel('Roll no.',fontsize =18,color='k')\n",
    "plt.ylabel('Average Mark',fontsize =18,color='k')\n",
    "plt.tick_params(axis='x', rotation= 45)\n",
    "plt.title(\"Roll no. wise mark distribution\",fontsize =24,color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Nepali</th>\n",
       "      <th>English</th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>Health</th>\n",
       "      <th>History</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Social</th>\n",
       "      <th>Moral</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roll no</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>52</td>\n",
       "      <td>88</td>\n",
       "      <td>24</td>\n",
       "      <td>65</td>\n",
       "      <td>93</td>\n",
       "      <td>23</td>\n",
       "      <td>64</td>\n",
       "      <td>92</td>\n",
       "      <td>24</td>\n",
       "      <td>54.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>82</td>\n",
       "      <td>81</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>47.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>95</td>\n",
       "      <td>63</td>\n",
       "      <td>37</td>\n",
       "      <td>81</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>37</td>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77</td>\n",
       "      <td>94</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>56</td>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "      <td>51.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>80</td>\n",
       "      <td>22</td>\n",
       "      <td>51</td>\n",
       "      <td>95</td>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79</td>\n",
       "      <td>65</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>46</td>\n",
       "      <td>74</td>\n",
       "      <td>42</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>56.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>31</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>92</td>\n",
       "      <td>63</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>54.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>54</td>\n",
       "      <td>77</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>52</td>\n",
       "      <td>71</td>\n",
       "      <td>68</td>\n",
       "      <td>87</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>51.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>49</td>\n",
       "      <td>86</td>\n",
       "      <td>23</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "      <td>45</td>\n",
       "      <td>49.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>69</td>\n",
       "      <td>67</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>73</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>41.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Nepali  English  Math  Science  Health  History  Geography  Social  \\\n",
       "Roll no                                                                       \n",
       "1            19       52    88       24      65       93         23      64   \n",
       "2            73       38    32       71      14       82         81      36   \n",
       "3            51        6    17       95      63       37         81      33   \n",
       "4            77       94    14       64      64       56         29      40   \n",
       "5            58       49    80       22      51       95          5      75   \n",
       "6            79       65    85       42      46       74         42      38   \n",
       "7            63        7    84       31      79       28         92      63   \n",
       "8            54       77    19       32      52       71         68      87   \n",
       "9            49       86    23       71      96        0          3      45   \n",
       "10           69       67    10       21      40        4         92      73   \n",
       "\n",
       "         Moral  Computer  Percentage  \n",
       "Roll no                               \n",
       "1           92        24        54.4  \n",
       "2           22        30        47.9  \n",
       "3           72        37        49.2  \n",
       "4           33        42        51.3  \n",
       "5           26         1        46.2  \n",
       "6           15        81        56.7  \n",
       "7           96         4        54.7  \n",
       "8           51         7        51.8  \n",
       "9           80        45        49.8  \n",
       "10          31        12        41.9  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Percentage']=100*np.sum(df,axis =1)/1000 #\n",
    "df   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.9"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "grade=[]\n",
    "for i in range(10):\n",
    "    if(df.iloc[i,0]>30 and df.iloc[i,1]>30 and df.iloc[i,2]>30 and df.iloc[i,3]>30 and df.iloc[i,4]>30 and df.iloc[i,5]>30 and df.iloc[i,6]>30 and df.iloc[i,7]>30 and df.iloc[i,8]>30 and df.iloc[i,9]>30):\n",
    "        if(df.iloc[i,10]>=40 and  df.iloc[i,10]<50):\n",
    "            y='E'\n",
    "        elif(df.iloc[i,10]>=50 and  df.iloc[i,10]<60):\n",
    "            y='D'\n",
    "        elif(df.iloc[i,10]>=60 and  df.iloc[i,10]<70):\n",
    "            y='C'\n",
    "        elif(df.iloc[i,10]>=70 and  df.iloc[i,10]<80):\n",
    "            y='B' \n",
    "        elif(df.iloc[i,10]>=80 and  df.iloc[i,10]<90):\n",
    "            y='A' \n",
    "        elif(df.iloc[i,10]>=90 and  df.iloc[i,10]<=100):\n",
    "            y='A+'\n",
    "        else:\n",
    "            y='F'\n",
