diff --git a/year/2016/16CE01.ipynb b/year/2016/16CE01.ipynb
new file mode 100644
index 0000000..2a9d7e2
--- /dev/null
+++ b/year/2016/16CE01.ipynb
@@ -0,0 +1,1323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# B.Tech 2016-20 Electrical Enginnering (Spring 2018)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime as dt\n",
+    "import json\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16MM01020 6.09\n",
+      "16MM01006 5.32\n",
+      "16MM01003 7.60\n",
+      "16MM01002 8.45\n",
+      "16MM01009 7.79\n",
+      "16MM01005 7.89\n",
+      "16MM01004 7.15\n",
+      "16MM01010 8.09\n",
+      "16MM01011 8.66\n",
+      "16MM01012 8.85\n",
+      "16MM01013 6.94\n",
+      "16MM01014 5.46\n",
+      "16MM01015 6.51\n",
+      "16MM01016 5.94\n",
+      "16MM01017 9.06\n",
+      "16MM01018 5.50\n",
+      "16MM01019 6.81\n",
+      "Total Stuents: 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('stres.json') as f:\n",
+    "    data = json.load(f)\n",
+    "    # Filter out 16EE data\n",
+    "    data = {k:v for (k,v) in data.items() if '16MM' in k[:6]}\n",
+    "    \n",
+    "    \n",
+    "for (k,v) in data.items():\n",
+    "    try:\n",
+    "        print(k,v['cgpa'][1])\n",
+    "    except:\n",
+    "        pass\n",
+    "    \n",
+    "with open('course.json') as c:\n",
+    "    cdata = json.load(c)\n",
+    "    \n",
+    "print (\"Total Stuents: %s\" % len(data))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Date of Birth analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Average:     June 09, 1998\n",
+      "  Median:     June 06, 1998\n",
+      "  Oldest:    March 25, 1997\n",
+      "Youngest:   August 17, 1999\n"
+     ]
+    }
+   ],
+   "source": [
+    "dob = [v['dob'] for (k, v) in data.items()]\n",
+    "\n",
+    "np_dob = (np.array(dob, dtype='datetime64[s]').view('i8'))\n",
+    "average_dob = np.mean(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "median_dob  = np.median(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "minimum_dob = np.min(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "maximum_dob = np.max(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "print (\" Average: %s\" % average_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Median: %s\" % median_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Oldest: %s\" % minimum_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"Youngest: %s\" % maximum_dob.strftime(\"%8B %d, %Y\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Branch Change Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no branch changer\n"
+     ]
+    }
+   ],
+   "source": [
+    "original_strengeth = 40\n",
+    "bc_cgpa = [v['cgpa'][1] for (k, v) in data.items() if (int(k[-2:]) > 40 and v['cgpa'][1] !='WH')]\n",
+    "#cgpa = [v['cgpa'][2] for (k,v) in data.items() if v['cgpa'][2] != 'WH' ]\n",
+    "\n",
+    "bc_cgpa = (np.array(bc_cgpa, dtype='float'))\n",
+    "cgpa = (np.array(cgpa, dtype='float'))\n",
+    "\n",
+    "if len(bc_cgpa>0):\n",
+    "    bc_count = bc_cgpa.size\n",
+    "    bc_highest = np.max(bc_cgpa)\n",
+    "    bc_lowest = np.min(bc_cgpa)\n",
+    "    bc_average = np.mean(bc_cgpa)\n",
+    "    bc_median = np.median(bc_cgpa)\n",
+    "    bc_std = np.std(bc_cgpa)\n",
+    "    print (\"Total branch changers: %s\" % bc_count)\n",
+    "    print (\"\\nCGPA (after 2nd sem) for branch change:-\")\n",
+    "    print (\"Highest: %s\" % bc_highest)\n",
+    "    print (\" Lowest: %s\" % bc_lowest)\n",
+    "    print (\"Average: %s\" % bc_average)\n",
+    "    print (\" Median: %s\" % bc_median)\n",
+    "    print(\"Standard Deviation: %s \" %bc_std)\n",
+    "else:\n",
+    "    print('no branch changer')\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Course wise analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Subject Code</th>\n",
+       "      <th>Subject Name</th>\n",
+       "      <th>Credits</th>\n",
+       "      <th>Students</th>\n",
+       "      <th>EX</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>P</th>\n",
+       "      <th>F</th>\n",
+       "      <th>WH</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>Average</th>\n",
+       "      <th>Median</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ME1L001</td>\n",
+       "      <td>Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.22</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>PH1L001</td>\n",
+       "      <td>Physics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.58</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MA1L002</td>\n",
+       "      <td>Mathematics - II</td>\n",
+       "      <td>4</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.94</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CE2L006</td>\n",
+       "      <td>Transportation Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>32</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.03</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CS1L001</td>\n",
+       "      <td>Introduction to Programing and Data Structures</td>\n",
+       "      <td>4</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.06</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MA2L003</td>\n",
+       "      <td>Probability Statistics and Stochastic Processes</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.09</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>CS1P001</td>\n",
+       "      <td>Introduction to Programing and Data Structures...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>33</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.09</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>HS2L002</td>\n",
+       "      <td>Speaking and Presentation</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.12</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>EE2L004</td>\n",
+       "      <td>Introduction to Electromagnetic Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.33</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>HS1L002</td>\n",
+       "      <td>Learning English</td>\n",
+       "      <td>4</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.34</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>EC2L005</td>\n",
+       "      <td>Analog Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.34</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>EE1L001</td>\n",
+       "      <td>Electrical Technology</td>\n",
+       "      <td>4</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.36</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CE2L004</td>\n",
+       "      <td>Structural Analysis</td>\n",
+       "      <td>4</td>\n",
+       "      <td>31</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.42</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>HS2L004</td>\n",
+       "      <td>Odissi Dance - I</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.43</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>CY1L001</td>\n",
+       "      <td>Chemistry</td>\n",
+       "      <td>4</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.48</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>ID2L001</td>\n",
+       "      <td>Entrepreneurship and Small Business Management</td>\n",
+       "      <td>3</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>ID3L003</td>\n",
+       "      <td>Environmental Science, Technology and Management</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>CE2L001</td>\n",
+       "      <td>Solid Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.52</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>CE2L005</td>\n",
+       "      <td>Hydraulics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>32</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.59</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>CE2S001</td>\n",
+       "      <td>Project Seminar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>32</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.66</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>MA1L001</td>\n",
+       "      <td>Mathematics -1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>ID2L002</td>\n",
+       "      <td>Introduction to Bioscience and Technology</td>\n",
+       "      <td>2</td>\n",
+       "      <td>32</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.75</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>PH1P001</td>\n",
+       "      <td>Physics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.85</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>CY1P001</td>\n",
+       "      <td>Chemistry Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>CE2L002</td>\n",
+       "      <td>Surveying</td>\n",
+       "      <td>3</td>\n",
+       "      <td>32</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>CE2L003</td>\n",
+       "      <td>Introduction to Civil Engineering and Construc...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>32</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>EC2L007</td>\n",
+       "      <td>Communication Systems</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>ID1T002</td>\n",
+       "      <td>Extra Academic Activities - 2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.03</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>EE1P001</td>\n",
+       "      <td>Electrical Technology Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>33</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.12</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>CS2L003</td>\n",
+       "      <td>Data Structure</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.17</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>CE2P001</td>\n",
+       "      <td>Surveying Practice</td>\n",
+       "      <td>2</td>\n",
+       "      <td>64</td>\n",
+       "      <td>9</td>\n",
+       "      <td>21</td>\n",
+       "      <td>16</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.31</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>ME1P001</td>\n",
+       "      <td>Introduction to Manufacturing Processes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.33</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>CE1P001</td>\n",
+       "      <td>Engineering Drawing and Graphics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>33</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.39</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>HS2L007</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>HS1L001</td>\n",
+       "      <td>English for Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>ME2L501</td>\n",
+       "      <td>Elements of Mechanical Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.71</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>EC2P005</td>\n",
+       "      <td>Analog Communication Lab</td>\n",
+       "      <td>2</td>\n",
+       "      <td>32</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>ID1T001</td>\n",
+       "      <td>Extra Academic Activities -1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Subject Code                                       Subject Name Credits  \\\n",
+       "0       ME1L001                                          Mechanics       4   \n",
+       "1       PH1L001                                            Physics       4   \n",
+       "2       MA1L002                                   Mathematics - II       4   \n",
+       "3       CE2L006                         Transportation Engineering       3   \n",
+       "4       CS1L001     Introduction to Programing and Data Structures       4   \n",
+       "5       MA2L003    Probability Statistics and Stochastic Processes       4   \n",
+       "6       CS1P001  Introduction to Programing and Data Structures...       2   \n",
+       "7       HS2L002                          Speaking and Presentation       4   \n",
+       "8       EE2L004        Introduction to Electromagnetic Engineering       3   \n",
+       "9       HS1L002                                   Learning English       4   \n",
+       "10      EC2L005                               Analog Communication       4   \n",
+       "11      EE1L001                              Electrical Technology       4   \n",
+       "12      CE2L004                                Structural Analysis       4   \n",
+       "13      HS2L004                                   Odissi Dance - I       3   \n",
+       "14      CY1L001                                          Chemistry       4   \n",
+       "15      ID2L001     Entrepreneurship and Small Business Management       3   \n",
+       "16      ID3L003   Environmental Science, Technology and Management       2   \n",
+       "17      CE2L001                                    Solid Mechanics       4   \n",
+       "18      CE2L005                                         Hydraulics       4   \n",
+       "19      CE2S001                                    Project Seminar       2   \n",
+       "20      MA1L001                                     Mathematics -1       4   \n",
+       "21      ID2L002          Introduction to Bioscience and Technology       2   \n",
+       "22      PH1P001                                 Physics Laboratory       2   \n",
+       "23      CY1P001                               Chemistry Laboratory       2   \n",
+       "24      CE2L002                                          Surveying       3   \n",
+       "25      CE2L003  Introduction to Civil Engineering and Construc...       3   \n",
+       "26      EC2L007                              Communication Systems       3   \n",
+       "27      ID1T002                      Extra Academic Activities - 2       1   \n",
+       "28      EE1P001                   Electrical Technology Laboratory       2   \n",
+       "29      CS2L003                                     Data Structure       3   \n",
+       "30      CE2P001                                 Surveying Practice       2   \n",
+       "31      ME1P001            Introduction to Manufacturing Processes       2   \n",
+       "32      CE1P001                   Engineering Drawing and Graphics       3   \n",
+       "33      HS2L007                          Introduction to Economics       4   \n",
+       "34      HS1L001                          English for Communication       4   \n",
+       "35      ME2L501                 Elements of Mechanical Engineering       3   \n",
+       "36      EC2P005                           Analog Communication Lab       2   \n",
+       "37      ID1T001                       Extra Academic Activities -1       1   \n",
+       "\n",
+       "    Students  EX   A   B   C  D   P  F  WH  Other  Average  Median  \n",
+       "0         33   0   0   4   9  9  10  0   0      1     6.22     6.0  \n",
+       "1         33   0   0   8  10  8   5  2   0      0     6.58     7.0  \n",
+       "2         33   0   4   8  10  4   6  1   0      0     6.94     7.0  \n",
+       "3         32   3   4   6   4  8   5  2   0      0     7.03     7.0  \n",
+       "4         33   1   4   8  10  3   6  1   0      0     7.06     7.0  \n",
+       "5         32   2   2   9  10  2   7  0   0      0     7.09     7.0  \n",
+       "6         33   0   5   5  15  4   4  0   0      0     7.09     7.0  \n",
+       "7          8   0   2   0   4  1   1  0   0      0     7.12     7.0  \n",
+       "8         12   2   2   2   2  0   2  2   0      0     7.33     7.5  \n",
+       "9         29   0   4   8  11  6   0  0   0      0     7.34     7.0  \n",
+       "10        32   0   9   8   4  7   2  2   0      0     7.34     8.0  \n",
+       "11        33   1   8   7   8  4   5  0   0      0     7.36     7.0  \n",
+       "12        31   5   5   6   4  4   4  3   0      0     7.42     8.0  \n",
+       "13         7   1   1   0   3  2   0  0   0      0     7.43     7.0  \n",
+       "14        33   3   8   6   7  3   4  2   0      0     7.48     8.0  \n",
+       "15        34   1   7  11   7  5   2  1   0      0     7.50     8.0  \n",
+       "16        30   1   6   8  10  2   3  0   0      0     7.50     7.5  \n",
+       "17        32   5   4   8   4  5   3  2   0      1     7.52     8.0  \n",
+       "18        32   5   8   7   1  3   5  3   0      0     7.59     8.0  \n",
+       "19        32   6   5   2  10  9   0  0   0      0     7.66     7.0  \n",
+       "20        33   2  10   8   6  3   4  0   0      0     7.70     8.0  \n",
+       "21        32   4   5   9   8  5   1  0   0      0     7.75     8.0  \n",
+       "22        33   1   8  13   8  2   1  0   0      0     7.85     8.0  \n",
+       "23        33   2   9  10  11  1   0  0   0      0     8.00     8.0  \n",
+       "24        32   5  10   5   6  4   0  2   0      0     8.00     8.0  \n",
+       "25        32   5   9   7   5  4   2  0   0      0     8.00     8.0  \n",
+       "26         1   0   0   1   0  0   0  0   0      0     8.00     8.0  \n",
+       "27        33   5   9   7   7  4   1  0   0      0     8.03     8.0  \n",
+       "28        33   3   9  11   9  1   0  0   0      0     8.12     8.0  \n",
+       "29        12   2   2   5   2  1   0  0   0      0     8.17     8.0  \n",
+       "30        64   9  21  16  17  1   0  0   0      0     8.31     8.0  \n",
+       "31        33   2  14  10   7  0   0  0   0      0     8.33     8.0  \n",
+       "32        33   5  14   8   2  3   1  0   0      0     8.39     9.0  \n",
+       "33        14   1   6   6   1  0   0  0   0      0     8.50     8.5  \n",
+       "34         4   0   2   2   0  0   0  0   0      0     8.50     8.5  \n",
+       "35         7   2   2   2   1  0   0  0   0      0     8.71     9.0  \n",
+       "36        32   8  13   6   5  0   0  0   0      0     8.75     9.0  \n",
+       "37        33  10  13  10   0  0   0  0   0      0     9.00     9.0  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "courses = dict()\n",
+    "for (k, v) in data.items():\n",
+    "    for (sem, scourses) in v['grades'].items():\n",
+    "        for (course, grade) in scourses.items():\n",
+    "            if course not in courses:\n",
+    "                courses[course] = list()\n",
+    "                courses[course].append(grade)\n",
+    "            else:\n",
+    "                courses[course].append(grade)\n",
+    "\n",
+    "clist = list()\n",
+    "\n",
+    "def other_grade(l):\n",
+    "    return len(l) - l.count('EX') - l.count('A') - l.count('B') - l.count('C') - l.count('D') - l.count('P') - l.count('F') - l.count('WH')\n",
+    "\n",
+    "def analyze_grade(l):\n",
+    "    grade_hash = {'EX': 10, 'A': 9, 'B': 8, 'C': 7, 'D': 6, 'P': 5, 'F': 5}\n",
+    "    hashed_grade = list()\n",
+    "    for grade in l:\n",
+    "        if grade in grade_hash:\n",
+    "            hashed_grade.append(grade_hash[grade])\n",
+    "    hashed_grade = (np.array(hashed_grade, dtype='float'))\n",
+    "    if hashed_grade.size == 0:\n",
+    "        return {'average': 0, 'median': 0}\n",
+    "    return {'average': round(np.mean(hashed_grade), 2), 'median': round(np.median(hashed_grade), 2)}\n",
+    "\n",
+    "grade_labels = 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other'\n",
+    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'orange', 'red', 'gray', 'black']\n",
+    "\n",
+    "for course, grades in courses.items():\n",
+    "    course_info = cdata[course]\n",
+    "    clist.append((course, course_info['subnane'], course_info['credit'], len(grades), grades.count('EX'), grades.count('A'), \n",
+    "                 grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "                 grades.count('F'), grades.count('WH'), other_grade(grades), analyze_grade(grades)['average'], analyze_grade(grades)['median']))\n",
+    "#     patches, texts = plt.pie([grades.count('EX'), grades.count('A'), \n",
+    "#                  grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "#                  grades.count('F'), grades.count('WH'), other_grade(grades)], labels=grade_labels, colors=colors)\n",
+    "#     plt.axis('equal')\n",
+    "#     plt.legend(patches, labels, loc=\"best\")\n",
+    "#     plt.show()\n",
+    "def sortByAverage(element):\n",
+    "    return element[-2]\n",
+    "\n",
+    "clist.sort(key=sortByAverage)\n",
+    "df = pd.DataFrame(data = clist)\n",
+    "df.columns = ['Subject Code', 'Subject Name', 'Credits', 'Students', 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other', 'Average', 'Median']\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CGPA Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 5 Students:\n",
+      "\n",
+      "16CE01004  AMAN SINGHAL  9.21\n",
+      "16CE01022  YOGENDRA PATEL  8.96\n",
+      "16CE01018  KUMAR SHUBHAM  8.61\n",
+      "16CE01003  DOSETTI KARTHIK DATTA  8.53\n",
+      "16CE01015  GAURAV GUPTA  8.35\n",
+      "\n",
+      "CGPA:\n",
+      "Highest: 9.21\n",
+      "lowest: 4.31\n",
+      " Median: 7.8\n",
+      "Average: 7.45\n",
+      "Standard Deviation: 1.11 \n",
+      "\n",
+      "   9.5+: 0\n",
+      "  9-9.5: 1\n",
+      "  8.5-9: 3\n",
+      "  8-8.5: 8\n",
+      "  7.5-8: 8\n",
+      "  7-7.5: 2\n",
+      "     7-: 10\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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eey5/+ctfGDJkCKWlpYwZM6b5tZSUFFavXs2IESPIyMjgpZdeAmDWrFnccMMNHD58mOLiYp555pnmdaZOncrtt9/O5s2bAYiPj+fVV1/l5ptvZv/+/TQ2NnLrrbdyyimneB1rly5dmDlzJldddRVHjx4F4N5776Vfv358//vfZ/DgwfTq1YtRo0Z5fQzKy8sZOXIk8fHxnH/++dx///089dRTfP/73yclJYWJEyeSkZHRvM748eP59re/zZdffsnVV1/NyJEj6dWrF7NmzWr3WPqNJ5NenWxfwBXAky0e/wq4o6N1/D354nff+K6xzbAZpnPC14ItC9pdPtonaAsWjdE/PIlxzZo1gQ/EjQMHDoQ6BLdCEePBgwebf/7tb39rbr75ZmOMMc8884y58cYb2yzvaYzt/c3xcPJFbVm17wvgPhHJAY4A5wPLWi8kItOAaQB5eXnMnz/fLzu3Gzuvr36dIalD+Fb+twCoa6xj+qbpPLfgOew97W3Wqaur89v+A0Vj9I/Oxrhi7wr6pfUjNTZwN756EmNGRgYHDx4MWAyeaGpqCnkM7oQixtdee40//OEPNDY20qNHD/72t79x8OBB6uvrOXbsWJt4PI2xvr7e5/euGL13p10i8j3gRqAOWAMcMcb8xNXyI0eONMuWtclnPllSvYSxT41t87xNbFzU7yJe/8brbV6bP38+EydO9Mv+A0Vj9I/OxLj94Ha6/6E79066l1+c/gv/BtaCJzGuXbuWAQMGBCwGT0TDiObhwNMY2/ubi8hyY8xId+tqgYULxpinjDHDjTGnA3uAoF2vmr1+NkLb6iO7sbOoSm8OVr77uOpjADbs2RDiSJTyjiYrF0Skq/W9J3Ap8ELHa/jPm+vfxNB+Qtp1eBdb9m0JVigqyjiT1Ze7vwxxJEp5R69Zufaadc2qAbjRGBOUYc8r91Wyurbj8tYl1UvoneXbBGbq5LaoynEDZ8WeoHUUKOUX2rJywRgzwRgz0Bgz1BjzfrD223rUitZsYmNxtY5kobxX31jPiu0rAKg9XMuhY4dCHJFSntNkFWaco1a4Yjd2PtqqNwcr7y2vWU6j/fgoBpv2bgphNKG3ZcsWRo8e7dU61157rdc33ir/0GQVRuqO1Z0waoUrq3au4kjDkSBFpaKF83qV08a9G0MUiVLe02QVRuZunNtm1Ir2NJkmlm9fHoSIVDT5uOpjYiSm+fHGPZqsGhsbueaaaxgyZAiXX35587BDnkzt8f777zNs2DAGDx7Mddddx9GjR1m6dGnz4LFvvvkmSUlJHDt2jPr6eoqL286YEC3Tg3Q01Yq/aIFFGJm9YXbzwLXuLK5azPie44MQlYoGxhgWbl1Ik2kCIEZiwqtldeutUO7fKUIoK4NHOh4gt6KigmeeeYZx48Zx3XXX8dhjj3HLLbe4ndqjvr6ea6+9lvfff59+/frxne98h8cff5ybbrqJlStXArBw4UIGDRrEp59+SmNjo8sux46mBykvLyczMzOspwcZOXKky6lW/ElbVmHCbuy8tf4tjxKVFlkob23au4ndR3Y3P7Ybu7asgMLCQsaNGwccnwrEk6k91q9fT+/evenXrx9wfLqQ2NhY+vTpw9q1a1m6dCk//elP+fDDD1m4cCETJkxoN4aOpgfZsmULS5cuDfvpQVxNteJP2rIKE59u+/SEk0lH7MbePHOwu6kLlIK216sMhg27w+jGYDctoEBpb7oLY4zbqT06ujF/woQJvPvuu8TFxXH22Wdz7bXX0tTUxEMPPdTu8u6mBwHCenoQb6Za6QxtWYWJ2RvaH7XCldrDtWzdvzWAEalo8nHVx22qTKsOVJ1QHXgyqqqqak5KL7zwAuPHj/doao/+/fuzZcuW5ik7Wk4Xcvrpp/PII48wduxYunTpwu7du1m3bp1Po60DjBo1KqynB+loqhV/0mQVJt5Y94bLUStc0a5A5amFWxe26WJuMk0n/Qee0tLS5qkt9uzZww9/+MPmqT3uvPNOhg4dSllZGR9/fGLLNDExkWeeeYYrrriiudvuhhtuAGD06NHs3LmT008/HYAhQ4YwZMgQn3tBcnNzm6cHGTJkCGPGjGHdunVkZmY2Tw8yZcoUn6YHueiiixg5ciRlZWXNLb+nnnqKadOmMXbsWIwx7U4PUlZWxmWXXcbIkSM599xzaWxsZMiQIfzqV78KzPQg6EC2ftOZgWwr91XS69FeXq0jCD8+9cc8et6jQPQPwBosgY6xYncF3VK7kZbg+8Ck3sZ44OgBMh/IbPfD0Nxvz+Xs4rN9jsUVHcjWf4IdY11dHampjhH5H3jgAbZv386jjz7KzJkzWbZsGX/5y198jlEHso1wcyrmeL2OwfBx9cfuF1Rho8nexKh/jOL2ubcHdb+fVH/istWuRRaqtTlz5lBWVsagQYNYuHAhv/zlL0MdEqAFFmHhrfVveVyy3lL5jnLqG+tJjE0MUGTKnyr2VLD/6H5eW/saj13wWIcjlfhT6+IKJ0HCq3xdhYWpU6cyderUNs9fe+21J9z7FWzasgqxumN1fLD5A68TFUCjvZHlNXpzcKRYud1x/82uw7v4dNunQdvvR1UftZsYbWILebLSyxAnj87+rTVZhZino1a4okUWkaN8Rzli/Xtr/VtB2WeTvYnF1Yvb/TDUZJpCWr6emJjI7t27NWGdBIwx7N69m8RE33uBtBswxMq6lTG6YDSfbPvE63VjJIYl1YEpE1X+t3LHSgTBYHh93evcd9Z9Ad/nmto11B2rc/n6pr2bQna/XmFhIdXV1dTW1gZ930719fWdOoEGQ7TEmJiYSGFhoc/70GQVYr2zejMif4RPyarJNDXfHKzCmzGG5duXY8fRwlm7ay2b9m6iOKvteHH+5Op6ldPhhsPUHq6la0rXgMbRnri4OHr3Du28bPPnz2fYsGEhjcEdjdFBuwEj3M5DO6k6UBXqMJQb2+u2s+fInhOem71+dsD3+3H1iYPXtkcrAlUk0GQVBRZX6XWrcFe+48RBWm1i4831bwZ8vwsrjw9e60qoiyyU8oQmqwhnE5tet4oArZOV3dhZULmAvUf2BmyfXx36is37NrtdTltWKhJosopwdmPnoyqdOTjcrdyxsk13nN3Y+c+X/wnYPj1pcYfdVCFKuaDJKgqs3LGSY/ZjAdt+zcEazn/u/KidBr32UC3/XPVPpr4ylUcqAjP69/Ka5W2642xiC2gJ+8dVH7sdHLnJNPHlni8DFoNS/qLVgFGg0d7IhoMbOIdzArL9X837Fe9++S5PrniS+8+6PyD7CCZjDOU7ynl7w9vM3jCbZTXLMJjmsvL5W+YzsddEv+3v4NGD7XbH2Y2dORVzaGhqIC4mzm/7c1pUtcij5TRZqUigLasosebAmoBsd23tWmaWzwTghS9eiOgy+Y+rPub7b32f/IfzGf7EcO6ef3dzogLHeIvpMen8ev6v/brfVTtXuXzt4LGDLNza/pTjnXGs6dgJv1tHag/XcujYIb/HoJQ/abKKAjESQ0Wd/2fmBPj5+z9v/nnLvi1tCgUixZwNczj9mdN5uvxpdh7a2fx865P5xOyJfFj5IR9Wfui3fXd0zAI1msXK7Ss51uR513C0dvGq6KHJKgo0mSZ21u90v6CXFlct5s31bzYP1SMIL69+2e/7CbR5m+dx6cuXYjBux2AcmzEWm9j82roq31Hu8l4n52gW/m6xursZuLWTrchi1+FdXPLiJWyq0yQdKTRZRYkdR3f4dXvGGG6fe/sJA6AaTMR1BS6pXsIFz19Ao73Ro8GC42xx2I2d+Vvms7DSP91zy7e3La5oaev+rayuXe3ydV98XOX+ZuCWTrby9f9++V/eWP8Gt392u7YqI4Qmqyix++huGpp8HxC3tXcq3uGjqo/anOAr91eycsdKv+0nkFbtWMXXnv0aR5uOej2qvb9aVw1NDaz+yn0i8mdXoDGGhVvd3wzsdDKWry/dthRBOGo/ypmzzmT7we2hDkm5ockqStixU32g2i/barI3ccd7d7Q7rUSkdAWu37WeM/95JnXH6nyafsVu7MzbMo9FWz2rqHNl3a51bkfVF4Q31r3Rqf20tHX/1hOuy7ljN/aTrmW1ZNsSDIbvdf8eVQeqOPufZwf0Bm3VeZqsokjl/kq/bOf5z59nTe2adk/yBsOLX7wY1l2BW/ZtYdKsSeyr3+dTonKyiY3p86d3KhZPWqEGw6c1n7Kjzj9dud5erzKYkE4VEmzHmo41F730TOqJ3dhZu2st5z13nlZFhjFNVlGkcl/nk9XRxqP83wf/1+HNpJX7K1mxfUWn9xUINQdrmDhzIjsP7exUogJHi+P9ze97ffJvqXxHucczAr+94W2f99PSx1Ufez0LcdWBKhrtjX7Zf7j7fOfnbSolDYZPtn3CJS9d4lUVpQoeTVZRxB8tq8eXPU71geoO788J167AXYd3cdass6g6UNXpROXU2dbVyh0rPWqF+nNg20VVi7z+/ZtME1v3b/XL/sPdpzWuZ2meu2ku3/r3t2iye3a9TwWPJqsokR6T3umW1f76/cxYMMPtED0Gw4urw6srcH/9fib/azLrd6/3W6ICR+tq7qa5Po1sb4xhRc0Kj27MtRs7czfO5XDDYV/CbFZ3rI7Pdn7m07rhdN3qaKP3RTGeWrptaYeVkq+seYUfvfOjsHp/K01WHRKRn4jIahH5QkReEJGwna4zMy6z0y2rhz5+iH31+zw6uW7dv5Xl25d3an/+cujYIc577jxW7VjlUeze8rV1tXX/Vg4cO+Dx8kebjvL+pve93s/RxqPM3TiXW/9zK4MfH+zzST6cKgLL/l7G9W9dH5BtL65e7LZS8onlT/DLD34ZkP0r32iyckFECoCbgZHGmEFADPCN0EblWlZsVqdONjvqdvDQ4oc8Xj5cugLrG+uZ8uIUFlcvDkiiAker53+b/uf1VCzejvbhzcC22w9u56kVT3HpS5eS/WA25zx7Dn/65E9s2bfFq306CRI29xvVHatj3a51PFP+jF9HEgHHOI3rd633aNn7F93PE8uf8Ov+le80WXUsFkgSkVggGagJcTwuZcVlse3ANp8/Vd+z4B6ONh71eHlvqwKrD1RTeajSr10rDU0NTH1lKu9tfs9v23TFl9aVt8nKbuy8sf6Ndv+GdmNn6bal/Hrer5m2fBrd/9Cd62dfz1vr32ruOuxMsraJLWxaVi0H1v3h2z/06/2Dy7cv9+o43fqfW33+AKD8S7Rf1jURuQW4DzgC/M8Y881Wr08DpgHk5eWNePHFF33az9b9W6k9XNupWFfXreaZmmd4dcyr5CTkeLXutiPbuObTaxidPppL8y71aJ2l+5fy8s6XeXzY4/RP79/hsrVHa7lh+Q3sadhDYVIhE3InMCF3AqVppV5XrTnZjZ37193P+1+9z5QuUxifNd6n7bRWmFBI9dG296t9sOcD3tn1Do8Ne4wB6QM82tYvv/glFQcruKPXHR4tv+LACp7f8Tx/HfZXBqYP5FDjIZbtXcaS3UtYsmcJ+xr2IQj9kvtRnFzMwJSBdIvvhkjH1xg98dS2pzjGMZ4c+WSntwVQV1dHamqqT+vOr53PjDUzmJw9mbl75nJjyY1cXni5X+J6YesLPLH5CWaUzCAlJsXl3xtgb8NeHqp8iEHpg/j9kN/75Tj7ojPHMlg6E+OkSZOWG2NGultOk5ULIpIFvAZMBfYBrwCvGmOebW/5kSNHmmXLlvm0rxvn3Mhjyx7zNVQArut+HU/XPM3i7y1mTOEYr9Z9YNEDJwxY6ylB+NlpP+PByQ+6XOZIwxEmPDOBlTtWcl7OeczZNad5Ko5uqd24fMDlXDLgEk4vOp1Ym2cz1hhjuGHODQHponmo30P8bMPP2n3NJja+VvI13vnmOx5tq8cfelB90LsbtQXhzN5nYjd2Pqz8kCbThE1sJ7S2OoqxM5Ljkqn7eZ1fTsrz589n4sSJPq17/8L7+cUHv2h+nBqfyoabNpCflt/puK54+QpeX/d68zUrT4/lPy78B9cPD8w1NHc6cyyDpTMxiohHyUq7AV07G9hsjKk1xjQA/wZOC3FMLmXFZQG+3Wu1fvd6r8aRc3I3VqAxhmmzp7F8+3Lsxs6k7EnN64G2vrhiAAAgAElEQVTjOtljyx7jrH+eRZffd+G7b36X2etnU99Y73qf1piFobiWYDd23v3yXZZuW+p22b1H9nqdqJze3/w+CyoXNJ9QA1UV19rhhsOdbuH7Q8WeihPej4eOHeKOuZ61Tt3xpLiiNUH4yX9/4rcRYpRvNFm5thUYIyLJ4vioeRawNsQxudScrHyoCFxXu87r/8BO1QeqXd638vDih3n283Ybos2cJ+J99fv456p/ctGLF5HzYA5XvnIlL37xIgeOnlhNd8+Ce3h48cM+xeoPNrExY8EMt8v5OpWKM5EHK0G1Fg7l6xt2bTjh/WgwPPv5s50utthRt4NtB7d5vZ7BcLjhMNNmT9Ny9hDSZOWCMeYT4FVgBfA5jmMVtqVBibZEYiTGp4vB63av83m/gvDK6lfaPP+fL//Dne/d6dW2nCfoww2HeW3ta1z12lXkPpjL+c+dz1MrnuL+hfczfcF0n2P1B7ux807FOyyr6bjLN1Ln/QqHIov1u9tW69nExg/n/LBTo2x8us31zcDuOFvVz37W8YcvFTiarDpgjPm1Maa/MWaQMebbxhjPy+VCwGC87gbcfXg3++r3dWqfrW8Q3rB7A1e+cqXP24TjiavB3sB/N/6X62dff8J1jFDypDKwfKfrOazCWahbVgeOHmD3kd1tnrcbO2tq1/DXpX/1edvOkdZ9JQg/fvfHfhvDUXlHk1UUsRs7m/Z5d6+MPwYwbdkVuL9+Pxc8fwGHGg75rSsrVF1irtiNnTkVc1he4/qm6GU1y3zuWg2VcJgqpGJ3xzNe/3LeL31OFktrlnaqeMRgOHD0AD98+4faHRgCWg3oJz5XA956Kxvef4Wag527haskuZiNhzdhs9mY0HOCx58fd9TtYJ2HN0l2pEdGIcVZxXy+8wv2HNnTYYzhzOMYBXKSchjcdVCbl+zGzsKtCwnUf61AHsf0hHSG5w/r9Hb27dtHZmam1+t9degr1tR2fGk4LzWPAbkd3y7RmgEWbV3UZsw/X4/lwC4D6ZrSxev1fOHrsQym6txcCl991ad1Pa0G9KxWWAVU76xe9Mzo0altHD54hKLMnlTu20rj0sXE2T1LV4fTGpFUOLXgVJ+7SNbtWs9Xe7chNdvZk9JE76xe5KXktRvjmMLRPu0jWDyNsfrANqoPVHNw+cekNZzYQXEozo7Jgb45fchJ8u6eN3/G6K2Nezexv24XLPFupI529egB67y/Fno4tRFSYVTBKGLauQev6kAV2w7UkL91D5nHPO8Yqo8xNHVpojirN11Tuh7fn5fH0mBY/dVqKr5aS2btl8R7+P+sU3w8lkGTmgrBKK03xuiXH75GjBhhQmnevHnm1dWvGqZjVnTDGDz7uuxKTOkjfTq175krZxqmY5iO+cHsH3QYY7jzNMZ9R/aZzPvSzcXfaHtMnxzmOBYVuytCGqO37vvwPsN0zMF4z98/rr7mPfSQT+t9+xJM4YP5LmM8dOyQ6flQgRl8o8002Dzf7nODHX+TVTtWdfpYfr7zcxM3I9Z847LOH6dAHsugfY0e3an3JLDMeHCO1WtWUaQoswiASi96DDZ0sdGvq2cjMrhycf+LSY5JYkLhOP503p86ta1IkZGYwa2n/ZQ3+8PKbie+Vt4NUmOSKc4qDk1wPirJKgFgcwh7nCpyhb5dXXfxJccl88j5f+bzLnb+Osrz7S4tgOSYRAZ2GdjpGAd1HcSvzribFwfDG971RqpO0GQVRYoyrGSV4dnydoGKbENpTmmn9puZmMkXN67mf9e8R3xMfKe2FUluGXMLGXFp3DPxxOfL84Wh+WU+DyUVKiXZjmS1MTt0MVTk2uib3bfDZab0n8I5xZO550wbh+I82+7SQmFE95Eej5Lizl3j76J/dj8eGheaIZhORpH1v0l1KDc5lyRbgsctq6p0R19+v5x+nd5376zeJMaG7QwqAZGZmMmtp/2UN/rDKusSnV0cyWpY/vDQBucDZ8tqY1Zo9r83EXYnNNE3p+NkJSL8euJ09iTYecqDw9xgg5X5MKrgVD9FCnExcUzu8zXK84UmzVdBockqiogIRRk9PW5Zrc91fPdHsjpZ3TL6FtJjU7nnDMfjTVlQF2enrFtZaAPzQVZSFllx6SFrWVVYtSjuWlYAp/U4jXGFY3l4vI0GN2exL7o6PpSd6sdkBTA8fziHYu18GcKW6MlEk1WUKcoupjLbsz/rBuvkUJrbuW7Ak1lWUha3nPYT/j0QPstzXK8CIjJZAZTk9AlZy6rCOum7a1k53Tn+52xNs/PyKR0vt7TA8T0QyQpgRefH11Ue0GQVZXpl9qIy07N+iQ05kBab0m6ZufLcrWNubW5dlXeDWInhlK5uzqBhqiSnLxu7hGbkjYocxygRnhamXNDvAgZm9+fBCbYOZ6haWgC5CVn0yuzllzidBuQOIMEWr8kqSDRZRZmijCJ2JTZ5dOF5fY6jVRWqeXqiRXZSNjePvZXXBsIrA2FATv+IvX5XnFVMZZqdxhCcGSqyoWdqgcfHziY2bp9wJ591tfPfPq6XW9rTxqk9xvr9fR4XE8fgroNY0d2vm1UuaLKKMt6Ur2/Ii6FfF6299YefjP0JabEpbMiFsu6RV1zhVJJVQqPNsNXD657+VJEr9PXy/Xj14KspSO7G7ya0n4jq4mFNjt3vXYBOw7uPZGX3jlt2yj80WUUZT8vX62OhMrWJftlaXOEPztYVwLBunR+uKFSay9eDfN3KYCUrD69XOcXHxPOTcT9jfpFpvjbV0op8R4XmqAIvbsrywrD8YexNsHt1b6PyjSarKONpy+rLbDCixRX+dNvY25h6ylSm9J8S6lB81ly+HuQKt93JsC/e7lElYGvTRkwjMy6dB8e1fc2ZwEZ1D0yy0iKL4NFkFWXyU/OJlRi3LStnJaCWrftPVlIWL17+Ir2zeoc6FJ8VpBeQYIsPesvK20rAltIS0vjR6Jv494Dj72unpQXQO60nXQI06OzgroOJwdZmFBPlf5qsokyMLYYeqQVuW1brvbinRZ08bGKjd0ZR0FtW3txj1Z6bR99MfEw8D4898fmlPWM4tefY9lfyg6S4JAbm9teWVRBosopCRdnFbsvXN+RA96Q80hLSghSVihQluf3YlBPcU0NFNsRg87lVmpeax7XDvsusYcKOVMdzX6VAZVpTwIornIYVjGRFYeRNtBlpNFlFoaLMIrc3Bm/IFfp1MGCoOnmVZJWwMZugVrhV5ECv9J6dGlvytrG3cSwGHrVm/PjUKikP1PUqp+HdhrMjuYntqQHdzUlPk1UUKsoooia5iWMdfNhb39VGv04OYKuiU0l2CXWxdmpTgrfPilxbh6Ote6JvTl8uG3AZj4+2cSDBcb3Khq25CCJQnNtfqV2BAaXJKgoVZRZhBKrT2399d5JjwFCtBFTtCfaAtgZHy8of10/vGHcH++PtPDHCkawG5Q4kJT6wWXdot6GAVgQGmiarKOTuXiutBFQdCfZUIV+lwME438rWWxtVMIpJRRP54zibNXLFGD9E2LH0hHT6ZhRrsgowTVZRyN29Vs0D2Go3oGpHr8xeCBK0llVzJaAPZevtuXP8XdSk2NmTELiRK1obXjiKlVpkEVCarKJQj/QeCMKWDpJVrMT4fWBPFR0SYxMpSO4WtJZV8z1WfrqN4pyScxjaZTDg/5HWXRmeP5wtaU3sSQrK7k5KmqyiUEJsAvlJXV12A67PheL0XsTFeDjNqjrplOT2ZWN2cAY4rrA+PDl7BDpLRPj91x7m6/2+HrTR751DbOnNwYGjySpKFWX3dt0N2MVGad7A4AakIkpJdh825gbn9FCR7fjw5K8p5wEml0xm9lWz/brNjgzLt5KVXrcKGE1WUaooqxeVOW370O0CFdn+mcpeRa+S7BJ2JjVR5/ttTx6r6NL5svVQy03OpWdKwUlZZDH8tM95pfqVgO9Hk1WUKsoooirVjr1VT05VumOKb01WqiPO8vVNAS6yMDhaVtEw7NfwHqNOupEsdqTCyozDCIHvMtZkFaWKMoposJk2d9VrJaDyRLCmCqlJg8Oxdr9VAobSsG7D2JAZnNZouFhlTTJeklIS8H1psopSrsrX1+c6vmvLSnUkWC2rzg5gG06G5w/HyPET+MlglVVQUpKqyUr5yNWNwRtyIDUmmW6pWrakXMtKyiIrLj3g5eudmRok3JyMc1uVd4MeKd1Jj3MxXI4fabKKUq5aVhtyHBMuigSnLFlFrpKcPgHvBqzIgXiJo0d6j8DuKAjyU/PpmphzUiWrVd1tlBWMCMq+NFm5ICKlIlLe4uuAiNwa6rg8lRqfSnZ8RpuW1fq8GPp1iezKKxUcJTl92dglsAUDFdlQktmbGFvkFyaICMMLRrGy4OQ4rR6JhXXZdobmDQ3K/k6Oo+oDY8x6Y0yZMaYMGAEcBl4PcVheKcrsdULLqj4WKlObtLhCeaQkq4TKNDuNATxLREPZekvD84ezOtdQH5zbu0JqdVfHrTBl3cqCsj9NVp45C9hojKkMdSDeKMruTWX28U+sX2aDES2uUJ4pziqm0WbY6mIklM6yC2zMMlFRXOE0PH84jTbDF11DHUnglVuXvZ2jzgfaSZD//eIbwAutnxSRacA0gLy8PObPnx/ksI6rq6trs/+YgzFsyolh3u8fQET4sPFzqJ/FgS0HmL97frvbCXaM4UZjPO7gvoMA/PvOaYyM9e4DTl1hIfMfeqjDZXba91J/+D7su+whO+b+PpZHjxwF4MUfX05dnH9GfPfkWIbCO0dfJ6lpBVtXbeXwocOB/xsaY8LqC3gEON36uTfwCVABvATEu1jnPqAKqGv1fIK13pfWdnpZzw8GZnoYTzywC8jraLkRI0aYUJo3b16b5/64+I+G6ZjaZIwB89vxGKZjDtQfCH6Apv0Yw43GeNzWfVsN0zGPj3S8f7z5mvfQQ26Xea+34/34/qb3g/L7tMffx9Jut5uM+9LMDRd4f8w6cyxD8TX+OjGn/WNsp48jsMx4cC4Oq25AEckGxhhjPrSe+h3wR2NMX2Av8D0Xq84G2hte+XvAXmNMH+CP1vYwxnwOFIpITw/COg9YYYzZ6flvEh5al6+vz4H8pK6kJaSFMCoVKQrSC0iwxQesIjCa7rFyEhGGdx/BioLorra1C6zKF8qsMRGDwW2yEpHviMhnIrJKRP5lPVciIktE5FMRuUdE6qznJ4rIhyLyuoisEZG/iYjNeu1xEVkmIqtFZIaL3V0O/MdaXoAzgVet12YBU9pbyRizxBizvZ2XLrbWw9rOWXK8Zns2ju49d66inS7ASNC6fH1DrmgloPKYTWz0zigK2L1WFdmQaEugIL0gMDsIkWH5w/ksj4AWpoTalkzHhJnBul4FbpKViJwC/AI40xgzFLjFeulR4FFjzCigptVqpwK34ehqKwEutZ7/hTFmJDAEOENEhrSzy3HAcuvnHGCfMabRelwNePuuLsDRPYi1nf3WdgGWARM6WllEkoHJwL+93G9YaN2y2tDFRmmuJivluZLcfgEbfb0iB/pkFWOT6DqrD88fTn2MYV1uqCMJHOcoHcGqBAT3LaszgVeNMbsAjDF7rOfHAs5hdp9vtc5SY8wmY0wTjhbJeOv5K0VkBbASOAVob46KfKDW+rm9drRxE29rHW3jK6B7RysbYw4bY3KMMfu93G9YyE7KJiUmicpM2JMEuxKbtBJQeaUkq4SNWd7/x/NERZcY+kZhS/9kGMliVTewYWNQ10FB26e7ZCV4/z5tvbwRkd7Az4CzjDFDgDlAYjvrHmnx/C4gU0ScFYuFQI2IxLS4UfceN7FUAz0ArO1kAM6Em2jtL2qJCEUZPanMaDGAba7eY6U8V5JdwqFYO7Up/t1uk8CmTHtUXa9y6pfTj+SYxKhOVuXdoG9mMclxyUHbp7tk9T6OFlEONBdAACwBLrN+bn3d51QR6W1dq5oKLALSgUPAfhHJw1G00J61QB8Aq0pkHo7rWADXAG8aY5qMdbOuMeZuN/G/Za2HtZ0PrO0C9AO+cLN+xCvKLqYy28Z6K1lpy0p5wzmgrb+LLLZmwDGbiYoxAVuLscUwtFsZK/Ojt8hiVUFM0IZZcuowWRljVuMoC18gIquAP1gv3Qr8VESW4ui6a9lNthh4AEci2Ay8boxZhaP7bzXwNPCRi13OASa2eHyntZ8vcVxreqq9lUTkQRGpBpJFpFpEplsvPQXkWOv/FLirxWqTrP1FtaKMIiozhQ3W1OG9M3uHOiQVQZqnCvFzkUU0VgK2NLz7CFZ2lzbzyUWDfYmwJa0paMMsObm9KdgYM4vjFXVO23CUmBsR+QaOYgWnw8aYqe1s51oP9rVQRH4rIpnGmH3GmE20X5Leer07gDvaeb4euKL18yKSAIzEkXSjWlFmEXsSmliR75g6PC4mLtQhqQjSO7M3grAxy79XraJptPX2DM8fzl/j7GzMgr573C8fST4LQXEF+D7c0gigXEQ+A36Eo/rPX24DPLn/qTN6Ane1qDSMWs6KwAW9oF/egJDGoiJPQmwChSn5AWlZpcQkkZ8anRd2nEUWK6Pw1wv2MEtOPg23ZIxZCLSJ1BgzH5jfmYCMMZ90Zn0P91GBY1SMqOe81+pIHPTL1utVynvFOX3YmL0df9YEVmRDn+w+UTtVzcAuA4mTWFbkN3Ll6lBH41+r8qBLQnbQP2hE1w0Oqg1nywq0ElD5piS7j9/vtaroGkPfLtH7foyPiWdwl0FRWRFY3t3G0O7Dgv5BQ5NVlMtPyyfOqv7XSkDli5LsEnYmNVEX75/tNdpgc3p0lq23NKJwFPN7C9++BOb0hWORP2UXjTZY3cVQ1i14wyw56ajrUc4mNnqmFbLxwBZNVsonzvL1TVkwxA8jZC4ogkabYUBudF9DnTFxBsYYXkt8hWeH7ifrqI1LV9v5xhcwcQvE2kMdoffW58DRGBP061WgLauTQlF2MamxyVF7MVsFVnP5uh/utWqwwc1ft9ErrQeXD7zc/QoRLD8tn39c9A923PEVb1/1NheMupqXRiUz+TtQcLuNm86HTyJsWERncUWwKwFBW1YnhStOuYLBeYOj9mK2CqzmG4P9UBH459GwJsfOWxf8laS4pM5vMALEx8RzQb8LuKDfBRz5+hO8U/EOL61+iadS3uKvpx7lrefhwg2hjtIzq7pBvMSFZLZxTVYngRtG3hDqEFQEy0rKIisunY1ZBzq1nZo0+PWZNi7ocy4Xll7op+giS1JcEpcNvIzLBl7GwaMHGf/kadxw8Vom/LmJzPpQR+deeTc4pcvAkNyvqd2ASim3SnL6dLpldftkaEiI5dHz/uSfoCJcWkIaT18yk50phtvOCXU07hmgvCCGsu7DQ7J/TVZKKbdKcvqyKdf3crb5veD5IXDn+Luar4EpGNF9BHeMv5Onh8N/w/yw7EiF2sTgD7PkpMlKKeVWSVYJlWl2nyYUbLDBjRc6iiruGn+X+xVOMnefcTcDsvrx/Sk2DiSEOhrXVoWwuAI0WSmlPFCSXUKjzbA1w/t1nUUVj17wl5OmqMIbibGJPH3pLKrTDHedHepoXHNWAg7Ja2/e3MDTZKWUcqs4qxjwvnz9eFHF+VzY7+QsqvDEmMIx/GTMT3h8FMzrFepo2rcqD4pSC8lK8vN8MR7SZKWUcsvX8vWWRRV660THfnPmb+iT0ZvrL7FxKAwnRygviGFoQWiKK0CTlVLKAwXpBSTY4r1qWWlRhXeS45J56pKZbMqw88szQx3NiY7EwoasJsryQnO9CjRZKaU8YBMbvTOKPG5ZNZombvq6FlV46/Si07lx1I08OgY+6hHqaI77oivYJfjTgrSkyUop5ZGS3H4ej77+74ZFrM7VogpfPHD2A/RMK+S6S20cCZNhG0I5zJKTJiullEdKskrYmOV+VquaNJjVMJfz+5ynRRU+SI1P5clLnmFDlp3pE0MdjcOqbpAWm0KvzF4hi0GTlVLKIyXZJRyKtfNVSsfL3T4ZGmjiT+f9WYsqfHR28dlcP+x6HhoHFU3bArYfx5Qf7pcrzxeGdivDJqFLGZqslFIe8aQicEGRo6jiqp5XaVFFJ/1u8u+wC3zStDZg+5hZBoNuhJ98DZpcfK6wC3zWTRiaH7ouQNBkpZTykLupQlqOVHF1j6uDGFl0yk7KpjA5n632rwK2jw05ju+PjIULr4b97YygsTkTDsbZQzbMkpMmK6WUR3pn9kYQNrlIVn85leaiioSYMB43KIKU5g2kyl4bsO3XpEHv1B78/et/Z26/GMZOs7X5MBLqYZacNFkppTySEJtAYUp+u92ANWnw67NsWlThZ6U5pVSZWrdFLb7alg7dM3swbcQ0/vftuezMT2P0D2wsKDq+THk3sGFjUNdBAYrCM5qslFIeK8nty8bsthc3bp8MxxJitajCz0pzSzlk6tmZGpjt12TGUJBRCMCk3pP45AfL6JJfwtnXCE8NcyyzKg9Ks/qE/BYETVZKKY8VZ5W0udfKWVRxx/g7tajCz/rn9gdgXa7/t22AbamG7qndm5/rk92HxdOWclafyVx/Mfz0a7CyMLTDLDlpslJKeawku4SdSU3UxTse6/QfgeWcPn59jv+3fTABDsXaKUgvOOH5zMRM3v7mHG4+9Wb+OBaqUkM7zJKTJiullMec5evOIgtnUcUj5/+Z5LjkEEYWnXpk9CDBlsD6ALSstqU5vndP697mtVhbLI+e9yh/u+BvZCVkMrlksv8D8JImK6WUx1qWr29PPV5UcVHpRSGOLDrZxEZBUkFAWlY1VrIqSCtwucwPRv6A3XfuYXi+dgOGNRHJFJFXRWSdiKwVkbGhjkmpUGp5Y/Dt58DR+Bid/iPAeib3ZF1ejN+3uy3d8b29llVL4fK3DZNhEsPWo8B/jDGXi0g8oP0c6qSWlZRFVlw6zw0+QHk+/GrCXfTJ7hPqsKJaj6QefJjexNEYSGjy33ZrOugGDEfasnJBRNKB04GnAIwxx4wx+0IblVKhV5LTh/J8x6yxWlQReD2Se2AX+NLLiS/d2ZYGGXFppMS7GewxTGjLyrVioBZ4RkSGAsuBW4wxh5wLiMg0YBpAXl4e8+fPD0WcANTV1YV0/57QGP0j1DGmNWUAcH3RD1j60dJ2lwl1jJ6KhDhzcVRXvHbbNdTGDvbbdlcdmUVm7H6//P5BOY7GGP1q5wsYCTQCo63HjwK/cbX8iBEjTCjNmzcvpPv3hMboH6GO8YNNH5gZ82cYu93ucplQx+ipSIhzztw5humY+8djDP77Gv19MWf/82y/xNiZ4wgsMx6ck7Vl5Vo1UG2M+cR6/CqgfR7qpDep9yQm9Z4U6jBOGsmxyXRPymNd7k6/brcmw8akCLleBXrNyiVjzA6gSkRKrafOAtaEMCSl1EmqNG8g67v4ryrPLrA92d5h2Xq40WTVsR8Dz4nIZ0AZcH+I41FKnYRKc0pZ30X8NqBtbTI02kzEVAKCFlh0yBhTjuPalVJKhUz/3P7si7dTmwJdD7lf3h3nPVbaslJKKeU3pbmOqxH+GtA20u6xAk1WSikV9vw9oK1zXMDWg9iGM01WSikV5npm9CTBFu+3AW1r0kAQ8lLy/LPBINBkpZRSYS7GFkO/7D7+a1mlQ15iLnExcf7ZYBBoslJKqQhQ2nWg3wa0rUmD7hmR0wUImqyUUioilOaUsjm9iWN+yFfbMm0UZPTo/IaCSJOVUkpFgP65/WmyOeYS66yaNImoSkDQZKWUUhGhuSKwk0UWR2NgV2JTRN1jBZqslFIqIvjrXqvtEXiPFWiyUkqpiJCekE63xC6drgiMxHusQJOVUkpFjP5+GNA2EkevAE1WSikVMUpz+7OukwPaRuK4gKDJSimlIkZpTil7E+zsSvZ9GzVpkGCLJzsp23+BBYEmK6WUihDOIovOVARuS4PuyXmI+G9+rGDQZKWUUhGif25/oHMD2takQ/fMyLohGDRZKaVUxCjKKOr0gLbbMmMoyCj0X1BBoslKKaUiRIwthj6ZxT63rAxQk2ronhpZlYCgyUoppSJKaZ7vA9oeSIBDsfaIu8cKNFkppVRE6Z/Tn00Zdhp8OHtH6j1WoMlKKaUiSmluKY02wyYfBrSN1HusQJOVUkpFlM4MaKstK6WUUkHRmQFtt2myUkopFQyZiZnkJeb6VBFYkwYZcWmkxKf4P7AA02SllFIRprTrAJ8GtN2WDgURWLYOmqyUUirilOb2Z30X70/fNelC96zIG70CNFkppVTEKc0pZVdiE7uTvFtvW6YtIisBQZOVUkpFnOYxAr0osrALbE9qisjiCtBkpZRSEad59HUviiy+SoEmW2TeYwWarJRSKuL0yuxFnMR61bKK5HusQJOVUkpFnFhbLH0yi72618p5j1UkjgsImqyUUioi9c87hfVdPR/QNtJbVrGhDiCcicgW4CDQBDQaY0aGNiKllHIozSnl7Uw7jTaItbtffls6CEK31G6BDy4ANFm5N8kYsyvUQSilVEuluaU02AybM6HvHvfL16RBXmIusbbIPO1rN6BSSkUgbwe03ZZGRM4Q7CTGmFDHELZEZDOwF8cEm383xjzR6vVpwDSAvLy8ES+++GLwg7TU1dWRmpoasv17QmP0D43RfyIhTlcxHmg4wMUfX8wN8V9navxEt9v53uE/0C2rF/cNui9oMXpi0qRJyz26xGKM0S8XX0B363tXYBVwuqtlR4wYYUJp3rx5Id2/JzRG/9AY/ScS4uwoxq4P5JhrpmAM7r9yfh5jbph9Q9BjdAdYZjw4H2s3YAeMMTXW96+A14FTQxuRUkodN7HkLN4ttdHkZkzbozGwOyFyR68AvWblkoikiEia82fgHOCL0EallFLHTSmdwldJdj5xcykq0svWQZNVR/KARSKyClgKzDHG/CfEMSmlVLPz+55PnMTyev+Ol6uJ8BuCQZOVS8aYTcaYodbXKcYY/1+VVEqpTiRHJUQAAAjuSURBVMhIzODM3mfy+iAbHZXKbUt3fNeWlVJKqZCYMuASNmbYWdPF9TLNLasIHcQWNFkppVREu7j0YgBeH+B6mW1pkGCLJzspO0hR+Z8mK6WUimD5afmM6X4qbwx0fTqvSYPuyXmIuCkbDGOarJRSKsJNGXApy7vZqUpv//Vt6dA9MzKns3fSZKWUUhHukgGXAPCmi6rAmsyYiB5qCTRZKaVUxOuX048B2aW8PqBtN58BtqUauqdGbiUgaLJSSqmoMGXgpSzoBXuSTnz+QAIcjrVH9D1WoMlKKaWiwiX9L6FJDHP6nvh8NNxjBZqslFIqKozoPoKC5G5tStij4R4r0GSllFJRwSY2Lh54Kf/pZ+Nw3PHnt0XBuICgyUoppaLGlP5TOBJj573i489FwyC2oMlKKaWixsReE8mISzthYNtt6ZARl0ZKfEroAvMDTVZKKRUl4mLi+Hr/i5g9wEajdXavSYOCCC9bB01WSikVVab0n8LuRDsfWQNWbMsQumdF9ugVoMlKKaWiyrl9ziXBFs8bVldgTYYt4isBQZOVUkpFldT4VCaXnMPrgxzT3W9Piuzp7J00WSmlVJSZ0n8KlWl25pZAky3y77ECTVZKKRV1Liq9CBs2HhvleKwtK6WUUmGnS0oXxvU4jbf7OR5H+riAoMlKKaWi0pQBl2CsQdi1ZaWUUiosTek/BQBB6JbaLcTRdJ4mK6WUikLFWcUMyR1EXlIusbbYUIfTaZH/GyillGrX7899mKr9VaEOwy80WSmlVJQ6p+ScUIfgN9oNqJRSKuxpslJKKRX2NFkppZQKe5qslFJKhT1NVkoppcKeJiullFJhT5OVUkqpsKfJSimlVNgTY0yoY4gKIlILVIYwhFxgVwj37wmN0T80Rv+JhDijPcYiY0wXdwtpsooSIrLMGDMy1HF0RGP0D43RfyIhTo3RQbsBlVJKhT1NVkoppcKeJqvo8USoA/CAxugfGqP/REKcGiN6zUoppVQE0JaVUkqpsKfJSimlVNjTZBXhRGSLiHwuIuUisizU8TiJyNMi8pWIfNHiuWwRmSsiFdb3rDCMcbqIbLOOZ7mInB/iGHuIyDwRWSsiq0XkFuv5sDmWHcQYNsdSRBJFZKmIrLJinGE931tEPrGO40siEh+GMc4Ukc0tjmNZqGJsEWuMiKwUkbetxwE/jpqsosMkY0xZmN2LMRM4t9VzdwHvG2P6Au9bj0NpJm1jBPijdTzLjDHvBDmm1hqB24wxA4AxwI0iMpDwOpauYoTwOZZHgTONMUOBMuBcERkD/M6KsS+wF/heGMYIcHuL41geuhCb3QKsbfE44MdRk5UKCGPMh8CeVk9fDMyyfp4FTAlqUK24iDGsGGO2G2NWWD8fxHGCKCCMjmUHMYYN41BnPYyzvgxwJvCq9Xyoj6OrGMOKiBQCFwBPWo+FIBxHTVaRzwD/E5HlIjIt1MG4kWeM2Q6OExzQNcTxuHKTiHxmdROGtKuyJRHpBQwDPiFMj2WrGCGMjqXVdVUOfAXMBTYC+4wxjdYi1YQ4ybaO0RjjPI73WcfxjyKSEMIQAR4B7gDs1uMcgnAcNVlFvnHGmOHAeTi6X04PdUAR7nGgBEc3zHbg4dCG4yAiqcBrwK3GmAOhjqc97cQYVsfSGNNkjCkDCoFTgQHtLRbcqFrtvFWMIjII+DnQHxgFZAN3hio+Efk68JUxZnnLp9tZ1O/HUZNVhDPG1FjfvwJex/GfMFztFJF8AOv7VyGOpw1jzE7rhGEH/kEYHE8RicORBJ4zxvzbejqsjmV7MYbjsQQwxuwD5uO4vpYpIrHWS4VATajiaqlFjOda3azGGHMUeIbQHsdxwEUisgV4EUf33yME4ThqsopgIpIiImnOn4FzgC86Xiuk3gKusX6+BngzhLG0y5kALJcQ4uNpXQ94ClhrjPlDi5fC5li6ijGcjqWIdBGRTOvnJOBsHNfW5gGXW4uF+ji2F+O6Fh9KBMe1oJAdR2PMz40xhcaYXsA3gA+MMd8kCMdRR7CIYCJSjKM1BRALPG+MuS+EITUTkReAiTimDtgJ/Bp4A3gZ6AlsBa4wxoSswMFFjBNxdFsZYAvwA+e1oVAQkfHAQuBzjl8j+D8c14TC4lh2EONVhMmxFJEhOC78x+D4kP6yMeYe6//Qizi611YC37JaMOEU4wdAFxzdbeXADS0KMUJGRCYCPzPGfD0Yx1GTlVJKqbCn3YBKKaXCniYrpZRSYU+TlVJKqbCnyUoppVTY02SllFIq7GmyUiqMiWNU/Vzr56CWK4vIfPn/9u4eNIooiuL4/2ATCaQQWyGNWGgTZBVtxBRWgkUEUfzCUrSxUizFQrTVRrAQRNBGWBCSQmMTI6KF2lgZK4sUuog2aq7Fu8TZIJtssjBjPL9qMtk3+zawuZkNnCM1KRzZ/mMeVmY1UrHu3oeVNAOzgVh3bxKzppM0mt1Pt4DXwBZJR1V6yd5JuraKa93ODqSpTD/oujOStDkjcpB0WtIjSe3sSTon6UL2E81K2lR5iuOSZnJfu3L9cAbTvsw1hyrXfSipDUwN5IdlljyszOqxDbgbEWPAD0of0Dgl8aElqZ+Kha3AzYjYDnwBJlawZgdwjJIzdxX4nnt5DpysPG44IvYCZ4E7ee4yJWanBewHrmfcF8Ae4FREjPexf7NleViZ1eNjRMzmcQuYjoj5rFm4B/STnv+hUsj3ChhdwZqnEfE1IuaBDtDO82+XrL8Pi91fI5lddwC4mFUW08AQJfYJSq1FozvC7N/kz5XN6vGtcvy3ioV+VDPYfgEb8/gnf/4gHeqxZqHy9QLdvxeW5rEFZb8TEfG++g1Ju+l+XWYD4zsrs/q9APbl/5U2UAJgnw3gunPAzjw+3ONxvRyBxbDaTkR0gEngfKaAI2lsjfs0W5bvrMxqFhGfJF2i1CwIeBwRg6hYuAE8kHQCeLLKa3yWNAOMAGfy3BVKh9GbHFhzwME17tWsJ6eum5lZ4/ljQDMzazwPKzMzazwPKzMzazwPKzMzazwPKzMzazwPKzMzazwPKzMza7zf8OnD4CgDceQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "roll_and_cgpa = []\n",
+    "for (k, v) in data.items():\n",
+    "    try:\n",
+    "        roll_and_cgpa.append((k,float(v['cgpa'][2])))\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "    \n",
+    "\n",
+    "def sortbycg(l):\n",
+    "   return l[1]\n",
+    "\n",
+    "def sortbyroll(l):\n",
+    "    return int(l[0][7:9])\n",
+    "\n",
+    "\n",
+    "sorted_cgpa = roll_and_cgpa[:]\n",
+    "sorted_cgpa.sort(key=sortbycg)\n",
+    "\n",
+    "\n",
+    "print(\"Top 5 Students:\\n\")\n",
+    "for element in sorted_cgpa[:-6:-1]:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "    \n",
+    "roll_and_cgpa.sort(key=sortbyroll)\n",
+    "\n",
+    "# for individual cgpa \n",
+    "\n",
+    "# print(\"Students with theri cgpa :\\n\")\n",
+    "# for element in roll_and_cgpa:\n",
+    "#     print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "\n",
+    "cgpa = np.array([element[1] for element in roll_and_cgpa], dtype='float')\n",
+    "\n",
+    "roll = np.array([element[0][7:9] for element in roll_and_cgpa],dtype='int')\n",
+    "\n",
+    "\n",
+    "cgpa_average = round(np.mean(cgpa), 2)\n",
+    "cgpa_median = round(np.median(cgpa), 2)\n",
+    "cgpa_highest = round(np.max(cgpa), 2)\n",
+    "cgpa_lowest = round(np.min(cgpa),2)\n",
+    "cgpa_standard_deviation = round(np.std(cgpa),2)\n",
+    "\n",
+    "print(\"\\nCGPA:\")\n",
+    "print(\"Highest: %s\" % cgpa_highest)\n",
+    "print(\"lowest: %s\"% cgpa_lowest)\n",
+    "print(\" Median: %s\" % cgpa_median)\n",
+    "print(\"Average: %s\" % cgpa_average)\n",
+    "print(\"Standard Deviation: %s \\n\"% cgpa_standard_deviation)\n",
+    "\n",
+    "print(\"   9.5+: %s\" % len([cg for cg in cgpa if cg >= 9.5]))\n",
+    "print(\"  9-9.5: %s\" % len([cg for cg in cgpa if cg >= 9 and cg < 9.5]))\n",
+    "print(\"  8.5-9: %s\" % len([cg for cg in cgpa if cg >= 8.5 and cg < 9]))\n",
+    "print(\"  8-8.5: %s\" % len([cg for cg in cgpa if cg >= 8 and cg < 8.5]))\n",
+    "print(\"  7.5-8: %s\" % len([cg for cg in cgpa if cg >= 7.5 and cg < 8]))\n",
+    "print(\"  7-7.5: %s\" % len([cg for cg in cgpa if cg >= 7 and cg < 7.5]))\n",
+    "print(\"     7-: %s\" % len([cg for cg in cgpa if cg < 7]))\n",
+    "\n",
+    "\n",
+    "unit_array = np.ones(len(roll),dtype='int')\n",
+    "fig,cgpa_plot = plt.subplots()\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "label=['above mean','below mean']\n",
+    "cgpa_plot.plot(roll,cgpa,label='above mean cgpa',color='green')\n",
+    "cgpa_plot.plot(roll,cgpa_median*unit_array,label='below mean cgpa',color='red')\n",
+    "cgpa_plot.grid(b = True)\n",
+    "cgpa_plot.legend()\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa >= cgpa_median*unit_array,facecolor = 'green',interpolate = True )\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa< cgpa_median*unit_array,facecolor = 'red',interpolate = True )\n",
+    "plt.xlabel('roll number')\n",
+    "plt.ylabel('cgpa (0-10)', horizontalalignment='right',rotation =0)\n",
+    "plt.title('cgpa distribution wrt roll number')\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\n')\n",
+    "#plt.fill(cgpa,cgpa_median*median,'r')g\n",
+    "sorted_cgpa=cgpa[:]\n",
+    "sorted_cgpa.sort()\n",
+    "fig,histo_cgpa = plt.subplots()\n",
+    "histo_cgpa.hist(sorted_cgpa,bins=40)\n",
+    "#histo_cgpa.fill_between(roll,histo_cgpa,where =sorted_cgpa >= 8.0,facecolor = 'green',interpolate = True )\n",
+    "#histo_cgpa.fill(histo_cgpa, where = sorted_cgpa >= cgpa_median)\n",
+    "plt.title('frequency distribution of class cgpa')\n",
+    "\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' number\\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.show()\n",
+    "#plt.plot(norm.pdf(cgpa_in_seq,cgpa_median,cgpa_standard_deviation))\n",
+    "\n",
+    "print('\\n')\n",
+    "fig = norm.pdf(sorted_cgpa, np.mean(sorted_cgpa), np.std(sorted_cgpa))\n",
+    "plt.plot(sorted_cgpa,fig,'-o')\n",
+    "plt.title('standard distribution of class cgpa')\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' fraction \\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.grid(b = True)\n",
+    "plt.show()\n",
+    "\n",
+    "above_mean = [cg for cg in cgpa if cg >= cgpa_median]\n",
+    "below_mean = [cg for cg in cgpa if cg < cgpa_median]\n",
+    "\n",
+    "print('\\n')\n",
+    "labels = 'above mean','below mean'\n",
+    "sizes = [len(above_mean),len(below_mean)]\n",
+    "#print(len(above_mean),len(below_mean))\n",
+    "explode = (0.1,0)\n",
+    "colors = ['green','red']\n",
+    "# Plot\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
+    "        autopct='%2.1f%%', shadow=True, startangle=140)\n",
+    " \n",
+    "plt.axis('equal')\n",
+    "plt.title('cgpa distribution wrt mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# results plot \n",
+    "this plot shows that students from roll number between 1 to 24 shows a good aveage performance while those at the roll number between 32 to 40 have very low average. these students need to receive better support in coming semester to impove their performance.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "https://analytics4all.org/2016/05/05/python-histograms-and-frequency-distribution/\n",
+    "\n",
+    "https://etav.github.io/python/count_basic_freq_plot.html for histogram frequency distribution\n",
+    "\n",
+    "https://stackoverflow.com/questions/20011494/plot-normal-distribution-with-matplotlib for ploting the standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/year/2016/16CE02.ipynb b/year/2016/16CE02.ipynb
new file mode 100644
index 0000000..00fd702
--- /dev/null
+++ b/year/2016/16CE02.ipynb
@@ -0,0 +1,1316 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# B.Tech 2016-20 Electrical Enginnering (Spring 2018)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime as dt\n",
+    "import json\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16CE02020 6.74\n",
+      "16CE02019 7.00\n",
+      "16CE02018 8.68\n",
+      "16CE02017 7.26\n",
+      "16CE02016 8.28\n",
+      "16CE02015 7.47\n",
+      "16CE02014 7.60\n",
+      "16CE02013 8.60\n",
+      "16CE02011 7.21\n",
+      "16CE02010 7.83\n",
+      "16CE02001 7.91\n",
+      "16CE02002 7.68\n",
+      "16CE02005 8.19\n",
+      "16CE02006 8.15\n",
+      "16CE02007 7.91\n",
+      "16CE02009 8.26\n",
+      "Total Stuents: 16\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('stres.json') as f:\n",
+    "    data = json.load(f)\n",
+    "    # Filter out 16EE data\n",
+    "    data = {k:v for (k,v) in data.items() if '16CE02' in k[:6]}\n",
+    "    \n",
+    "for (k,v) in data.items():\n",
+    "    print(k,v['cgpa'][1])\n",
+    "    \n",
+    "with open('course.json') as c:\n",
+    "    cdata = json.load(c)\n",
+    "    \n",
+    "print (\"Total Stuents: %s\" % len(data))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Date of Birth analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Average:    April 27, 1998\n",
+      "  Median:      May 26, 1998\n",
+      "  Oldest:    March 23, 1996\n",
+      "Youngest: December 12, 1999\n"
+     ]
+    }
+   ],
+   "source": [
+    "dob = [v['dob'] for (k, v) in data.items()]\n",
+    "\n",
+    "np_dob = (np.array(dob, dtype='datetime64[s]').view('i8'))\n",
+    "average_dob = np.mean(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "median_dob  = np.median(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "minimum_dob = np.min(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "maximum_dob = np.max(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "print (\" Average: %s\" % average_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Median: %s\" % median_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Oldest: %s\" % minimum_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"Youngest: %s\" % maximum_dob.strftime(\"%8B %d, %Y\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Branch Change Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no branch changer\n"
+     ]
+    }
+   ],
+   "source": [
+    "original_strengeth = 40\n",
+    "bc_cgpa = [v['cgpa'][1] for (k, v) in data.items() if (int(k[-2:]) > 20 and v['cgpa'][2] !='WH')]\n",
+    "cgpa = [v['cgpa'][2] for (k,v) in data.items() if v['cgpa'][2] != 'WH' ]\n",
+    "\n",
+    "bc_cgpa = (np.array(bc_cgpa, dtype='float'))\n",
+    "cgpa = (np.array(cgpa, dtype='float'))\n",
+    "\n",
+    "if len(bc_cgpa>0):\n",
+    "    bc_count = bc_cgpa.size\n",
+    "    bc_highest = np.max(bc_cgpa)\n",
+    "    bc_lowest = np.min(bc_cgpa)\n",
+    "    bc_average = np.mean(bc_cgpa)\n",
+    "    bc_median = np.median(bc_cgpa)\n",
+    "    bc_std = np.std(bc_cgpa)\n",
+    "    print (\"Total branch changers: %s\" % bc_count)\n",
+    "    print (\"\\nCGPA (after 2nd sem) for branch change:-\")\n",
+    "    print (\"Highest: %s\" % bc_highest)\n",
+    "    print (\" Lowest: %s\" % bc_lowest)\n",
+    "    print (\"Average: %s\" % bc_average)\n",
+    "    print (\" Median: %s\" % bc_median)\n",
+    "    print(\"Standard Deviation: %s \" %bc_std)\n",
+    "else:\n",
+    "    print('no branch changer')\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Course wise analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Subject Code</th>\n",
+       "      <th>Subject Name</th>\n",
+       "      <th>Credits</th>\n",
+       "      <th>Students</th>\n",
+       "      <th>EX</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>P</th>\n",
+       "      <th>F</th>\n",
+       "      <th>WH</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>Average</th>\n",
+       "      <th>Median</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>HS2L002</td>\n",
+       "      <td>Speaking and Presentation</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ME1L001</td>\n",
+       "      <td>Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.50</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MA2L003</td>\n",
+       "      <td>Probability Statistics and Stochastic Processes</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.19</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CE2L006</td>\n",
+       "      <td>Transportation Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.19</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MA1L002</td>\n",
+       "      <td>Mathematics - II</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.25</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>ME2L501</td>\n",
+       "      <td>Elements of Mechanical Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.25</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>EE1L001</td>\n",
+       "      <td>Electrical Technology</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.31</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>PH1L001</td>\n",
+       "      <td>Physics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.31</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>HS1L002</td>\n",
+       "      <td>Learning English</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.36</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>CS1L001</td>\n",
+       "      <td>Introduction to Programing and Data Structures</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.62</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>CE2L004</td>\n",
+       "      <td>Structural Analysis</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.62</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>ID3L003</td>\n",
+       "      <td>Environmental Science, Technology and Management</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.62</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>EC2L007</td>\n",
+       "      <td>Communication Systems</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.67</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>MA1L001</td>\n",
+       "      <td>Mathematics -1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.75</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>CE2L001</td>\n",
+       "      <td>Solid Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.75</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>ID2L001</td>\n",
+       "      <td>Entrepreneurship and Small Business Management</td>\n",
+       "      <td>3</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.78</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>EC2L005</td>\n",
+       "      <td>Analog Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.88</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>ID2L002</td>\n",
+       "      <td>Introduction to Bioscience and Technology</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.12</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>CS1P001</td>\n",
+       "      <td>Introduction to Programing and Data Structures...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.12</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>CE2L005</td>\n",
+       "      <td>Hydraulics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.12</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>CY1L001</td>\n",
+       "      <td>Chemistry</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.19</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>CE2S001</td>\n",
+       "      <td>Project Seminar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.19</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>CE2L002</td>\n",
+       "      <td>Surveying</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.31</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>PH1P001</td>\n",
+       "      <td>Physics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.31</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>CE2L003</td>\n",
+       "      <td>Introduction to Civil Engineering and Construc...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.31</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>HS2L007</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.40</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>HS1L001</td>\n",
+       "      <td>English for Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>CE2P001</td>\n",
+       "      <td>Surveying Practice</td>\n",
+       "      <td>2</td>\n",
+       "      <td>32</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.53</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>ME1P001</td>\n",
+       "      <td>Introduction to Manufacturing Processes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.56</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>CE1P001</td>\n",
+       "      <td>Engineering Drawing and Graphics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.56</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>ID1T002</td>\n",
+       "      <td>Extra Academic Activities - 2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.56</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>EE1P001</td>\n",
+       "      <td>Electrical Technology Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.69</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>CY1P001</td>\n",
+       "      <td>Chemistry Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.75</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>CS2L003</td>\n",
+       "      <td>Data Structure</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.80</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>EC2P005</td>\n",
+       "      <td>Analog Communication Lab</td>\n",
+       "      <td>2</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.88</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>HS2L004</td>\n",
+       "      <td>Odissi Dance - I</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>EE2L004</td>\n",
+       "      <td>Introduction to Electromagnetic Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.25</td>\n",
+       "      <td>9.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>ID1T001</td>\n",
+       "      <td>Extra Academic Activities -1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.62</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Subject Code                                       Subject Name Credits  \\\n",
+       "0       HS2L002                          Speaking and Presentation       4   \n",
+       "1       ME1L001                                          Mechanics       4   \n",
+       "2       MA2L003    Probability Statistics and Stochastic Processes       4   \n",
+       "3       CE2L006                         Transportation Engineering       3   \n",
+       "4       MA1L002                                   Mathematics - II       4   \n",
+       "5       ME2L501                 Elements of Mechanical Engineering       3   \n",
+       "6       EE1L001                              Electrical Technology       4   \n",
+       "7       PH1L001                                            Physics       4   \n",
+       "8       HS1L002                                   Learning English       4   \n",
+       "9       CS1L001     Introduction to Programing and Data Structures       4   \n",
+       "10      CE2L004                                Structural Analysis       4   \n",
+       "11      ID3L003   Environmental Science, Technology and Management       2   \n",
+       "12      EC2L007                              Communication Systems       3   \n",
+       "13      MA1L001                                     Mathematics -1       4   \n",
+       "14      CE2L001                                    Solid Mechanics       4   \n",
+       "15      ID2L001     Entrepreneurship and Small Business Management       3   \n",
+       "16      EC2L005                               Analog Communication       4   \n",
+       "17      ID2L002          Introduction to Bioscience and Technology       2   \n",
+       "18      CS1P001  Introduction to Programing and Data Structures...       2   \n",
+       "19      CE2L005                                         Hydraulics       4   \n",
+       "20      CY1L001                                          Chemistry       4   \n",
+       "21      CE2S001                                    Project Seminar       2   \n",
+       "22      CE2L002                                          Surveying       3   \n",
+       "23      PH1P001                                 Physics Laboratory       2   \n",
+       "24      CE2L003  Introduction to Civil Engineering and Construc...       3   \n",
+       "25      HS2L007                          Introduction to Economics       4   \n",
+       "26      HS1L001                          English for Communication       4   \n",
+       "27      CE2P001                                 Surveying Practice       2   \n",
+       "28      ME1P001            Introduction to Manufacturing Processes       2   \n",
+       "29      CE1P001                   Engineering Drawing and Graphics       3   \n",
+       "30      ID1T002                      Extra Academic Activities - 2       1   \n",
+       "31      EE1P001                   Electrical Technology Laboratory       2   \n",
+       "32      CY1P001                               Chemistry Laboratory       2   \n",
+       "33      CS2L003                                     Data Structure       3   \n",
+       "34      EC2P005                           Analog Communication Lab       2   \n",
+       "35      HS2L004                                   Odissi Dance - I       3   \n",
+       "36      EE2L004        Introduction to Electromagnetic Engineering       3   \n",
+       "37      ID1T001                       Extra Academic Activities -1       1   \n",
+       "\n",
+       "    Students  EX   A   B  C  D  P  F  WH  Other  Average  Median  \n",
+       "0          3   0   0   0  1  1  1  0   0      0     6.00     6.0  \n",
+       "1         16   0   0   3  5  5  3  0   0      0     6.50     6.5  \n",
+       "2         16   0   1   4  9  1  1  0   0      0     7.19     7.0  \n",
+       "3         16   0   3   5  2  4  1  1   0      0     7.19     7.5  \n",
+       "4         16   0   1   5  7  3  0  0   0      0     7.25     7.0  \n",
+       "5          4   0   0   1  3  0  0  0   0      0     7.25     7.0  \n",
+       "6         16   0   0   8  6  1  1  0   0      0     7.31     7.5  \n",
+       "7         16   0   3   2  8  3  0  0   0      0     7.31     7.0  \n",
+       "8         14   0   2   7  1  2  2  0   0      0     7.36     8.0  \n",
+       "9         16   1   4   3  6  0  2  0   0      0     7.62     7.5  \n",
+       "10        16   0   3   5  7  1  0  0   0      0     7.62     7.5  \n",
+       "11        16   0   2   9  2  3  0  0   0      0     7.62     8.0  \n",
+       "12         3   0   0   2  1  0  0  0   0      0     7.67     8.0  \n",
+       "13        16   0   2   8  6  0  0  0   0      0     7.75     8.0  \n",
+       "14        16   0   5   5  3  3  0  0   0      0     7.75     8.0  \n",
+       "15        18   1   3   8  4  1  1  0   0      0     7.78     8.0  \n",
+       "16        16   0   2  11  2  1  0  0   0      0     7.88     8.0  \n",
+       "17        16   0   8   5  1  1  1  0   0      0     8.12     8.5  \n",
+       "18        16   2   5   4  4  0  1  0   0      0     8.12     8.0  \n",
+       "19        16   1   6   6  1  1  1  0   0      0     8.12     8.0  \n",
+       "20        16   1   5   7  2  1  0  0   0      0     8.19     8.0  \n",
+       "21        16   2   6   2  5  1  0  0   0      0     8.19     8.5  \n",
+       "22        16   3   5   5  0  3  0  0   0      0     8.31     8.5  \n",
+       "23        16   0   6   9  1  0  0  0   0      0     8.31     8.0  \n",
+       "24        16   0   9   5  0  2  0  0   0      0     8.31     9.0  \n",
+       "25        10   0   6   2  2  0  0  0   0      0     8.40     9.0  \n",
+       "26         2   0   1   1  0  0  0  0   0      0     8.50     8.5  \n",
+       "27        32   4  16   5  7  0  0  0   0      0     8.53     9.0  \n",
+       "28        16   3   5   6  2  0  0  0   0      0     8.56     8.5  \n",
+       "29        16   1   8   6  1  0  0  0   0      0     8.56     9.0  \n",
+       "30        16   2   6   7  1  0  0  0   0      0     8.56     8.5  \n",
+       "31        16   1  10   4  1  0  0  0   0      0     8.69     9.0  \n",
+       "32        16   1  10   5  0  0  0  0   0      0     8.75     9.0  \n",
+       "33         5   1   3   0  1  0  0  0   0      0     8.80     9.0  \n",
+       "34        16   2  11   2  1  0  0  0   0      0     8.88     9.0  \n",
+       "35         1   0   1   0  0  0  0  0   0      0     9.00     9.0  \n",
+       "36         4   2   1   1  0  0  0  0   0      0     9.25     9.5  \n",
+       "37        16  10   6   0  0  0  0  0   0      0     9.62    10.0  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "courses = dict()\n",
+    "for (k, v) in data.items():\n",
+    "    for (sem, scourses) in v['grades'].items():\n",
+    "        for (course, grade) in scourses.items():\n",
+    "            if course not in courses:\n",
+    "                courses[course] = list()\n",
+    "                courses[course].append(grade)\n",
+    "            else:\n",
+    "                courses[course].append(grade)\n",
+    "\n",
+    "clist = list()\n",
+    "\n",
+    "def other_grade(l):\n",
+    "    return len(l) - l.count('EX') - l.count('A') - l.count('B') - l.count('C') - l.count('D') - l.count('P') - l.count('F') - l.count('WH')\n",
+    "\n",
+    "def analyze_grade(l):\n",
+    "    grade_hash = {'EX': 10, 'A': 9, 'B': 8, 'C': 7, 'D': 6, 'P': 5, 'F': 5}\n",
+    "    hashed_grade = list()\n",
+    "    for grade in l:\n",
+    "        if grade in grade_hash:\n",
+    "            hashed_grade.append(grade_hash[grade])\n",
+    "    hashed_grade = (np.array(hashed_grade, dtype='float'))\n",
+    "    if hashed_grade.size == 0:\n",
+    "        return {'average': 0, 'median': 0}\n",
+    "    return {'average': round(np.mean(hashed_grade), 2), 'median': round(np.median(hashed_grade), 2)}\n",
+    "\n",
+    "grade_labels = 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other'\n",
+    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'orange', 'red', 'gray', 'black']\n",
+    "\n",
+    "for course, grades in courses.items():\n",
+    "    course_info = cdata[course]\n",
+    "    clist.append((course, course_info['subnane'], course_info['credit'], len(grades), grades.count('EX'), grades.count('A'), \n",
+    "                 grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "                 grades.count('F'), grades.count('WH'), other_grade(grades), analyze_grade(grades)['average'], analyze_grade(grades)['median']))\n",
+    "#     patches, texts = plt.pie([grades.count('EX'), grades.count('A'), \n",
+    "#                  grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "#                  grades.count('F'), grades.count('WH'), other_grade(grades)], labels=grade_labels, colors=colors)\n",
+    "#     plt.axis('equal')\n",
+    "#     plt.legend(patches, labels, loc=\"best\")\n",
+    "#     plt.show()\n",
+    "def sortByAverage(element):\n",
+    "    return element[-2]\n",
+    "\n",
+    "clist.sort(key=sortByAverage)\n",
+    "df = pd.DataFrame(data = clist)\n",
+    "df.columns = ['Subject Code', 'Subject Name', 'Credits', 'Students', 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other', 'Average', 'Median']\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CGPA Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 5 Students:\n",
+      "\n",
+      "16CE02013  SHIVAM SETHI  8.67\n",
+      "16CE02018  SANDEEP KUMAR  8.60\n",
+      "16CE02016  ASHISH ANAND  8.28\n",
+      "16CE02009  ADITYA PARPE  8.26\n",
+      "16CE02005  VIPUL JAIN  8.26\n",
+      "\n",
+      "CGPA:\n",
+      "Highest: 8.67\n",
+      "lowest: 6.51\n",
+      " Median: 8.0\n",
+      "Average: 7.86\n",
+      "Standard Deviation: 0.56 \n",
+      "\n",
+      "   9.5+: 0\n",
+      "  9-9.5: 0\n",
+      "  8.5-9: 2\n",
+      "  8-8.5: 7\n",
+      "  7.5-8: 4\n",
+      "  7-7.5: 2\n",