    "    else:\n",
    "        y='F'\n",
    "    grade.append(y)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Nepali</th>\n",
       "      <th>English</th>\n",
       "      <th>Math</th>\n",
       "      <th>Science</th>\n",
       "      <th>Health</th>\n",
       "      <th>History</th>\n",
       "      <th>Geography</th>\n",
       "      <th>Social</th>\n",
       "      <th>Moral</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Percentage</th>\n",
       "      <th>Grade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roll no</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>52</td>\n",
       "      <td>88</td>\n",
       "      <td>24</td>\n",
       "      <td>65</td>\n",
       "      <td>93</td>\n",
       "      <td>23</td>\n",
       "      <td>64</td>\n",
       "      <td>92</td>\n",
       "      <td>24</td>\n",
       "      <td>54.4</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73</td>\n",
       "      <td>38</td>\n",
       "      <td>32</td>\n",
       "      <td>71</td>\n",
       "      <td>14</td>\n",
       "      <td>82</td>\n",
       "      <td>81</td>\n",
       "      <td>36</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>47.9</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>95</td>\n",
       "      <td>63</td>\n",
       "      <td>37</td>\n",
       "      <td>81</td>\n",
       "      <td>33</td>\n",
       "      <td>72</td>\n",
       "      <td>37</td>\n",
       "      <td>49.2</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77</td>\n",
       "      <td>94</td>\n",
       "      <td>14</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>56</td>\n",
       "      <td>29</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "      <td>51.3</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58</td>\n",
       "      <td>49</td>\n",
       "      <td>80</td>\n",
       "      <td>22</td>\n",
       "      <td>51</td>\n",
       "      <td>95</td>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>46.2</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>79</td>\n",
       "      <td>65</td>\n",
       "      <td>85</td>\n",
       "      <td>42</td>\n",
       "      <td>46</td>\n",
       "      <td>74</td>\n",
       "      <td>42</td>\n",
       "      <td>38</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "      <td>56.7</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>31</td>\n",
       "      <td>79</td>\n",
       "      <td>28</td>\n",
       "      <td>92</td>\n",
       "      <td>63</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>54.7</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>54</td>\n",
       "      <td>77</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "      <td>52</td>\n",
       "      <td>71</td>\n",
       "      <td>68</td>\n",
       "      <td>87</td>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>51.8</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>49</td>\n",
       "      <td>86</td>\n",
       "      <td>23</td>\n",
       "      <td>71</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "      <td>80</td>\n",
       "      <td>45</td>\n",
       "      <td>49.8</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>69</td>\n",
       "      <td>67</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>92</td>\n",
       "      <td>73</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>41.9</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Nepali  English  Math  Science  Health  History  Geography  Social  \\\n",
       "Roll no                                                                       \n",
       "1            19       52    88       24      65       93         23      64   \n",
       "2            73       38    32       71      14       82         81      36   \n",
       "3            51        6    17       95      63       37         81      33   \n",
       "4            77       94    14       64      64       56         29      40   \n",
       "5            58       49    80       22      51       95          5      75   \n",
       "6            79       65    85       42      46       74         42      38   \n",
       "7            63        7    84       31      79       28         92      63   \n",
       "8            54       77    19       32      52       71         68      87   \n",
       "9            49       86    23       71      96        0          3      45   \n",
       "10           69       67    10       21      40        4         92      73   \n",
       "\n",
       "         Moral  Computer  Percentage Grade  \n",
       "Roll no                                     \n",
       "1           92        24        54.4     F  \n",
       "2           22        30        47.9     F  \n",
       "3           72        37        49.2     F  \n",
       "4           33        42        51.3     F  \n",
       "5           26         1        46.2     F  \n",
       "6           15        81        56.7     F  \n",
       "7           96         4        54.7     F  \n",
       "8           51         7        51.8     F  \n",
       "9           80        45        49.8     F  \n",
       "10          31        12        41.9     F  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Grade']=grade\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data/result.csv\")  #save data in csv format in excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "correlation coefficient\n",