+      "     7-: 1\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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5YRfitvJATlNFIrsBPwMqgZUAl8LYcXCEL9XkttNU4GBUq8MuxBnvQ05TpaozgeuwQO4DcBu88xA8XgU+f0EL1ACjgeOD6+cDewC7A6cCGxp5TBVwDrAbtmrsjcHt5cCBwK7AxHrbnwgsjnfhsdsf8DP5UogHcgqTEhkoJRL1BJBS1YXAtdj6aoMA+ReU/RHu3QDrklNl+ruNbZfivgX4GPgE65j/WyOPeRIb4jITmA7cjf0SHsOCeirwp2DbScAYgnfN1HMFIvuGXYQzHsgpSkqkE/Ay8KaUSNT/5VLVtcCfgbeAwUDeNFh2FfxjOSxKTrXpayE2LdqP693WMfiu2KJ1jfX/CLARG4e4GcgPHpcXXK/A/rmqseExVyag9jjJAe5GJDfsQpwHcsqRiORIoZzHeqYAw7FhSu9JiYyJ9phS1QrgPuCfQD+gYAFsvAQmfA6fJqXwNHUZNgas4T/CeUAvbJzhxY087lSgHdAba0X/AugCnAm8BBwDXAP8HTgbKIh/6fG0B3Bp2EU4D+RUdCT78nM6sFu92/oCU6RETon2oFLV2lLVf2OfwLsAO22C6l/Av6bAZD90+23PAT2wM24auh/r8x0BPN7I/e8BucE2c7GPKF8BnbAW9wdYN8VzwCnABViIT43rK4irEkT6h11EtvNATiESkV0YzlUM2SaM6xQAT0qJ/KapfZSqfghcj33i7gXwJ3jjn/BUtX2CdoG3gVKs8/37wOvAWfXuzwVOB/7VyGMfxVrBeVioH4CFcH3XAr/B+pX3xD7CpPDy0O2B28MuItt5IKcIiUgv2nMlo9ivqc2A66VEHpISibqqdKnq11geLME+UctjMOsWuH9T44MGstKNWB/y11hfz2HAQ8CXwf2KHZArbOSxA7AAV6wv+d0G232BtZ4PATZh/2gCbInza4izkxApCruIbOaBnAIkIvnATxnLQbTarmk1zwJelxLpEW2DUtVVwB+A97FGYKspsPg3MH4FLI1H3ZlI2TqcbTfsHe3q4L7Sepd/ir2z7QrsjfU5715vP7/BPqaAnVo5AdgP62tOcX9FJMW7vDOXnxiSAiQiJzKCyxnNwS186DzgeC3WqAfuikRygCLgZCxfNneEvGvh5CGNN/6cuxLVm8MuIht5IIdMIjKIdtzIdyii1Q4djF8PnKHF+nxTGxXZWNMLgbXAWgF+BYfvZ+cxOFdfOTAY1Y1hF5JtvMsiREFXxfkcwN47GMYAHYBSKZGfN7VRqeo04PdAK6CHAjfAa/+CiTV2sppzdbpjp+W7JPMWcogkIkUUcjljOCROuxwP/FSLoy/9XiTSDRtz2gc7pqWHQf+L4PttUn64rEuilcAgVP0gcBJ5CzkkEpGBtOMMdmfvOO72AuAlKZEu0TYoVV2BDTCYgZ3Zl/s6LLgaxq+2j6rOgU1mf0nYRWQbbyGHIOiq+B1HcQbd2DkBT/EFcIIWa1m0DYrsVNmTgROwU6wrukDrEjh1IAxNQE0u/azC+pJ9XpQk8RZyOI5mZ/ZLUBgDDAPelRI5PNoGpao1wFNYN0cvoOMqqPg5PDrdhtU61wVvJSeVt5CTTCIyACjhRI6hnZ1Jl0DVwMVarHc1tVGRSATrV1aCbosLYM/vwHE5/qad7ZYD/VGtDLuQbOD/bEkkERHg+xTSOwlhDDai4k4pkdulJPpsXqWqZUAJNmVnP4DxMP0ueLgi5U8ucwnWA/he2EVkCw/k5Iog7MIIos7cliAXA89JiXSMtkGp6jJsWNxn2MG+nBdh7nVwz1rrS3TZ66dhF5AtPJCTJGgdn8ou9KYt3UMo4RhgqpTIkGgblNoQp9uweZgHAfmfwMorYPwim9TMZaf9ERkddhHZwAM5eXYhh2FE2CvEGkYC06REDoq2Qamtr/YoNgNlH6D9cthyCTw80xbHcNnpx81v4mLlB/WSQCKSA1zDKMYykqgjH5KoErhQi3VCUxsVieyCHWWvAlYA/BT2PRKO9oVUs84aoDeqfkwhgbyFnBy7k8sghsb1JJBY5AP3S4n8QUok6t9Aqeos7GDfJoIl4e6AaffBo5W2SpHLHp2xufZdAnkgJ5hExOY5H0V/8ol6UC0kvwSelhJpF22DUtXF2EySc7B+5ZxS+PJGuHe9tZpc9jg77AIynQdy4o0hhz4MCbXvuCknAm9JSfTle0rtTK1bgMlYKOdNh/IrYfwSmJ+UKl0qGIdEH6njYueBnEASkTzgdIbRiTzah11PE0ZhC6nuE22DUjsx4AHgEWyscrvFsOlSePAz+DhJdbpw5QHHhl1EJvNATqxdgK4MSYuJ4HsBb0iJnB5tg1JVLVV9CVvTsxPQZQvUXAUT/wOv+eHhrOBLPCWQB3JiHU4HlM4MC7uQ7dQG+KeUyDVSIlFHUZSqfgJch52a3RvgFnjrYXi8ykZkuMx1LCKtwi4iU3kgJ4hEpCuwC7vQH0m7n3Mx8JiUSNT1/UpVF2ALqS4ABgLyJMy+Ge7baKdgu8y0E77KTMKkW1Ckk70BpQ+jwi5kB50OTJYSiTrnRqnqGuBPwDvY6datpsLS/4Px5bbosstM3m2RIB7ICRCcCHIkA2lNG7qFXU8M9gHelxKJ+qZSqloB3As8AQwA2s6DDZfC/V/ArCTV6ZLrhLALyFQeyIkxFOjCMEaGXUgc9MOGxZ0UbYNS1dpS1eeA24FuQOcNUP0LeOoteCNZhbqkGYrIoLCLyEQeyIlxEPnU0pVdwi4kTtphJ5Bc1dRGpaofYCeR5AA9FfgjTH4c/lVtBwBd5og6RNLtOA/kOJOItAf2ZyTdyCU/7HriSICbpEQmSIlEfV2lqnOx062XY10Y8gh8eitM2AS+YGbm2DfsAjKRB3L87QHk0ocRYReSIOcAr0mJRO0bL1VdCdwEfICd2Zf7Jiz6LYxfCcuSU6ZLMG8hJ4AHcvwdSSvW05FBYReSQAdiZ/ZF7SMvVd0M3A2UYsPi2nwJ6y6D++ZC1MVXXdoY4+OR488DOY4kIjsBAxhCZ3LIC7ueBBuMTXh/TLQNgoVUnwbuBHoCndZC5c/h8fdsqJxLXwXArmEXkWk8kONrKAC9g++ZryO2NFTUlYmD062nYstD5QE9akGvh1eegWdroCZZxbq4826LOPNAjq8xwBa6ZE0gA+QCt0mJ3Ckl0T/Clqp+iR3sWw30B7gfZvwdHtpi8y279OMH9uLMAzlOgnmPR9GRypDWzAvbRcCLUiI7RdugVLUcuAGYiXV55L4C84rhntVQnqQ6Xfxk6oHr0Hggx88AIJ8BtrJGljoceFdKJOpkSqWqG4G/AS9gB/tafwarL4d759sk+C59DAq7gEzjgRw/gwGhOwPDLiRkw7FQHhdtg2Ah1ceBe7DZ4jqshIrL4JGP4L0k1eli1wuR1mEXkUk8kONnFLCBTlkfyABdgJekRC6ItkFwsO9NbLxyAdCtGrQYXngenq+F2mQV63aYYJ8MXZx4IMeBRKQVEKGACtrSI+x6UkQe8A8pkVuaWUh1NnawbyPQF+Bu+OAf8EgF+ArHqW9Q2AVkEg/k+OgDtKI33RCiTuyepS4DJkmJdIi2QanqUmwOjDKChVT/DV9dD/esg1XJKdPtoEFhF5BJPJDjYwAgdKJr2IWkqOOAd6REBkXboFR1PXAb8Br2T57/May8Au5ZDF8noUa3YwaFXUAm8UCOj8FAJR08kJuwK3a69dhoG5SqVgEPY4up9gXaL4PNl8BDn8KHSarTtYwfM4kjD+T46AdspoAuYReS4roDr0uJnBVtg+Bg32vYSiQdga6VUPtrmPQKvFwLvpZqaok67ty1nAdyjCQiggXyJtp4C3k7tAYekhK5oZmFVD/F1uyrwPro+StMnQCPVUJlUip126Mg7AIyiQdy7NoCbRGqae0t5Bb4FfCUlEjUf+hS1YXY6tZzCRZSnQhf/AHu3QBrk1Sna5oHchx5IMeuK1BLVzqRQ27YxaSZk7HlofpG26BUdS3wZ2AK1lef9z4s/yWMX2orXrtweSDHkQdy7LoCOXTx7oodNBpbSHWvaBuUqlYC9wOPYRMTFSyEjZfCA7PhkyTV6RrXNuwCMokHcuxs5Qwf8haL3sCbUiLfi7ZBcLDvBeAv2IGkLpuh5pfwzGR43Y/0hcZbyHHkgRy7AcAW2nv/cYzaAo9LifyuqY1KVWdgJ5HUAL0A/gJTHoUnqqEq8WW6BjyQ40hUvW0RC4lIMdCRozmGrkRd0si1yKPA+VqsUU+dLhLZCbgY61eeD+g+0LMI9hoKhQXQPkm1ZrtKVH2CoTjxQI5BMOTtTmAFx3EanYk67aRrsXeBk7RYoy6KWiTSBjgXGAsswobIIcAh0O9AGBGBwk74p5cE2oiqv/nFiQdyDCQiecA/gHkcz9l0ZHDYNWWY+cAJWqxRD9wVieQAR2OnZ7fDThxZSb1VSPaE7uNgxAgo7G791S5+lqLqP9M48UCOgUSkAJtsfT5FnE97+oVdUwbaAJypxTqpqY2KRHKx/vw9sFWx61rFa4B1ddsNg05HQuHuUNgLBubgk0HF6EtU/ZNhnHggx0Ai0hG4BVjASVxIgR1kcnFXC1ylxXrz9mxcJCJYS3hXLJz7Yy3n9VhAK0AvaHssREZBYX/YuRX4svYtNwPV0WEXkSk8kGMgEekK/AFYyMn8zE+dTrj7gIu0WFs0mqJIpBu2/tsB2IomAJuxqT1rADpC3jEwdC8oHAzDW0ObONadyd5C9aCwi8gUHsgxkIj0xJa3X8ipXEY+ncKuKQu8AZyixbpyRx5cJNIBKAT2A3bHVs2uxPqdqwDyIOdIGLQfFA63ERtR53J2vIjqsWEXkSk8kGMgEekHXAMs5HtcSZ6PyUySOdjBvs9i2UmRSFtgGLAXsA+Qj7WYVxKsViLAQdD3QCgshBGd8U9BDTyFatQTelzLeCDHQCIyCPgtsJDT+RW55IdbUVZZC/wf8LQW6/JYd1YkkgcMwdZGPAAbx6xYt8bGuu1GQ7dxULgLjOgGffyIIPej+qOwi8gUHsgxkIgMA64CFnIGV/vyTaGoBd4BngGe0WKdG+sOg6F0A7AujQOxeZyVBiM2doaOR0HhblDYx0ZsZOOZr9ehenXYRWQKD+QYSERGApdjgVzscZwSPmFrOH8c686CERu9gF2wcK5bIWM9sJpgxEZPaHsMDB8NhQNsxEZerM+dJs5F9YGwi8gUHsgxkIjsDlwCLOQ0rqKVH5lPMXOBiVhAv63FWhvrDotEumIjNsZiBwcFOwnlmxEbHSDvaNh5bygcYiM2MnlGtINRnRJ2EZnCAzkGQSBfCizgZC6mjZ+im8LKgVIsnF/VYq2IdYfBiI0INmJjD2zERhWwgnojNg6HgftD4TAobG/LUmWSfqguCruITOGBHINt+pCL+DHtiTrRuksp64EXsXB+Xot1XTPbNyuYV6NuxMbe2DjmbUZsABwIfQ6CwhE2YqNbrM8bsi1AAR4iceOBHINthr0dx5k+uVBaqgT+g4Xzs1qsS2PdYZFIK7aO2BiLtYoV63PeULfd7tD1MCjcFUZ0h75peAhiNqojwi4ik3ggx0Ai0gX4I7CQIzmJ7uwRdk0uJrXANLYeFPwy1h0GIzb6A7thBwV7YuG8FhuxoQCDoMPRENkdRvSBQbnpMWLj36h+J+wiMokHcgwkIm2BO4D5HMrR9GG/sGtycfUpwUFBLdYPY91ZMGKjJzZi4wD4ZnbADVjruRagG7Q5FoaNgREDYGhe6o7YuB3VS8MuIpN4IMcgmA/5HmAhYzmQQRwWdk0uYeZh4TwRmKLFWhPrDoNJ9guxcB6BtYrr5tioBiiAVsfAkL1hxM4QaZNaIzbORvWhsIvIJB7IMZKI3AZsZC92Zzj+8S07rAAmYV0brzS1ssn2KhJpj43Y2Adb+LUVNlJjJdbPTSuQuhEbw23ERthzpwxDY+/WcVt5IMdIInI90JpdGcju+Dn92Wcj247YWBPrDotEWmMjNvYE9sVGbNRi4by5brux0Ptgm2OjsAv0iPV5W0KhXFST+pzZwAM5RhKRXwK92ZnO7Mu5YdfjQlUFTGbriI3Fse4wGLExmK0T7zc6YmMX6HKEzbFR2BP6J2HExiRUixL/NNnFAzlGEpGLgF3pzBaO44qw63EpQ4EtO1igAAASKklEQVT32Dpi4/NYdxiM2OjHtiM2wEZrrA2ekwHQ/phgxEZfG7GRG+tzN+LXqN6YgP1mNQ/kGElETgOOABb76dOuCZ+xNZw/iHVnwYiNHsBILJwHY6dxbzNioyu0PgaG7QkjBtqIjXjNSDgO1clx2pcLeCDHSCIyFvgxMJ8TOI8ODAi7JpfyFgDPYgH9phZrdaw7LBLpzNYRGyOxERtbsH7naoC2kHs0DNkHRgy1ERs7NH+3Qo1AJ1Q3Nr+1awkP5Bhtc/r04RxPT/YMuyaXVlYBz2Hh/JIW6+Zmtm9WkUg7bKmqfYEx2IiNamx0SCVADshh0P8AGDEcCjtA5xY8xXRU94q1TvdtHsgxkoh0whY6nc/e7M0wjgu7Jpe2NgEvYWOdJ2mxro51h8GIjZ2xERv7YeOYa7A3gm/Cf1/oeQiMGGkjNno2urOtfA7kBPFAjlFwcsgdwCoG05v98dUTXDxUY+sHTgQmarEujHWHRSK5wCC2ropS1ypejU24BMAI2OkIm2OjsCf0z+FbM33vg+r7sdbjvs0DOQ4kIlcBvclnA6fwKyQt5iFw6UOBD9h6GndMawnCNwcF+wG7YgcFewd3bTNiox+0OwYie0BhXxiSA+U50MdneEsMD+Q4kIicBBwPLOQkLqKg2Y98zsWijGDEBvC+Fsf2TxyEc3fsYOABWBeHYCe9rMJGbHTcCbY8AE+jsY8ScY3zQI4Dicgo4GJgAYdzAj0ZE3ZNLmssYuuIjclxGrHRCRuxMRabCCkHaAfcV6r6Uqz7d9F5IMeBRKQ7cBOwgDGMoZATwq7JZaXVwPNYOL+oxbop1h0WiRRgIzZGAi+Vqq6MdZ8uOg/kOJCI5GAH9lbSjfYchU9J6MK2GXgFC+dJWuxBmg48kONEInI5dgR7Fd/lJ7RN7mQvzjWhBpiChfNELdb5IdfjovBAjhOJyEHAecB8DmQcAzg47Jqci2I6W0dszAq7GLeVB3KcBP3IfwDmM4A+HMgFYdfk3Hb4giCcgXdjHbHhYuOBHCfBCSK/xyZv2cCpXE4+HUIuy7mWWIKN2JgIvK7FWhVyPVnHT2CIEy1TBd4BdgJgNTFPt+hckvUGLsIm3C+XEnlESmSHJiByO8YDOb4+/ebSIspCrMO5WHUCxsRj6Jzbfh7I8bUAmyCmNXOYSw3+kc+lsyfDLiDbeCDHkZZpDTAN6EoV1axjTtg1OReDJ8IuINt4IMffR9j8s7CE2eGW4twOqmW2FuunzW/o4skDOf7mYJOx5PJfZlFNzBOOO5d0OTwSdgnZyAM5zrRMtwAfAl2ppJqlfBh2Tc61iFIJ3B12GdnIAzkxXsdWZoBPeQ/FB3u79FHDP7VYy8MuIxt5ICfG58AyoAOrWMdq70t2aaQVfwq7hGzlgZwAWqa1wCSgCwBfMC3UgpzbXtVM8YN54fFATpwPgQognznMYxNLwy7IuWblcmPYJWQzD+QE0TKtm4/WlnOa561kl+Kq+QrhxbDLyGYeyIn1JvYzFmYykyr8NFSXunL4o8/2Fi4P5ATSMi3Hui56UE0NS5kedk3ONaqWteTwYNhlZDsP5MR7GWgDwAzepYaKcMtxrhHKXVqsfhJTyDyQE+8LbJ7ZjqxnE/OYEnZBzm2jhnXkclPYZTgP5IQLhsA9Rd0QuA94lwpWh1qUc/VVcK0W65qwy3B1k+C4RJuBtZT7UE05ZbzC7pwWdlFNugVoDQj2tn1hcPs04L3gtmHAUQ0etxZbDGhD8Ng9gf2C+17Bfgq9gJOD2z7G1kfeDxeGCuZRwC1hl+GMB3ISaJnWSkQeBYqBlXzKZwxmHh0YGHZtTToHaFfv+lxgNvAT7C9nQyOPycFCug82CvtuYAjQEZst+n+Bf2HnMXbB3qrOSkz5bjtUcpHeoLVhl+GMd1kkiZbpXGwp9t4AzOCltJvj4n3gQLa+jbdvZJsOWBiDtbC7A+ux1nINoEAV9pf3NrAvkJu4kl0TNvEfvVl93HEK8UBOronB93wWsIRyPg61mqYI8BDWwv0guG0lMB8YD9wPLGpmH6uxw5l9sXAeAdyFrTrYBlgMFMa7cLddaqkGzg+7DLctX3U6ySQix2M9qPPpTHuO5mJyyQ+7rm9Zh3UzbMCC+VjgeWBwcHkRdqjyUiy8G6oAJgAHASMbuf9ZYB8slOdg5zMeEs8X4Jq0gTv0T/qzsMtw2/IWcvK9in2Ib8caNrCAt8MuqFEdg+/tsVbsouC2EVgA9wu+N3buYQ22+M9uNB7GS4LvXbGDeqcBy7EWuEu8KlbTil+GXYb7Ng/kJAsmsH8E6AHAe7zNZpaHWlRDlfDN6SuVWAu2BxbMc4PbV2DB23CReMVav92AsVH2/zowjq19ymDh7kvCJscWrtQbfTXpVORdFiGQiOQAv8MOea2gLz05iAvISZHDW6uAx4PLtVhL92CgGgvbpdiBuKOwERTrgFJstMQ8rH+5B1u7Mg4HhgeXP8NGWBwaXH+JrV0WpyTo9bit1jNZb9ZxYZfhGueBHBKJyEDgGqwXtYr9GMsQjgy3KpfRtrCGRQzVB9U7h1KUd1mERMt0HvAk1hsL05jKeuaFWpTLXLXUspDzPIxTmwdyuF7GTrXoiaK8zTO+SrVLiKXcow/pxOY3dGHyQA6Rlmk1cC/WI9uWVaxlJhPT7HQRl+rWMIvJ/DTsMlzzPJBDpmW6HBux2xvI4TM+ZzHvhFuVyxgVbGABJwRv/i7FeSCnhqnAZOr6k6fwGhtYEGZBLgPUoiziJ/qUzm1+Y5cKPJBTgJapAo9hp0x0p5ZapvAklawPuTSXzpbxmD6gD4ddhtt+HsgpIlgU9e9APtCW1aznbR6imi0hl+bS0Upm8iHnhl2GaxkP5BSiZboIuAfrT85jCeW8z6PBRDDObZ81LOYDjtX31M99TDMeyClGy3Qadmp1fyCXuSzgY55E8TlrXfM2sJoPOEXf0ebm4nMpyAM5Nb0MPAcMAITP+JzZTAq5JpfqtrCRD/mRTtF3wy7F7RgP5BQUHOR7CngTglVFPmIGc3ktzLpcCqtgM9O5nIU8G3Ypbsd5IKeoYHHUB7FFjgYAMJW3WIy3fty2qqjgA65lHuODN3OXpjyQU5iWaRW2ZsdX1C2MNJmXWMHMMOtyKaSKSt7nZuZxs4dx+vNATnHBcLjbsRmIbQ7lV5nIcj4Ksy6XAqqoZDp/5WtK/Ey8zODTb6YJiUg34DdAHhbOcACHMPCbmYVdNtnCet7lDhZzjZZpRfMPcOnAAzmNSER6Ab/A1na2hZBGM4pCTkD8007WWEc5U7iLtdwQrEDjMoQHcpqRiHQGLsFGX9h8F8PZmdGclpKLpbr4Ws58pnAzFfzDW8aZxwM5DUlE2gL/A4zBFk2qpR+92J8fkEf7cKtzCfM1/2UqV6NM1DKtCbscF38eyGlKItIKOBM4ApgPVNOFThzCD2hL93Crc3GlKP9lGh9zFTDFR1NkLg/kNCYREeA44HRsbb4tFNCGwzidjgwKtTgXHzVUMZ3X+JJfaJnOCrscl1geyBlAIjIWuABYCWwgB2E/DmQAh5LjB/vS1iZWM43nWcKvtUx9fuws4IGcISQiI7GDfbXAcgAG0Ie9OJk2dA2zNtdCCixgFtOYSBV/0TJdFXZJLjk8kDNIMCzuf4AhwEKgmnzyOJCj6MVe4VbntksF6/iAd5nHJOA+LdNNYZfkkscDOcNIRPKwfuWTgLXAGgAKGcZunEge7UIszzVlKWW8zTtUcA8w1Q/eZR8P5AwlERkKXAh0ARYBtXSkHQdQxE4MD7c6t40qNjGDaXzBi8D9WqblYZfkwuGBnMEkIgXYCIxxwDJgIwBjGMMwjiKX1iGW5wBWMpe3mMpG7gMm+/ji7OaBnOGCoXGjsFEYudSdct2RduzNOHowBkFCLDE7bWEVs/iEMt4AxgfLd7ks54GcJSQiOwHnAXsA5cAGAPrSk9Ec4+OWk6SKDXzBB3zCfGp5CngxmGbVOQ/kbCIRyQFGA2cBnbHWciUAIykkwmF+ll+C1FDB13zAh8ylis+Bh7VM54ZdlkstHshZSCLSBjgM+C426nUpUIsg7M5uDGUcrekcapGZopYaFvMh7/Mlm1kAPAZ8EqwI49w2PJCzmESkC3AicBBQhQWzkksOo9mTgYz1YN5BirKcmXzAbNayFHgceM8nkndN8UB2SET6YK3lvYHN2Jl+iiBEGMoQ9qYTQ/3g33aoZgvL+JiZzGcVq4CngTd93mK3PTyQmyAiG1Q1a6azlIgMAk4BdsP6lpcD1qLrRmdGshc9GU0eBWHVmLI2s5x5TGcmy6miGngBeFnLdH3Ypbn04YHchGwLZPhmmFx/4BDgYKAVsAqwYGlFLrswkoHsTXv6h1ZoKqihghXM4gs+Zz5bgArgFWw88cqQq3NpyAMZEJGJWAi1AW5T1X8Et2/AVn0eB6wGvq+q5SIyCrgLKADmAD8CegEPqOo+wWMHAaWquruI7An8BWiPrYd3rqouaVDDBKy7oBBbDeQ84Bxgf2Caqp4bbHcUUAK0Dp77PFXdICJXAycAbYF3gAtVVUVkMjAteA2dgfNVdcp2/Vwi0g7YEzsVuycWOMuxCYxsyNxwxtCVCPl02p59pj1FWc88FvAx/2UlVeRiZ0I+D3wULErr3A7xQAZEpIuqrhKRtsD7wCGqulJEFDhLVR8JAq+Hqv5MRD4BLlbVN0TkWqCjql4mIjOAk1X1KxG5CluQ9A/AG8CJQZifDhytqj9qUMME7A3hDKAIeAg4AJgV1HQ+NmHQ08CxqroxeI7Wqnpt3WsI9vUQ8ISqTgoCebqqXiEixwGXq+oRLfr52HC5oVio7wMINpZ5a/j0pjsDGUY3htGeARk17Wcl61nDlyxjDnNYwSZaAzXAu8CbwJc+74SLh1ZhF5AiLhGR7waX+wPDsLmFa7Gj4wAPA0+LSCegs6q+Edz+APBkcPkJ4DTgJuyU5dOBCLAr8IqIQP2z5b5tUtCqnQksU9WZACIyCxgE9ANGAm8H+8oHpgaPHSciv8Ra7V2wIJ8U3Pd08H16sJ8WCYZofQ58LhF5AtgXOAa+GbO8liWsYAnlwDu0pTVDGEJvhtGZoeTToaXPGapaqlnPfFbwJfP5iiVUYZ9uBPvdvYINXdsYap0u42R9IIvIodgySPur6qagRdkmyubNtYIeB54UkacBVdUvRGQ3YJaq7r8d5dQtWllb73Ld9VZYq+wVVT2jwWtoA/wd2EtVF4jINQ1eQ92+aojxd65luhp4USLyMtAXe8PZD5vyE6CSzaxiFp8xi88A6Ecv+jGEDvSkHT1oQzdyUuhvr5pNbKac9SxhCXP4inKq6IAFcB7wFfAeUAYs89awS5TU+acITydgdRDGhVi41MkBTgX+ia1f95aqrhWR1SJyUNAX+0OsSwJVnSMiNcDv2NqyLgO6i8j+qjpVRPKA4ao7tBzPu8AdIjJUVb8UkQKs1bw8uH+FiLQPan5qB/a/3YJW84Lg61WJSAesW2M01u9c94awjoWUs5Cl3zxYEHrQhW70oDM9aE8PCuhBG7ogCezqqGQ9mylnE+Wso5w1rGA55aynEtgJ+3Sh2AiTF4D/AnN9yJpLFg9keBG4KOgXLsNCr85GYBcRmY7NLXx6cPs5wF1BIH6FHYCr8zjwJ2AwgKpWisipwO1Bd0cr4FasS6FFgj7oc4HHRKRuprbfqurnIjIemAl8jfU5J1UwvOsj4COJyAPYG0Uh1ufcH3vdCuSgbGYZG1nGbAha0WAjOLrThfa0pw0FtKGA/OArl/zgK48c8sglH6WGGiqopvKb79VUUEMlVVRSRQVVVLKR9SxnBZupwUK3AL7pB+6KnRTzCdalMwdY6a1gFwY/qOcSLjgo2AXogY3WGIK9YfXGQlqCryqsdVpV76uGpruKBOuXz8VCP7feVz5b3wgk2PdC7E1rHnZgshxY7acyu1TggexCIxFpBXTDgroX1lrtjHUjdQy+t8G6jrTeV90Zg3W3bwa2BN831fu+BpiPDTUsB9Z6y9elMg9kl9KCE1VysNZuXvC9FmvtVgDVHrIuU3ggO+dcisicwfvOOZfmPJCdcy5FeCA751yK8EB2zrkU4YHsnHMpwgPZOedShAeyc86lCA9k55xLER7IzjmXIjyQnXMuRXggO+dcivBAds65FOGB7JxzKcID2TnnUoQHsnPOpQgPZOecSxEeyM45lyL+H0Zwz/KrGQGWAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "roll_and_cgpa = []\n",
+    "for (k, v) in data.items():\n",
+    "    try:\n",
+    "        roll_and_cgpa.append((k,float(v['cgpa'][2])))\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "    \n",
+    "\n",
+    "def sortbycg(l):\n",
+    "   return l[1]\n",
+    "\n",
+    "def sortbyroll(l):\n",
+    "    return int(l[0][7:9])\n",
+    "\n",
+    "\n",
+    "sorted_cgpa = roll_and_cgpa[:]\n",
+    "sorted_cgpa.sort(key=sortbycg)\n",
+    "\n",
+    "\n",
+    "print(\"Top 5 Students:\\n\")\n",
+    "for element in sorted_cgpa[:-6:-1]:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "    \n",
+    "roll_and_cgpa.sort(key=sortbyroll)\n",
+    "\n",
+    "# for individual cgpa \n",
+    "\n",
+    "# print(\"Students with theri cgpa :\\n\")\n",
+    "# for element in roll_and_cgpa:\n",
+    "#     print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "\n",
+    "cgpa = np.array([element[1] for element in roll_and_cgpa], dtype='float')\n",
+    "\n",
+    "roll = np.array([element[0][7:9] for element in roll_and_cgpa],dtype='int')\n",
+    "\n",
+    "\n",
+    "cgpa_average = round(np.mean(cgpa), 2)\n",
+    "cgpa_median = round(np.median(cgpa), 2)\n",
+    "cgpa_highest = round(np.max(cgpa), 2)\n",
+    "cgpa_lowest = round(np.min(cgpa),2)\n",
+    "cgpa_standard_deviation = round(np.std(cgpa),2)\n",
+    "\n",
+    "print(\"\\nCGPA:\")\n",
+    "print(\"Highest: %s\" % cgpa_highest)\n",
+    "print(\"lowest: %s\"% cgpa_lowest)\n",
+    "print(\" Median: %s\" % cgpa_median)\n",
+    "print(\"Average: %s\" % cgpa_average)\n",
+    "print(\"Standard Deviation: %s \\n\"% cgpa_standard_deviation)\n",
+    "\n",
+    "print(\"   9.5+: %s\" % len([cg for cg in cgpa if cg >= 9.5]))\n",
+    "print(\"  9-9.5: %s\" % len([cg for cg in cgpa if cg >= 9 and cg < 9.5]))\n",
+    "print(\"  8.5-9: %s\" % len([cg for cg in cgpa if cg >= 8.5 and cg < 9]))\n",
+    "print(\"  8-8.5: %s\" % len([cg for cg in cgpa if cg >= 8 and cg < 8.5]))\n",
+    "print(\"  7.5-8: %s\" % len([cg for cg in cgpa if cg >= 7.5 and cg < 8]))\n",
+    "print(\"  7-7.5: %s\" % len([cg for cg in cgpa if cg >= 7 and cg < 7.5]))\n",
+    "print(\"     7-: %s\" % len([cg for cg in cgpa if cg < 7]))\n",
+    "\n",
+    "\n",
+    "unit_array = np.ones(len(roll),dtype='int')\n",
+    "fig,cgpa_plot = plt.subplots()\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "label=['above mean','below mean']\n",
+    "cgpa_plot.plot(roll,cgpa,label='above mean cgpa',color='green')\n",
+    "cgpa_plot.plot(roll,cgpa_median*unit_array,label='below mean cgpa',color='red')\n",
+    "cgpa_plot.grid(b = True)\n",
+    "cgpa_plot.legend()\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa >= cgpa_median*unit_array,facecolor = 'green',interpolate = True )\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa< cgpa_median*unit_array,facecolor = 'red',interpolate = True )\n",
+    "plt.xlabel('roll number')\n",
+    "plt.ylabel('cgpa (0-10)', horizontalalignment='right',rotation =0)\n",
+    "plt.title('cgpa distribution wrt roll number')\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\n')\n",
+    "#plt.fill(cgpa,cgpa_median*median,'r')g\n",
+    "sorted_cgpa=cgpa[:]\n",
+    "sorted_cgpa.sort()\n",
+    "fig,histo_cgpa = plt.subplots()\n",
+    "histo_cgpa.hist(sorted_cgpa,bins=40)\n",
+    "#histo_cgpa.fill_between(roll,histo_cgpa,where =sorted_cgpa >= 8.0,facecolor = 'green',interpolate = True )\n",
+    "#histo_cgpa.fill(histo_cgpa, where = sorted_cgpa >= cgpa_median)\n",
+    "plt.title('frequency distribution of class cgpa')\n",
+    "\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' number\\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.show()\n",
+    "#plt.plot(norm.pdf(cgpa_in_seq,cgpa_median,cgpa_standard_deviation))\n",
+    "\n",
+    "print('\\n')\n",
+    "fig = norm.pdf(sorted_cgpa, np.mean(sorted_cgpa), np.std(sorted_cgpa))\n",
+    "plt.plot(sorted_cgpa,fig,'-o')\n",
+    "plt.title('standard distribution of class cgpa')\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' fraction \\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.grid(b = True)\n",
+    "plt.show()\n",
+    "\n",
+    "above_mean = [cg for cg in cgpa if cg >= cgpa_median]\n",
+    "below_mean = [cg for cg in cgpa if cg < cgpa_median]\n",
+    "\n",
+    "print('\\n')\n",
+    "labels = 'above mean','below mean'\n",
+    "sizes = [len(above_mean),len(below_mean)]\n",
+    "#print(len(above_mean),len(below_mean))\n",
+    "explode = (0.1,0)\n",
+    "colors = ['green','red']\n",
+    "# Plot\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
+    "        autopct='%2.1f%%', shadow=True, startangle=140)\n",
+    " \n",
+    "plt.axis('equal')\n",
+    "plt.title('cgpa distribution wrt mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# results plot \n",
+    "this class has very less deviation compared to other department batches.However, the average grades are lesser than other department batches. An overall performance improvement is required.\n",
+    "\n",
+    "\n",
+    "https://analytics4all.org/2016/05/05/python-histograms-and-frequency-distribution/\n",
+    "\n",
+    "https://etav.github.io/python/count_basic_freq_plot.html for histogram frequency distribution\n",
+    "\n",
+    "https://stackoverflow.com/questions/20011494/plot-normal-distribution-with-matplotlib for ploting the standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/year/2016/16CS.ipynb b/year/2016/16CS.ipynb
new file mode 100644
index 0000000..f6c5077
--- /dev/null
+++ b/year/2016/16CS.ipynb
@@ -0,0 +1,1390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# B.Tech 2016-20 Electrical Enginnering (Spring 2018)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime as dt\n",
+    "import json\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16CS01044 9.60\n",
+      "16CS01038 6.45\n",
+      "16CS01036 4.65\n",
+      "16CS01037 6.91\n",
+      "16CS01034 7.81\n",
+      "16CS01035 6.28\n",
+      "16CS01032 7.40\n",
+      "16CS01033 5.79\n",
+      "16CS01030 6.32\n",
+      "16CS01031 7.91\n",
+      "16CS01003 8.40\n",
+      "16CS01002 8.40\n",
+      "16CS01001 7.91\n",
+      "16CS01007 9.40\n",
+      "16CS01006 8.00\n",
+      "16CS01005 8.66\n",
+      "16CS01004 8.66\n",
+      "16CS01009 6.25\n",
+      "16CS01008 7.28\n",
+      "16CS01014 9.55\n",
+      "16CS01015 8.40\n",
+      "16CS01016 7.60\n",
+      "16CS01017 9.70\n",
+      "16CS01010 9.13\n",
+      "16CS01011 8.00\n",
+      "16CS01012 8.36\n",
+      "16CS01013 8.43\n",
+      "16CS01018 8.26\n",
+      "16CS01019 8.40\n",
+      "16CS01024 8.09\n",
+      "16CS01040 6.38\n",
+      "16CS01045 9.47\n",
+      "16CS01043 9.60\n",
+      "16CS01042 9.66\n",
+      "16CS01041 9.72\n",
+      "16CS01029 9.34\n",
+      "16CS01028 8.43\n",
+      "16CS01025 8.11\n",
+      "16CS01027 6.98\n",
+      "16CS01026 7.34\n",
+      "16CS01021 6.70\n",
+      "16CS01020 6.68\n",
+      "16CS01023 7.21\n",
+      "16CS01022 5.13\n",
+      "Total Stuents: 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('stres.json') as f:\n",
+    "    data = json.load(f)\n",
+    "    # Filter out 16EE data\n",
+    "    data = {k:v for (k,v) in data.items() if '16CS' in k[:4]}\n",
+    "    \n",
+    "for (k,v) in data.items():\n",
+    "    print(k,v['cgpa'][1])\n",
+    "    \n",
+    "with open('course.json') as c:\n",
+    "    cdata = json.load(c)\n",
+    "    \n",
+    "print (\"Total Stuents: %s\" % len(data))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Date of Birth analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Average:  October 11, 1998\n",
+      "  Median: December 27, 1998\n",
+      "  Oldest:    March 14, 1997\n",
+      "Youngest:     June 01, 2000\n"
+     ]
+    }
+   ],
+   "source": [
+    "dob = [v['dob'] for (k, v) in data.items()]\n",
+    "\n",
+    "np_dob = (np.array(dob, dtype='datetime64[s]').view('i8'))\n",
+    "average_dob = np.mean(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "median_dob  = np.median(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "minimum_dob = np.min(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "maximum_dob = np.max(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "print (\" Average: %s\" % average_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Median: %s\" % median_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Oldest: %s\" % minimum_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"Youngest: %s\" % maximum_dob.strftime(\"%8B %d, %Y\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Branch Change Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total branch changers: 5\n",
+      "\n",
+      "CGPA (after 2nd sem) for branch change:-\n",
+      "Highest: 9.72\n",
+      " Lowest: 9.47\n",
+      "Average: 9.61\n",
+      " Median: 9.6\n",
+      "Standard Deviation: 0.08294576541331088 \n"
+     ]
+    }
+   ],
+   "source": [
+    "original_strengeth = 40\n",
+    "bc_cgpa = [v['cgpa'][1] for (k, v) in data.items() if (int(k[-2:]) > 40 and v['cgpa'][2] !='WH')]\n",
+    "cgpa = [v['cgpa'][2] for (k,v) in data.items() if v['cgpa'][2] != 'WH' ]\n",
+    "\n",
+    "bc_cgpa = (np.array(bc_cgpa, dtype='float'))\n",
+    "cgpa = (np.array(cgpa, dtype='float'))\n",
+    "\n",
+    "if len(bc_cgpa>0):\n",
+    "    bc_count = bc_cgpa.size\n",
+    "    bc_highest = np.max(bc_cgpa)\n",
+    "    bc_lowest = np.min(bc_cgpa)\n",
+    "    bc_average = np.mean(bc_cgpa)\n",
+    "    bc_median = np.median(bc_cgpa)\n",
+    "    bc_std = np.std(bc_cgpa)\n",
+    "    print (\"Total branch changers: %s\" % bc_count)\n",
+    "    print (\"\\nCGPA (after 2nd sem) for branch change:-\")\n",
+    "    print (\"Highest: %s\" % bc_highest)\n",
+    "    print (\" Lowest: %s\" % bc_lowest)\n",
+    "    print (\"Average: %s\" % bc_average)\n",
+    "    print (\" Median: %s\" % bc_median)\n",
+    "    print(\"Standard Deviation: %s \" %bc_std)\n",
+    "else:\n",
+    "    print('no branch changer')\n",
+    "    pass\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Course wise analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Subject Code</th>\n",
+       "      <th>Subject Name</th>\n",
+       "      <th>Credits</th>\n",
+       "      <th>Students</th>\n",
+       "      <th>EX</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>P</th>\n",
+       "      <th>F</th>\n",
+       "      <th>WH</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>Average</th>\n",
+       "      <th>Median</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CS2S001</td>\n",
+       "      <td>Project Seminar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ME1L001</td>\n",
+       "      <td>Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ME2L501</td>\n",
+       "      <td>Elements of Mechanical Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HS2L002</td>\n",
+       "      <td>Speaking and Presentation</td>\n",
+       "      <td>4</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.15</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>EC2L001</td>\n",
+       "      <td>Introduction to Electronics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.16</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CS2L001</td>\n",
+       "      <td>Discrete Structures</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9</td>\n",
+       "      <td>14</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.16</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>CE2L011</td>\n",
+       "      <td>Building materials and Construction</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.20</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>PH1L001</td>\n",
+       "      <td>Physics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.36</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>EC2L004</td>\n",
+       "      <td>Digital Electronics Circuits</td>\n",
+       "      <td>4</td>\n",
+       "      <td>40</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.39</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>CS2P002</td>\n",
+       "      <td>Data Structure Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.41</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>CS2L003</td>\n",
+       "      <td>Data Structure</td>\n",
+       "      <td>3</td>\n",
+       "      <td>44</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>EC2L002</td>\n",
+       "      <td>Signals and Systems</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.51</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>MA1L002</td>\n",
+       "      <td>Mathematics - II</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.52</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CS2L002</td>\n",
+       "      <td>Design and Analysis of Algorithms</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.60</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>MA2L006</td>\n",
+       "      <td>Combinatorics, Probability and Statistics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>42</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.63</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>EE1L001</td>\n",
+       "      <td>Electrical Technology</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.64</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>HS1L002</td>\n",
+       "      <td>Learning English</td>\n",
+       "      <td>4</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.72</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>CS1P001</td>\n",
+       "      <td>Introduction to Programing and Data Structures...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.91</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>EC2L007</td>\n",
+       "      <td>Communication Systems</td>\n",
+       "      <td>3</td>\n",
+       "      <td>36</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.91</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>ID2L001</td>\n",
+       "      <td>Entrepreneurship and Small Business Management</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.93</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>ID1T002</td>\n",
+       "      <td>Extra Academic Activities - 2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.98</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>CS2P001</td>\n",
+       "      <td>Design and Analysis of Algorithms Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>5</td>\n",
+       "      <td>10</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.02</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>MA1L001</td>\n",
+       "      <td>Mathematics -1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.07</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>ID2L002</td>\n",
+       "      <td>Introduction to Bioscience and Technology</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.09</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>CY1L001</td>\n",
+       "      <td>Chemistry</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.11</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>CE1P001</td>\n",
+       "      <td>Engineering Drawing and Graphics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.14</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>EC2P001</td>\n",
+       "      <td>Introduction to Electronics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.14</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>HS2L004</td>\n",
+       "      <td>Odissi Dance - I</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.17</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>EE1P001</td>\n",
+       "      <td>Electrical Technology Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>13</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.23</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>EC2P002</td>\n",
+       "      <td>Signals and Systems Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>19</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.25</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>ID2L003</td>\n",
+       "      <td>Environmental Science Technology and Management</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.28</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>CS1L001</td>\n",
+       "      <td>Introduction to Programing and Data Structures</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.30</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>HS2L003</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>31</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.32</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>CY1P001</td>\n",
+       "      <td>Chemistry Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>16</td>\n",
+       "      <td>13</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.39</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>PH1P001</td>\n",
+       "      <td>Physics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>15</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.57</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>ME1P001</td>\n",
+       "      <td>Introduction to Manufacturing Processes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.59</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>EC2P004</td>\n",
+       "      <td>Digital Electronics Circuits Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>40</td>\n",
+       "      <td>8</td>\n",
+       "      <td>15</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.63</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>HS2L007</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.82</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>HS1L001</td>\n",
+       "      <td>English for Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.20</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>ID1T001</td>\n",
+       "      <td>Extra Academic Activities -1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>22</td>\n",
+       "      <td>14</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.27</td>\n",
+       "      <td>9.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Subject Code                                       Subject Name Credits  \\\n",
+       "0       CS2S001                                    Project Seminar       2   \n",
+       "1       ME1L001                                          Mechanics       4   \n",
+       "2       ME2L501                 Elements of Mechanical Engineering       3   \n",
+       "3       HS2L002                          Speaking and Presentation       4   \n",
+       "4       EC2L001                        Introduction to Electronics       4   \n",
+       "5       CS2L001                                Discrete Structures       4   \n",
+       "6       CE2L011                Building materials and Construction       3   \n",
+       "7       PH1L001                                            Physics       4   \n",
+       "8       EC2L004                       Digital Electronics Circuits       4   \n",
+       "9       CS2P002                          Data Structure Laboratory       2   \n",
+       "10      CS2L003                                     Data Structure       3   \n",
+       "11      EC2L002                                Signals and Systems       4   \n",
+       "12      MA1L002                                   Mathematics - II       4   \n",
+       "13      CS2L002                  Design and Analysis of Algorithms       4   \n",
+       "14      MA2L006          Combinatorics, Probability and Statistics       4   \n",
+       "15      EE1L001                              Electrical Technology       4   \n",
+       "16      HS1L002                                   Learning English       4   \n",
+       "17      CS1P001  Introduction to Programing and Data Structures...       2   \n",
+       "18      EC2L007                              Communication Systems       3   \n",
+       "19      ID2L001     Entrepreneurship and Small Business Management       3   \n",
+       "20      ID1T002                      Extra Academic Activities - 2       1   \n",
+       "21      CS2P001       Design and Analysis of Algorithms Laboratory       2   \n",
+       "22      MA1L001                                     Mathematics -1       4   \n",
+       "23      ID2L002          Introduction to Bioscience and Technology       2   \n",
+       "24      CY1L001                                          Chemistry       4   \n",
+       "25      CE1P001                   Engineering Drawing and Graphics       3   \n",
+       "26      EC2P001             Introduction to Electronics Laboratory       2   \n",
+       "27      HS2L004                                   Odissi Dance - I       3   \n",
+       "28      EE1P001                   Electrical Technology Laboratory       2   \n",
+       "29      EC2P002                     Signals and Systems Laboratory       2   \n",
+       "30      ID2L003    Environmental Science Technology and Management       2   \n",
+       "31      CS1L001     Introduction to Programing and Data Structures       4   \n",
+       "32      HS2L003                          Introduction to Economics       3   \n",
+       "33      CY1P001                               Chemistry Laboratory       2   \n",
+       "34      PH1P001                                 Physics Laboratory       2   \n",
+       "35      ME1P001            Introduction to Manufacturing Processes       2   \n",
+       "36      EC2P004            Digital Electronics Circuits Laboratory       2   \n",
+       "37      HS2L007                          Introduction to Economics       4   \n",
+       "38      HS1L001                          English for Communication       4   \n",
+       "39      ID1T001                       Extra Academic Activities -1       1   \n",
+       "\n",
+       "    Students  EX   A   B   C   D  P  F  WH  Other  Average  Median  \n",
+       "0         44   0   0   0   0   0  0  0  44      0     0.00     0.0  \n",
+       "1         44   4   4   6  11  10  8  0   0      1     7.00     7.0  \n",
+       "2          1   0   0   0   1   0  0  0   0      0     7.00     7.0  \n",
+       "3         22   0   3   3  10   2  1  1   2      0     7.15     7.0  \n",
+       "4         44   5   4   8  10  10  3  4   0      0     7.16     7.0  \n",
+       "5         44   1   7   9  14   7  6  0   0      0     7.16     7.0  \n",
+       "6          6   0   0   1   4   0  0  0   1      0     7.20     7.0  \n",
+       "7         44   5   8   7   8  10  6  0   0      0     7.36     7.0  \n",
+       "8         40   4   5   4  16   7  1  1   2      0     7.39     7.0  \n",
+       "9         44   6   7   8   6  12  5  0   0      0     7.41     7.0  \n",
+       "10        44   5   6  11   9  10  2  1   0      0     7.50     7.5  \n",
+       "11        44   6   5  13   8   3  4  4   0      1     7.51     8.0  \n",
+       "12        44   6   9  10   5   5  8  1   0      0     7.52     8.0  \n",
+       "13        44   5   8   8   9   5  3  2   3      1     7.60     8.0  \n",
+       "14        42   5   4  12   8   7  2  0   4      0     7.63     8.0  \n",
+       "15        44   5   8  13   9   2  7  0   0      0     7.64     8.0  \n",
+       "16        39   0   8  16  12   2  1  0   0      0     7.72     8.0  \n",
+       "17        44   8  10   8   8   8  2  0   0      0     7.91     8.0  \n",
+       "18        36   3   5  14   7   2  1  0   3      1     7.91     8.0  \n",
+       "19        16   1   3   7   2   2  0  0   1      0     7.93     8.0  \n",
+       "20        44   5  10  14   9   3  2  0   0      1     7.98     8.0  \n",
+       "21        44   5  10  12   8   4  1  0   4      0     8.02     8.0  \n",
+       "22        44  13   8   7   6   5  5  0   0      0     8.07     8.0  \n",
+       "23        44   7  11  17   0   6  3  0   0      0     8.09     8.0  \n",
+       "24        44  13   9   7   4   7  3  1   0      0     8.11     8.5  \n",
+       "25        44   6  13  14   5   4  2  0   0      0     8.14     8.0  \n",
+       "26        44   8   7  17   8   3  1  0   0      0     8.14     8.0  \n",
+       "27         6   1   0   4   1   0  0  0   0      0     8.17     8.0  \n",
+       "28        44   6  13  18   1   4  2  0   0      0     8.23     8.0  \n",
+       "29        44   7   8  19   9   1  0  0   0      0     8.25     8.0  \n",
+       "30        44   3  15  15   5   1  1  0   4      0     8.28     8.0  \n",
+       "31        44  13   8  10   5   8  0  0   0      0     8.30     8.0  \n",
+       "32        31   6  11   7   4   0  3  0   0      0     8.32     9.0  \n",
+       "33        44   6  16  13   7   2  0  0   0      0     8.39     8.5  \n",
+       "34        44   6  15  22   0   1  0  0   0      0     8.57     8.0  \n",
+       "35        44   7  15  19   3   0  0  0   0      0     8.59     8.5  \n",
+       "36        40   8  15   9   5   1  0  0   2      0     8.63     9.0  \n",
+       "37        13   3   3   5   0   0  0  0   2      0     8.82     9.0  \n",
+       "38         5   2   2   1   0   0  0  0   0      0     9.20     9.0  \n",
+       "39        44  22  14   6   2   0  0  0   0      0     9.27     9.5  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "courses = dict()\n",
+    "for (k, v) in data.items():\n",
+    "    for (sem, scourses) in v['grades'].items():\n",
+    "        for (course, grade) in scourses.items():\n",
+    "            if course not in courses:\n",
+    "                courses[course] = list()\n",
+    "                courses[course].append(grade)\n",
+    "            else:\n",
+    "                courses[course].append(grade)\n",
+    "\n",
+    "clist = list()\n",
+    "\n",
+    "def other_grade(l):\n",
+    "    return len(l) - l.count('EX') - l.count('A') - l.count('B') - l.count('C') - l.count('D') - l.count('P') - l.count('F') - l.count('WH')\n",
+    "\n",
+    "def analyze_grade(l):\n",
+    "    grade_hash = {'EX': 10, 'A': 9, 'B': 8, 'C': 7, 'D': 6, 'P': 5, 'F': 5}\n",
+    "    hashed_grade = list()\n",
+    "    for grade in l:\n",
+    "        if grade in grade_hash:\n",
+    "            hashed_grade.append(grade_hash[grade])\n",
+    "    hashed_grade = (np.array(hashed_grade, dtype='float'))\n",
+    "    if hashed_grade.size == 0:\n",
+    "        return {'average': 0, 'median': 0}\n",
+    "    return {'average': round(np.mean(hashed_grade), 2), 'median': round(np.median(hashed_grade), 2)}\n",
+    "\n",
+    "grade_labels = 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other'\n",
+    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'orange', 'red', 'gray', 'black']\n",
+    "\n",
+    "for course, grades in courses.items():\n",
+    "    course_info = cdata[course]\n",
+    "    clist.append((course, course_info['subnane'], course_info['credit'], len(grades), grades.count('EX'), grades.count('A'), \n",
+    "                 grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "                 grades.count('F'), grades.count('WH'), other_grade(grades), analyze_grade(grades)['average'], analyze_grade(grades)['median']))\n",
+    "#     patches, texts = plt.pie([grades.count('EX'), grades.count('A'), \n",
+    "#                  grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "#                  grades.count('F'), grades.count('WH'), other_grade(grades)], labels=grade_labels, colors=colors)\n",
+    "#     plt.axis('equal')\n",
+    "#     plt.legend(patches, labels, loc=\"best\")\n",
+    "#     plt.show()\n",
+    "def sortByAverage(element):\n",
+    "    return element[-2]\n",
+    "\n",
+    "clist.sort(key=sortByAverage)\n",
+    "df = pd.DataFrame(data = clist)\n",
+    "df.columns = ['Subject Code', 'Subject Name', 'Credits', 'Students', 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other', 'Average', 'Median']\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CGPA Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 5 Students:\n",
+      "\n",
+      "16CS01041  TUMMALA MADHAV  9.71\n",
+      "16CS01042  SAKSHAM ARNEJA  9.70\n",
+      "16CS01017  ADITYA PAL  9.70\n",
+      "16CS01014  ANKIT PRADHAN  9.55\n",
+      "16CS01007  YATAM VENU MADHAV  9.52\n",
+      "\n",
+      "CGPA:\n",
+      "Highest: 9.71\n",
+      "lowest: 4.51\n",
+      " Median: 7.87\n",
+      "Average: 7.72\n",
+      "Standard Deviation: 1.34 \n",
+      "\n",
+      "   9.5+: 5\n",
+      "  9-9.5: 4\n",
+      "  8.5-9: 1\n",
+      "  8-8.5: 10\n",
+      "  7.5-8: 7\n",
+      "  7-7.5: 7\n",
+      "     7-: 10\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "roll_and_cgpa = []\n",
+    "for (k, v) in data.items():\n",
+    "    try:\n",
+    "        roll_and_cgpa.append((k,float(v['cgpa'][2])))\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "    \n",
+    "\n",
+    "def sortbycg(l):\n",
+    "   return l[1]\n",
+    "\n",
+    "def sortbyroll(l):\n",
+    "    return int(l[0][7:9])\n",
+    "\n",
+    "\n",
+    "sorted_cgpa = roll_and_cgpa[:]\n",
+    "sorted_cgpa.sort(key=sortbycg)\n",
+    "\n",
+    "\n",
+    "print(\"Top 5 Students:\\n\")\n",
+    "for element in sorted_cgpa[:-6:-1]:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "    \n",
+    "roll_and_cgpa.sort(key=sortbyroll)\n",
+    "\n",
+    "#for individual cgpa \n",
+    "\n",
+    "# print(\"Students with theri cgpa :\\n\")\n",
+    "# for element in roll_and_cgpa:\n",
+    "#     print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "\n",
+    "cgpa = np.array([element[1] for element in roll_and_cgpa], dtype='float')\n",
+    "\n",
+    "roll = np.array([element[0][7:9] for element in roll_and_cgpa],dtype='int')\n",
+    "\n",
+    "\n",
+    "cgpa_average = round(np.mean(cgpa), 2)\n",
+    "cgpa_median = round(np.median(cgpa), 2)\n",
+    "cgpa_highest = round(np.max(cgpa), 2)\n",
+    "cgpa_lowest = round(np.min(cgpa),2)\n",
+    "cgpa_standard_deviation = round(np.std(cgpa),2)\n",
+    "\n",
+    "print(\"\\nCGPA:\")\n",
+    "print(\"Highest: %s\" % cgpa_highest)\n",
+    "print(\"lowest: %s\"% cgpa_lowest)\n",
+    "print(\" Median: %s\" % cgpa_median)\n",
+    "print(\"Average: %s\" % cgpa_average)\n",
+    "print(\"Standard Deviation: %s \\n\"% cgpa_standard_deviation)\n",
+    "\n",
+    "print(\"   9.5+: %s\" % len([cg for cg in cgpa if cg >= 9.5]))\n",
+    "print(\"  9-9.5: %s\" % len([cg for cg in cgpa if cg >= 9 and cg < 9.5]))\n",
+    "print(\"  8.5-9: %s\" % len([cg for cg in cgpa if cg >= 8.5 and cg < 9]))\n",
+    "print(\"  8-8.5: %s\" % len([cg for cg in cgpa if cg >= 8 and cg < 8.5]))\n",
+    "print(\"  7.5-8: %s\" % len([cg for cg in cgpa if cg >= 7.5 and cg < 8]))\n",
+    "print(\"  7-7.5: %s\" % len([cg for cg in cgpa if cg >= 7 and cg < 7.5]))\n",
+    "print(\"     7-: %s\" % len([cg for cg in cgpa if cg < 7]))\n",
+    "\n",
+    "\n",
+    "unit_array = np.ones(len(roll),dtype='int')\n",
+    "fig,cgpa_plot = plt.subplots()\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "label=['above mean','below mean']\n",
+    "cgpa_plot.plot(roll,cgpa,label='above mean cgpa',color='green')\n",
+    "cgpa_plot.plot(roll,cgpa_median*unit_array,label='below mean cgpa',color='red')\n",
+    "cgpa_plot.grid(b = True)\n",
+    "cgpa_plot.legend()\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa >= cgpa_median*unit_array,facecolor = 'green',interpolate = True )\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa< cgpa_median*unit_array,facecolor = 'red',interpolate = True )\n",
+    "plt.xlabel('roll number')\n",
+    "plt.ylabel('cgpa (0-10)', horizontalalignment='right',rotation =0)\n",
+    "plt.title('cgpa distribution wrt roll number')\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\n')\n",
+    "#plt.fill(cgpa,cgpa_median*median,'r')g\n",
+    "sorted_cgpa=cgpa[:]\n",
+    "sorted_cgpa.sort()\n",
+    "fig,histo_cgpa = plt.subplots()\n",
+    "histo_cgpa.hist(sorted_cgpa,bins=40)\n",
+    "#histo_cgpa.fill_between(roll,histo_cgpa,where =sorted_cgpa >= 8.0,facecolor = 'green',interpolate = True )\n",
+    "#histo_cgpa.fill(histo_cgpa, where = sorted_cgpa >= cgpa_median)\n",
+    "plt.title('frequency distribution of class cgpa')\n",
+    "\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' number\\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.show()\n",
+    "#plt.plot(norm.pdf(cgpa_in_seq,cgpa_median,cgpa_standard_deviation))\n",
+    "\n",
+    "print('\\n')\n",
+    "fig = norm.pdf(sorted_cgpa, np.mean(sorted_cgpa), np.std(sorted_cgpa))\n",
+    "plt.plot(sorted_cgpa,fig,'-o')\n",
+    "plt.title('standard distribution of class cgpa')\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' fraction \\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.grid(b = True)\n",
+    "plt.show()\n",
+    "\n",
+    "above_mean = [cg for cg in cgpa if cg >= cgpa_median]\n",
+    "below_mean = [cg for cg in cgpa if cg < cgpa_median]\n",
+    "\n",
+    "print('\\n')\n",
+    "labels = 'above mean','below mean'\n",
+    "sizes = [len(above_mean),len(below_mean)]\n",
+    "explode = (0.1,0)\n",
+    "colors = ['green','red']\n",
+    "# Plot\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
+    "        autopct='%2.1f%%', shadow=True, startangle=140)\n",
+    " \n",
+    "plt.axis('equal')\n",
+    "plt.title('cgpa distribution wrt mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# results plot \n",
+    "this plot shows that students from roll number between 1 to 19 shows a good aveage performance while those at the roll number between 30 to 40 have very low average. these students need to receive better support in coming semester to impove their performance.Also the class has very large deviation showing a one sided growth and lack of overall improvement.\n",
+    "\n",
+    "\n",
+    "https://analytics4all.org/2016/05/05/python-histograms-and-frequency-distribution/\n",
+    "\n",
+    "https://etav.github.io/python/count_basic_freq_plot.html for histogram frequency distribution\n",
+    "\n",
+    "https://stackoverflow.com/questions/20011494/plot-normal-distribution-with-matplotlib for ploting the standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/year/2016/16ECE.ipynb b/year/2016/16ECE.ipynb
new file mode 100644
index 0000000..3bb3041
--- /dev/null
+++ b/year/2016/16ECE.ipynb
@@ -0,0 +1,1358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# B.Tech 2016-20 Electrical Enginnering (Spring 2018)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime as dt\n",
+    "import json\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16EC01016 8.04\n",
+      "16EC01017 8.36\n",
+      "16EC01014 8.60\n",
+      "16EC01012 9.15\n",
+      "16EC01013 8.74\n",
+      "16EC01010 8.15\n",
+      "16EC01011 8.55\n",
+      "16EC01018 8.51\n",
+      "16EC01007 8.57\n",
+      "16EC01029 8.00\n",
+      "16EC01028 9.02\n",
+      "16EC01023 9.17\n",
+      "16EC01022 7.51\n",
+      "16EC01021 7.96\n",
+      "16EC01020 6.20\n",
+      "16EC01027 8.72\n",
+      "16EC01026 6.89\n",
+      "16EC01025 6.89\n",
+      "16EC01024 6.40\n",
+      "16EC01038 6.38\n",
+      "16EC01039 7.11\n",
+      "16EC01034 6.40\n",
+      "16EC01035 5.96\n",
+      "16EC01036 6.62\n",
+      "16EC01037 6.13\n",
+      "16EC01030 8.40\n",
+      "16EC01031 8.89\n",
+      "16EC01032 7.62\n",
+      "16EC01033 7.94\n",
+      "16EC01005 8.26\n",
+      "16EC01041 9.53\n",
+      "16EC01040 7.74\n",
+      "16EC01043 9.21\n",
+      "16EC01042 9.43\n",
+      "16EC01045 8.74\n",
+      "16EC01044 8.91\n",
+      "16EC01046 8.62\n",
+      "16EC01004 9.17\n",
+      "16EC01006 7.02\n",
+      "16EC01001 8.09\n",
+      "16EC01003 8.55\n",
+      "16EC01002 9.06\n",
+      "16EC01009 8.47\n",
+      "16EC01008 8.66\n",
+      "Total Stuents: 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('stres.json') as f:\n",
+    "    data = json.load(f)\n",
+    "    # Filter out 16EE data\n",
+    "    data = {k:v for (k,v) in data.items() if '16EC' in k[:4]}\n",
+    "    \n",
+    "for (k,v) in data.items():\n",
+    "    print(k,v['cgpa'][1])\n",
+    "    \n",
+    "with open('course.json') as c:\n",
+    "    cdata = json.load(c)\n",
+    "    \n",
+    "print (\"Total Stuents: %s\" % len(data))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Date of Birth analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Average:  October 07, 1998\n",
+      "  Median: November 07, 1998\n",
+      "  Oldest:    April 13, 1997\n",
+      "Youngest: December 30, 1999\n"
+     ]
+    }
+   ],
+   "source": [
+    "dob = [v['dob'] for (k, v) in data.items()]\n",
+    "\n",
+    "np_dob = (np.array(dob, dtype='datetime64[s]').view('i8'))\n",
+    "average_dob = np.mean(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "median_dob  = np.median(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "minimum_dob = np.min(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "maximum_dob = np.max(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "print (\" Average: %s\" % average_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Median: %s\" % median_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Oldest: %s\" % minimum_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"Youngest: %s\" % maximum_dob.strftime(\"%8B %d, %Y\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Branch Change Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total branch changers: 6\n",
+      "\n",
+      "CGPA (after 2nd sem) for branch change:-\n",
+      "Highest: 9.53\n",
+      " Lowest: 8.62\n",
+      "Average: 9.073333333333334\n",
+      " Median: 9.06\n",
+      "Standard Deviation: 0.34101156708957675 \n"
+     ]
+    }
+   ],
+   "source": [
+    "original_strengeth = 40\n",
+    "bc_cgpa = [v['cgpa'][1] for (k, v) in data.items() if (int(k[-2:]) > 40 and v['cgpa'][2] !='WH')]\n",
+    "cgpa = [v['cgpa'][2] for (k,v) in data.items() if v['cgpa'][2] != 'WH' ]\n",
+    "\n",
+    "bc_cgpa = (np.array(bc_cgpa, dtype='float'))\n",
+    "cgpa = (np.array(cgpa, dtype='float'))\n",
+    "\n",
+    "if len(bc_cgpa>0):\n",
+    "    bc_count = bc_cgpa.size\n",
+    "    bc_highest = np.max(bc_cgpa)\n",
+    "    bc_lowest = np.min(bc_cgpa)\n",
+    "    bc_average = np.mean(bc_cgpa)\n",
+    "    bc_median = np.median(bc_cgpa)\n",
+    "    bc_std = np.std(bc_cgpa)\n",
+    "    print (\"Total branch changers: %s\" % bc_count)\n",
+    "    print (\"\\nCGPA (after 2nd sem) for branch change:-\")\n",
+    "    print (\"Highest: %s\" % bc_highest)\n",
+    "    print (\" Lowest: %s\" % bc_lowest)\n",
+    "    print (\"Average: %s\" % bc_average)\n",
+    "    print (\" Median: %s\" % bc_median)\n",
+    "    print(\"Standard Deviation: %s \" %bc_std)\n",
+    "else:\n",
+    "    print('no branch changer')\n",
+    "    pass\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Course wise analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Subject Code</th>\n",
+       "      <th>Subject Name</th>\n",
+       "      <th>Credits</th>\n",
+       "      <th>Students</th>\n",
+       "      <th>EX</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>P</th>\n",
+       "      <th>F</th>\n",
+       "      <th>WH</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>Average</th>\n",
+       "      <th>Median</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PH1L001</td>\n",
+       "      <td>Physics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>13</td>\n",
+       "      <td>13</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.09</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ME1L001</td>\n",
+       "      <td>Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>13</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.18</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EC2L001</td>\n",
+       "      <td>Introduction to Electronics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5</td>\n",
+       "      <td>16</td>\n",
+       "      <td>22</td>\n",
+       "      <td>15</td>\n",
+       "      <td>11</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HS2L004</td>\n",
+       "      <td>Odissi Dance - I</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.25</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MA2L003</td>\n",
+       "      <td>Probability Statistics and Stochastic Processes</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>12</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.36</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>EC2L002</td>\n",
+       "      <td>Signals and Systems</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>19</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.61</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>ME2L501</td>\n",
+       "      <td>Elements of Mechanical Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.62</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MA1L002</td>\n",
+       "      <td>Mathematics - II</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.68</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>EC2L005</td>\n",
+       "      <td>Analog Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "      <td>17</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.72</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>ID2L001</td>\n",
+       "      <td>Entrepreneurship and Small Business Management</td>\n",
+       "      <td>3</td>\n",
+       "      <td>62</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11</td>\n",
+       "      <td>23</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>HS1L002</td>\n",
+       "      <td>Learning English</td>\n",
+       "      <td>4</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>21</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.83</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>HS2L002</td>\n",
+       "      <td>Speaking and Presentation</td>\n",
+       "      <td>4</td>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.91</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CE2L011</td>\n",
+       "      <td>Building materials and Construction</td>\n",
+       "      <td>3</td>\n",
+       "      <td>18</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>EE1L001</td>\n",
+       "      <td>Electrical Technology</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>9</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.02</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>CS1P001</td>\n",
+       "      <td>Introduction to Programing and Data Structures...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.05</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>ID1T002</td>\n",
+       "      <td>Extra Academic Activities - 2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13</td>\n",
+       "      <td>16</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.05</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>CS1L001</td>\n",
+       "      <td>Introduction to Programing and Data Structures</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.14</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>ID2L002</td>\n",
+       "      <td>Introduction to Bioscience and Technology</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.14</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>EC2L004</td>\n",
+       "      <td>Digital Electronics Circuits</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>9</td>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.18</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>MA1L001</td>\n",
+       "      <td>Mathematics -1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.20</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>ID2L003</td>\n",
+       "      <td>Environmental Science Technology and Management</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>14</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.23</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>CY1L001</td>\n",
+       "      <td>Chemistry</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44</td>\n",
+       "      <td>9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.30</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>EE1P001</td>\n",
+       "      <td>Electrical Technology Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.32</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>HS2L003</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.39</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>EC2P004</td>\n",
+       "      <td>Digital Electronics Circuits Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>15</td>\n",
+       "      <td>14</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.40</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>HS2L007</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>EC2P002</td>\n",
+       "      <td>Signals and Systems Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17</td>\n",
+       "      <td>13</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.55</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>CE1P001</td>\n",
+       "      <td>Engineering Drawing and Graphics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>44</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.61</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>PH1P001</td>\n",
+       "      <td>Physics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>4</td>\n",
+       "      <td>25</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.61</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>EC2P001</td>\n",
+       "      <td>Introduction to Electronics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>21</td>\n",
+       "      <td>12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.64</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>EE2S001</td>\n",
+       "      <td>Project Seminar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>8</td>\n",
+       "      <td>14</td>\n",
+       "      <td>21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.66</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>EC2P005</td>\n",
+       "      <td>Analog Communication Lab</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>9</td>\n",
+       "      <td>21</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.79</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>ME1P001</td>\n",
+       "      <td>Introduction to Manufacturing Processes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>25</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.80</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>HS1L001</td>\n",
+       "      <td>English for Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.88</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>CY1P001</td>\n",
+       "      <td>Chemistry Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>44</td>\n",
+       "      <td>10</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.89</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>ID1T001</td>\n",
+       "      <td>Extra Academic Activities -1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>16</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.34</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Subject Code                                       Subject Name Credits  \\\n",
+       "0       PH1L001                                            Physics       4   \n",
+       "1       ME1L001                                          Mechanics       4   \n",
+       "2       EC2L001                        Introduction to Electronics       4   \n",
+       "3       HS2L004                                   Odissi Dance - I       3   \n",
+       "4       MA2L003    Probability Statistics and Stochastic Processes       4   \n",
+       "5       EC2L002                                Signals and Systems       4   \n",
+       "6       ME2L501                 Elements of Mechanical Engineering       3   \n",
+       "7       MA1L002                                   Mathematics - II       4   \n",
+       "8       EC2L005                               Analog Communication       4   \n",
+       "9       ID2L001     Entrepreneurship and Small Business Management       3   \n",
+       "10      HS1L002                                   Learning English       4   \n",
+       "11      HS2L002                          Speaking and Presentation       4   \n",
+       "12      CE2L011                Building materials and Construction       3   \n",
+       "13      EE1L001                              Electrical Technology       4   \n",
+       "14      CS1P001  Introduction to Programing and Data Structures...       2   \n",
+       "15      ID1T002                      Extra Academic Activities - 2       1   \n",
+       "16      CS1L001     Introduction to Programing and Data Structures       4   \n",
+       "17      ID2L002          Introduction to Bioscience and Technology       2   \n",
+       "18      EC2L004                       Digital Electronics Circuits       4   \n",
+       "19      MA1L001                                     Mathematics -1       4   \n",
+       "20      ID2L003    Environmental Science Technology and Management       2   \n",
+       "21      CY1L001                                          Chemistry       4   \n",
+       "22      EE1P001                   Electrical Technology Laboratory       2   \n",
+       "23      HS2L003                          Introduction to Economics       3   \n",
+       "24      EC2P004            Digital Electronics Circuits Laboratory       2   \n",
+       "25      HS2L007                          Introduction to Economics       4   \n",
+       "26      EC2P002                     Signals and Systems Laboratory       2   \n",
+       "27      CE1P001                   Engineering Drawing and Graphics       3   \n",
+       "28      PH1P001                                 Physics Laboratory       2   \n",
+       "29      EC2P001             Introduction to Electronics Laboratory       2   \n",
+       "30      EE2S001                                    Project Seminar       2   \n",
+       "31      EC2P005                           Analog Communication Lab       2   \n",
+       "32      ME1P001            Introduction to Manufacturing Processes       2   \n",
+       "33      HS1L001                          English for Communication       4   \n",
+       "34      CY1P001                               Chemistry Laboratory       2   \n",
+       "35      ID1T001                       Extra Academic Activities -1       1   \n",
+       "\n",
+       "    Students  EX   A   B   C   D   P  F  WH  Other  Average  Median  \n",
+       "0         44   0   5  13  13   7   6  0   0      0     7.09     7.0  \n",
+       "1         44   1   8   9  13   6   7  0   0      0     7.18     7.0  \n",
+       "2         88   5  16  22  15  11  15  4   0      0     7.23     7.0  \n",
+       "3          9   0   2   2   0   4   0  0   1      0     7.25     7.0  \n",
+       "4         44   2   9  10  12   4   7  0   0      0     7.36     7.0  \n",
+       "5         44   1   9  19   8   1   5  1   0      0     7.61     8.0  \n",
+       "6         26   1   5   8   8   3   1  0   0      0     7.62     8.0  \n",
+       "7         44   4  13   9   7   5   6  0   0      0     7.68     8.0  \n",
+       "8         44   1  11  17   7   3   3  1   1      0     7.72     8.0  \n",
+       "9         62   5  11  23  14   5   3  1   0      0     7.76     8.0  \n",
+       "10        37   0   6  21   7   1   1  0   0      1     7.83     8.0  \n",
+       "11        22   0   5  11   5   1   0  0   0      0     7.91     8.0  \n",
+       "12        18   3   3   4   3   3   0  0   1      1     8.00     8.0  \n",
+       "13        44   6   9  17   5   6   1  0   0      0     8.02     8.0  \n",
+       "14        44   5  12  13   8   6   0  0   0      0     8.05     8.0  \n",
+       "15        44   3  13  16   8   3   1  0   0      0     8.05     8.0  \n",
+       "16        44  10  10  10   7   4   3  0   0      0     8.14     8.0  \n",
+       "17        44   5  14  11  10   4   0  0   0      0     8.14     8.0  \n",
+       "18        44   9  11  12   5   5   2  0   0      0     8.18     8.0  \n",
+       "19        44   8  13   9  10   2   2  0   0      0     8.20     8.0  \n",
+       "20        44   4  15  14   9   2   0  0   0      0     8.23     8.0  \n",
+       "21        44   9  18   7   2   3   5  0   0      0     8.30     9.0  \n",
+       "22        44   1  25   9   5   4   0  0   0      0     8.32     9.0  \n",
+       "23        23   4   8   6   3   2   0  0   0      0     8.39     9.0  \n",
+       "24        44   6  15  14   4   3   0  0   2      0     8.40     8.5  \n",
+       "25        16   1   9   3   3   0   0  0   0      0     8.50     9.0  \n",
+       "26        44   7  17  13   7   0   0  0   0      0     8.55     9.0  \n",
+       "27        44   7  21  12   1   2   1  0   0      0     8.61     9.0  \n",
+       "28        44   4  25   9   6   0   0  0   0      0     8.61     9.0  \n",
+       "29        44   6  21  12   5   0   0  0   0      0     8.64     9.0  \n",
+       "30        44   8  14  21   1   0   0  0   0      0     8.66     8.5  \n",
+       "31        44   9  21   9   3   1   0  0   1      0     8.79     9.0  \n",
+       "32        44   6  25  11   2   0   0  0   0      0     8.80     9.0  \n",
+       "33         8   1   6   0   1   0   0  0   0      0     8.88     9.0  \n",
+       "34        44  10  22   9   3   0   0  0   0      0     8.89     9.0  \n",
+       "35        44  16  27   1   0   0   0  0   0      0     9.34     9.0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "courses = dict()\n",
+    "for (k, v) in data.items():\n",
+    "    for (sem, scourses) in v['grades'].items():\n",
+    "        for (course, grade) in scourses.items():\n",
+    "            if course not in courses:\n",
+    "                courses[course] = list()\n",
+    "                courses[course].append(grade)\n",
+    "            else:\n",
+    "                courses[course].append(grade)\n",
+    "\n",
+    "clist = list()\n",
+    "\n",
+    "def other_grade(l):\n",
+    "    return len(l) - l.count('EX') - l.count('A') - l.count('B') - l.count('C') - l.count('D') - l.count('P') - l.count('F') - l.count('WH')\n",
+    "\n",
+    "def analyze_grade(l):\n",
+    "    grade_hash = {'EX': 10, 'A': 9, 'B': 8, 'C': 7, 'D': 6, 'P': 5, 'F': 5}\n",
+    "    hashed_grade = list()\n",
+    "    for grade in l:\n",
+    "        if grade in grade_hash:\n",
+    "            hashed_grade.append(grade_hash[grade])\n",
+    "    hashed_grade = (np.array(hashed_grade, dtype='float'))\n",
+    "    if hashed_grade.size == 0:\n",
+    "        return {'average': 0, 'median': 0}\n",
+    "    return {'average': round(np.mean(hashed_grade), 2), 'median': round(np.median(hashed_grade), 2)}\n",
+    "\n",
+    "grade_labels = 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other'\n",
+    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'orange', 'red', 'gray', 'black']\n",
+    "\n",
+    "for course, grades in courses.items():\n",
+    "    course_info = cdata[course]\n",
+    "    clist.append((course, course_info['subnane'], course_info['credit'], len(grades), grades.count('EX'), grades.count('A'), \n",
+    "                 grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "                 grades.count('F'), grades.count('WH'), other_grade(grades), analyze_grade(grades)['average'], analyze_grade(grades)['median']))\n",
+    "#     patches, texts = plt.pie([grades.count('EX'), grades.count('A'), \n",
+    "#                  grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "#                  grades.count('F'), grades.count('WH'), other_grade(grades)], labels=grade_labels, colors=colors)\n",
+    "#     plt.axis('equal')\n",
+    "#     plt.legend(patches, labels, loc=\"best\")\n",
+    "#     plt.show()\n",
+    "def sortByAverage(element):\n",
+    "    return element[-2]\n",
+    "\n",
+    "clist.sort(key=sortByAverage)\n",
+    "df = pd.DataFrame(data = clist)\n",
+    "df.columns = ['Subject Code', 'Subject Name', 'Credits', 'Students', 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other', 'Average', 'Median']\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CGPA Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 5 Students:\n",
+      "\n",
+      "16EC01023  ARVIND T.K.R  9.46\n",
+      "16EC01041  MEGHNA CHINMOY SAHA  9.44\n",
+      "16EC01004  ADYASHA CHAKRAVARTY  9.17\n",
+      "16EC01043  KUMAR ANKUL  9.00\n",
+      "16EC01042  MARRI K L MOUNIKA  8.97\n",
+      "Students with theri cgpa :\n",
+      "\n",
+      "16EC01001  ARPIT BAL  8.21\n",
+      "16EC01002  DONGALA SUDEEPA  8.88\n",
+      "16EC01003  ADITYA TERKAR  8.68\n",
+      "16EC01004  ADYASHA CHAKRAVARTY  9.17\n",
+      "16EC01005  RAHUL SABNANI  8.04\n",
+      "16EC01006  JAMPALA VISHNU TEJA  7.03\n",
+      "16EC01007  KADIVENDI ROHITH  8.51\n",
+      "16EC01008  MANDAVA SAI RAGHAVENDRA DINESH  8.26\n",
+      "16EC01009  MUTHALURU CHAITANYA SHIVA KUMAR REDDY  8.60\n",
+      "16EC01010  DHULIPALLA SAI KIRAN  8.18\n",
+      "16EC01011  ABDUL AZGAR TAJ  8.11\n",
+      "16EC01012  VIJAY SURYA VEMPATI  8.96\n",
+      "16EC01013  NIGHUT CHIRAG ASHOK  8.75\n",
+      "16EC01014  PRAHARSH DEEP SINGH  8.41\n",