    "$(r)=\\frac{cov(x,y)}{\\sqrt{\\sigma_x \\sigma_y}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-344.3555555555555"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov=np.cov(df.iloc[:,1],df.iloc[:,2])\n",
    "cov[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.14908476],\n",
       "       [-0.14908476,  1.        ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv=np.corrcoef(df.iloc[:,1],df.iloc[:,3])\n",
    "cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.1490847615170243"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "r=np.eye(10,10) # construction of 10 x 10 identical matrix\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        a=np.corrcoef(df.iloc[:,i],df.iloc[:,j])\n",
    "        r[i][j]=\"{:.3}\".format(a[0,1])       \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>6</th>\n",
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       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.1430</td>\n",
       "      <td>-0.2420</td>\n",
       "      <td>0.1480</td>\n",
       "      <td>-0.4230</td>\n",
       "      <td>-0.1410</td>\n",
       "      <td>0.3090</td>\n",
       "      <td>-0.3020</td>\n",
       "      <td>-0.7250</td>\n",
       "      <td>0.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.143</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.3330</td>\n",
       "      <td>-0.1490</td>\n",
       "      <td>0.0743</td>\n",
       "      <td>-0.0398</td>\n",
       "      <td>-0.5360</td>\n",
       "      <td>0.1390</td>\n",
       "      <td>-0.3670</td>\n",
       "      <td>0.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.242</td>\n",
       "      <td>-0.3330</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.5130</td>\n",
       "      <td>0.0865</td>\n",
       "      <td>0.5050</td>\n",
       "      <td>-0.2710</td>\n",
       "      <td>0.1360</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>0.0115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.148</td>\n",
       "      <td>-0.1490</td>\n",
       "      <td>-0.5130</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0953</td>\n",
       "      <td>-0.2400</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>-0.8210</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>0.4790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.423</td>\n",
       "      <td>0.0743</td>\n",
       "      <td>0.0865</td>\n",
       "      <td>0.0953</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.4780</td>\n",
       "      <td>-0.4040</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.7440</td>\n",
       "      <td>0.0254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.141</td>\n",
       "      <td>-0.0398</td>\n",
       "      <td>0.5050</td>\n",
       "      <td>-0.2400</td>\n",
       "      <td>-0.4780</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.2790</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>-0.3190</td>\n",
       "      <td>-0.0159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.309</td>\n",
       "      <td>-0.5360</td>\n",
       "      <td>-0.2710</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>-0.4040</td>\n",
       "      <td>-0.2790</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0275</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>-0.2460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.302</td>\n",
       "      <td>0.1390</td>\n",
       "      <td>0.1360</td>\n",
       "      <td>-0.8210</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.0275</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>-0.7630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.725</td>\n",
       "      <td>-0.3670</td>\n",
       "      <td>0.1710</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>0.7440</td>\n",
       "      <td>-0.3190</td>\n",
       "      <td>0.0113</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>-0.2640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.290</td>\n",
       "      <td>0.2700</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.4790</td>\n",
       "      <td>0.0254</td>\n",
       "      <td>-0.0159</td>\n",
       "      <td>-0.2460</td>\n",
       "      <td>-0.7630</td>\n",
       "      <td>-0.2640</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0       1       2       3       4       5       6       7       8  \\\n",
       "0  1.000  0.1430 -0.2420  0.1480 -0.4230 -0.1410  0.3090 -0.3020 -0.7250   \n",
       "1  0.143  1.0000 -0.3330 -0.1490  0.0743 -0.0398 -0.5360  0.1390 -0.3670   \n",
       "2 -0.242 -0.3330  1.0000 -0.5130  0.0865  0.5050 -0.2710  0.1360  0.1710   \n",
       "3  0.148 -0.1490 -0.5130  1.0000  0.0953 -0.2400  0.0598 -0.8210  0.0504   \n",
       "4 -0.423  0.0743  0.0865  0.0953  1.0000 -0.4780 -0.4040  0.0101  0.7440   \n",
       "5 -0.141 -0.0398  0.5050 -0.2400 -0.4780  1.0000 -0.2790  0.1020 -0.3190   \n",
       "6  0.309 -0.5360 -0.2710  0.0598 -0.4040 -0.2790  1.0000  0.0275  0.0113   \n",
       "7 -0.302  0.1390  0.1360 -0.8210  0.0101  0.1020  0.0275  1.0000  0.1030   \n",
       "8 -0.725 -0.3670  0.1710  0.0504  0.7440 -0.3190  0.0113  0.1030  1.0000   \n",
       "9  0.290  0.2700  0.0115  0.4790  0.0254 -0.0159 -0.2460 -0.7630 -0.2640   \n",
       "\n",
       "        9  \n",
       "0  0.2900  \n",
       "1  0.2700  \n",
       "2  0.0115  \n",
       "3  0.4790  \n",
       "4  0.0254  \n",
       "5 -0.0159  \n",
       "6 -0.2460  \n",
       "7 -0.7630  \n",
       "8 -0.2640  \n",
       "9  1.0000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(r)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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