+      "16EC01016  SAHUKAR NIKHIL PANDA  8.08\n",
+      "16EC01017  LAKSHMIREDDIPALLE VARUN KUMAR REDDY  8.42\n",
+      "16EC01018  CHIRAG MALHAN  8.55\n",
+      "16EC01020  RAHUL KUMAR SINGH  6.18\n",
+      "16EC01021  KAMALAPADU SAHITHYA  7.86\n",
+      "16EC01022  SURISETTI ANIL  7.82\n",
+      "16EC01023  ARVIND T.K.R  9.46\n",
+      "16EC01024  DUPPALA VIVEK  6.24\n",
+      "16EC01025  NASANURU ABHISHEK SAI  6.79\n",
+      "16EC01026  YENNI KAUSHIK  7.63\n",
+      "16EC01027  VIMAN CHANDRA DAS  8.61\n",
+      "16EC01028  ADITYA PRATAP SINGH  8.85\n",
+      "16EC01029  SANGEETHAM SAI TEJA  7.86\n",
+      "16EC01030  AVANI PATIDAR  8.21\n",
+      "16EC01031  SOUMYAJIT GHOSH  8.86\n",
+      "16EC01032  SUPRIYA ATHOTA  7.88\n",
+      "16EC01033  PURETI AJAY  8.01\n",
+      "16EC01034  KAKARA AKASH  6.35\n",
+      "16EC01035  TAGILI MADHAN MOHAN RAO  6.25\n",
+      "16EC01036  K SUDEK  6.46\n",
+      "16EC01037  NELAPARTHI LIYANTH RAJU  6.07\n",
+      "16EC01038  TULASI THUDUMU  5.88\n",
+      "16EC01039  AMGOTHU MURALI KRISHNA  7.08\n",
+      "16EC01040  NITIN MEENA  7.60\n",
+      "16EC01041  MEGHNA CHINMOY SAHA  9.44\n",
+      "16EC01042  MARRI K L MOUNIKA  8.97\n",
+      "16EC01043  KUMAR ANKUL  9.00\n",
+      "16EC01044  ABHISHEK PATNAIK  8.86\n",
+      "16EC01045  RAHUL MAHANOT  8.11\n",
+      "16EC01046  MOHIT KANODIA  8.29\n",
+      "\n",
+      "CGPA:\n",
+      "Highest: 9.46\n",
+      "lowest: 5.88\n",
+      " Median: 8.2\n",
+      "Average: 7.99\n",
+      "Standard Deviation: 0.96 \n",
+      "\n",
+      "   9.5+: 0\n",
+      "  9-9.5: 4\n",
+      "  8.5-9: 12\n",
+      "  8-8.5: 12\n",
+      "  7.5-8: 6\n",
+      "  7-7.5: 2\n",
+      "     7-: 8\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "roll_and_cgpa = []\n",
+    "for (k, v) in data.items():\n",
+    "    try:\n",
+    "        roll_and_cgpa.append((k,float(v['cgpa'][2])))\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "    \n",
+    "\n",
+    "def sortbycg(l):\n",
+    "   return l[1]\n",
+    "\n",
+    "def sortbyroll(l):\n",
+    "    return int(l[0][7:9])\n",
+    "\n",
+    "\n",
+    "sorted_cgpa = roll_and_cgpa[:]\n",
+    "sorted_cgpa.sort(key=sortbycg)\n",
+    "\n",
+    "\n",
+    "print(\"Top 5 Students:\\n\")\n",
+    "for element in sorted_cgpa[:-6:-1]:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "    \n",
+    "roll_and_cgpa.sort(key=sortbyroll)\n",
+    "\n",
+    "# for individual cgpa \n",
+    "\n",
+    "print(\"Students with theri cgpa :\\n\")\n",
+    "for element in roll_and_cgpa:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "\n",
+    "cgpa = np.array([element[1] for element in roll_and_cgpa], dtype='float')\n",
+    "\n",
+    "roll = np.array([element[0][7:9] for element in roll_and_cgpa],dtype='int')\n",
+    "\n",
+    "\n",
+    "cgpa_average = round(np.mean(cgpa), 2)\n",
+    "cgpa_median = round(np.median(cgpa), 2)\n",
+    "cgpa_highest = round(np.max(cgpa), 2)\n",
+    "cgpa_lowest = round(np.min(cgpa),2)\n",
+    "cgpa_standard_deviation = round(np.std(cgpa),2)\n",
+    "\n",
+    "print(\"\\nCGPA:\")\n",
+    "print(\"Highest: %s\" % cgpa_highest)\n",
+    "print(\"lowest: %s\"% cgpa_lowest)\n",
+    "print(\" Median: %s\" % cgpa_median)\n",
+    "print(\"Average: %s\" % cgpa_average)\n",
+    "print(\"Standard Deviation: %s \\n\"% cgpa_standard_deviation)\n",
+    "\n",
+    "print(\"   9.5+: %s\" % len([cg for cg in cgpa if cg >= 9.5]))\n",
+    "print(\"  9-9.5: %s\" % len([cg for cg in cgpa if cg >= 9 and cg < 9.5]))\n",
+    "print(\"  8.5-9: %s\" % len([cg for cg in cgpa if cg >= 8.5 and cg < 9]))\n",
+    "print(\"  8-8.5: %s\" % len([cg for cg in cgpa if cg >= 8 and cg < 8.5]))\n",
+    "print(\"  7.5-8: %s\" % len([cg for cg in cgpa if cg >= 7.5 and cg < 8]))\n",
+    "print(\"  7-7.5: %s\" % len([cg for cg in cgpa if cg >= 7 and cg < 7.5]))\n",
+    "print(\"     7-: %s\" % len([cg for cg in cgpa if cg < 7]))\n",
+    "\n",
+    "\n",
+    "unit_array = np.ones(len(roll),dtype='int')\n",
+    "fig,cgpa_plot = plt.subplots()\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "label=['above mean','below mean']\n",
+    "cgpa_plot.plot(roll,cgpa,label='above mean cgpa',color='green')\n",
+    "cgpa_plot.plot(roll,cgpa_median*unit_array,label='below mean cgpa',color='red')\n",
+    "cgpa_plot.grid(b = True)\n",
+    "cgpa_plot.legend()\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa >= cgpa_median*unit_array,facecolor = 'green',interpolate = True )\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa< cgpa_median*unit_array,facecolor = 'red',interpolate = True )\n",
+    "plt.xlabel('roll number')\n",
+    "plt.ylabel('cgpa (0-10)', horizontalalignment='right',rotation =0)\n",
+    "plt.title('cgpa distribution wrt roll number')\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\n')\n",
+    "#plt.fill(cgpa,cgpa_median*median,'r')g\n",
+    "sorted_cgpa=cgpa[:]\n",
+    "sorted_cgpa.sort()\n",
+    "fig,histo_cgpa = plt.subplots()\n",
+    "histo_cgpa.hist(sorted_cgpa,bins=40)\n",
+    "#histo_cgpa.fill_between(roll,histo_cgpa,where =sorted_cgpa >= 8.0,facecolor = 'green',interpolate = True )\n",
+    "#histo_cgpa.fill(histo_cgpa, where = sorted_cgpa >= cgpa_median)\n",
+    "plt.title('frequency distribution of class cgpa')\n",
+    "\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' number\\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.show()\n",
+    "#plt.plot(norm.pdf(cgpa_in_seq,cgpa_median,cgpa_standard_deviation))\n",
+    "\n",
+    "print('\\n')\n",
+    "fig = norm.pdf(sorted_cgpa, np.mean(sorted_cgpa), np.std(sorted_cgpa))\n",
+    "plt.plot(sorted_cgpa,fig,'-o')\n",
+    "plt.title('standard distribution of class cgpa')\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' fraction \\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.grid(b = True)\n",
+    "plt.show()\n",
+    "\n",
+    "above_mean = [cg for cg in cgpa if cg >= cgpa_median]\n",
+    "below_mean = [cg for cg in cgpa if cg < cgpa_median]\n",
+    "\n",
+    "print('\\n')\n",
+    "labels = 'above mean','below mean'\n",
+    "sizes = [len(above_mean),len(below_mean)]\n",
+    "explode = (0.1,0)\n",
+    "colors = ['green','red']\n",
+    "# Plot\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
+    "        autopct='%2.1f%%', shadow=True, startangle=140)\n",
+    " \n",
+    "plt.axis('equal')\n",
+    "plt.title('cgpa distribution wrt mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# results plot \n",
+    "this plot shows that students from roll number between 1 to 18 shows a good aveage performance while those at the roll number between 34 to 40 have very low average. these students need to receive better support in coming semester to impove their performance.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "https://analytics4all.org/2016/05/05/python-histograms-and-frequency-distribution/\n",
+    "\n",
+    "https://etav.github.io/python/count_basic_freq_plot.html for histogram frequency distribution\n",
+    "\n",
+    "https://stackoverflow.com/questions/20011494/plot-normal-distribution-with-matplotlib for ploting the standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/year/2016/16EE.ipynb b/year/2016/16EE.ipynb
index 2431771..cf62728 100644
--- a/year/2016/16EE.ipynb
+++ b/year/2016/16EE.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,18 +18,59 @@
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import datetime as dt\n",
-    "import json"
+    "import json\n",
+    "from scipy.stats import norm"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 429,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "16EE01010 8.89\n",
+      "16EE01015 7.87\n",
+      "16EE01019 8.55\n",
+      "16EE01042 8.94\n",
+      "16EE01012 7.36\n",
+      "16EE01041 9.47\n",
+      "16EE01040 6.79\n",
+      "16EE01004 8.00\n",
+      "16EE01029 7.40\n",
+      "16EE01028 8.34\n",
+      "16EE01001 9.26\n",
+      "16EE01021 8.21\n",
+      "16EE01023 7.19\n",
+      "16EE01022 7.94\n",
+      "16EE01025 7.89\n",
+      "16EE01024 9.60\n",
+      "16EE01027 6.53\n",
+      "16EE01026 6.55\n",
+      "16EE01039 6.04\n",
+      "16EE01032 6.85\n",
+      "16EE01031 7.79\n",
+      "16EE01036 8.00\n",
+      "16EE01037 6.11\n",
+      "16EE01034 6.87\n",
+      "16EE01033 7.64\n",
+      "16EE01030 8.72\n",
+      "16EE01006 8.15\n",
+      "16EE01003 8.53\n",
+      "16EE01011 9.00\n",
+      "16EE01009 9.21\n",
+      "16EE01035 7.77\n",
+      "16EE01013 8.64\n",
+      "16EE01014 8.38\n",
+      "16EE01016 8.23\n",
+      "16EE01002 7.30\n",
+      "16EE01008 8.57\n",
+      "16EE01017 8.30\n",
+      "16EE01005 6.85\n",
       "Total Stuents: 38\n"
      ]
     }
@@ -40,10 +81,13 @@
     "    # Filter out 16EE data\n",
     "    data = {k:v for (k,v) in data.items() if '16EE' in k[:4]}\n",
     "    \n",
+    "for (k,v) in data.items():\n",
+    "    print(k,v['cgpa'][1])\n",
+    "    \n",
     "with open('course.json') as c:\n",
     "    cdata = json.load(c)\n",
     "    \n",
-    "print (\"Total Stuents: %s\" % len(data))"
+    "print (\"Total Stuents: %s\" % len(data))\n"
    ]
   },
   {
@@ -55,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 375,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 376,
+   "execution_count": 9,
    "metadata": {
     "scrolled": true
    },
@@ -107,27 +151,36 @@
       "Highest: 9.47\n",
       " Lowest: 8.94\n",
       "Average: 9.205\n",
-      " Median: 9.205\n"
+      " Median: 9.205\n",
+      "Standard Deviation: 0.26500000000000057 \n"
      ]
     }
    ],
    "source": [
     "original_strengeth = 40\n",
-    "bc_cgpa = [v['cgpa'][1] for (k, v) in data.items() if int(k[-2:]) > 40]\n",
-    "bc_cgpa = (np.array(bc_cgpa, dtype='float'))\n",
+    "bc_cgpa = [v['cgpa'][1] for (k, v) in data.items() if (int(k[-2:]) > 40 and v['cgpa'][2] !='WH')]\n",
+    "cgpa = [v['cgpa'][2] for (k,v) in data.items() if v['cgpa'][2] != 'WH' ]\n",
     "\n",
-    "bc_count = bc_cgpa.size\n",
-    "bc_highest = np.max(bc_cgpa)\n",
-    "bc_lowest = np.min(bc_cgpa)\n",
-    "bc_average = np.mean(bc_cgpa)\n",
-    "bc_median = np.median(bc_cgpa)\n",
+    "bc_cgpa = (np.array(bc_cgpa, dtype='float'))\n",
+    "cgpa = (np.array(cgpa, dtype='float'))\n",
     "\n",
-    "print (\"Total branch changers: %s\" % bc_count)\n",
-    "print (\"\\nCGPA (after 2nd sem) for branch change:-\")\n",
-    "print (\"Highest: %s\" % bc_highest)\n",
-    "print (\" Lowest: %s\" % bc_lowest)\n",
-    "print (\"Average: %s\" % bc_average)\n",
-    "print (\" Median: %s\" % bc_median)"
+    "if len(bc_cgpa>0):\n",
+    "    bc_count = bc_cgpa.size\n",
+    "    bc_highest = np.max(bc_cgpa)\n",
+    "    bc_lowest = np.min(bc_cgpa)\n",
+    "    bc_average = np.mean(bc_cgpa)\n",
+    "    bc_median = np.median(bc_cgpa)\n",
+    "    bc_std = np.std(bc_cgpa)\n",
+    "    print (\"Total branch changers: %s\" % bc_count)\n",
+    "    print (\"\\nCGPA (after 2nd sem) for branch change:-\")\n",
+    "    print (\"Highest: %s\" % bc_highest)\n",
+    "    print (\" Lowest: %s\" % bc_lowest)\n",
+    "    print (\"Average: %s\" % bc_average)\n",
+    "    print (\" Median: %s\" % bc_median)\n",
+    "    print(\"Standard Deviation: %s \" %bc_std)\n",
+    "else:\n",
+    "    print('no branch changer')\n",
+    "    pass\n"
    ]
   },
   {
@@ -139,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 438,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
@@ -419,16 +472,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>EE1L001</td>\n",
-       "      <td>Electrical Technology</td>\n",
-       "      <td>4</td>\n",
+       "      <td>CS1P001</td>\n",
+       "      <td>Introduction to Programing and Data Structures...</td>\n",
+       "      <td>2</td>\n",
        "      <td>38</td>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>11</td>\n",
+       "      <td>7</td>\n",
        "      <td>8</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -437,16 +490,16 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>CS1P001</td>\n",
-       "      <td>Introduction to Programing and Data Structures...</td>\n",
-       "      <td>2</td>\n",
+       "      <td>EE1L001</td>\n",
+       "      <td>Electrical Technology</td>\n",
+       "      <td>4</td>\n",
        "      <td>38</td>\n",
-       "      <td>7</td>\n",
-       "      <td>8</td>\n",
-       "      <td>7</td>\n",
+       "      <td>4</td>\n",
        "      <td>9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -689,39 +742,39 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
-       "      <td>ME1P001</td>\n",
-       "      <td>Introduction to Manufacturing Processes</td>\n",
+       "      <td>PH1P001</td>\n",
+       "      <td>Physics Laboratory</td>\n",
        "      <td>2</td>\n",
        "      <td>38</td>\n",
-       "      <td>3</td>\n",
-       "      <td>17</td>\n",
+       "      <td>5</td>\n",
        "      <td>14</td>\n",
-       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>5</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>8.50</td>\n",
-       "      <td>9.0</td>\n",
+       "      <td>8.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
-       "      <td>PH1P001</td>\n",
-       "      <td>Physics Laboratory</td>\n",
+       "      <td>ME1P001</td>\n",
+       "      <td>Introduction to Manufacturing Processes</td>\n",
        "      <td>2</td>\n",
        "      <td>38</td>\n",
-       "      <td>5</td>\n",
-       "      <td>14</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
        "      <td>14</td>\n",
-       "      <td>5</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>8.50</td>\n",
-       "      <td>8.5</td>\n",
+       "      <td>9.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
@@ -868,8 +921,8 @@
        "10      CS1L001     Introduction to Programing and Data Structures       4   \n",
        "11      EC2L002                                Signals and Systems       4   \n",
        "12      ID2L001     Entrepreneurship and Small Business Management       3   \n",
-       "13      EE1L001                              Electrical Technology       4   \n",
-       "14      CS1P001  Introduction to Programing and Data Structures...       2   \n",
+       "13      CS1P001  Introduction to Programing and Data Structures...       2   \n",
+       "14      EE1L001                              Electrical Technology       4   \n",
        "15      EC2P001             Introduction to Electronics Laboratory       2   \n",
        "16      ID2L002          Introduction to Bioscience and Technology       2   \n",
        "17      ID2L003    Environmental Science Technology and Management       2   \n",
@@ -883,8 +936,8 @@
        "25      EE2L003                                    Electric Machin       4   \n",
        "26      ME2L501                 Elements of Mechanical Engineering       3   \n",
        "27      HS2L007                          Introduction to Economics       4   \n",
-       "28      ME1P001            Introduction to Manufacturing Processes       2   \n",
-       "29      PH1P001                                 Physics Laboratory       2   \n",
+       "28      PH1P001                                 Physics Laboratory       2   \n",
+       "29      ME1P001            Introduction to Manufacturing Processes       2   \n",
        "30      EE1P001                   Electrical Technology Laboratory       2   \n",
        "31      EE2P003                       Electric Machines Laboratory       2   \n",
        "32      CY1P001                               Chemistry Laboratory       2   \n",
@@ -907,8 +960,8 @@
        "10        38   5  10  10   3   5  5  0   0      0     7.79     8.0  \n",
        "11        38   2   9  13  10   3  1  0   0      0     7.84     8.0  \n",
        "12        56   3  14  20  14   3  1  1   0      0     7.89     8.0  \n",
-       "13        38   4   9  11   8   6  0  0   0      0     7.92     8.0  \n",
-       "14        38   7   8   7   9   5  2  0   0      0     7.92     8.0  \n",
+       "13        38   7   8   7   9   5  2  0   0      0     7.92     8.0  \n",
+       "14        38   4   9  11   8   6  0  0   0      0     7.92     8.0  \n",
        "15        38   2   7  18  10   1  0  0   0      0     7.97     8.0  \n",
        "16        38   8   9   7   7   3  3  1   0      0     8.00     8.0  \n",
        "17        38   2  11  12   7   4  0  0   2      0     8.00     8.0  \n",
@@ -922,8 +975,8 @@
        "25        38   5  10  12   9   0  0  0   2      0     8.31     8.0  \n",
        "26        18   2   7   4   4   0  0  0   1      0     8.41     9.0  \n",
        "27         8   2   2   0   3   0  0  0   1      0     8.43     9.0  \n",
-       "28        38   3  17  14   4   0  0  0   0      0     8.50     9.0  \n",
-       "29        38   5  14  14   5   0  0  0   0      0     8.50     8.5  \n",
+       "28        38   5  14  14   5   0  0  0   0      0     8.50     8.5  \n",
+       "29        38   3  17  14   4   0  0  0   0      0     8.50     9.0  \n",
        "30        38   5  17  13   1   2  0  0   0      0     8.58     9.0  \n",
        "31        38   5  16  11   4   0  0  0   2      0     8.61     9.0  \n",
        "32        38   6  18   9   5   0  0  0   0      0     8.66     9.0  \n",
@@ -933,7 +986,7 @@
        "36        38  17  15   6   0   0  0  0   0      0     9.29     9.0  "
       ]
      },
-     "execution_count": 438,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -998,9 +1051,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 437,
+   "execution_count": 8,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -1009,58 +1062,245 @@
      "text": [
       "Top 5 Students:\n",
       "\n",
-      "MANAPURAM JYOTHI VENKATA SAI ADITYA\n",
-      "AKASH MOHAPATRA\n",
-      "AYUSH SHARMA\n",
-      "DEBJIT CHATTOPADHYAY\n",
-      "KAUSTAV BHATTACHARYA\n",
+      "16EE01024  AKASH MOHAPATRA  9.41\n",
+      "16EE01041  AYUSH SHARMA  9.36\n",
+      "16EE01001  MANAPURAM JYOTHI VENKATA SAI ADITYA  9.30\n",
+      "16EE01011  DEBJIT CHATTOPADHYAY  9.21\n",
+      "16EE01010  KAUSTAV BHATTACHARYA  9.08\n",
       "\n",
       "CGPA:\n",
-      "Highest: 9.43\n",
+      "Highest: 9.41\n",
+      "lowest: 5.63\n",
       " Median: 8.02\n",
-      "Average: 7.89\n",
+      "Average: 7.87\n",
+      "Standard Deviation: 0.95 \n",
+      "\n",
       "   9.5+: 0\n",
       "  9-9.5: 5\n",
       "  8.5-9: 5\n",
-      "  8-8.5: 8\n",
-      "  7.5-8: 7\n",
+      "  8-8.5: 10\n",
+      "  7.5-8: 6\n",
       "  7-7.5: 4\n",
-      "     7-: 7\n"
+      "     7-: 8\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "cgpa = list()\n",
+    "\n",
+    "roll_and_cgpa = []\n",
     "for (k, v) in data.items():\n",
     "    try:\n",
-    "        cgpa.append((float(v['cgpa'][-1]), k))\n",
+    "        roll_and_cgpa.append((k,float(v['cgpa'][2])))\n",
     "    except:\n",
     "        pass\n",
     "\n",
+    "    \n",
+    "\n",
     "def sortbycg(l):\n",
-    "   return l[0]\n",
+    "   return l[1]\n",
+    "\n",
+    "def sortbyroll(l):\n",
+    "    return int(l[0][7:9])\n",
+    "\n",
+    "\n",
+    "sorted_cgpa = roll_and_cgpa[:]\n",
+    "sorted_cgpa.sort(key=sortbycg)\n",
+    "\n",
     "\n",
-    "cgpa.sort(key=sortbycg)\n",
     "print(\"Top 5 Students:\\n\")\n",
-    "for element in cgpa[:-6:-1]:\n",
-    "    print('%s' % (data[element[1]]['name']))\n",
-    "cgpa = np.array([element[0] for element in cgpa], dtype='float')\n",
+    "for element in sorted_cgpa[:-6:-1]:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "    \n",
+    "roll_and_cgpa.sort(key=sortbyroll)\n",
+    "\n",
+    "# for individual cgpa \n",
+    "\n",
+    "# print(\"Students with theri cgpa :\\n\")\n",
+    "# for element in roll_and_cgpa:\n",
+    "#     print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "\n",
+    "cgpa = np.array([element[1] for element in roll_and_cgpa], dtype='float')\n",
+    "\n",
+    "roll = np.array([element[0][7:9] for element in roll_and_cgpa],dtype='int')\n",
+    "\n",
+    "\n",
     "cgpa_average = round(np.mean(cgpa), 2)\n",
     "cgpa_median = round(np.median(cgpa), 2)\n",
     "cgpa_highest = round(np.max(cgpa), 2)\n",
+    "cgpa_lowest = round(np.min(cgpa),2)\n",
+    "cgpa_standard_deviation = round(np.std(cgpa),2)\n",
     "\n",
     "print(\"\\nCGPA:\")\n",
     "print(\"Highest: %s\" % cgpa_highest)\n",
+    "print(\"lowest: %s\"% cgpa_lowest)\n",
     "print(\" Median: %s\" % cgpa_median)\n",
     "print(\"Average: %s\" % cgpa_average)\n",
+    "print(\"Standard Deviation: %s \\n\"% cgpa_standard_deviation)\n",
+    "\n",
     "print(\"   9.5+: %s\" % len([cg for cg in cgpa if cg >= 9.5]))\n",
     "print(\"  9-9.5: %s\" % len([cg for cg in cgpa if cg >= 9 and cg < 9.5]))\n",
     "print(\"  8.5-9: %s\" % len([cg for cg in cgpa if cg >= 8.5 and cg < 9]))\n",
     "print(\"  8-8.5: %s\" % len([cg for cg in cgpa if cg >= 8 and cg < 8.5]))\n",
     "print(\"  7.5-8: %s\" % len([cg for cg in cgpa if cg >= 7.5 and cg < 8]))\n",
     "print(\"  7-7.5: %s\" % len([cg for cg in cgpa if cg >= 7 and cg < 7.5]))\n",
-    "print(\"     7-: %s\" % len([cg for cg in cgpa if cg < 7]))"
+    "print(\"     7-: %s\" % len([cg for cg in cgpa if cg < 7]))\n",
+    "\n",
+    "\n",
+    "unit_array = np.ones(len(roll),dtype='int')\n",
+    "fig,cgpa_plot = plt.subplots()\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "label=['above mean','below mean']\n",
+    "cgpa_plot.plot(roll,cgpa,label='above mean cgpa',color='green')\n",
+    "cgpa_plot.plot(roll,cgpa_median*unit_array,label='below mean cgpa',color='red')\n",
+    "cgpa_plot.grid(b = True)\n",
+    "cgpa_plot.legend()\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa >= cgpa_median*unit_array,facecolor = 'green',interpolate = True )\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa< cgpa_median*unit_array,facecolor = 'red',interpolate = True )\n",
+    "plt.xlabel('roll number')\n",
+    "plt.ylabel('cgpa (0-10)', horizontalalignment='right',rotation =0)\n",
+    "plt.title('cgpa distribution wrt roll number')\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\n')\n",
+    "#plt.fill(cgpa,cgpa_median*median,'r')g\n",
+    "sorted_cgpa=cgpa[:]\n",
+    "sorted_cgpa.sort()\n",
+    "fig,histo_cgpa = plt.subplots()\n",
+    "histo_cgpa.hist(sorted_cgpa,bins=40)\n",
+    "#histo_cgpa.fill_between(roll,histo_cgpa,where =sorted_cgpa >= 8.0,facecolor = 'green',interpolate = True )\n",
+    "#histo_cgpa.fill(histo_cgpa, where = sorted_cgpa >= cgpa_median)\n",
+    "plt.title('frequency distribution of class cgpa')\n",
+    "\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' number\\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.show()\n",
+    "#plt.plot(norm.pdf(cgpa_in_seq,cgpa_median,cgpa_standard_deviation))\n",
+    "\n",
+    "print('\\n')\n",
+    "fig = norm.pdf(sorted_cgpa, np.mean(sorted_cgpa), np.std(sorted_cgpa))\n",
+    "plt.plot(sorted_cgpa,fig,'-o')\n",
+    "plt.title('standard distribution of class cgpa')\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' fraction \\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.grid(b = True)\n",
+    "plt.show()\n",
+    "\n",
+    "above_mean = [cg for cg in cgpa if cg >= cgpa_median]\n",
+    "below_mean = [cg for cg in cgpa if cg < cgpa_median]\n",
+    "\n",
+    "print('\\n')\n",
+    "labels = 'above mean','below mean'\n",
+    "sizes = [len(above_mean),len(below_mean)]\n",
+    "#print(len(above_mean),len(below_mean))\n",
+    "explode = (0.1,0)\n",
+    "colors = ['green','red']\n",
+    "# Plot\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
+    "        autopct='%2.1f%%', shadow=True, startangle=140)\n",
+    " \n",
+    "plt.axis('equal')\n",
+    "plt.title('cgpa distribution wrt mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# results plot \n",
+    "this plot shows that students from roll number between 1 to 24 shows a good aveage performance while those at the roll number between 32 to 40 have very low average. these students need to receive better support in coming semester to impove their performance.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "https://analytics4all.org/2016/05/05/python-histograms-and-frequency-distribution/\n",
+    "\n",
+    "https://etav.github.io/python/count_basic_freq_plot.html for histogram frequency distribution\n",
+    "\n",
+    "https://stackoverflow.com/questions/20011494/plot-normal-distribution-with-matplotlib for ploting the standard deviation"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1079,7 +1319,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,
diff --git a/year/2016/16ME01.ipynb b/year/2016/16ME01.ipynb
new file mode 100644
index 0000000..1dd2162
--- /dev/null
+++ b/year/2016/16ME01.ipynb
@@ -0,0 +1,1407 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# B.Tech 2016-20 Electrical Enginnering (Spring 2018)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime as dt\n",
+    "import json\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16ME01007 7.98\n",
+      "16ME01006 8.04\n",
+      "16ME01005 7.60\n",
+      "16ME01004 9.21\n",
+      "16ME01003 8.04\n",
+      "16ME01002 8.26\n",
+      "16ME01001 6.81\n",
+      "16ME01009 8.77\n",
+      "16ME01008 6.48\n",
+      "16ME01019 8.40\n",
+      "16ME01011 7.06\n",
+      "16ME01012 8.32\n",
+      "16ME01014 8.21\n",
+      "16ME01017 6.67\n",
+      "16ME01040 6.02\n",
+      "16ME01021 7.83\n",
+      "16ME01023 7.38\n",
+      "16ME01022 8.49\n",
+      "16ME01025 5.87\n",
+      "16ME01024 6.36\n",
+      "16ME01027 7.30\n",
+      "16ME01026 7.32\n",
+      "16ME01029 7.79\n",
+      "16ME01028 6.89\n",
+      "16ME01032 6.47\n",
+      "16ME01033 6.72\n",
+      "16ME01030 7.02\n",
+      "16ME01031 8.19\n",
+      "16ME01036 6.02\n",
+      "16ME01037 6.49\n",
+      "16ME01034 5.68\n",
+      "16ME01038 6.91\n",
+      "16ME01039 6.30\n",
+      "16ME01041 7.09\n",
+      "Total Stuents: 34\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('stres.json') as f:\n",
+    "    data = json.load(f)\n",
+    "    # Filter out 16EE data\n",
+    "    data = {k:v for (k,v) in data.items() if '16ME01' in k[:6]}\n",
+    "    \n",
+    "for (k,v) in data.items():\n",
+    "    try:\n",
+    "        print(k,v['cgpa'][1])\n",
+    "    except:\n",
+    "        pass\n",
+    "    \n",
+    "with open('course.json') as c:\n",
+    "    cdata = json.load(c)\n",
+    "    \n",
+    "print (\"Total Stuents: %s\" % len(data))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Date of Birth analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Average:     June 09, 1998\n",
+      "  Median:     June 06, 1998\n",
+      "  Oldest:    March 25, 1997\n",
+      "Youngest:   August 17, 1999\n"
+     ]
+    }
+   ],
+   "source": [
+    "dob = [v['dob'] for (k, v) in data.items()]\n",
+    "\n",
+    "np_dob = (np.array(dob, dtype='datetime64[s]').view('i8'))\n",
+    "average_dob = np.mean(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "median_dob  = np.median(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "minimum_dob = np.min(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "maximum_dob = np.max(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "print (\" Average: %s\" % average_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Median: %s\" % median_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Oldest: %s\" % minimum_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"Youngest: %s\" % maximum_dob.strftime(\"%8B %d, %Y\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Branch Change Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total branch changers: 1\n",
+      "\n",
+      "CGPA (after 2nd sem) for branch change:-\n",
+      "Highest: 7.09\n",
+      " Lowest: 7.09\n",
+      "Average: 7.09\n",
+      " Median: 7.09\n",
+      "Standard Deviation: 0.0 \n"
+     ]
+    }
+   ],
+   "source": [
+    "original_strengeth = 40\n",
+    "bc_cgpa = [v['cgpa'][1] for (k, v) in data.items() if (int(k[-2:]) > 40 and v['cgpa'][1] !='WH')]\n",
+    "cgpa = [v['cgpa'][2] for (k,v) in data.items() if v['cgpa'][2] != 'WH' ]\n",
+    "\n",
+    "bc_cgpa = (np.array(bc_cgpa, dtype='float'))\n",
+    "cgpa = (np.array(cgpa, dtype='float'))\n",
+    "\n",
+    "if len(bc_cgpa>0):\n",
+    "    bc_count = bc_cgpa.size\n",
+    "    bc_highest = np.max(bc_cgpa)\n",
+    "    bc_lowest = np.min(bc_cgpa)\n",
+    "    bc_average = np.mean(bc_cgpa)\n",
+    "    bc_median = np.median(bc_cgpa)\n",
+    "    bc_std = np.std(bc_cgpa)\n",
+    "    print (\"Total branch changers: %s\" % bc_count)\n",
+    "    print (\"\\nCGPA (after 2nd sem) for branch change:-\")\n",
+    "    print (\"Highest: %s\" % bc_highest)\n",
+    "    print (\" Lowest: %s\" % bc_lowest)\n",
+    "    print (\"Average: %s\" % bc_average)\n",
+    "    print (\" Median: %s\" % bc_median)\n",
+    "    print(\"Standard Deviation: %s \" %bc_std)\n",
+    "else:\n",
+    "    print('no branch changer')\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Course wise analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Subject Code</th>\n",
+       "      <th>Subject Name</th>\n",
+       "      <th>Credits</th>\n",
+       "      <th>Students</th>\n",
+       "      <th>EX</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>P</th>\n",
+       "      <th>F</th>\n",
+       "      <th>WH</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>Average</th>\n",
+       "      <th>Median</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MA2L004</td>\n",
+       "      <td>Partial Differential Equations</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.88</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ME1L001</td>\n",
+       "      <td>Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.32</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EC2L005</td>\n",
+       "      <td>Analog Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.50</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PH1L001</td>\n",
+       "      <td>Physics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "      <td>11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.59</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CE2L011</td>\n",
+       "      <td>Building materials and Construction</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.60</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>HS2L002</td>\n",
+       "      <td>Speaking and Presentation</td>\n",
+       "      <td>4</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.62</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>ID2L001</td>\n",
+       "      <td>Entrepreneurship and Small Business Management</td>\n",
+       "      <td>3</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.77</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>EE2L004</td>\n",
+       "      <td>Introduction to Electromagnetic Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.86</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>ME2L003</td>\n",
+       "      <td>Thermodynamics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.88</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>MA1L002</td>\n",
+       "      <td>Mathematics - II</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.94</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>HS1L002</td>\n",
+       "      <td>Learning English</td>\n",
+       "      <td>4</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.96</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>ME2L001</td>\n",
+       "      <td>Theory of Machines - I</td>\n",
+       "      <td>4</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>EC2L007</td>\n",
+       "      <td>Communication Systems</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CS1P001</td>\n",
+       "      <td>Introduction to Programing and Data Structures...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.09</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>CS1L001</td>\n",
+       "      <td>Introduction to Programing and Data Structures</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.12</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>EE1L001</td>\n",
+       "      <td>Electrical Technology</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.12</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>ME2L002</td>\n",
+       "      <td>Fluid Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.18</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>ID2L002</td>\n",
+       "      <td>Introduction to Bioscience and Technology</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.18</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>MA2L005</td>\n",
+       "      <td>Transform Calculus</td>\n",
+       "      <td>3</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.24</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>CY1L001</td>\n",
+       "      <td>Chemistry</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.26</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>ID3L003</td>\n",
+       "      <td>Environmental Science, Technology and Management</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.30</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>MA1L001</td>\n",
+       "      <td>Mathematics -1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.38</td>\n",
+       "      <td>7.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>ME2L005</td>\n",
+       "      <td>Theory of Machines - II</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>12</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.42</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>ME2L004</td>\n",
+       "      <td>Mechanics of Solids</td>\n",
+       "      <td>4</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.70</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>CS2L003</td>\n",
+       "      <td>Data Structure</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>ME2P004</td>\n",
+       "      <td>Materials Testing Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.81</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>EE1P001</td>\n",
+       "      <td>Electrical Technology Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>14</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.85</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>PH1P001</td>\n",
+       "      <td>Physics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>18</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.91</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>ID1T002</td>\n",
+       "      <td>Extra Academic Activities - 2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7.94</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>ME2P003</td>\n",
+       "      <td>Machines &amp; Mechanisms Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>17</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.09</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>HS2L007</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.11</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>EC2P005</td>\n",
+       "      <td>Analog Communication Lab</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.12</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>CE1P001</td>\n",
+       "      <td>Engineering Drawing and Graphics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>12</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.15</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>ME2S001</td>\n",
+       "      <td>Project Seminar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>11</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.24</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>ME1P001</td>\n",
+       "      <td>Introduction to Manufacturing Processes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>14</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.29</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>CY1P001</td>\n",
+       "      <td>Chemistry Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>16</td>\n",
+       "      <td>12</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.35</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>ME2P001</td>\n",
+       "      <td>Workshop Processes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>33</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.48</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>HS2L004</td>\n",
+       "      <td>Odissi Dance - I</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.50</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>ME2P002</td>\n",
+       "      <td>Fluid Mechanics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>34</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.74</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>HS1L001</td>\n",
+       "      <td>English for Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.83</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>ID1T001</td>\n",
+       "      <td>Extra Academic Activities -1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>15</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.18</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Subject Code                                       Subject Name Credits  \\\n",
+       "0       MA2L004                     Partial Differential Equations       4   \n",
+       "1       ME1L001                                          Mechanics       4   \n",
+       "2       EC2L005                               Analog Communication       4   \n",
+       "3       PH1L001                                            Physics       4   \n",
+       "4       CE2L011                Building materials and Construction       3   \n",
+       "5       HS2L002                          Speaking and Presentation       4   \n",
+       "6       ID2L001     Entrepreneurship and Small Business Management       3   \n",
+       "7       EE2L004        Introduction to Electromagnetic Engineering       3   \n",
+       "8       ME2L003                                     Thermodynamics       3   \n",
+       "9       MA1L002                                   Mathematics - II       4   \n",
+       "10      HS1L002                                   Learning English       4   \n",
+       "11      ME2L001                             Theory of Machines - I       4   \n",
+       "12      EC2L007                              Communication Systems       3   \n",
+       "13      CS1P001  Introduction to Programing and Data Structures...       2   \n",
+       "14      CS1L001     Introduction to Programing and Data Structures       4   \n",
+       "15      EE1L001                              Electrical Technology       4   \n",
+       "16      ME2L002                                    Fluid Mechanics       4   \n",
+       "17      ID2L002          Introduction to Bioscience and Technology       2   \n",
+       "18      MA2L005                                 Transform Calculus       3   \n",
+       "19      CY1L001                                          Chemistry       4   \n",
+       "20      ID3L003   Environmental Science, Technology and Management       2   \n",
+       "21      MA1L001                                     Mathematics -1       4   \n",
+       "22      ME2L005                            Theory of Machines - II       4   \n",
+       "23      ME2L004                                Mechanics of Solids       4   \n",
+       "24      CS2L003                                     Data Structure       3   \n",
+       "25      ME2P004                       Materials Testing Laboratory       2   \n",
+       "26      EE1P001                   Electrical Technology Laboratory       2   \n",
+       "27      PH1P001                                 Physics Laboratory       2   \n",
+       "28      ID1T002                      Extra Academic Activities - 2       1   \n",
+       "29      ME2P003                   Machines & Mechanisms Laboratory       2   \n",
+       "30      HS2L007                          Introduction to Economics       4   \n",
+       "31      EC2P005                           Analog Communication Lab       2   \n",
+       "32      CE1P001                   Engineering Drawing and Graphics       3   \n",
+       "33      ME2S001                                    Project Seminar       2   \n",
+       "34      ME1P001            Introduction to Manufacturing Processes       2   \n",
+       "35      CY1P001                               Chemistry Laboratory       2   \n",
+       "36      ME2P001                                 Workshop Processes       2   \n",
+       "37      HS2L004                                   Odissi Dance - I       3   \n",
+       "38      ME2P002                         Fluid Mechanics Laboratory       2   \n",
+       "39      HS1L001                          English for Communication       4   \n",
+       "40      ID1T001                       Extra Academic Activities -1       1   \n",
+       "\n",
+       "    Students  EX   A   B   C   D   P  F  WH  Other  Average  Median  \n",
+       "0         34   1   2   3   2   3  17  5   1      0     5.88     5.0  \n",
+       "1         34   0   1   5   8  10  10  0   0      0     6.32     6.0  \n",
+       "2         34   1   2   6   8   4   6  7   0      0     6.50     6.5  \n",
+       "3         34   0   1   5  12  11   4  1   0      0     6.59     7.0  \n",
+       "4          6   0   0   1   2   1   1  0   1      0     6.60     7.0  \n",
+       "5         13   0   0   3   3   6   1  0   0      0     6.62     6.0  \n",
+       "6         40   1   4   6  11   8   7  2   1      0     6.77     7.0  \n",
+       "7          7   0   2   0   2   1   2  0   0      0     6.86     7.0  \n",
+       "8         34   1   2   8   8   9   5  0   1      0     6.88     7.0  \n",
+       "9         34   1   5   9   5   4  10  0   0      0     6.94     7.0  \n",
+       "10        28   0   2   6  10   7   2  0   0      1     6.96     7.0  \n",
+       "11        33   2   2   6  11   6   3  2   0      1     7.00     7.0  \n",
+       "12         4   0   0   1   2   1   0  0   0      0     7.00     7.0  \n",
+       "13        34   1   3   7  10  13   0  0   0      0     7.09     7.0  \n",
+       "14        34   0   6   7  11   5   5  0   0      0     7.12     7.0  \n",
+       "15        34   1   3   7  15   4   4  0   0      0     7.12     7.0  \n",
+       "16        34   0   6   8   9   8   3  0   0      0     7.18     7.0  \n",
+       "17        34   3   2   8   7  13   1  0   0      0     7.18     7.0  \n",
+       "18        33   2   3   6  16   2   2  2   0      0     7.24     7.0  \n",
+       "19        34   3   6   6   8   4   7  0   0      0     7.26     7.0  \n",
+       "20        34   0   7   7  10   7   2  0   1      0     7.30     7.0  \n",
+       "21        34   3   4  10   9   2   6  0   0      0     7.38     7.5  \n",
+       "22        34   1   5   8  12   7   0  0   1      0     7.42     7.0  \n",
+       "23        34   1   3  16  11   2   0  0   1      0     7.70     8.0  \n",
+       "24        17   1   4   5   5   1   1  0   0      0     7.76     8.0  \n",
+       "25        34   0   6  14  12   0   0  0   2      0     7.81     8.0  \n",
+       "26        34   1   8  14   8   2   1  0   0      0     7.85     8.0  \n",
+       "27        34   0   8  18   6   1   1  0   0      0     7.91     8.0  \n",
+       "28        34   1   8  13   8   2   0  0   0      2     7.94     8.0  \n",
+       "29        34   3   5  17   8   0   0  0   1      0     8.09     8.0  \n",
+       "30         9   1   0   7   1   0   0  0   0      0     8.11     8.0  \n",
+       "31        34   3  11  10   7   3   0  0   0      0     8.12     8.0  \n",
+       "32        34   1  13  12   6   2   0  0   0      0     8.15     8.0  \n",
+       "33        34   3  12  11   6   2   0  0   0      0     8.24     8.0  \n",
+       "34        34   2  12  14   6   0   0  0   0      0     8.29     8.0  \n",
+       "35        34   1  16  12   4   1   0  0   0      0     8.35     8.5  \n",
+       "36        33   2  14  15   2   0   0  0   0      0     8.48     8.0  \n",
+       "37         6   2   1   2   0   1   0  0   0      0     8.50     8.5  \n",
+       "38        34   7  13  12   2   0   0  0   0      0     8.74     9.0  \n",
+       "39         6   0   5   1   0   0   0  0   0      0     8.83     9.0  \n",
+       "40        34  15  11   7   1   0   0  0   0      0     9.18     9.0  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "courses = dict()\n",
+    "for (k, v) in data.items():\n",
+    "    for (sem, scourses) in v['grades'].items():\n",
+    "        for (course, grade) in scourses.items():\n",
+    "            if course not in courses:\n",
+    "                courses[course] = list()\n",
+    "                courses[course].append(grade)\n",
+    "            else:\n",
+    "                courses[course].append(grade)\n",
+    "\n",
+    "clist = list()\n",
+    "\n",
+    "def other_grade(l):\n",
+    "    return len(l) - l.count('EX') - l.count('A') - l.count('B') - l.count('C') - l.count('D') - l.count('P') - l.count('F') - l.count('WH')\n",
+    "\n",
+    "def analyze_grade(l):\n",
+    "    grade_hash = {'EX': 10, 'A': 9, 'B': 8, 'C': 7, 'D': 6, 'P': 5, 'F': 5}\n",
+    "    hashed_grade = list()\n",
+    "    for grade in l:\n",
+    "        if grade in grade_hash:\n",
+    "            hashed_grade.append(grade_hash[grade])\n",
+    "    hashed_grade = (np.array(hashed_grade, dtype='float'))\n",
+    "    if hashed_grade.size == 0:\n",
+    "        return {'average': 0, 'median': 0}\n",
+    "    return {'average': round(np.mean(hashed_grade), 2), 'median': round(np.median(hashed_grade), 2)}\n",
+    "\n",
+    "grade_labels = 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other'\n",
+    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'orange', 'red', 'gray', 'black']\n",
+    "\n",
+    "for course, grades in courses.items():\n",
+    "    course_info = cdata[course]\n",
+    "    clist.append((course, course_info['subnane'], course_info['credit'], len(grades), grades.count('EX'), grades.count('A'), \n",
+    "                 grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "                 grades.count('F'), grades.count('WH'), other_grade(grades), analyze_grade(grades)['average'], analyze_grade(grades)['median']))\n",
+    "#     patches, texts = plt.pie([grades.count('EX'), grades.count('A'), \n",
+    "#                  grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "#                  grades.count('F'), grades.count('WH'), other_grade(grades)], labels=grade_labels, colors=colors)\n",
+    "#     plt.axis('equal')\n",
+    "#     plt.legend(patches, labels, loc=\"best\")\n",
+    "#     plt.show()\n",
+    "def sortByAverage(element):\n",
+    "    return element[-2]\n",
+    "\n",
+    "clist.sort(key=sortByAverage)\n",
+    "df = pd.DataFrame(data = clist)\n",
+    "df.columns = ['Subject Code', 'Subject Name', 'Credits', 'Students', 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other', 'Average', 'Median']\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CGPA Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 5 Students:\n",
+      "\n",
+      "16ME01004  ABHISHEK MISHRA  9.34\n",
+      "16ME01009  VIMAL SHARMA  8.65\n",
+      "16ME01019  RAJNEESH AWASTHI  8.59\n",
+      "16ME01014  SHIVAM SHARMA  8.53\n",
+      "16ME01031  METTA SRIRAM  8.43\n",
+      "\n",
+      "CGPA:\n",
+      "Highest: 9.34\n",
+      "lowest: 5.21\n",
+      " Median: 7.3\n",
+      "Average: 7.22\n",
+      "Standard Deviation: 1.06 \n",
+      "\n",
+      "   9.5+: 0\n",
+      "  9-9.5: 1\n",
+      "  8.5-9: 3\n",
+      "  8-8.5: 5\n",
+      "  7.5-8: 5\n",
+      "  7-7.5: 8\n",
+      "     7-: 12\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "roll_and_cgpa = []\n",
+    "for (k, v) in data.items():\n",
+    "    try:\n",
+    "        roll_and_cgpa.append((k,float(v['cgpa'][2])))\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "    \n",
+    "\n",
+    "def sortbycg(l):\n",
+    "   return l[1]\n",
+    "\n",
+    "def sortbyroll(l):\n",
+    "    return int(l[0][7:9])\n",
+    "\n",
+    "\n",
+    "sorted_cgpa = roll_and_cgpa[:]\n",
+    "sorted_cgpa.sort(key=sortbycg)\n",
+    "\n",
+    "\n",
+    "print(\"Top 5 Students:\\n\")\n",
+    "for element in sorted_cgpa[:-6:-1]:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "    \n",
+    "roll_and_cgpa.sort(key=sortbyroll)\n",
+    "\n",
+    "# for individual cgpa \n",
+    "\n",
+    "#print(\"Students with their cgpa :\\n\")\n",
+    "# for element in roll_and_cgpa:\n",
+    "#   #   print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "\n",
+    "cgpa = np.array([element[1] for element in roll_and_cgpa], dtype='float')\n",
+    "\n",
+    "roll = np.array([element[0][7:9] for element in roll_and_cgpa],dtype='int')\n",
+    "\n",
+    "\n",
+    "cgpa_average = round(np.mean(cgpa), 2)\n",
+    "cgpa_median = round(np.median(cgpa), 2)\n",
+    "cgpa_highest = round(np.max(cgpa), 2)\n",
+    "cgpa_lowest = round(np.min(cgpa),2)\n",
+    "cgpa_standard_deviation = round(np.std(cgpa),2)\n",
+    "\n",
+    "print(\"\\nCGPA:\")\n",
+    "print(\"Highest: %s\" % cgpa_highest)\n",
+    "print(\"lowest: %s\"% cgpa_lowest)\n",
+    "print(\" Median: %s\" % cgpa_median)\n",
+    "print(\"Average: %s\" % cgpa_average)\n",
+    "print(\"Standard Deviation: %s \\n\"% cgpa_standard_deviation)\n",
+    "\n",
+    "print(\"   9.5+: %s\" % len([cg for cg in cgpa if cg >= 9.5]))\n",
+    "print(\"  9-9.5: %s\" % len([cg for cg in cgpa if cg >= 9 and cg < 9.5]))\n",
+    "print(\"  8.5-9: %s\" % len([cg for cg in cgpa if cg >= 8.5 and cg < 9]))\n",
+    "print(\"  8-8.5: %s\" % len([cg for cg in cgpa if cg >= 8 and cg < 8.5]))\n",
+    "print(\"  7.5-8: %s\" % len([cg for cg in cgpa if cg >= 7.5 and cg < 8]))\n",
+    "print(\"  7-7.5: %s\" % len([cg for cg in cgpa if cg >= 7 and cg < 7.5]))\n",
+    "print(\"     7-: %s\" % len([cg for cg in cgpa if cg < 7]))\n",
+    "\n",
+    "\n",
+    "unit_array = np.ones(len(roll),dtype='int')\n",
+    "fig,cgpa_plot = plt.subplots()\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "label=['above mean','below mean']\n",
+    "cgpa_plot.plot(roll,cgpa,label='above mean cgpa',color='green')\n",
+    "cgpa_plot.plot(roll,cgpa_median*unit_array,label='below mean cgpa',color='red')\n",
+    "cgpa_plot.grid(b = True)\n",
+    "cgpa_plot.legend()\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa >= cgpa_median*unit_array,facecolor = 'green',interpolate = True )\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa< cgpa_median*unit_array,facecolor = 'red',interpolate = True )\n",
+    "plt.xlabel('roll number')\n",
+    "plt.ylabel('cgpa (0-10)', horizontalalignment='right',rotation =0)\n",
+    "plt.title('cgpa distribution wrt roll number')\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\n')\n",
+    "#plt.fill(cgpa,cgpa_median*median,'r')g\n",
+    "sorted_cgpa=cgpa[:]\n",
+    "sorted_cgpa.sort()\n",
+    "fig,histo_cgpa = plt.subplots()\n",
+    "histo_cgpa.hist(sorted_cgpa,bins=40)\n",
+    "#histo_cgpa.fill_between(roll,histo_cgpa,where =sorted_cgpa >= 8.0,facecolor = 'green',interpolate = True )\n",
+    "#histo_cgpa.fill(histo_cgpa, where = sorted_cgpa >= cgpa_median)\n",
+    "plt.title('frequency distribution of class cgpa')\n",
+    "\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' number\\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.show()\n",
+    "#plt.plot(norm.pdf(cgpa_in_seq,cgpa_median,cgpa_standard_deviation))\n",
+    "\n",
+    "print('\\n')\n",
+    "fig = norm.pdf(sorted_cgpa, np.mean(sorted_cgpa), np.std(sorted_cgpa))\n",
+    "plt.plot(sorted_cgpa,fig,'-o')\n",
+    "plt.title('standard distribution of class cgpa')\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' fraction \\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.grid(b = True)\n",
+    "plt.show()\n",
+    "\n",
+    "above_mean = [cg for cg in cgpa if cg >= cgpa_median]\n",
+    "below_mean = [cg for cg in cgpa if cg < cgpa_median]\n",
+    "\n",
+    "print('\\n')\n",
+    "labels = 'above mean','below mean'\n",
+    "sizes = [len(above_mean),len(below_mean)]\n",
+    "#print(len(above_mean),len(below_mean))\n",
+    "explode = (0.1,0)\n",
+    "colors = ['green','red']\n",
+    "# Plot\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
+    "        autopct='%2.1f%%', shadow=True, startangle=140)\n",
+    " \n",
+    "plt.axis('equal')\n",
+    "plt.title('cgpa distribution wrt mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# results plot \n",
+    "this class has lowest median among all other department batches.This is because of poor performance of students in roll number from 31 to 40.These students should be supported in improving their academic performance.\n",
+    "\n",
+    "\n",
+    "\n",
+    "https://analytics4all.org/2016/05/05/python-histograms-and-frequency-distribution/\n",
+    "\n",
+    "https://etav.github.io/python/count_basic_freq_plot.html for histogram frequency distribution\n",
+    "\n",
+    "https://stackoverflow.com/questions/20011494/plot-normal-distribution-with-matplotlib for ploting the standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/year/2016/16ME02.ipynb b/year/2016/16ME02.ipynb
new file mode 100644
index 0000000..4c76dba
--- /dev/null
+++ b/year/2016/16ME02.ipynb
@@ -0,0 +1,1409 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# B.Tech 2016-20 Electrical Enginnering (Spring 2018)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime as dt\n",
+    "import json\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16ME02021 9.34\n",
+      "16ME02019 6.64\n",
+      "16ME02018 7.74\n",
+      "16ME02010 8.49\n",
+      "16ME02013 WH\n",
+      "16ME02012 7.77\n",
+      "16ME02015 7.06\n",
+      "16ME02014 9.15\n",
+      "16ME02017 6.36\n",
+      "16ME02016 7.91\n",
+      "16ME02008 6.21\n",
+      "16ME02009 6.51\n",
+      "16ME02002 8.13\n",
+      "16ME02003 7.55\n",
+      "16ME02001 6.91\n",
+      "16ME02006 WH\n",
+      "16ME02007 7.30\n",
+      "16ME02004 8.36\n",
+      "16ME02005 8.06\n",
+      "16ME02020 9.43\n",
+      "16ME02022 8.96\n",
+      "Total Stuents: 21\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('stres.json') as f:\n",
+    "    data = json.load(f)\n",
+    "    # Filter out 16EE data\n",
+    "    data = {k:v for (k,v) in data.items() if '16ME02' in k[:6]}\n",
+    "    \n",
+    "for (k,v) in data.items():\n",
+    "    print(k,v['cgpa'][1])\n",
+    "    \n",
+    "with open('course.json') as c:\n",
+    "    cdata = json.load(c)\n",
+    "    \n",
+    "print (\"Total Stuents: %s\" % len(data))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Date of Birth analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Average: September 24, 1998\n",
+      "  Median:   August 19, 1998\n",
+      "  Oldest: December 18, 1996\n",
+      "Youngest:     July 10, 2000\n"
+     ]
+    }
+   ],
+   "source": [
+    "dob = [v['dob'] for (k, v) in data.items()]\n",
+    "\n",
+    "np_dob = (np.array(dob, dtype='datetime64[s]').view('i8'))\n",
+    "average_dob = np.mean(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "median_dob  = np.median(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "minimum_dob = np.min(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "maximum_dob = np.max(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "print (\" Average: %s\" % average_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Median: %s\" % median_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Oldest: %s\" % minimum_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"Youngest: %s\" % maximum_dob.strftime(\"%8B %d, %Y\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Branch Change Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total branch changers: 2\n",
+      "\n",
+      "CGPA (after 2nd sem) for branch change:-\n",
+      "Highest: 9.34\n",
+      " Lowest: 8.96\n",
+      "Average: 9.15\n",
+      " Median: 9.15\n",
+      "Standard Deviation: 0.1899999999999995 \n"
+     ]
+    }
+   ],
+   "source": [
+    "original_strengeth = 40\n",
+    "bc_cgpa = [v['cgpa'][1] for (k, v) in data.items() if (int(k[-2:]) > 20 and v['cgpa'][2] !='WH')]\n",
+    "cgpa = [v['cgpa'][2] for (k,v) in data.items() if v['cgpa'][2] != 'WH' ]\n",
+    "\n",
+    "bc_cgpa = (np.array(bc_cgpa, dtype='float'))\n",
+    "cgpa = (np.array(cgpa, dtype='float'))\n",
+    "\n",
+    "if len(bc_cgpa>0):\n",
+    "    bc_count = bc_cgpa.size\n",
+    "    bc_highest = np.max(bc_cgpa)\n",
+    "    bc_lowest = np.min(bc_cgpa)\n",
+    "    bc_average = np.mean(bc_cgpa)\n",
+    "    bc_median = np.median(bc_cgpa)\n",
+    "    bc_std = np.std(bc_cgpa)\n",
+    "    print (\"Total branch changers: %s\" % bc_count)\n",
+    "    print (\"\\nCGPA (after 2nd sem) for branch change:-\")\n",
+    "    print (\"Highest: %s\" % bc_highest)\n",
+    "    print (\" Lowest: %s\" % bc_lowest)\n",
+    "    print (\"Average: %s\" % bc_average)\n",
+    "    print (\" Median: %s\" % bc_median)\n",
+    "    print(\"Standard Deviation: %s \" %bc_std)\n",
+    "else:\n",
+    "    print('no branch changer')\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Course wise analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Subject Code</th>\n",
+       "      <th>Subject Name</th>\n",
+       "      <th>Credits</th>\n",
+       "      <th>Students</th>\n",
+       "      <th>EX</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>P</th>\n",
+       "      <th>F</th>\n",
+       "      <th>WH</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>Average</th>\n",
+       "      <th>Median</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EE2L004</td>\n",
+       "      <td>Introduction to Electromagnetic Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ME1L001</td>\n",
+       "      <td>Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.67</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>EC2L005</td>\n",
+       "      <td>Analog Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.76</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>MA2L004</td>\n",
+       "      <td>Partial Differential Equations</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.79</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HS2L002</td>\n",
+       "      <td>Speaking and Presentation</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.86</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>PH1L001</td>\n",
+       "      <td>Physics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.90</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>EC2L007</td>\n",
+       "      <td>Communication Systems</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>HS2L004</td>\n",
+       "      <td>Odissi Dance - I</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>ID2L001</td>\n",
+       "      <td>Entrepreneurship and Small Business Management</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.16</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>MA1L002</td>\n",
+       "      <td>Mathematics - II</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.19</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>MA2L005</td>\n",
+       "      <td>Transform Calculus</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.29</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>HS1L002</td>\n",
+       "      <td>Learning English</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.40</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CS1P001</td>\n",
+       "      <td>Introduction to Programing and Data Structures...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.43</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>ME2L001</td>\n",
+       "      <td>Theory of Machines - I</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>ID2L002</td>\n",
+       "      <td>Introduction to Bioscience and Technology</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.52</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>EE1L001</td>\n",
+       "      <td>Electrical Technology</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.57</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>ID3L003</td>\n",
+       "      <td>Environmental Science, Technology and Management</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.58</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>ME2L002</td>\n",
+       "      <td>Fluid Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.62</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>CE2L011</td>\n",
+       "      <td>Building materials and Construction</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.75</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>MA1L001</td>\n",
+       "      <td>Mathematics -1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.76</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>ME2L003</td>\n",
+       "      <td>Thermodynamics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.84</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>CS1L001</td>\n",
+       "      <td>Introduction to Programing and Data Structures</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.90</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>CY1L001</td>\n",
+       "      <td>Chemistry</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.90</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>ME2L005</td>\n",
+       "      <td>Theory of Machines - II</td>\n",
+       "      <td>4</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.95</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>HS2L007</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>ID1T002</td>\n",
+       "      <td>Extra Academic Activities - 2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8.06</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>EE1P001</td>\n",
+       "      <td>Electrical Technology Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.10</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>ME2L004</td>\n",
+       "      <td>Mechanics of Solids</td>\n",
+       "      <td>4</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.11</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>ME2P003</td>\n",
+       "      <td>Machines &amp; Mechanisms Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.11</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>CE1P001</td>\n",
+       "      <td>Engineering Drawing and Graphics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.19</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>PH1P001</td>\n",
+       "      <td>Physics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.29</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>EC2P005</td>\n",
+       "      <td>Analog Communication Lab</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.33</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>ME2S001</td>\n",
+       "      <td>Project Seminar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.57</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>ME2P004</td>\n",
+       "      <td>Materials Testing Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.58</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>ME1P001</td>\n",
+       "      <td>Introduction to Manufacturing Processes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.67</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>CY1P001</td>\n",
+       "      <td>Chemistry Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.67</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>HS1L001</td>\n",
+       "      <td>English for Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.67</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>CS2L003</td>\n",
+       "      <td>Data Structure</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.70</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>ME2P001</td>\n",
+       "      <td>Workshop Processes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>6</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.81</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>ID1T001</td>\n",
+       "      <td>Extra Academic Activities -1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.05</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>ME2P002</td>\n",
+       "      <td>Fluid Mechanics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.10</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Subject Code                                       Subject Name Credits  \\\n",
+       "0       EE2L004        Introduction to Electromagnetic Engineering       3   \n",
+       "1       ME1L001                                          Mechanics       4   \n",
+       "2       EC2L005                               Analog Communication       4   \n",
+       "3       MA2L004                     Partial Differential Equations       4   \n",
+       "4       HS2L002                          Speaking and Presentation       4   \n",
+       "5       PH1L001                                            Physics       4   \n",
+       "6       EC2L007                              Communication Systems       3   \n",
+       "7       HS2L004                                   Odissi Dance - I       3   \n",
+       "8       ID2L001     Entrepreneurship and Small Business Management       3   \n",
+       "9       MA1L002                                   Mathematics - II       4   \n",
+       "10      MA2L005                                 Transform Calculus       3   \n",
+       "11      HS1L002                                   Learning English       4   \n",
+       "12      CS1P001  Introduction to Programing and Data Structures...       2   \n",
+       "13      ME2L001                             Theory of Machines - I       4   \n",
+       "14      ID2L002          Introduction to Bioscience and Technology       2   \n",
+       "15      EE1L001                              Electrical Technology       4   \n",
+       "16      ID3L003   Environmental Science, Technology and Management       2   \n",
+       "17      ME2L002                                    Fluid Mechanics       4   \n",
+       "18      CE2L011                Building materials and Construction       3   \n",
+       "19      MA1L001                                     Mathematics -1       4   \n",
+       "20      ME2L003                                     Thermodynamics       3   \n",
+       "21      CS1L001     Introduction to Programing and Data Structures       4   \n",
+       "22      CY1L001                                          Chemistry       4   \n",
+       "23      ME2L005                            Theory of Machines - II       4   \n",
+       "24      HS2L007                          Introduction to Economics       4   \n",
+       "25      ID1T002                      Extra Academic Activities - 2       1   \n",
+       "26      EE1P001                   Electrical Technology Laboratory       2   \n",
+       "27      ME2L004                                Mechanics of Solids       4   \n",
+       "28      ME2P003                   Machines & Mechanisms Laboratory       2   \n",
+       "29      CE1P001                   Engineering Drawing and Graphics       3   \n",
+       "30      PH1P001                                 Physics Laboratory       2   \n",
+       "31      EC2P005                           Analog Communication Lab       2   \n",
+       "32      ME2S001                                    Project Seminar       2   \n",
+       "33      ME2P004                       Materials Testing Laboratory       2   \n",
+       "34      ME1P001            Introduction to Manufacturing Processes       2   \n",
+       "35      CY1P001                               Chemistry Laboratory       2   \n",
+       "36      HS1L001                          English for Communication       4   \n",
+       "37      CS2L003                                     Data Structure       3   \n",
+       "38      ME2P001                                 Workshop Processes       2   \n",
+       "39      ID1T001                       Extra Academic Activities -1       1   \n",
+       "40      ME2P002                         Fluid Mechanics Laboratory       2   \n",
+       "\n",
+       "    Students  EX   A   B  C  D  P  F  WH  Other  Average  Median  \n",
+       "0          5   0   0   0  0  3  1  0   0      1     5.75     6.0  \n",
+       "1         21   0   4   2  5  3  7  0   0      0     6.67     7.0  \n",
+       "2         21   1   3   3  4  3  4  3   0      0     6.76     7.0  \n",
+       "3         21   2   3   1  4  1  6  2   1      1     6.79     7.0  \n",
+       "4          8   0   0   2  2  3  0  0   0      1     6.86     7.0  \n",
+       "5         21   2   0   6  3  6  4  0   0      0     6.90     7.0  \n",
+       "6          1   0   0   0  1  0  0  0   0      0     7.00     7.0  \n",
+       "7          1   0   0   0  1  0  0  0   0      0     7.00     7.0  \n",
+       "8         26   1   3   6  8  3  3  1   1      0     7.16     7.0  \n",
+       "9         21   1   3   6  4  3  4  0   0      0     7.19     7.0  \n",
+       "10        21   2   2   3  8  5  0  1   0      0     7.29     7.0  \n",
+       "11        15   0   3   3  6  3  0  0   0      0     7.40     7.0  \n",
+       "12        21   2   3   5  4  6  1  0   0      0     7.43     7.0  \n",
+       "13        21   3   2   7  2  2  2  2   0      1     7.50     8.0  \n",
+       "14        21   1   5   6  3  4  1  1   0      0     7.52     8.0  \n",
+       "15        21   2   4   4  6  4  1  0   0      0     7.57     7.0  \n",
+       "16        21   0   2   9  6  2  0  0   1      1     7.58     8.0  \n",
+       "17        21   1   4   7  5  3  0  1   0      0     7.62     8.0  \n",
+       "18         5   1   0   0  3  0  0  0   1      0     7.75     7.0  \n",
+       "19        21   4   4   4  4  2  3  0   0      0     7.76     8.0  \n",
+       "20        21   3   4   5  3  2  2  0   1      1     7.84     8.0  \n",
+       "21        21   1   6   6  4  3  0  0   0      1     7.90     8.0  \n",
+       "22        21   5   2   5  5  3  1  0   0      0     7.90     8.0  \n",
+       "23        20   3   4   4  5  3  0  0   1      0     7.95     8.0  \n",
+       "24         7   0   2   3  2  0  0  0   0      0     8.00     8.0  \n",
+       "25        21   2   5   4  6  1  0  0   0      3     8.06     8.0  \n",
+       "26        21   3   2  12  2  2  0  0   0      0     8.10     8.0  \n",
+       "27        21   3   4   5  6  1  0  0   1      1     8.11     8.0  \n",
+       "28        21   1   3  12  3  0  0  0   1      1     8.11     8.0  \n",
+       "29        21   2   5   9  5  0  0  0   0      0     8.19     8.0  \n",
+       "30        21   2   7   7  5  0  0  0   0      0     8.29     8.0  \n",
+       "31        21   2   7   9  2  1  0  0   0      0     8.33     8.0  \n",
+       "32        21   2  10   7  2  0  0  0   0      0     8.57     9.0  \n",
+       "33        21   3   8   5  3  0  0  0   1      1     8.58     9.0  \n",
+       "34        21   3   8  10  0  0  0  0   0      0     8.67     9.0  \n",
+       "35        21   2  10   9  0  0  0  0   0      0     8.67     9.0  \n",
+       "36         6   0   4   2  0  0  0  0   0      0     8.67     9.0  \n",
+       "37        10   3   3   2  2  0  0  0   0      0     8.70     9.0  \n",
+       "38        21   6   6   8  1  0  0  0   0      0     8.81     9.0  \n",
+       "39        21   8   7   5  1  0  0  0   0      0     9.05     9.0  \n",
+       "40        21   7  10   3  1  0  0  0   0      0     9.10     9.0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "courses = dict()\n",
+    "for (k, v) in data.items():\n",
+    "    for (sem, scourses) in v['grades'].items():\n",
+    "        for (course, grade) in scourses.items():\n",
+    "            if course not in courses:\n",
+    "                courses[course] = list()\n",
+    "                courses[course].append(grade)\n",
+    "            else:\n",
+    "                courses[course].append(grade)\n",
+    "\n",
+    "clist = list()\n",
+    "\n",
+    "def other_grade(l):\n",
+    "    return len(l) - l.count('EX') - l.count('A') - l.count('B') - l.count('C') - l.count('D') - l.count('P') - l.count('F') - l.count('WH')\n",
+    "\n",
+    "def analyze_grade(l):\n",
+    "    grade_hash = {'EX': 10, 'A': 9, 'B': 8, 'C': 7, 'D': 6, 'P': 5, 'F': 5}\n",
+    "    hashed_grade = list()\n",
+    "    for grade in l:\n",
+    "        if grade in grade_hash:\n",
+    "            hashed_grade.append(grade_hash[grade])\n",
+    "    hashed_grade = (np.array(hashed_grade, dtype='float'))\n",
+    "    if hashed_grade.size == 0:\n",
+    "        return {'average': 0, 'median': 0}\n",
+    "    return {'average': round(np.mean(hashed_grade), 2), 'median': round(np.median(hashed_grade), 2)}\n",
+    "\n",
+    "grade_labels = 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other'\n",
+    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'orange', 'red', 'gray', 'black']\n",
+    "\n",
+    "for course, grades in courses.items():\n",
+    "    course_info = cdata[course]\n",
+    "    clist.append((course, course_info['subnane'], course_info['credit'], len(grades), grades.count('EX'), grades.count('A'), \n",
+    "                 grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "                 grades.count('F'), grades.count('WH'), other_grade(grades), analyze_grade(grades)['average'], analyze_grade(grades)['median']))\n",
+    "#     patches, texts = plt.pie([grades.count('EX'), grades.count('A'), \n",
+    "#                  grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "#                  grades.count('F'), grades.count('WH'), other_grade(grades)], labels=grade_labels, colors=colors)\n",
+    "#     plt.axis('equal')\n",
+    "#     plt.legend(patches, labels, loc=\"best\")\n",
+    "#     plt.show()\n",
+    "def sortByAverage(element):\n",
+    "    return element[-2]\n",
+    "\n",
+    "clist.sort(key=sortByAverage)\n",
+    "df = pd.DataFrame(data = clist)\n",
+    "df.columns = ['Subject Code', 'Subject Name', 'Credits', 'Students', 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other', 'Average', 'Median']\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CGPA Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 5 Students:\n",
+      "\n",
+      "16ME02021  SHIVAM HANDA  9.45\n",
+      "16ME02020  CHIRAG CHIRANJIB  9.41\n",
+      "16ME02014  SANJAY SRINIVAAS M R  9.11\n",
+      "16ME02022  GAURAV KHANDELWAL  8.77\n",
+      "16ME02004  RAMIT ASHUTOSH MACHHAN  8.62\n",
+      "Students with theri cgpa :\n",
+      "\n",
+      "16ME02001  BANGALE ADWAIT RAJENDRA  7.12\n",
+      "16ME02002  Manas Bagai  8.43\n",
+      "16ME02003  POLURI BHARADWAJ REDDY  7.53\n",
+      "16ME02004  RAMIT ASHUTOSH MACHHAN  8.62\n",
+      "16ME02005  JISHNU A K  8.00\n",
+      "16ME02007  AYUSH RAJ ARYA  7.18\n",
+      "16ME02008  KARRA VENKATA SUMANTH  5.68\n",
+      "16ME02009  KHETHAVATH DEEPAK  6.14\n",
+      "16ME02010  KATAM RISHWANTH  8.27\n",
+      "16ME02012  BHARATH RAM  7.65\n",
+      "16ME02014  SANJAY SRINIVAAS M R  9.11\n",
+      "16ME02015  PODILI SAI DEEKSHITH  7.09\n",
+      "16ME02016  ABHISHEK ANAND  7.99\n",
+      "16ME02017  VIKAS BHUPARIA  6.12\n",
+      "16ME02018  AMBOLKAR MANTHAN RAMNATH  7.74\n",
+      "16ME02019  AJAY KUMAR MEENA  6.61\n",
+      "16ME02020  CHIRAG CHIRANJIB  9.41\n",
+      "16ME02021  SHIVAM HANDA  9.45\n",
+      "16ME02022  GAURAV KHANDELWAL  8.77\n",
+      "\n",
+      "CGPA:\n",
+      "Highest: 9.45\n",
+      "lowest: 5.68\n",
+      " Median: 7.74\n",
+      "Average: 7.73\n",
+      "Standard Deviation: 1.08 \n",
+      "\n",
+      "   9.5+: 0\n",
+      "  9-9.5: 3\n",
+      "  8.5-9: 2\n",
+      "  8-8.5: 3\n",
+      "  7.5-8: 4\n",
+      "  7-7.5: 3\n",
+      "     7-: 4\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MSC3G6GhEFQ5KSkpYsmQJK1asoHv37syYMcPjOqqOjMM33HAD1113Hddccw0iwvDhw/nyyy8ZPXo0K1as6FCWlJQUwK5MndvO/ebmZhISErjwwgt58cUXW13X0NDAj370I9auXcuAAQO47777Wn0HZ10JCQk0N7ddnWFfFN32u3VkIm1bXkT86kuDIRrsq9tHr8ReVDcF7tcmCE+teYqfTf0ZFomtsVFsSWOICseOHSMzM5Pu3buzefNmVq5c2XLOZrPxyiuvAPDPf/6Ts88+m/T0dDIzM1tsU88//3zLqG3o0KEkJCTw+9//vmWEN2LECA4dOtSi1JqamlqNovxhypQpfPrpp2zfvh2A+vp6tm7d2qI4cnJyqKura5HZVy666CLmzZtHfX09AEeOHCEzM5OePXu29Me//vWvVtd88MEHHDlyhJMnT/L6669z1llnee1LgyEW2FdrV2rBoCgVRyv4qOKjEEkVOsxIzcDFF1/MnDlzGDduHCNGjGDKlCkt53r06MHGjRuZMGEC6enpLFq0CICFCxdy6623Ul9fz5AhQ5g/f37LNTfccAN33303FRUVACQnJ/PKK69w++23c+zYMZqbm7nzzjsZPdr/ICy5ubksWLCA73znO5w6dQqABx98kMLCQm655RbGjh1LQUEBkyZN8rsPSktLmThxIsnJyVx66aX88Y9/ZO7cudxyyy306NGDGTNmkJ6e3nLN2WefzY033sj27dv57ne/y8SJExk7dqzHvjQYYoG9x/fSMyH4daAWsfD02qe5YMgFIZAqdIjxQo8sEydO1LZJQjdt2sSoUaOiJ1SMEUuLr+vq6khLSwPgoYceoqqqiscee4wFCxawdu1annzyyYDqDeY3NwuLO8b0kWfS/pjG6T1O55OjnwRdl0Us7PnvPfTt2TcEknlHRD5X1YkdyhR2SQyGOObtt9+muLiYMWPGsHz5cn7zm99EWySDIWDqGus40XQi6OlHJza1MW99ey/maGKmHw0GL9xwww0ttkFXZs6c2WrtnMEQD1TV2teHhkqpATy99ml+efYvSbAkhKzOYDAjNYPBYOgiVNU5lFpC6JTavtp9vLv93ZDVFyxGqRkMBkMXwTlS65kYOpu102EkVjBKzWAwGLoI+2r3AZCemN5BSd+xqY13t73LrqO7QlZnMBilZjAYDF2EqroqBKGbpVtI61WUv637W0jrDBSj1Azs3LmzJVCvr8ycOdPvBc4GgyG6VNVVYRFLWNLGPPP5MzRZm0Jer78YpWYwGAxdhH3H92FVa1jqPlx/mMVbFoelbn8wSs0A2KPY33zzzYwbN45vfetbLeGifEkZ8+GHH3L66aczduxYfvCDH3Dq1ClWr17dEkR48eLFdOvWjcbGRhoaGhgyZEi7OpxpZ84991zGjRtn0s50cQ6eOMjt795OfVN9tEXpVFQedx9IPBRYxMJTa54KW/2+YtapxRp33gmloU09Q3ExPOo9UPKWLVuYO3cuZ511Fj/4wQ946qmnuOOOOzpMGdPQ0MDMmTP58MMPKSws5KabbuLpp5/mJz/5CevXrwdg+fLljBkzhjVr1tDc3MwZZ5zhVoaamho++ugjFi1aZNLOdHGeXP0kT6x+gsuGX8Y3hn0j2uJ0Gpwu/eHApjaW7lzK1uqtFGYXhq2djjAjNQMAAwYM4KyzzgK+TjHjS8qYLVu2MHjwYAoL7TexMw1NYmIiw4YNY9OmTaxevZqf/exnfPzxxyxfvpxzzjnHrQzOtDOnnXaaSTvThXGNUrGjZkeUpek8nGw6SW1jbVjbEIRn1j4T1jY6wozUYo0ORlThwl0aFVXtMGWMt9ih55xzDu+++y5JSUlccMEFzJw5E6vVyuzZs92WN2lnDAAfVXzE3tq9AOw4YpRaqAjnKM2JosxdP5cHz3uQbkmh9bD0FTNS84KI3CEiZSKyUUTudHNeRORxEdkuIl+IyPhoyBkKdu/e3aK8XnzxRc4++2yfUsaMHDmSnTt3tqSCcU1DM23aNB599FGmTp1Kbm4u1dXVbN68OaDo/GDSznQV5q+fj0UsWMRiRmohxLnwOtwcO3WMV76Knme0UWoeEJExwC3AZKAI+KaIDG9T7BJguOMzC4idZfV+MmrUKBYuXMi4ceM4cuQIt912W0vKmHvuuYeioiKKi4v57LPPWl2XmprK/Pnzue6661qmC2+99VYAzjjjDA4cOMC0adMAGDduHOPGjQvYndg17cy4ceOYMmUKmzdvJiMjoyXtzFVXXRVQ2pkrrriCiRMnUlxc3DKSnDt3LrNmzWLq1Kmoqtu0M8XFxVx77bVMnDiRiy++mObmZsaNG8dvf/tbk3YmAI42HOWVTa9gUxs2tbG1emu0Reo0RGKkBg6HkbXRcxgxqWc8ICLXAd9Q1f907P8WOKWqf3Ep8wxQoqovOva3ADNU1ePdY1LPdEyspJ4JV9oZMKlnPPHM2me49e1bW/ZTE1Op/1W93y9CnbmPAuWJVU9w+3u3AzC7cDZ3bb0rrO1tuHUD4/LHhaw+X1PPGJuaZ8qAP4hINnASuBRY26ZMP2CPy36l41grpSYis7CP5MjPz6euro6SkpKW8+np6dTWhteAG09YrdaY6I9XX32Vv/71rzQ3NzNgwADmzJlDbW0tDQ0NNDY2BiVjQ0NDq3vAH9reP52Jx9Y9Ru/k3vx80M/59OinvH7odf79/r/JTvHPg7Qz91GgrCxfiQULDw1/iP4p/Zld6N62HSz11noeKH+Ae9+4lzuHt7PahB0zUvOCiPwQ+DFQB3wFnFTV/3Y5/zbwJ1X9xLH/IfA/qvq5pzrNSK1jYmWkFk7MSK09mw5t4rSnTmt3fPn3l3P2wLP9qquz9lEwzHx9Ji988QJWtUZkpNYjqQf779pPWnJaSOozSUJDgKrOVdXxqjoNOAK09c+uBAa47PcH9gXYVmBCGuIO81u7Z37pfIT204zGAzI0VNVWYVNbxNo70XSCF798seOCIcYoNS+ISJ7j70DgGqDtL/QGcJPDC3IKcMybPc0TqampVFdXm4ddF0BVqa6uJjU1NdqixBTNtmYWlC5Aaf8/YDwgQ0NlbaXb/g0XFrHwv2v+N+LPNWNT886rDptaE/BjVa0RkVsBVHUO8A52W9t2oB74fiCN9O/fn8rKSg4dOhQiseObhoaGTv3QT01NpX///tEWI6Z4b/t7HKpvf/8nSIJRaiHCmXYmUtjUxoYDG1i7by2T+vnnkRwMRql5QVXbhb5wKDPntmK3uQVFUlISgwcPDraaTkNJSQmnn356tMUwRJB56+dhEUu76TGrWtlWbaKyBEujtZGjDUcj3q4zgWgklZqZfjQYDFHlcP1h3tz6pkd7z/Yj2yMsUedjf93+qLRrUxv//PKfEVWoRqkZDIao8o8v/kGzrX34Mic1DTUcP3U8ghJ1PiIVTcQdp6yneH7D8xFrzyg1g8EQVeaun+vW69EV4wEZHJG2p7kiSEQdRoxSMxgMUWN91Xq+PPhlh155xlkkOCIVIssdirKleguf7P4kIu0ZpWYwGKLGgtIFHY7SwIzUgiWa049gdxiZs3ZOxwVD0VZEWjEYDIY2NFobee6L5zocpRm3/uDZV7uPBEmIWvs2tfHyVy9z6ET4ly0ZpWYwGKLCm1ve9MkrzqY24wEZJFV1kY0m4o6xeWOpa6wLeztGqRkMhqgwr9S+Nq0jFDVr1YKk8nhko4m445YJtzA4M/zrcY1SMxgMEaeqtor3tr/n8+hhX90+Gq2NYZaq8xJtm1okMUrNYDBEnOe/eN6v6TCb2th5dGf4BOrENNuaqT5ZHW0xIoZRagaDIaKoKn9f93efvB5dMR6QgXGg7kDUpx4jiYn9aDAYIsqqvavYdsR/G5nxgAyMaK5RiwZmpGYwGCLK/PXu86Z5wyIWM1ILkK5kTwOj1AxhYMeRHQx8ZCCf7/OYANzQRalvquefZf8MaDrMjNQCI5ohsqKBUWqGkPPGljfYc3wPf/zkj9EWxRBjvLbptYDWKtnUxtbqrWGQqPNjph8NhiD5aOdHALy++XXKa8qjLI0hlnDmTQuEnUd3Rn0BcTxSVVsV1WgikcYoNUNIsdqsLNu5DLB7uT2+6vEoS2SIFXYd3cXSnUsDVkynrKe6nH0oFFTVVXUp70ej1LwgIv8tIhtFpExEXhSR1DbnZ4rIIREpdXz+M1qyxgrr96+ntrEWsEeC+Nu6v0Ul464h9li4YWHQD1djV/OfyuOVXWqEa5SaB0SkH3A7MFFVxwAJwLfdFF2kqsWOz98jKmQMUrKzpNV+fVM9f1/X5buly2NTG3PXzw26HuMB6T/GUcTgSiLQTUQSge5A17o7AuCjio9a2UwE4ZGVj9BkbYqiVIZo8/Guj9l9bHfQ9ZjAxv5htVk5XH842mJEFLP42gOquldEZgO7gZPA+6r6vpui14rINGAr8N+quqdtARGZBcwCyM/Pp66ujpKSkvAJHyWsaqWkooQzep3BtfnXAvBV3VfM2zePB159gPPzzvepns7aP6EiHvvnT5v/RKollXuH3EuyJTmwOir+xIotKyhJKOmwbDz2UTg40ngEq1q5Ou9qzso4q+V4/5T+zC6cHVFZsg9mR+Q3kUil2I43RCQTeBW4ATgKvAy8oqovuJTJBupU9ZSI3Apcr6rneat34sSJOnv2bGbMmBE+4aPEqspVTJk7pd1xi1gozi9m7ay1iHS86LakpKRT9k+oiLf+qT1VS97sPBqaG4Kua3yf8Xw+q+P1j/HWR+FifdV6xj87vt3x2YWzuWvrXRGV5enLnubWibcGfL2IfK6qEzsqZ6YfPXMBUKGqh1S1Cfg3cKZrAVWtVtVTjt2/ARMiLGNMsXTnUrfHbWpj3f51EUvnbogtXv7q5ZAoNDDTj/7S1daogVFq3tgNTBGR7mIfXpwPbHItICJ9XHavaHs+FiivKSfv/+Wxdt/asLfV1p7mikUsPLzi4bDLYIg95q6fG/DatLYcP3WcmpM1IamrK9AVl0AYpeYBVV0FvAKsA77E3lfPisgDInKFo9jtDpf/Ddg9JWdGRVgvfFj+IYfqD/GPL/4R1nYarY0s373co+uwTW28seUN86bdxdhWvY3P9nwWUpdy49bvO13N8xGMUvOKqv5OVUeq6hhVvVFVT6nqvar6huP8L1V1tKoWqeq5qro52jK3ZfXe1QD8e/O/Caf9dM3eNT5NMT228rGwyWCIPRaULgh5ncat33eq6rpWNBEwSq3Ts6JyBQC7j+1m46GNYWvHkz3NFUX5+/q/m+mjLoLVZmVe6byQ12tGar7T1aKJgFFqnZoTjSfYdPhrM9/izYvD1pY3e5orDc0NPPP5M2GTwxA7LClfwv66/SGtM0ESzEjND/Ye39uloomAUWqdmnVV61puaEF4bfNrYWnnVPMpPt3zqU//PILw6MpHabQ2hkUWQ+wwv3R+yBxEnFjVyvYaY5f1lcrjldEWIeIYpdaJcdrTwD7193nV52ExHK+sXOmzklKUAycO8NLGl0IuhyF2qDlZw2ubXwvLKGFbtf9Zs7siqsqh+kPRFiPiGKXWiVm9b3U7I/GbW94MeTu+2NNcsYiF2Z/NDqvjiiG6vFj2YthG4/vr9ods3VtnpvpkNc225miLEXGMUuvErNizAqtaW/YtYmHxltDb1ZZWLEXoOFKIE5va2HBgA8t2LQu5LJ2dYw3HmLNjDodOxPYb+Nx1oVub1hZFqaipCEvdnYmuuEYNjFLrtBw8cZA9x1uHobSpjQ8rPgwo87AnTjadZEXlCr89rCxi4eHPzGJsf1lSvoRFlYu45B+XcKLxRLTFcUvZwTLW7V8XVgcF4wHZMV1xjRoYpdZpWbN3jdvjjdZG/m/7/4WsnRWVK2iy+R+B36Y23tr2Flurt4ZMlq5AxVH7COXzqs+57uXrYnJ6af76+X6N3APBeEB2TFcMkQVGqXVaXJ1EXLGIhTe2vhGydvydenRFEB5Z8UjIZOkKVNRU0N3SHYB3t7/LrDdnxZRtssnaFJJkoN6wiMWM1HzATD8aOhWr9q5ya9OwqY3FmxeH7A3/w4oPA75WUeaXzu9y+Z6CobymnKykrJb9+aXzua/kvugJ1IZ3tr1D9cnqsLdjRmods69DiT+6AAAgAElEQVR2X9jsmrFM1/vGXQBVZWXlSo82jWOnjvHp7k+DbudE4wnW7FsT1Fv5KespnllrFmP7yvYj28lMymx17IGPH+DZz5+NkkStmVc6L+wPUpva2HrETFt3RFVdVdingWMRo9Q6IeU15Rw7dczjeUF4Y0vwU5Cf7vk0JCO+x1Y9xqnmUx0X7OLY1Mbu47vJTspud+62t28LyW8aDAdPHOTtrW9HJILFrqO7sNqsHRfswuyr3dfK+7mrYJRaJ8STPc2JoiEJcByMPc2VQ/WH+FfZv4Kup7Ozv24/jdbGVtOPTlSV61++nhV7VkRBMjsvfPFCxB6iTbYm9tbujUhb8UpXjCYCRql1SlbvXd3hFNDOoztbxYUMhGDsaa6Yxdi+4Vyb5VapoTTZmrj0n5ey5fCWSIuGqjJ33dyITncZu5pnVO2Re7oiRql1QlbuXemTgggmwHHtqVo+r/o8JF5uNrVRdqiMjyo+Crquzkx5TTngXqmBvR+PnzrOhc9fGHHPt3VV6/jq8FcRjQhvPCA9c7ThaJeNr2qUWiejydrEuqp1HT5cgg1w7C0haCBYxMLsFbNDVl9nxLlGLTMx02MZm9rYW7uXb7zwDY6fOh4p0ZhfGv61aa4IYkZqXuiqa9TAKLVOR9nBMp/e0BRlzb41AacGCZU9zYlNbby3/T2+OvRVyOrsbFQcrSBBEkiyJHktZ1MbZQfLuHrR1RF5W29obuD5L56P6CjNrFXzTlddowZGqXlFRP5bRDaKSJmIvCgiqW3Op4jIIhHZLiKrRKQgOpJ+TUdOIm0JNMBxqOxprjjT0hjcU15T7rMjhqJ8VPERP1z8w7DbKt/Y8kZER4VgT0Gz7YiJ1u+JrhoiC4xS84iI9ANuByaq6hggAfh2m2I/BGpUdRjwCPDnyErZntV720fm94RFLLy+5XW/2zjacJQNBzaE/M1cURZuWMjRxqMhrbezsP2I/3nEXvjyBX754S/DIM3XzFsf/rVp7thxZIdxLvKAmX40eCIR6CYiiUB3oO3rz5XAQsf2K8D5IhLV1Y4rKlf4/DZvUxtLypf4HRh3+a7Q2tNcabQ2snhf+DJ0xyuN1saAp5T+/OmfeWLVEyGWyM7e43v5oPyDqGRXrm2s5cjJIxFvNx6oqq3qktFEwP7QNrhBVfeKyGxgN3ASeF9V329TrB+wx1G+WUSOAdlAq7hPIjILmAWQn59PXV0dJSUlIZe5vrmezYc3c1H2RVyUfVGH5bfVb+OZymf46xt/5Zycc3xu57ntz5Eoifx+6O87tO/4y/y98/nXnn9x0bsX0adbn5DWHc/sPbkXRbkh/wb6p/RndqFvTjU2tbFw30LueO8Ojuw+wvTc6SGV6x+7/4FNbfyi4BfkJOeEtG5vbKzbyPx983lpyUuM6jWq3flA/se2123n95t+zyNFj5CV7N7DNF7YUL6B7MRs7hl8j9vz/txDoSL7YHZYnnttMUrNAyKSiX0kNhg4CrwsIt9T1Rdci7m5tN18iKo+CzwLMHHiRE1LS2PGjBkhl3nZzmXop8r71e/zfnVb/esei1goTyrntzN+63M7d2y6g2Zt5pfbwzOtlWpJZc6BOSy9eWmXfdtsywc7PoDVsOjAIialT+KurXf5db0g/HHzHzlv8nmcM8j3FxhvqCo/fOKHCMJDOx8KSZ3+kjE4gxljZ7Q7XlJS4vf/2OL3FrO7fjeJgxKZMcK/a2ON31b8lkOHDnm8T2YXzvb7HgqWpy97musmXhf2dswTwzMXABWqekhVm4B/A2e2KVMJDABwTFGmA1GbD/HXSQS+DnDsa8ihIyeP8OXBL/1uxx8uz72cj3d9zFNrngprO/GE050/UBSlWZv55j+/ycaDG0Mi02d7PqO8pjyiXo9tCZUHpKo9yg7QKdIhddVoImCUmjd2A1NEpLvDTnY+0DYExxvAzY7tbwEfaRQt1/44ibhS01DDZ3s+86nssp3Lwv4Qm9xrMgB3f3B3QM4RnZHymvKgl1DY1EZdUx0XPn9hSB5680vnR3UknSAJIVNqGw9tZPex3UD8KzVV5UBd14wmAkapeURVV2F3/lgHfIm9r54VkQdE5ApHsblAtohsB34G/CIqwjrwx0nEFX8CHC/dGf4pQaevTaO1kZteu8kErsU+UgtFv9vUxoETB7jo+Ys42hC4l+mJxhO8WPZiVBxEnFjVyvbq0Lz0uEbX2VId+TBjoaS2sZaTzSejLUbUMErNC6r6O1UdqapjVPVGVT2lqveq6huO8w2qep2qDlPVyapaHi1Z99ftDzjAqz8BjpeUL4nYg8ymNlZUruCxVY9FpL1YZseRHSELFmxTG5sPb+bKf10ZcHaEVze9Sn1TfUjkCYZQrVV7ffPrLSPhzYc3h6TOaNGVF16DUWqdhjV71wR1fXlNeYdvqIdOHAo6CHIg/PLDX8b9gyZYnHEfQ4WifLzrY2587caAXlKitTatLQdOHOBkU3Cjkn21+1hbtbZlWv3giYPUnqoNhXhRoSuvUQOj1DoNgTiJtKWjAMclO0uCbiMQmm3N3PjajSHL1h1v1J6qpaahJix1v/zVy9z1vn9ecOU15SzbtSyqU4+uBKvw39r6Vrtj8WzLNSM1Q6dg1d5VWIL4OX0JcBwJe5o7bGpj7b61zP6sawY8DtbzsSMeWfkID3/2sM/lF5Yu7LhQBAlWAb2++fV293U8O4t05RBZYJRap0BVWVm5EhuBvzkryuq9q716TUXSnuaOe5feS9nBsqi1Hy2cedTCyV0f3MWLX77YYTmb2phXOi/s8vhDMB6QdY11fFjxYbv7Op6VWlVdVUQzJsQaRql1ArYf2U5tY/A2AEXdTsWA3REl2gFkrWrlxtdupMnaFFU5Ik24R2pgH6nf9PpNHea0W1qxNKbWQFnEElQKmvd3vN8uk0GCJLD1SHwrtViwd0aLrvvNOxGhsKeB9wDH0bKnuWJTG6X7S/nTJ3+KtigRpaImNO783lAUm9q44sUr2LB/g8dyC0oXxNQDU1WDGqkt3rK43fexqjWuHZOqaqtC5ikbj8TO3WkImNV7V4dsDdP7O95366q9tCJ2QlY9sOwBSveXRluMiBFqz0dP2NTGyeaTXPTCRew6uqvd+WMNx3j5q5djxkEE7Mo40KnCZlszizcvdvt9thzeErcZAPYc3xNtEaKKxOsPF69MnDhRZ8+e7X/sxzvvhNXuR2TrDn9JbdMJkizBhfK0qdJsa2ZM1ghyUlsHdF11cB2nrI0kBtmGLwxMGcTuU+0fqmAPrNlsa6Z7Yjcm5IyNGUUbTlYfLKXB2tDS9976J1gU+z2QmpDK+Jyxre6pffUH2Hq0nMSERCwxYrOxqhWb2pjWewquCTKOnjhBRo8eXq892nic0sMbSbQktLqPrGrDarNyVu+JIQ/Y3ZaTzQ1sPVbOaZmFQf//Olm+fxUKJHqJLhTOe8gTGVPO5bR//F/A14vI56o6saNyMRfQWEQeBf6tqh+LyGDgX0AW9sgeN6pqu1S+IvIH4CYgU1XTXI6nAM8BE4Bq4AZV3SkiY4Gfq+rMsH+hULJqFdhav1XagLpB0D+9P0MzhwZVvU2Vz/Z8yuHdW8ip/vr4qQQ42R+GZg1lQK/+QbXhC0ePHuXMvLZhNr/mcH01ZQfL2LV5FYM7eeo1BRoGQt/0/gzLsv++HfVPsNjz5X1B2a41jNtvTyQIsL83dE/rxqR+k2NEpdntR1sOb6VhzUq6uc64DRkCX3zh9drDmSDpwpT+U0m0fK0Aqk8e4csDX1Jfupb0wNam+0x1T6jJgiNfriE/BGvZrQLWgTAkczAD0wd6LBfue8gtue2zKYQFVY2ZD3bltdJl/yXg247tOcBtHq6bAvQB6toc/xEwx7H9bWCRy7klwMBIf8cJEybo0qVLNSC6d1eFVp+1fVDuQ18qeymwOtvwnVe+o7m/sGizfN3G8+Psbazbty4kbXSEL/1z82s3a8LvRFf3pV2fdKbPgR72vn985eN+9U+wvFT2ksp9old9G20WdFOOXY6/fPKXsLftDx+Vf6Tch34wpHW/LZ0922u/2kCH/rdFL3nh4nZ1bqveptyHzi8O/+97y+X2fv2vb4amvu2Z9voWrF/gtd8icQ+FGmCt+vCM7XDuRkRuEpEvRGSDiDzvODZURFaKyBpHLMQ6x/EZIvKxiLwmIl+JyBwR+7heRJ4WkbUislFE7vfQ3LeA9xzlBTgPe/xFsCfjvMqDYl6pqu5WHHpL4vkm7TNZxx2r+9n/ntH/jJDUd8WIKziUamOly4BsaQFkJveiqHdRSNoIBY9e/Ci9e+Rz87UWGmJuviF0VGTY/w7OHBzRdq8bfR2PXvwor4+E2y+BBcWQgIUbi26MqBwdMSxrGAA7Mv27blMu7Ei3ccWIK9udK8goIFES2JodCgm9U5ZvfxwtGxKaafR9Pe1/+/TsurkIvfakiIwGfg2cp6pFwB2OU48Bj6nqJNpng54M/BwYCwwFrnEc/7Xa50PHAdNFZJybJs8CPndsZwNHVdUZRqISe1JOf2iVxBNwJvEEWAuEJrFUFFndD/JTcxjQa0BI6rtk2CUkSgJvjPj62NJhCUwfcl5M2a8yUjOYe/UCNmXb+N2MaEsTPsodD+vBGZFVagC3n3E7d595N09Nhr9OhUuGX0LvtN4Rl8Mb/Xr1I8WSzA4/c3oudtzflxde3u5coiWRIb0Kwq7UFNiYL6QkpLA5y8ZB7yZAn6hyKLW+PfsGX1mc0tFT6jzgFVU9DKCqzlxhU4GXHdv/bHPNalUtV1Ur8CJwtuP49SKyDlgPjAZOc9NeH+CQY9unBJwd4K2Og0Dc//KrByYweeDUVkbyYEhPTWdGwQwWn2a/NXalQ0UvK+cWnBuS+kPJN4Z9g1vG38Lss2BF+E19UaHCqdQiPFJz8tAFD/EfY/+DpgT4wek/jIoM3rCIhcG9Bvk9UntjlDCx93j69XL/nlyYP4qtueF9iavsBceTbHx7jH3C6ONBwddZ5fAo6JNmRmqeEPxXJG3Lq8Ph4y7gfFUdB7wNpLq59qTL8cNAhiP5JkB/YJ+IJIhIqePzQAeyeEvimepoL245ngKbsqxM7jc5pPVeOfIqtmTZ2JINSx3P0lhUagAPX/QwA3r25+ZrLdSH11EtKlRk2Efi3ZO6R6V9i1iYf+V8ln9/OVeNdDv7H3WG5hayI8d3BbQ/DVb1Va4cdbXHMoVZhWzPAlsYPWLK8ux/byq6ie4JqSFRavt6QrIkkdXNz6FrJ6KjO+FD7COsbAARcfbUSuBax3Zbu9RkERnssKXdAHwC9AJOAMdEJB+4xEN7m4BhAA7D4FLsdjawJ+NcrKpWVS12fO7tQH5vSTwLgbiOufR5H1Ah5ErtihH2dHFvjLDb03JSMhmdNzqkbYSKnik9mXfNQrZl2vj1edGWJvRUZMLgrOC8WoMlKSGJsweeHbLZgFAzNHMoOzJ9f/t+s9D+f3OlG3uak8LsQk4m2NjbMzQyumOjQ6kV9y7mzIFnhcSuVtUTenfPi9nfKhJ47UVV3Qj8AVgmIhuAvzpO3Qn8TERWY58yPOZy2QrgIewKowJ4TVU3YJ923AjMAz710OTbwAyX/Xsc7WzHbgub6+4iEfmLiFQC3UWkUkTuc5zylsTzXEd7cYvTSWRi3w6XbvjFwPSBFOeOY/EoiUl7WlvOG3weP570Yx6bEpopnFiiPCeBwVlDoi1GTDM0ayh1STYO+WiTemMkFPQcwJi8MR7LFGYXAoTVrlaWB3275ZPVLYvpBTP4MsfGkW7B1VmVZl/+0ZXp0G9MVRfytQehk73AFFVVEfk2dqcLJ/WqeoObemb60NZyEfmTiGSo6lG1J93scBiiqv8D/I+b4w3AdW2PO9avTcSunOOWVf1hePqQsEw1XHna1dx/6AvAyj0xOvXoyp8v+DPvbnmL71+zhw3/ayOt3WrG+KPZArvTrHw30yg1bzjXZ+7IhLwT3sueSIIlQ4X/Ou0ar6MZV6V2fkXIRG1FWW8Lo/vY/eWmDZqGCnwyEK4IIvF2VUYChb3i3lUgKAJ9/Z4AlIrIF9jXgv08dCLxc8DzqsHQMBD4hYtnZTtEZISL7a5URI6LyJ1tyswQkWMuZTqaDg0pTieRcOA6NXPu4NhXaj2Se7DgmuepSFfuuSDa0oSGyl5gtUTH8zGeGOqYnvXFA/L9odCQoF6nHsHuPdg9ITVsIzWbwFe5tIwWJ/ebTIolmWVBzjTs69m1nUQgwIgiqrocaLdoSVVLgJJgBFLVVcFc72Mb2wCvIedVdQtQDCAiCdhHp+4Sji1X1W+GXMgO2NsT9vYIvZOIk+LexQzo0ZdGayOjciIUCSBIzhl0DndOuZNHeIRrNoXvDTtSRGuNWrwxOGMwgrAjs2Or2hsjICOpF2cPPNtrORFheNZwtmZ/GSoxW1GRAScTbC1KLTUxlSn9p7BsyCcQYAqphkSoSbZ2aXd+MAGNfeV8YIeqRjZYmhfWOOxp4VJqIsKjlz3Bw5c8GldG5z+c9wcKM4byg2sSOJ4SbWmCoyKKa9TiiZTEFPr36NPhSM0q8NaoBC4beTlJCR27yhbmjmRrnuf4icHg9Hwcnfu1A9a0gumsz7cFfN/ud7rzd+GF1xCDsR9jlG9jX3PnjqkOJ5p9wF0O55pWiMgsYBZAfn4+dXV1lJSU+C/Fgw+2xH58+dQ7JDQt4/jW45RsD6AuH8jC/pQISNYgCLh/HNwx+Gf89OhP+e5vz+Cu1HYm1bhh6al3sTQtpby0nF3y9ftUsP3TGclOzGPd6cmUnPUTAOr696dkdutM6V9aKzh88n8Z2jzUp/5LqUuhPENZ8v/+7DU4cCC82fghNL5L9ebqlv/f9KPp2ASevv+HnJHo/+zIRutOOPkkh8oPUXKsxGvZTn0P+RJLqyt/gGTsa+by3ZzrBaQ5ti8FtnVUX6hiP55/Ezrh6dMDqyfGCUVcuns+uEe5D313WGhi6kXj891r0MEPDwxL/3Q2frj4h5p/T0JL37mL/XjXhWjS/Yl6rOGYT3UuLF2o3IduyQ79b/uda9GC2QNatVd3qk4T70/Qey4IrM5XRtnjPq6vWt/hd4vHe4hQxX40cAmwTlUPtD2hqsdVtc6x/Q6QJCI54RbIJrBmgIXJA6aEu6m45b4Z93Fa1kj+8yoLR90t848DKrKEwdnDoi1GXDA0cygHulmpS3Z/XoHFoy2cN/g8eqX08qnOcLr1u3o+OumR3INJfSfxcUFg0/0mRJYdo9Q65jt4mHoUkd7OAMkiMhl7f1a7KxtKtmbbw+uEy57WGUhNTOW5a19gfxrceXG0pQmMimyLcRLxEacHZLmHcFlbcmBbho0rRnr3enRleNZwIPRKrckCm7PV7Tq56QUzWNPXvvTAX6rS7DnUcrqH/b06pjFKzQsi0h24EPi3y7FbReRWx+63gDKHTe1x7Gly/A0r5jerw+wk0lmY0HcCv5r2axYW0ypAczxQnwT7u1mNk4iPuK5Vc4czgLEzWo4vZHfPJis5PeRKbXsWNFncK7Vpg6bRbNFWWTJ8paon5HfLielACZGga3/7DlDVelXNVtVjLsfmqOocx/aTqjpaVYtUdYqqfhYJuVb3g56JPRiRHWdP6ijwm2m/oSh3LP91ZQLVQUZriCQ7jTu/X3S0Vm3xKGFC/un09zPJbWHOyJArNafnozuldtbAs7BgYVmB//Xu6wl9PARo7koYpRaHrB5gYWK/SSRYwuNu3JlITkhm4TXPU91D+Oml0ZbGd5xr1IaYaCI+kZGaQVZyutuR2oEesLKfcsUo/wMyF+aOYFuI3frL8sCChZE5I9ud65XSi/G9i1kWgF2tKt3S5UNkgVFqccepBCjNVyaHKCloV6CodxH3zvgdL46FV+NjHblZoxYAQ7OHuR2pveVDAGNPFGYVUtnDGpCNyxMb82BY+mBSE917ME0rmMGq/vid/Laqp3T5aCJglFrcsaG3fT7e2NP8456z7mFC/unceqUlJMkYw01FBnRPSCWvR160RYkbhmYPZ0du+1HV4pEwKK0/4/Ld5SX2jtMDcnsIw6uW9UlgTF/PWeSnF0znVIK22M59ockCh1KtRqlhlFrcYZxEAiMpIYmF1zzP8W4J/Ogy/5MERpryTChIHxRX0VyizdDMoexKs9Lk8lSrT4IPhglXnHZ1QH0Zarf+hkTYlmFtFUmkLecMPAdB/IoD6Ywm0tXd+cEotbhjdT/o0y2Pfj2NQdhfRueN5oHzfs+rp8Eiz1lHYoKKbAtDcoZHW4y4YmjmUKwW2J3+9bEPhvgWwNgTw7Ls6wRDpdS2ZNvXmXpLe5PZLZOxOaP5uMD3ep1r1Lp6iCwwSi3ucEbmN2/wgXHXmXcxpe9kfny5peXtNtZQHMlBjT3NL9x5QL4xAtKTejJt0LSA6uyR3IN+3XuHTKl583x0ZfqQc/lsoKXVqNMbVc64j2b60Si1eOJoirIlM3yR+bsCCZYEFlz9HPXdEpl1eWxOQ9Z0sy+uN0rNP9quVbMKvHmahUtHfNOnAMaeKMwbxdac0LxEluVBkiS2LOz2xLRB06hPtLHWx9nEfWak1oJRanHE2t72YMZGqQXHiJwR/PGCh3hzBDzv2V4fNZxRMcwaNf/o07MPqZaUlpHayv5wKNUW8NSjk8LsQrblhuZRuTEPRmQN71DJOkeWvmZyr+ppXyZgHIuMUosrVvexK7WJfSdGWZL4544pd3BO/7O4/TILlb6FAowYLXnUzEjNLyxiYUhGQctI7Y0R9lHRxcOCi5NWmF1IdYo1JIv3y/okMKZPx29SeT3yGJVZ6PMi7Ko0yEvNJtFiEq8YpRZHrO5jY0TGMDJSM6ItStxjEQvzr15IU7dkbrlCYmoassKM1AJmaE4hO3Lsj7XFp1mYUTCD9NT0Dq7yjtMDcluQdrW6ZKjo5d3z0ZVpQ87lkwILVh9mPqt6Qp9exvMRjFKLG1SVVf2FyQOnRluUTsPQrKH85aLZvDdMmXd6tKX5mooMyE7O8DmavOFrhmYOZUcm7LYdZEuWfwGMPREqt/5NjjjDHTmJOJk+aDq1STZKe3dcdl+6hT7pxiMajFKLG/bW7mV/t2ZjTwsxt026jcl9J/Hk1Nj5V6jIhMFZJjxWIAzNGkp9oo03m1YC/gUw9sTgjMEkYAlaqfnq+ejEaVdbVtBx2apeQt80M1IDo9TihtV7VwPGSSTUWMTCuYPPY2OO0hgjoTTLcxIY7HBPN/iH0wPynaZVnJ5XxMD0gUHXmZSQxOBeg0Ki1FItKT7bSvv16sfQXgUdOotYBQ6mWo3nowOj1OKE1XtXkySJFOXHoLtenFOUX0STRdkcA2mobAK7etlMIOMAca5Vq+dUQAGMPVGYPypoD8iyPDgtd5RfgcinDzmP5YMt2LzY1Q72sN83Zo2aHaPU4oTVe1dT3LuYlMSUaIvS6SjqbX9R2JAfZUGwrzdqtKjxfAyQgowCLI7HWrCu/K4UZhWyNSu4dY0b+yQwprd/8SenF0znSIqtZerSHftMxutWGKUWJ7xy/Ss8d83z0RajU1KYXUiKJZkNPhjkw02FyaMWFMkJyQxI60teci7FvYtDVm9hdiH1ibYWBeIvNamwt4fvno9OfFmvZkJktcYotTghq1uW2/xLhuBJtCQyJnd0TCi1cpNyJmjumfYr/nPILSENJResB+RGP51EnBRkFDAwrZ/X4MYmRFZrjFIzGIDivuPZ0Dch6uvVKjJBkJA4OHRVbpt0GxfmXxjSOodn28NaBazUcu1//VVqYF+v9vEQz/emc6SWnxYD8+cxgFFqHhCRESJS6vI5LiJ3tikjIvK4iGwXkS9EZHy05DUER1F+EYdSrS0PiGhRkQH9e/QxttMYo3+v/qRaUgJWamV50DOxBwN6DfD72umDpnOwm5UtHhyZ9vWE3JQskhOSAxOuk2GUmgdUdYuqFqtqMTABqAdea1PsEmC44zMLeDqyUhpCRaw4i1RkweBs484fa1jEwvDMoQFHFSnLh9F5YwKaEp0+aDqAxynIqjRjT3PFKDXfOB/Yoaq72hy/EnhO7awEMkTE3F1xiDMrcrTtahXZCQw27vwxSWHeKLbmBbaYcWPvBMbkjw3o2mFZw+jdLdejs0hVL6FPev+A6u6MmOiXvvFt4EU3x/sBe1z2Kx3HqlwLicgs7CM58vPzqauro6SkJDySdgKi1T/5Kfl8cHEfplz1vYi3DdCozew98UvkqHj9/ub+6Zhw9FFKXQrbM5UP/9+fSRDflVuNrZZD9feTcjwlYJlGpY3h/aJ1LJ36m3ajvYr6B8k+4f2eaUunvodU1Xy8fIBk4DCQ7+bc28DZLvsfAhO81TdhwgRdunSpGjwTrf654sUrdNTtFlWIymdLNsp96HOlz3mV09w/HROOPpq/fr5yH7oty7/f9cPB9t/1gx0fBNz2U6ufUu5Dt2e2rtsqaOK9or9a8iu/6ovHewhYqz48s830Y8dcAqxT1QNuzlUCrpbf/sC+iEhlCDnF+cVsybRxMkrzF2aNWmwTqFt/MJ6PTjytVzvcHZotamxqLhil1jHfwf3UI8AbwE0OL8gpwDFVrfJQ1hDjFPUuwiZ4jd4QTirMGrWYxpmt2l+lVpYHWcnp5PcI3AvptNzTyEnJbBfc2KxRa49Ral4Qke7AhcC/XY7dKiK3OnbfAcqB7cDfgB9FXEhDyHDG1YyWs0h5JqRYks1bd4yS0z2HjKSe/iu1fGFM73FBLQYXEaYNOZdlQ1rb8kyIrPYYpeYFVa1X1WxVPeZybI6qznFsq6r+WFWHqupYVV0bPWkNwTI4czBpCd2j5tZfkQGDeg7AIubfMhYREQpzRrAty/drFNiYL4zJC8zz0ZVpA6exs5eV3S45T02IrPaY/x6DwYFFLIzrXcSGPqELr+QPFdkWhuQOj0rbBt8ozB3J1nzfPR/39oJjyTZG5/kX8xpyTxsAABKmSURBVNEd0wvs69Vc7WrO6cfeaTEQ4y1GMErNYHChqE8xX/SWqITLqsgUY0+LcQqzCtmdZvXZmcjfxKDeGJs3lvSknq0WYVf1hMykXqQmpgZdf2fBKDWDwYXi3sUcS7axKyOy7R5LgSMpVqPUYhynB+R2H6cgnUrN3+j87kiwJHBOwXQ+drGr7etp7GltMUrNYHDB6SxSGuHZnBbPR+POH9P469a/MRd6p+aS3T3ItNkOphdMZ2umtWXasaqX0CfDRBNxxSg1g8GFMXljECTiziLONWom43VsMyxrGOC7UivrbWFMH/8Sg3rDuV5tuWMKsqqXxbjzt8EoNYPBhR7JPRieMSTibv1mjVp80DOlJ3265fkU2Ngm8FUuIfF8dDK+z3jSErqzbJDds7Kqh81MP7bBKDWDoQ1FjtxqkaQiA9KTepLZLTOi7Rr8pzB3JFtzOvaQ3ZkB9Ymh8Xx0kmhJ5KyCs1k2xMKRbtBoUTNSa4NRagZDG4p7F1OebuV4BFOaVWSaUVq8UJgzgq25HT86Q+n56Mq0QdPZmGNrqd+sUWuNUWoGQxucziJfRjBcVnlOgsmjFicUZhdyKNVKTQde9E6lc1ruaSFt35lfbZFDV5qRWmuMUjMY2uBMGBopD0gFdqarcRKJE5wekB3Z1TbmwsAe/eiV0iuk7U/qN4lUSwqvOHSlsam1xig1g6EN/Xr2Iys5PWLOIgfS4GSCzUw/xgm+BjYu62NhTN+ikLefnJDM1AFTOdTDvm+mH1tjlJrB0AYRoajP6RELl2VSzsQXQzKHYMHiNQZkswU2ZytjckNrT3MyvWAGAL0S0+ie1D0sbcQrRqkZDG4o6l3Ml/mCNQJ6rdy488cVKYkpFPQc4HWktj3L7pkYSs9HV5zr1fqYmI/tMErNYHBDce9iTibYfA6HFAzONWoFGQXhb8wQEgrzR3n1gAyX56OTKf2nkCxJ9M0Y0HHhLoZRagaDG5zOIpGwq1VkQJ9ueXRL6hb+xgwhoTCrkK3ZeAx8XZYHgjAqZ1RY2u+W1I1ZE/+LywsvD0v98UyUEtcbDLHNqJxRJEoCpb2tXL8xvG1VZMLgLOPOH08UZhdSl2Rjfxr0qWt/fmMuDEsfHNYXlScufSJsdcczZqRmMLghJTGFUdkjIhIDsiIngcFZxp0/nugosHFZnwRGhzDmo8F3jFLzgohkiMgrIrJZRDaJyNQ252eIyDERKXV87o2WrIbQU9R3PBv6hTdcVpMFdqeZlDPxxvBsz279DYmwLcMaNs9Hg3eMUvPOY8B7qjoSKAI2uSmzXFWLHZ8HIiueIZwU5Rext4eV6jCauvak2wPfmoXX8cWAXgNIsSS7XYC9JRuslvA5iRi8Y5SaB0SkFzANmAugqo2qejS6UhkiSXHvYiC8ziJmjVp8kmBJYFjGELcjtY3OxKBhcuc3eMc4inhmCHAImC8iRcDnwB2qeqJNuakisgHYB9ylqu3cCkRkFjALID8/n7q6OkpKSsIqfDwTK/1T12j3AHj1liuwJE8LSxvvNa2CUy+zf/N+SnaW+CZXjPRPLBOJPsqy5FJ6Wg0ls+9udfztU++Q0LSMqrIqDlsOh1WGQOnU95Cqmo+bDzARaAbOcOw/Bvy+TZleQJpj+1JgW0f1TpgwQZcuXaoGz8RS//T5c57edBWqhOfzy/PRxPsTtNna7LNMsdQ/sUok+uieD+7RpHtFm6X1b3r5d9DRj48Me/vBEI/3ELBWfXh2m+lHz1QClaq6yrH/CjDetYCqHlfVOsf2O0CSiOREVkxDOLHnVgvfv0lFBgxM60+CJbL52wzBU5hdSJNF2ZXR+vhG4/kYVYxS84Cq7gf2iMgIx6Hzga9cy4hIbxERx/Zk7P1ZHVFBDWGlqHcRX+UojWHSORVZwpCc4eGp3BBW3Ln1n0iC8l7G8zGaGKXmnZ8C/xCRL4Bi4I8icquI3Oo4/y2gzGFTexz4tmOYbOgkFOUX0WRRNodp/F2RbTFOInGKM1q/a2Djr3Ltf43nY/QwjiJeUNVS7LY1V+a4nH8SeDKiQhkiSosHZD6MOxDauk8kwcFUs0YtXsnrkUevxDS2Zn8dUsR4PkYfM1IzGLwwPHs4qZaUsCQMdQYyNiO1+EREKMwpbDX9WJYHKZZkhmaasGfRwig1g8ELiZZExuSNDstaNecaNbPwOn4pzB3J1vyvDa5leXBazijj+BNFjFIzGDqgqM/pbOib4DEie6BUmDxqcU9hViG70qw0OAw5G/skMLr32OgK1cUxSs1g6ICi/CIOp1qp6hnaeisyoEdCN3K6m1Ug8UphdiEqsCMTjqZCZQ/j+RhtjKOIwdABrs4ifWtDV295JgzOKMCxKsQQhzgDG2/Ltis1MJ6P0caM1AyGDhiXb19IG2q7WkW2hcHZw0JbqSGiON36t2Z/ne3aeD5GF6PUDIYOSE9NpyBtQEg9IBW7Tc04icQ36anp5KfmsDXb7s6fltCdgekDoy1Wl8YoNYPBB4r6hTZcVnV3qEu0GSeRTkBh7ki25thHaqPzRmMR81iNJqb3DQYfKMovYmumjZMhskKblDOdh8KcEWzNTbBnu843no/Rxig1g+H/t3f3QVbVdRzH3x+WXXlYcOOhC64IigiieBEEy5DErBHwocaa1FGx/iANSaf6w2qmHB+aUCtpfKAyymbMxnx2MsIUfBjTSQsfijRGMEhS7IFcdFR2v/1xD3Jdd+/evSz33D3385q5s+f8zrmHL785e797zvnd768M+TF5OrT7ucmeetHD+TPjkJGH8MrgdrYNavcgkRrgpGZWhr6eMNTVRLJjV2Fj8MjHWuCkZlaGCS0TGDZwKE/n+uZ4G1tg9D4jaG5q7psDWmp2jYAEj3ysBU5qZmUYoAEcMSbPurF7/p2yAJ7NwYEjPPIxCyaOmIgQH2gcztjmsWmHU/ec1MzKlB8znWfGao/LZd07GX4/Dk6fdmafxGXpGjRwEOOH7c9huWn+In0NcEURszLlx+S5vrGDTS1w4H8rO8YbjfClhQM4fOQULph9Qd8GaKlZvvBaWga19Lyj7XVOamZlyufyQGGwSKVJ7Ttz4KXhHTx08g00NjT2YXSWplMmn5J2CJbw7UezMk3LTWMAAyoeLLJhBCw7Vpx1xFnMHT+3b4MzM8BJrSRJLZJuk/RXSeslfbjTdkn6gaQNkp6RNCOtWG3vG9I4hEktB1U0rD+ALy0Q+zQN4coTruzz2MyswEmttOXAqoiYAuSB9Z22zwcmJa/FwA3VDc+qLd86g3WtvZ8A8u4p8JuDg0s/djljh3mEnNne4qTWDUnDgbnATwAi4u2I6Pwk5VTg51HwONAiyZ9YGZbP5dk4vJ3/7VP+e95ohIsWDmDaqMM8OMRsL/NAke4dBGwDfiopDzwFXBgRO4r2aQU2F61vSdq2Fh9I0mIKV3Lkcjna2tpYu3btXgy9f6vl/tG/CkO2b7p0CdMayqsGsvKtVbz0zu9Y3rqYRx9+dI9jqOX+qRXuo9Iy3T8R4VcXL+AoYCdwdLK+HLis0z6/BuYUrT8AzCx13JkzZ8aaNWvCulfL/bN5++bgEuLaWUTQ8+uFEUTTNxVn33F2n8VQy/1TK9xHpfXH/gGejDI+u337sXtbgC0R8USyfhvQeSDIFmBc0fr+wMtViM1S0jqslZFNLWUNFglg6UIxqGkoV37cg0PMqsFJrRsR8U9gs6TJSdPHgL902u0e4JxkFOSHgO0RsRXLLEnk9zuSp8sol3XXFPjtxMLgkDHNfTxttpl1yc/USlsK3CypCXgR+Jyk8wAiYgVwH7AA2AC8AXwurUCtevJjprMi9xDtChq6qZm1oxEuWtjAtFFTWDJ7SXUDNKtjTmolRMQ6Cs/Wiq0o2h6AP7HqTD6X582GDv42Eqa81vU+3z4W/j6snYdPuoGBA/xrZlYtvv1o1kv5MUm5rG4qi7wwEq6aI87Jn8Ox44+tYmRm5qRm1ktTR0+lUQO7HCwSwNIFYnDTUFcOMUuBk5pZLzU1NHHoyMldXqndeSisnhhcfsK3yTX30YyiZlY2JzWzCuT3m8HTncpl7WiEixYM4IhRh3P+rPNTisysvjmpmVUgn8vzj6HtvDZkd9sVc2HzsA6uO9mDQ8zS4qRmVoHOg0WeHwlXf0Qsyi9izgFzUozMrL45qZlVoHjC0F2VQ4bs08yyE5alG5hZnXNSM6vA6KGj2W9wjqdzcPtUuP8gDw4xqwW+8W9WoXzrDB4/YBUPHiymjz6c8446L+2QzOqer9TMKpTP5XlhRLCl2YNDzGqFk5pZhXYNFjl3+rkcM+6YlKMxM3BSM6vY/IPnc+HRF3LVx69KOxQzS/h+iVmF9h20L9eceE3aYZhZEV+pmZlZZjipmZlZZjipmZlZZjipmZlZZnigSAmSNgGvA+3Azog4qtP244C7gY1J0x0RcWk1YzQzs92c1Ho2LyJeK7H9kYg4qWrRmJlZt3z70czMMkMRkXYMNUvSRuA/FAqx/zAiftRp+3HA7cAW4GXgqxHx5y6OsxhYDJDL5WbeeOONNDc37+Xo+6+2tjb3Twnun565j0rrj/0zb968pzo/AuqKk1oJkvaLiJclfRC4H1gaEQ8XbR8OdEREm6QFwPKImNTDMbcBO4BStzTr3SjcP6W4f3rmPiqtP/bP+IgY3dNOTmplknQJ0BYRV5fYZxNwVA/P4JD0ZDl/cdQr909p7p+euY9Ky3L/+JlaNyQNlTRs1zLwCeC5TvuMkaRkeTaF/vxXtWM1M7MCj37sXg64M8lZA4FfRMQqSecBRMQK4NPA+ZJ2Am8Cp4cvfc3MUuOk1o2IeBHId9G+omj5WuDaCg7/o553qWvun9LcPz1zH5WW2f7xMzUzM8sMP1MzM7PMcFIzM7PMcFKrIkknSnpe0gZJF6cdTy2StEnSs5LWSXoy7XjSJmmlpFclPVfUNkLS/ZL+lvz8QJoxpq2bPrpE0j+S82hd8j3SuiRpnKQ1ktZL+rOkC5P2TJ5HTmpVIqkBuA6YD0wFzpA0Nd2oata8iJie1e/R9NLPgBM7tV0MPJB80f+BZL2e/Yz39xHA95PzaHpE3FflmGrJTuArEXEo8CFgSfLZk8nzyEmtemYDGyLixYh4G/glcGrKMVmNSyrY/LtT86nATcnyTcAnqxpUjemmjywREVsj4o/J8uvAeqCVjJ5HTmrV0wpsLlrfkrTZewWwWtJTSc1Me79cRGyFwgcW8MGU46lVF0h6Jrk9mYlba3tK0gTgSOAJMnoeOalVj7po8/cp3u8jETGDwm3aJZLmph2Q9Us3ABOB6cBW4LvphpM+Sc0UCrBfFBH/SzuevcVJrXq2AOOK1venUNnfikTEy8nPV4E7Kdy2tfd6RdJYgOTnqynHU3Mi4pWIaI+IDuDH1Pl5JKmRQkK7OSLuSJozeR45qVXPH4BJkg6U1AScDtyTckw1pZx6mwYUzptFyfIiCrOvW5FdH9aJT1HH51FSn/YnwPqI+F7RpkyeR64oUkXJsOJrgAZgZURckXJINUXSQRSuzmB3vc267iNJtwDHUZgq5BXgW8BdwK3AAcDfgc9ERN0OlOimj46jcOsxgE3AF3Y9P6o3kuYAjwDPAh1J89cpPFfL3HnkpGZmZpnh249mZpYZTmpmZpYZTmpmZpYZTmpmZpYZTmpmZpYZTmpm/Vwys8GoZLmtyv/2WkkuPG01w0nNrMapIHO/q5IGph2DZU/mflHMskDShGT+q+uBPwLjJJ2RzDX3nKRlFRzrx8l8WqslDU62vXulJWmUpE3J8rmS7pJ0r6SNki6Q9GVJf5L0uKQRRf/EWZIeS+Kanbx/aFJI+A/Je04tOu6vJN0LrO6TzjIr4qRmVrsmAz+PiCOBd4BlwPEUKmXMktSbqUImAddFxGHAf4HTynjP4cCZFOomXgG8kcTye+Ccov2GRsQxwBeBlUnbN4AHI2IWMA+4Kil9BvBhYFFEHN+L+M3K4qRmVrteiojHk+VZwNqI2BYRO4Gbgd7MYLAxItYly08BE8p4z5qIeD0itgHbgXuT9mc7vf8WeHdes+GSWijU7bxY0jpgLTCIQjkmgPuzUI7JapPvaZvVrh1Fy11NXdQbbxUttwODk+Wd7P7jdlCJ93QUrXfw3s+OzrX2gkK8p0XE88UbJB3Ne/9fZn3KV2pm/cMTwEeT514NwBnAQ31w3E3AzGT50xUe47PwbuHc7RGxHfgtsDSpEI+kI/cwTrOy+ErNrB+IiK2SvgasoXAVdF9E9MVUIVcDt0o6G3iwwmP8R9JjwHDg80nbZRRmpHgmSWybgJP2MFazHrlKv5mZZYZvP5qZWWY4qZmZWWY4qZmZWWY4qZmZWWY4qZmZWWY4qZmZWWY4qZmZWWb8H139MvoXG6jNAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "roll_and_cgpa = []\n",
+    "for (k, v) in data.items():\n",
+    "    try:\n",
+    "        roll_and_cgpa.append((k,float(v['cgpa'][2])))\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "    \n",
+    "\n",
+    "def sortbycg(l):\n",
+    "   return l[1]\n",
+    "\n",
+    "def sortbyroll(l):\n",
+    "    return int(l[0][7:9])\n",
+    "\n",
+    "\n",
+    "sorted_cgpa = roll_and_cgpa[:]\n",
+    "sorted_cgpa.sort(key=sortbycg)\n",
+    "\n",
+    "\n",
+    "print(\"Top 5 Students:\\n\")\n",
+    "for element in sorted_cgpa[:-6:-1]:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "    \n",
+    "roll_and_cgpa.sort(key=sortbyroll)\n",
+    "\n",
+    "# for individual cgpa \n",
+    "\n",
+    "print(\"Students with theri cgpa :\\n\")\n",
+    "for element in roll_and_cgpa:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "\n",
+    "cgpa = np.array([element[1] for element in roll_and_cgpa], dtype='float')\n",
+    "\n",
+    "roll = np.array([element[0][7:9] for element in roll_and_cgpa],dtype='int')\n",
+    "\n",
+    "\n",
+    "cgpa_average = round(np.mean(cgpa), 2)\n",
+    "cgpa_median = round(np.median(cgpa), 2)\n",
+    "cgpa_highest = round(np.max(cgpa), 2)\n",
+    "cgpa_lowest = round(np.min(cgpa),2)\n",
+    "cgpa_standard_deviation = round(np.std(cgpa),2)\n",
+    "\n",
+    "print(\"\\nCGPA:\")\n",
+    "print(\"Highest: %s\" % cgpa_highest)\n",
+    "print(\"lowest: %s\"% cgpa_lowest)\n",
+    "print(\" Median: %s\" % cgpa_median)\n",
+    "print(\"Average: %s\" % cgpa_average)\n",
+    "print(\"Standard Deviation: %s \\n\"% cgpa_standard_deviation)\n",
+    "\n",
+    "print(\"   9.5+: %s\" % len([cg for cg in cgpa if cg >= 9.5]))\n",
+    "print(\"  9-9.5: %s\" % len([cg for cg in cgpa if cg >= 9 and cg < 9.5]))\n",
+    "print(\"  8.5-9: %s\" % len([cg for cg in cgpa if cg >= 8.5 and cg < 9]))\n",
+    "print(\"  8-8.5: %s\" % len([cg for cg in cgpa if cg >= 8 and cg < 8.5]))\n",
+    "print(\"  7.5-8: %s\" % len([cg for cg in cgpa if cg >= 7.5 and cg < 8]))\n",
+    "print(\"  7-7.5: %s\" % len([cg for cg in cgpa if cg >= 7 and cg < 7.5]))\n",
+    "print(\"     7-: %s\" % len([cg for cg in cgpa if cg < 7]))\n",
+    "\n",
+    "\n",
+    "unit_array = np.ones(len(roll),dtype='int')\n",
+    "fig,cgpa_plot = plt.subplots()\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "label=['above mean','below mean']\n",
+    "cgpa_plot.plot(roll,cgpa,label='above mean cgpa',color='green')\n",
+    "cgpa_plot.plot(roll,cgpa_median*unit_array,label='below mean cgpa',color='red')\n",
+    "cgpa_plot.grid(b = True)\n",
+    "cgpa_plot.legend()\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa >= cgpa_median*unit_array,facecolor = 'green',interpolate = True )\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa< cgpa_median*unit_array,facecolor = 'red',interpolate = True )\n",
+    "plt.xlabel('roll number')\n",
+    "plt.ylabel('cgpa (0-10)', horizontalalignment='right',rotation =0)\n",
+    "plt.title('cgpa distribution wrt roll number')\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\n')\n",
+    "#plt.fill(cgpa,cgpa_median*median,'r')g\n",
+    "sorted_cgpa=cgpa[:]\n",
+    "sorted_cgpa.sort()\n",
+    "fig,histo_cgpa = plt.subplots()\n",
+    "histo_cgpa.hist(sorted_cgpa,bins=40)\n",
+    "#histo_cgpa.fill_between(roll,histo_cgpa,where =sorted_cgpa >= 8.0,facecolor = 'green',interpolate = True )\n",
+    "#histo_cgpa.fill(histo_cgpa, where = sorted_cgpa >= cgpa_median)\n",
+    "plt.title('frequency distribution of class cgpa')\n",
+    "\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' number\\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.show()\n",
+    "#plt.plot(norm.pdf(cgpa_in_seq,cgpa_median,cgpa_standard_deviation))\n",
+    "\n",
+    "print('\\n')\n",
+    "fig = norm.pdf(sorted_cgpa, np.mean(sorted_cgpa), np.std(sorted_cgpa))\n",
+    "plt.plot(sorted_cgpa,fig,'-o')\n",
+    "plt.title('standard distribution of class cgpa')\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' fraction \\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.grid(b = True)\n",
+    "plt.show()\n",
+    "\n",
+    "above_mean = [cg for cg in cgpa if cg >= cgpa_median]\n",
+    "below_mean = [cg for cg in cgpa if cg < cgpa_median]\n",
+    "\n",
+    "print('\\n')\n",
+    "labels = 'above mean','below mean'\n",
+    "sizes = [len(above_mean),len(below_mean)]\n",
+    "#print(len(above_mean),len(below_mean))\n",
+    "explode = (0.1,0)\n",
+    "colors = ['green','red']\n",
+    "# Plot\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
+    "        autopct='%2.1f%%', shadow=True, startangle=140)\n",
+    " \n",
+    "plt.axis('equal')\n",
+    "plt.title('cgpa distribution wrt mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# results plot \n",
+    "Certain students need support that would improve their academic performance.\n",
+    "\n",
+    "\n",
+    "https://analytics4all.org/2016/05/05/python-histograms-and-frequency-distribution/\n",
+    "\n",
+    "https://etav.github.io/python/count_basic_freq_plot.html for histogram frequency distribution\n",
+    "\n",
+    "https://stackoverflow.com/questions/20011494/plot-normal-distribution-with-matplotlib for ploting the standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/year/2016/16MM.ipynb b/year/2016/16MM.ipynb
new file mode 100644
index 0000000..b173fe4
--- /dev/null
+++ b/year/2016/16MM.ipynb
@@ -0,0 +1,1357 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# B.Tech 2016-20 Mechanical Engineering (Spring 2018)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import datetime as dt\n",
+    "import json\n",
+    "from scipy.stats import norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16MM01020 6.09\n",
+      "16MM01006 5.32\n",
+      "16MM01003 7.60\n",
+      "16MM01002 8.45\n",
+      "16MM01009 7.79\n",
+      "16MM01005 7.89\n",
+      "16MM01004 7.15\n",
+      "16MM01010 8.09\n",
+      "16MM01011 8.66\n",
+      "16MM01012 8.85\n",
+      "16MM01013 6.94\n",
+      "16MM01014 5.46\n",
+      "16MM01015 6.51\n",
+      "16MM01016 5.94\n",
+      "16MM01017 9.06\n",
+      "16MM01018 5.50\n",
+      "16MM01019 6.81\n",
+      "Total Stuents: 17\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open('stres.json') as f:\n",
+    "    data = json.load(f)\n",
+    "    # Filter out 16EE data\n",
+    "    data = {k:v for (k,v) in data.items() if '16MM' in k[:6]}\n",
+    "    \n",
+    "for (k,v) in data.items():\n",
+    "    try:\n",
+    "        print(k,v['cgpa'][1])\n",
+    "    except:\n",
+    "        pass\n",
+    "    \n",
+    "with open('course.json') as c:\n",
+    "    cdata = json.load(c)\n",
+    "    \n",
+    "print (\"Total Stuents: %s\" % len(data))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Date of Birth analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Average:   August 10, 1998\n",
+      "  Median:     July 09, 1998\n",
+      "  Oldest:     June 25, 1997\n",
+      "Youngest:     June 17, 1999\n"
+     ]
+    }
+   ],
+   "source": [
+    "dob = [v['dob'] for (k, v) in data.items()]\n",
+    "\n",
+    "np_dob = (np.array(dob, dtype='datetime64[s]').view('i8'))\n",
+    "average_dob = np.mean(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "median_dob  = np.median(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "minimum_dob = np.min(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "maximum_dob = np.max(np_dob).astype('datetime64[s]').astype(dt.datetime)\n",
+    "print (\" Average: %s\" % average_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Median: %s\" % median_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"  Oldest: %s\" % minimum_dob.strftime(\"%8B %d, %Y\"))\n",
+    "print (\"Youngest: %s\" % maximum_dob.strftime(\"%8B %d, %Y\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Branch Change Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no branch changer\n"
+     ]
+    }
+   ],
+   "source": [
+    "original_strengeth = 40\n",
+    "bc_cgpa = [v['cgpa'][1] for (k, v) in data.items() if (int(k[-2:]) > 40 and v['cgpa'][1] !='WH')]\n",
+    "#cgpa = [v['cgpa'][2] for (k,v) in data.items() if v['cgpa'][2] != 'WH' ]\n",
+    "\n",
+    "bc_cgpa = (np.array(bc_cgpa, dtype='float'))\n",
+    "#cgpa = (np.array(cgpa, dtype='float'))\n",
+    "\n",
+    "if len(bc_cgpa>0):\n",
+    "    bc_count = bc_cgpa.size\n",
+    "    bc_highest = np.max(bc_cgpa)\n",
+    "    bc_lowest = np.min(bc_cgpa)\n",
+    "    bc_average = np.mean(bc_cgpa)\n",
+    "    bc_median = np.median(bc_cgpa)\n",
+    "    bc_std = np.std(bc_cgpa)\n",
+    "    print (\"Total branch changers: %s\" % bc_count)\n",
+    "    print (\"\\nCGPA (after 2nd sem) for branch change:-\")\n",
+    "    print (\"Highest: %s\" % bc_highest)\n",
+    "    print (\" Lowest: %s\" % bc_lowest)\n",
+    "    print (\"Average: %s\" % bc_average)\n",
+    "    print (\" Median: %s\" % bc_median)\n",
+    "    print(\"Standard Deviation: %s \" %bc_std)\n",
+    "else:\n",
+    "    print('no branch changer')\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Course wise analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>Subject Code</th>\n",
+       "      <th>Subject Name</th>\n",
+       "      <th>Credits</th>\n",
+       "      <th>Students</th>\n",
+       "      <th>EX</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "      <th>D</th>\n",
+       "      <th>P</th>\n",
+       "      <th>F</th>\n",
+       "      <th>WH</th>\n",
+       "      <th>Other</th>\n",
+       "      <th>Average</th>\n",
+       "      <th>Median</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CE2L011</td>\n",
+       "      <td>Building materials and Construction</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>EE2L004</td>\n",
+       "      <td>Introduction to Electromagnetic Engineering</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.17</td>\n",
+       "      <td>5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ME1L001</td>\n",
+       "      <td>Mechanics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.18</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>HS2L002</td>\n",
+       "      <td>Speaking and Presentation</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.57</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>EC2L005</td>\n",
+       "      <td>Analog Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.67</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MA1L002</td>\n",
+       "      <td>Mathematics - II</td>\n",
+       "      <td>4</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6.67</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>PH1L001</td>\n",
+       "      <td>Physics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.71</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>EE1L001</td>\n",
+       "      <td>Electrical Technology</td>\n",
+       "      <td>4</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.94</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>MA1L001</td>\n",
+       "      <td>Mathematics -1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.24</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>ID2L001</td>\n",
+       "      <td>Entrepreneurship and Small Business Management</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.36</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>ID2L002</td>\n",
+       "      <td>Introduction to Bioscience and Technology</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.40</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>CS1L001</td>\n",
+       "      <td>Introduction to Programing and Data Structures</td>\n",
+       "      <td>4</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.41</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CY1L001</td>\n",
+       "      <td>Chemistry</td>\n",
+       "      <td>4</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.47</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>CE1P001</td>\n",
+       "      <td>Engineering Drawing and Graphics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>17</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.47</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>HS1L002</td>\n",
+       "      <td>Learning English</td>\n",
+       "      <td>4</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>ML2L005</td>\n",
+       "      <td>Physical Metallurgy</td>\n",
+       "      <td>3</td>\n",
+       "      <td>14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>CS1P001</td>\n",
+       "      <td>Introduction to Programing and Data Structures...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.59</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>ML2L002</td>\n",
+       "      <td>Thermodynamics of Materials</td>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.60</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>ML2L003</td>\n",
+       "      <td>Materials Processing</td>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.69</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>ID3L003</td>\n",
+       "      <td>Environmental Science, Technology and Management</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.77</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>HS2L003</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.82</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>PH1P001</td>\n",
+       "      <td>Physics Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>ID1T002</td>\n",
+       "      <td>Extra Academic Activities - 2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>ML2L004</td>\n",
+       "      <td>Transport Phenomena and Kinetics of Metallurgi...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>HS2L004</td>\n",
+       "      <td>Odissi Dance - I</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>EE1P001</td>\n",
+       "      <td>Electrical Technology Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.12</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>EC2P005</td>\n",
+       "      <td>Analog Communication Lab</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.27</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>CS2L003</td>\n",
+       "      <td>Data Structure</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.33</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>MA2L007</td>\n",
+       "      <td>Numerical Methods</td>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.40</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>ML2P001</td>\n",
+       "      <td>Introduction to Materials Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.53</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>ME1P001</td>\n",
+       "      <td>Introduction to Manufacturing Processes</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.59</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>ML2S001</td>\n",
+       "      <td>Seminar</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.67</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>ML2P003</td>\n",
+       "      <td>Physical Metallurgy Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.67</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>HS1L001</td>\n",
+       "      <td>English for Communication</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.67</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>ML2P004</td>\n",
+       "      <td>Thermodynamics of Materials Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.83</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>CY1P001</td>\n",
+       "      <td>Chemistry Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.88</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>ML2P002</td>\n",
+       "      <td>Materials Processing Laboratory</td>\n",
+       "      <td>2</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.92</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>ID1T001</td>\n",
+       "      <td>Extra Academic Activities -1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>HS2L007</td>\n",
+       "      <td>Introduction to Economics</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Subject Code                                       Subject Name Credits  \\\n",
+       "0       CE2L011                Building materials and Construction       3   \n",
+       "1       EE2L004        Introduction to Electromagnetic Engineering       3   \n",
+       "2       ME1L001                                          Mechanics       4   \n",
+       "3       HS2L002                          Speaking and Presentation       4   \n",
+       "4       EC2L005                               Analog Communication       4   \n",
+       "5       MA1L002                                   Mathematics - II       4   \n",
+       "6       PH1L001                                            Physics       4   \n",
+       "7       EE1L001                              Electrical Technology       4   \n",
+       "8       MA1L001                                     Mathematics -1       4   \n",
+       "9       ID2L001     Entrepreneurship and Small Business Management       3   \n",
+       "10      ID2L002          Introduction to Bioscience and Technology       2   \n",
+       "11      CS1L001     Introduction to Programing and Data Structures       4   \n",
+       "12      CY1L001                                          Chemistry       4   \n",
+       "13      CE1P001                   Engineering Drawing and Graphics       3   \n",
+       "14      HS1L002                                   Learning English       4   \n",
+       "15      ML2L005                                Physical Metallurgy       3   \n",
+       "16      CS1P001  Introduction to Programing and Data Structures...       2   \n",
+       "17      ML2L002                        Thermodynamics of Materials       3   \n",
+       "18      ML2L003                               Materials Processing       3   \n",
+       "19      ID3L003   Environmental Science, Technology and Management       2   \n",
+       "20      HS2L003                          Introduction to Economics       3   \n",
+       "21      PH1P001                                 Physics Laboratory       2   \n",
+       "22      ID1T002                      Extra Academic Activities - 2       1   \n",
+       "23      ML2L004  Transport Phenomena and Kinetics of Metallurgi...       4   \n",
+       "24      HS2L004                                   Odissi Dance - I       3   \n",
+       "25      EE1P001                   Electrical Technology Laboratory       2   \n",
+       "26      EC2P005                           Analog Communication Lab       2   \n",
+       "27      CS2L003                                     Data Structure       3   \n",
+       "28      MA2L007                                  Numerical Methods       4   \n",
+       "29      ML2P001               Introduction to Materials Laboratory       2   \n",
+       "30      ME1P001            Introduction to Manufacturing Processes       2   \n",
+       "31      ML2S001                                            Seminar       2   \n",
+       "32      ML2P003                     Physical Metallurgy Laboratory       2   \n",
+       "33      HS1L001                          English for Communication       4   \n",
+       "34      ML2P004             Thermodynamics of Materials Laboratory       2   \n",
+       "35      CY1P001                               Chemistry Laboratory       2   \n",
+       "36      ML2P002                    Materials Processing Laboratory       2   \n",
+       "37      ID1T001                       Extra Academic Activities -1       1   \n",
+       "38      HS2L007                          Introduction to Economics       4   \n",
+       "\n",
+       "    Students  EX   A   B  C  D  P  F  WH  Other  Average  Median  \n",
+       "0          2   0   0   0  0  1  0  0   1      0     6.00     6.0  \n",
+       "1          6   0   1   0  1  1  2  1   0      0     6.17     5.5  \n",
+       "2         17   0   0   3  3  5  6  0   0      0     6.18     6.0  \n",
+       "3          8   0   1   1  1  2  2  0   1      0     6.57     6.0  \n",
+       "4         15   1   1   2  2  6  0  3   0      0     6.67     6.0  \n",
+       "5         17   0   4   0  2  5  4  0   0      2     6.67     6.0  \n",
+       "6         17   0   1   3  6  4  2  1   0      0     6.71     7.0  \n",
+       "7         17   1   2   4  2  4  4  0   0      0     6.94     7.0  \n",
+       "8         17   0   4   2  6  4  1  0   0      0     7.24     7.0  \n",
+       "9         23   3   3   4  5  3  3  1   1      0     7.36     7.0  \n",
+       "10        15   1   1   6  4  1  2  0   0      0     7.40     8.0  \n",
+       "11        17   2   3   4  3  1  4  0   0      0     7.41     8.0  \n",
+       "12        17   3   2   5  2  0  5  0   0      0     7.47     8.0  \n",
+       "13        17   2   1   6  4  2  2  0   0      0     7.47     8.0  \n",
+       "14        14   1   1   3  5  2  0  0   0      2     7.50     7.0  \n",
+       "15        14   1   1   5  2  2  1  0   2      0     7.50     8.0  \n",
+       "16        17   3   1   5  3  4  1  0   0      0     7.59     8.0  \n",
+       "17        15   2   2   6  1  1  2  1   0      0     7.60     8.0  \n",
+       "18        15   1   3   4  2  2  0  1   2      0     7.69     8.0  \n",
+       "19        15   1   2   5  4  0  1  0   2      0     7.77     8.0  \n",
+       "20        11   2   2   3  2  0  2  0   0      0     7.82     8.0  \n",
+       "21        17   0   2  13  2  0  0  0   0      0     8.00     8.0  \n",
+       "22        17   0   3   8  3  0  0  0   0      3     8.00     8.0  \n",
+       "23        15   3   2   3  2  3  0  0   2      0     8.00     8.0  \n",
+       "24         2   0   1   0  1  0  0  0   0      0     8.00     8.0  \n",
+       "25        17   3   3   6  3  2  0  0   0      0     8.12     8.0  \n",
+       "26        15   1   4   9  0  1  0  0   0      0     8.27     8.0  \n",
+       "27         7   0   3   2  1  0  0  0   1      0     8.33     8.5  \n",
+       "28        15   3   6   2  2  2  0  0   0      0     8.40     9.0  \n",
+       "29        15   3   5   4  3  0  0  0   0      0     8.53     9.0  \n",
+       "30        17   0  10   7  0  0  0  0   0      0     8.59     9.0  \n",
+       "31        15   1   8   6  0  0  0  0   0      0     8.67     9.0  \n",
+       "32        14   2   6   2  2  0  0  0   2      0     8.67     9.0  \n",
+       "33         3   0   2   1  0  0  0  0   0      0     8.67     9.0  \n",
+       "34        14   2   6   4  0  0  0  0   2      0     8.83     9.0  \n",
+       "35        17   4   8   4  1  0  0  0   0      0     8.88     9.0  \n",
+       "36        15   5   4   3  0  1  0  0   2      0     8.92     9.0  \n",
+       "37        17   7   6   1  3  0  0  0   0      0     9.00     9.0  \n",
+       "38         1   0   1   0  0  0  0  0   0      0     9.00     9.0  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "courses = dict()\n",
+    "for (k, v) in data.items():\n",
+    "    for (sem, scourses) in v['grades'].items():\n",
+    "        for (course, grade) in scourses.items():\n",
+    "            if course not in courses:\n",
+    "                courses[course] = list()\n",
+    "                courses[course].append(grade)\n",
+    "            else:\n",
+    "                courses[course].append(grade)\n",
+    "\n",
+    "clist = list()\n",
+    "\n",
+    "def other_grade(l):\n",
+    "    return len(l) - l.count('EX') - l.count('A') - l.count('B') - l.count('C') - l.count('D') - l.count('P') - l.count('F') - l.count('WH')\n",
+    "\n",
+    "def analyze_grade(l):\n",
+    "    grade_hash = {'EX': 10, 'A': 9, 'B': 8, 'C': 7, 'D': 6, 'P': 5, 'F': 5}\n",
+    "    hashed_grade = list()\n",
+    "    for grade in l:\n",
+    "        if grade in grade_hash:\n",
+    "            hashed_grade.append(grade_hash[grade])\n",
+    "    hashed_grade = (np.array(hashed_grade, dtype='float'))\n",
+    "    if hashed_grade.size == 0:\n",
+    "        return {'average': 0, 'median': 0}\n",
+    "    return {'average': round(np.mean(hashed_grade), 2), 'median': round(np.median(hashed_grade), 2)}\n",
+    "\n",
+    "grade_labels = 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other'\n",
+    "colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue', 'orange', 'red', 'gray', 'black']\n",
+    "\n",
+    "for course, grades in courses.items():\n",
+    "    course_info = cdata[course]\n",
+    "    clist.append((course, course_info['subnane'], course_info['credit'], len(grades), grades.count('EX'), grades.count('A'), \n",
+    "                 grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "                 grades.count('F'), grades.count('WH'), other_grade(grades), analyze_grade(grades)['average'], analyze_grade(grades)['median']))\n",
+    "#     patches, texts = plt.pie([grades.count('EX'), grades.count('A'), \n",
+    "#                  grades.count('B'), grades.count('C'), grades.count('D'), grades.count('P'),\n",
+    "#                  grades.count('F'), grades.count('WH'), other_grade(grades)], labels=grade_labels, colors=colors)\n",
+    "#     plt.axis('equal')\n",
+    "#     plt.legend(patches, labels, loc=\"best\")\n",
+    "#     plt.show()\n",
+    "def sortByAverage(element):\n",
+    "    return element[-2]\n",
+    "\n",
+    "clist.sort(key=sortByAverage)\n",
+    "df = pd.DataFrame(data = clist)\n",
+    "df.columns = ['Subject Code', 'Subject Name', 'Credits', 'Students', 'EX', 'A', 'B', 'C', 'D', 'P', 'F', 'WH', 'Other', 'Average', 'Median']\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CGPA Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top 5 Students:\n",
+      "\n",
+      "16MM01012  SAMPURNA BORAH  9.15\n",
+      "16MM01017  SHUBHAJIT MONDAL  9.04\n",
+      "16MM01002  MANTRI HARSH RAKESH  8.58\n",
+      "16MM01011  BASANT KUMAR  8.40\n",
+      "16MM01010  GUPTA CHINMAY AMIT  8.19\n",
+      "Students with their cgpa :\n",
+      "\n",
+      "16MM01002  MANTRI HARSH RAKESH  8.58\n",
+      "16MM01003  PIYUSH G KHATRI  7.66\n",
+      "16MM01004  PALLI CHAITANYA SAI SRI KRISHNA  7.32\n",
+      "16MM01005  MD ZAHID AHMED  8.13\n",
+      "16MM01006  RAJ NANDAN  4.80\n",
+      "16MM01009  MADDINA N V G PRUDHVI  7.94\n",
+      "16MM01010  GUPTA CHINMAY AMIT  8.19\n",
+      "16MM01011  BASANT KUMAR  8.40\n",
+      "16MM01012  SAMPURNA BORAH  9.15\n",
+      "16MM01013  BOLEM SURYA PRAKASH  7.11\n",
+      "16MM01015  CHIMALADHARI BHANU VASISHT  6.72\n",
+      "16MM01016  KASUKURTHI ROHIT  5.71\n",
+      "16MM01017  SHUBHAJIT MONDAL  9.04\n",
+      "16MM01019  DHARMENDRA MEENA  7.06\n",
+      "16MM01020  VISLAVATH RANJEETH KUMAR  5.89\n",
+      "\n",
+      "CGPA:\n",
+      "Highest: 9.15\n",
+      "lowest: 4.8\n",
+      " Median: 7.66\n",
+      "Average: 7.45\n",
+      "Standard Deviation: 1.22 \n",
+      "\n",
+      "   9.5+: 0\n",
+      "  9-9.5: 2\n",
+      "  8.5-9: 1\n",
+      "  8-8.5: 3\n",
+      "  7.5-8: 2\n",
+      "  7-7.5: 3\n",
+      "     7-: 4\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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C7M0COcUERbKAb+P6lNcCpW0g5wY4YzAc4G91JoVUAaNRneN3IWYPC+QUFRQZAVwKbAdKBPgVjD0GRvlbmUkhy4BDUN3udyHGsUBOYUGRfYCf4cYkrwc4D4aeCcEcsKuzTDSmovp9v4swjgVykpKQnAIM0SK9vaHtgiJdcCf7+gBfAToa+lwG3y2w8comOt9BdabfRRgb9pZ0JCDt5TwZCzwG3CYhuVdCkeciCKtuBG4GPgAGANmz4atrYcpmr9Vsmq4KOBQ41ftZgWuAfXGLFN5Rzz6vA8Nq3fKBZ7zHzgUOBn5ba/sbgWdjXXjz3G9D4ZKDBXISkYDkU8Bv6M0zQHvv7h8BL0pIOkbaL+yW7bkHCAP9gfwvYOsV8NBysKXim+F29l4ddiqwCvgM+BT4bj37jAU+9G6vAYXAicBC7/GFuImKt+LOxi4ATot96c3RGbjf7yKMBXLSkIBkIVzAKCZTSLs6D58AzJOQ7Btp/7BqFTATuBfoDrTbCuU/gxnvuLlxTZS+Al4AfljrvnuA69nzhunWyDGeAsbjQjkXKAWqgXLc+lrXAzfEruRYOBWRk/0uItNZICePb3EoF9OZvhEe3xcXysdHOkBYVcOqbwM3Aa2AbtWgN8Irz0C4ymWCacTPgD+x95tjCfAEcDguaL9o5BgzgEne9/sD/XCXWp4DfInrAjk0diXHyl9tqk5/WSAnAQnIAXThUvblkEY27Qi8JCG5pKGNwqqfAyGgBHeyj4fgg7th+m7XWDMRPI9r/Q6vc38Zrk/4XeBi4KIGjrEWWAScVOu+23BdGVcB1+Fax3/ABfSUWBQeGwfghlIan9goC59JQNog/JEJnE0bejRh19uBq7RIqyJtEBRpDVyCO8e0Aqg+ADr+GiZ3gC4tqzw9/QZ4BDdmcDewDTgTF8Qv4s6aKtAB1xdcn9uBxdTfKfssLpgn47otHscNHH8R172RBDYDg1Hd4nchmchayD6SgAgwkcMZ2cQwBrgCCEtI6vY3/0/YrTx8J3uypNUnsOVKeGCV+xRu6rgJ14e8HNftcDzwD+B03Ik6gNm4/qNIHmdPd0VtFbiw/iWwCxDv/pq+5STRCfid30VkKgtkfx1MN05jULO7E08B3paQDIi0QVi1EpctDwE9gbaboOwKePRDeKeZz5txfg38CxiKa0U/4N3/Lnuf/FuOG40xup5j3AVcgGsJH4xraQ8FjsG1uJPIZYjs53cRmci6LHwiAWlHFn9gAmfTmu4tPNwG4AwtanhegqDI/riWdSWwEeBSGHEynJxlf5zN3v6N6rf9LiLT2JvQB15XxSRGcHQMwhigK/BfCcn3GtoorPop7mTfLqA3wL3wzhR4tNyba9kYzymI1D23aeLMAtkfw+jBqQwklqs3tAKmS0j+KCGRSBuFVdfiLhL7AtevnPUCLP09PLAN7ESOqe2XfheQaazLIsEkIO2BPzKB02kbtzmMZwLf0yLdFWmDoEgu7tzTONx5rPIeUPA7mNjLXe1nTBUwBNVlfheSKayFnEBeV8Vk9mVIHMMY3EittyQkfSJtEHarRTzi3XoDrddB6eUwfbGbF8OYbOBKv4vIJNZCTiAJyBCEazmd8RTQNQFPuRY4TYu0wdEUQZGhuBnjyoBNAFfA0WNhXNae0VkmM+0E+qG62e9CMoG1kBPEax2fw1B6JyiMwQ1zmy0hOaehjcKqi3D9yuV4S0HdDm9PhxkVSTVE1vigNXCZ30VkCgvkxDmIbALsy+EJft4CYIaE5PqGNgqrrsKF8nLcyT6ZCZ/fAg/tiHxRmskMP8Wt52jizAI5ASQg2cAkDqU/ef+bVjOhJQAhCcljEor8xgqrlgB/Bd7CrW6duwDWXw1T1rsTfyYzdQPO87uITGCBnBjDaUU/9kl467iuScDrEpKIY5/DqmW4q/pmAH2BwlWw83KYWuzmzDGZ6Xy/C8gEdlIvziQgecBNHM1IBjDS73o8K4EJWqQLG9ooKHIYrv9wJ94Y5V/AqONgrJ3pyzgK9Md1bZk4sRZy/B1NIT3p+40ZHf3UD5gjIZnQ0EZh1feB3+PejD0A/gJvPA5PVrrLr03mEOpfKMXEkAVyHElACoGzGEovssnzu5462gDPSEgavBorrLocN33vWlyQywz45FZ4eBfY8vGZpb5J7EwMWSDH1zFAa/pwsN+FRJAF/ElC8qCEJDfSRmE3BvUW3OxwA4Cct2DNb2DKRhfUJjMcarPAxZcFcpx4IyvGM5hCWhFxgdIkcRHwqoSkc6QNwqq7gfuAp3Et5YJlsP0KeHiJW/fTZAZrJceRBXL8BICODGao34VEaRQwX0Kyf6QNwqrVYdVngLtxM8y13w4VP4d/znULKpv0Z4EcRxbI8XMC7RA6EvC7kCYYBMyVkJzU0EZh1Xm4JeFygO4K3ASvPQVPV7kJaUz6GmLTcsaPBXIcSEA6AYdyEP2RlPsdtwdekJD8pKGNwqpLcCf7NuLGK8t0WHgnTNvt5ls26Wu83wWkq1QLi1RxFAL0SsaV3qOSDdwpIblLQpGXhQ+rbsAtQ/cR7sq+7Ndg1XUwZTN8naBaTeJ9y+8C0pUFcoxJQHKAkxhCW58uk46ly4B/S0giLvkWVt2FWy7uedw8yq2KoeRKeHCFmwTfpJ+jcCuamxizQI69/YF2DEqZk3mN+RauX3lwpA3CqlXAk8AU3Axz7bZA+RXw+HswL0F1msTJw50ENjFmgRx742hFBe2JGGApaD/cCIz6FlMGIKyqYdU3cV0Y+UDXatAQvBSG56rcavcmfVggx4EFcgxJQDoDQxlCR7LI9rueGOsEvCIh+UFDG4VVi3ELqW4D+gA8AO/fB4+UQWn8yzQJcqzfBaQjC+TYcmN4e7Gvz3XESy7wgITkLxKSiP93wqrrccPiPsWd7Mt6EZaH4IESb0USk/IORyTZpgNIeRbIsTWSLHbSgSF+FxJnV+HmwWgTaYOw6g7gduBl3OXWeR/D5p/DA1+BLZqZ+vLB9+lk044FcoxIQFoD+zGAtuRQ4Hc9CTABeFtCEnGF6rBqJfAY8DBuaag2G2H3FfCPhfBuguo08XOY3wWkGwvk2BkCCL0Y6HchCTQUWCAhiTjPs3ey73Xgz0BboEsFVF8LL7wIL1a7qT1Nakqlq1BTggVy7BwGlNOJQX4XkmDdcKuQnNvQRmHVxbiTfbvwFlK9G+Y/BI+Vu9WuTeqxQI4xC+QYkIBkAYdSyE5au7DJMK2Af0hIfi8hibiYSFh1DW7C+yW4fuWsMHz5R3hwO5QkplQTQzYVZ4xZIMdGD6A1A+mNkMmrG10D/FNCUhhpg7DqNuBvwCxcKOe+Dxt+AVPWuqWlTOrog0T+tzZNZ4EcG+4ikC7087mOZHAW8IaEJOInhbBqOTANeBQ3Vrn1Wth1BUz/xM2LYVKDQNoO8fSFBXJsHAbspA0RV3POMMNxJ/sinoX3Tva9BPwV6AB02g1Vv4ZnXoNX7UxfyrB+5BiyQG4hCYjg/lNupdACuZbewJsSku80tFFYdSFuGs8K3DwY3AZzHoEnKtx9JrlZP3IMWSC3XAegFW3JIxebAWtvhcCTEpJrGtoo7JaWvxFYhbeQ6lPw2Z/hoR3uEmyTvNL9IqiEskBuue5ANd2tdRyBAL+XkDwiIWkVaaOwaglurPJcvIVU58G6X8OUr2F1Yko1zdDJ7wLSiQVyy3UHsuhEN78LSXLnAa9JSCL+nsKqZcCDuKk8+wEFK2HH5TD1C1icoDpN00ScK9s0nQVyyw0CymhnLeQoHI072RdxrmhvIdXngTuALkCHXVB5FTz1lhsqZ5JLqi/CkFQskFtuH2Anra2FHKX+wBwJyakNbRRWfRd3EUkW7lMIf4LZM+CpSqiMf5kmStZCjiEL5BbwlmvqibCLfAvkJmgLPCsh+XlDG4VVl+Eut/4a72TfY7D4Npi6C3YkoE7TOGshx5AFcst0BZQutCebXL+LSTFZwF8lJFMkJBF/d2HVTcDNuNnhBgDZb8Dqa2HKJliXmFJNA1ojkRfCNU1jgdwy3QChHe38LiSF/RB4WUIS8Wx9WLUUuA8I47o88r+EbVfAQ8ugOEF1msislRwjFsgt0xMQWmXE/MfxNAa3Zl/Eq768hVRnAvfg+pTbb4OKK+GJBTAnMWWaCCyQY8QCuWU6A+UWyDExGJgnIRkXaQPvcuu5uOWhcoFu1aC/h1efhmeroCpRxZq92FJOMWKB3DLtgEoL5JjpAPxHQvLjhjYKq36JO9m3BegL8DB8eCdM2wzr41+mqWO33wWkCwvklmkLVJBrgRxDOcDdEpI7JCQRV+4Oq24A/ggswi2kmv0arLoI7nsSZm5zYW0SwwI5RiyQW8a1kPMskOPgp8DzEpKIJ0zDqjuBvwPP4abx7FENPAKLLoK//xv+XQo7E1NuRrNAjhFRtYkOm0sCchuwi5M4jc4c4Hc9aeoTYIIW6dKGNgqK9AROB47ELRP1NUA7yL0ERh4FR+e5lU1M7BWgaqEcAxbIzeRNuzkFWMOpnEe7jFrcNNE2Amdqkb7Z2IZBkYG4SfIPArYCmwF6QMHFcNyhMCLHdYuY2MnCgiQmrMui+XKBbKCaHOuyiLMuwKsSkgsb29C7uu8vuItJNuEuJmm3DkpvhJd/CXd+DB/YatcxU2ZhHDvWQm4mCUh74FZgFd/hclrR0e+aMsSfgV9rkVY3tmFQJAs4BPgubuzyBrw+5WHQ5QI4fhDsH89iM8BWVG0+ixixQG4mCUgP3OQ3X3Em/0c+XfyuKYM8C5yrRRrVCbugu7T3CGAi7kTserwTUcdBr8kwrjfW5dRMa1HNxJXW48K6LJpvz2D4apt9LMFOA96TkJwRzcZh1cqw6tvAr3ALq7bHTVaU+yas+TFMvw8e2QBr41dy2vrK7wLSiQVy8+25KswC2Q8BYKaEZJ6EZEw0O4RVy8KqrwK/xLWyu+GGy2W/AEt/CPc/Bk9udX3PJjor/C4gnVggN18FbnkiqLbFOH10JPC6hOQlCcmh0ewQVt0RVn0W12L+L25B1l4KWTPgk4vg7jA8txO2x7HudGGBHEMWyM1XSc2ZemshJ4MTcd0YMyQkg6PZIay6Jaz6OPAb4B1cN0b3CtAH4P2L4Y7X4NUyu/ChIcv9LiCd2Em9ZpKAtANuA1ZyMmfTyS4MSSKVuLX5QlqkUfcLB0X6Ad8BhuFaxxsBukD+JXDM4XBkDjbvdR1BVJ/zu4h0YYHcTBKQAuAuYCXf4gy6crDfNZlv2AXcCdysRVoSzQ5BEcHNPDcRt8T9FqAEoB+0uRhGHwSHZdunyxqHoLrQ7yLShQVyM0lAcoH7gRWcwKl0Z7jfNZmItgC3AHdokZZGs4MXzENxY5h74VrLOwAOgI4XwfFD4CCJU8EppD2q2/wuIl1YIDeTd+n0Q8AKxnAyvTjS75pMo9YANwAPapFG1e8fFMkGDse1mDvi5sgoBTgKepwLJ/R3LepMVIKqXRAVQxbILSABuR9Yx7GMph+j/K7HRO0L4Drgn1oU3RsgKJIHHIPrYy7EredXDnAi9D8bxnV3Q+gyyRxUj/W7iHRi/WAtUw5ks4utfhdimmQIMAN4V0JyUjQ7hFXLw6qv44bK/Qu3WkxfIOdlWHExPDgNZmxxl2dnivl+F5BurIXcAhKQWwAYQk9G8D2fyzHN9zrwGy3SqAMmKNIeN9TuZKAad5VfdRbI+XDwiTC2TfqvNXcOqk/6XUQ6sUBuAQnIVUA/uiGM46d+12Na7GngGi3ST6PdISjSBZgAjALKcPNkaAFk/wBGjILj8l0XRzrqj+pKv4tIJxbILSABmQyMJoevOZtrsVPu6aAKmA4UaZGuinanoEhv4ExgOG5GuQ0AHSHvYjj6CBiZl0aLgSqsFZtUKOYskFtAAjIWOB9YwVlcSR4RlxsyKacMuBv4gxZp1HNbBEX2Ac7BTetZgre2Xy8ovARGHQKHZ7t5tFPdM6hGNbmTiZ6d1GuZElz/IZS6q7pM2mgFXAkslZBcJyFpHc1OYdWluDHPt+BWLBkItF0Du34HL14Nd34CH6XBBPnz/C4gHVkLuQUkIP1xw6e+Yiwn09PGIqexr3HzX9+nRVoezQ7eBPmH4i4u6eodYxfAcOh6Ppww0M1al4oGsNcIAAATeklEQVTGojrL7yLSjQVyC0hAWuNWPV7B4RzOvnzb75pM3C0DioBHo1m1BCAokgscBZwNtMWNYS4DGAN9JsG4ntA/PuXGnkKZQGc0ugUCTPQskFtIAnInsJ3B9OIILvC7HpMwi3AjMqKeWCcokg+MBs7AneBbi5vGlSAMPgNO6Aw94lFsjL2A6ql+F5GOLJBbSALyK6AXBZRyOlcjNtYiw8zBrfH3VrQ7BEXaAt8Cxnt3rQOqBJgMB42H49uR1Gs0XozqA34XkY4skFtIAjIROAFYw+lcQiE9/a7J+OLfuItLop75LCjSCfg2cDzuqs91gOZB1vfhsLEwuhDaxKfc5lGoFuiJ6td+15KOLJBbSAJyOHAZsJLRnEhvRvpdk/FNNfA4cJ0W6bJodwqK9MCtEzgSN3HR14C2hdyL4cij4dg8N+rDd9UwJ8vmr4gbC+QWkoB0Af4ErGR/9uVQJvldk/FdBW5q1hu1SNdHu1NQZABu8qKDgW14a/t1h4KL4djD4IgcyIl9uU1yFaq3+lxD2rJAbiFvGs5bgTIKUOtHNrXsxK0q8yctim7OYG8e5n1x033ug7uwZCvAQGj7QxhzIAzL8u8agkG4sdYmDiyQY0ACciFuWNM6TudHFKbEmXKTOJuAm4C7tEijWp/PG8N8MG4Mcw/cpdg7cXd2vhCOHwQHJPIvfzUsylK1lXHiyAI5BvbqRx7DSfTiKL9rMklpFRACpmqRVkWzQ1AkBxiBuxy7A27yot0Ax0DPyTCur2tJJ0IRqjck6LkykgVyDOzVj3wAAYbxXb9rMkntM+BaLdJ/RbtDUKQVcCyujzmfWhPknwwDz4ZxXd1SU3GhblheP1TXxOs5jAVyTOzVj1wInMavrB/ZRGEBbqjca9HuEBRpjRsmNwHXj7wOt8o2E2H/b8PxHaBLrAsth2fzVE+P9XHN3iyQY2SvfuTTuJjW8WutmLTzKu7ikvei3SEo0gE3Of6JuEBeB1TngFwAw8bBmNbEdPbBMajOjuHxTD0skGNkr37kIzmKQUS1NJAxHgWewnVlfB7tTkGRbrjW8rG4vuWvAS2EnB/CiGPdBPkFLSmsHD7PU03VSZBSigVyjEhAOgN/AVbSjkK+zVXWbWGaoRJ4GAhpka6OdqegSF/cBPmHAdvBTQfbCVpdAkePgJG5kNucghQuE9V7mrOvaRoL5BiSgPwW6A5s4RTOpUPGLg9vWq4UN5PgTVqkW6LZwRvDPAg3hnkIbr7uEoC+0PpiGD0Uhmc3YQxzFezMhh6o7mjyKzBNZoEcQxKQI4FLgRUczEEcxHf8rsmkvBLgz8BtWqS7otnBC+YDcWOY++DGQW8H2A86/ACOHwIHZdH4J7gKuCdX9bJmV2+axAI5hrz5kW8H1pKHcAa/JDt91lEzvloL3AhM0SKtjGaHoEg2boL8SUAnXP9yKcAR0P08OGGAa0nXS0EF9ke1uKXFm+hYIMeYBORS4BBgPeM4jW4M87smk1a+BK4HZmhRdG/eoEgecDRwFm4F7PV4E+SfAP0mwrge0LfufrvgX4WqZ8WsctMoC+QYk4AcCPwCWMFgBnIE5/tdk0lLHwK/1SL9T7Q7BEUKgLG4meWyqTWG+QzY9zQ4oRN0A6iG6iwIoPpl7Es3kVggx5gEJBf4G67PrsxWozZxNht3ccncaHcIirTDjV8+GTfcbi0ugOU8OHgcjMuC/7RTPSc+JZtILJDjQAJyDm5FiNUcx/H05Ti/azJpL4xrMS+OdoegSGfgVGAMeybI71YAXz4MDxVqdKM7TOz4NYVfuluA+0gIC1lANVGdhDGmBYLAQgnJVAlJv2h2CKtuCqtOA64BFuIWWm1dCk9ZGPvDWshx4M1tcRNuIP52xjKenhzhc1kmc5QB9wJ/0CLdEO1OQZF9gO5hjb77w8SWBXKcSECOwo1JXk5H2nESl5PltZqNSYztuEmv/qpFut3vYkzjrMsift7DDchvwxa2sYEP/S7IZJy2QBGwREJyhYTExsQnOQvkONFirQBmUjMV4ke8hVLta1EmU3XFLSX1uYTkAgmJve+TlHVZxJEEJA932WsZsItvcTpdOcTnsr7pb7g1jQX3J/pHwGu4adQFaA2czjcncywBnsCttVwNHIFb26ISt/byNu/nmt7zsPdzz/i9FBOVxcA1WqTP+l2I2ZsFcpxJQEYDFwIr6E5njuf/km4WuL8Bl+CCt8Zu3LoUAPNwK7pNqLNfzdiRHNyfnLuBHwBrgNW4SxDuA36MG1A1H3dJgkkWc3HzML/hdyHGsY8u8TcP2AEUsJ5NbCLqcaK+yq/1fQX1T0OTw55F6atwlxiAG/BXAXt10LyGC2iTTEYC1/pdhNnDAjnOtFjLgGfwLkllIbOTri9ZgEdwrdl3a93/X9w5+oVEDtOtuJbxrbgp0tvhltzcATwAHIPr+uhFbNevMC3n/h/+0u8yzB7WZZEAEpAC4K+4XtXdSbcy9TZcWO7ABfN4YECtx9/EdU801MLdBswAJgNtat1f5R1zEvA6LsAPAfaLTemmBUp5Vm+2dfKSibWQE0CLtRTXSu4OwHxmUUHyTPhd03JtgwvKuutUDAU+ieIY3YAVde5/BxgGfIXryjgbsB5L/1VTQR7/53cZZm8WyIkzCzcfbXtKKeNzXva5HqccbyJG7/sluGDdVGubYupfx3grrq8Y3Cy7K+tsVwp8jmsR1+6HtgvJ/VfKnXpD9EtEmcTIaXwTEwtarOUSkGnA1cA2PmIR/RhOW/r7WtgO3NA1cCfhhuKmLH8CtyqbAB1wU9CAaz2/ixstsRF4ydtGcTPudq917NnAKO/xQbgZPu4BDo/bqzHRKGUFrbna7zLMN1kfcgJ5c1xchmszrqE7nRnLpWTZH0aTINVUsY5Rep++7Xcp5pusyyKBtFgV1/YUoBXr2cQKZvlblckom3nAwjh5WSAnmBbrRtx1bL0AmM/b7GStr0WZzLCTFZTwE7/LMJFZIPtjNu50V1eqUd7lWaqTbGyySS9VVLKFifpIdAukGn9YIPtAi7UKmAoUALmsZj1LeMnfqkxa28xdOkXn+12GaZgFsk+0WFcD/wL6APAOC9jIQl+LMulpB0tYx5V+l2EaZ4Hsr5eAj6mZ/2wWz7OL9b5WZNJLBWWU8B19yoZTpQILZB9psVYC9+MuoehAORXM4Qkq2e1zaSYdVFPNSq7UKfqR36WY6Fgg+0yLdStwB+7i41ZsYAuLeBprz5iWWsZjzOdev8sw0bNATgJarEuB6UBvQPiUz1llMz6YFljDPOZziTf23aQIC+TkMdu79QVgDrPYyhJfKzKpaTPLmccEb1Irk0IskJOE15J5FFgFdENRZvEvdu81zY8xDdvJFhYyQT/SjX6XYprOAjmJaLHuxk337lay20kprzOdMkp8Ls2kgnJ28wkX6Ov6sd+lmOaxQE4yWqzrgbtwKwXns4VtzGYa5WzzuTSTzKqp5jOu0+f1Ob9LMc1ngZyEtFg/Bu7FjU9uxUZKeINpSTWpvUkeVVTxMXfyMbf6XYppGQvkJKXFOg+3Kl1vII+v2cxbTKeCXT6XZpJJFZW8zzQ+5motVpsPJcVZICcxLdY3gYdxl1fnspYNvM10u3DEAFBJBQuYzhdc6S2ma1KcBXKS02J9HTf6oi+Qw2rWM49HqMLegJmsknLm8yDLuEKL1c4vpAkL5NTwMvBPoB+QzUrWsIBHqcTGmWaiCnYzl3tZwVVarHZeIY3YEk4pwlv+6XTgDNzazlV0oxPHMpl8OvtbnUmYckqZy99ZTZFd+JF+LJBTiBfKZwETgK+AcgrJ53jOoR0D/a3OxN1udjCX21nLjdZnnJ4skFOMF8pjgAtw6z7vIIssxjCeHraec9oqYT1z+Btb+ZsWa7nf5Zj4sEBOURKQA4HLgQpcMMMRHMkgTkIQP2szMbaKz3ibO6jiAS3WCr/LMfFjgZzCJCC9gJ8BHYA1AAQYzDDOIptWftZmYqCaKhYzn0X8GXjOW/rLpDEL5BQnAWkH/BjYDzcxUTU96crRTKIVHf2tzjTbbrYyl9dYyx+0WN/zuxyTGBbIaUACkgdMBo7HhXIF+eRxLOPpxjB/qzNNtoFlvMnz7ObPWqyr/C7HJI4FcprwTvadCEwCNoM3GdGB7M+BTCCHAh/LM9GoppLP+IAPeQSYqsW63e+STGJZIKcZCch+wI+AtsBqQOlAG44mSAeG+FudiaiEFczjPTZzH/CqzUuRmSyQ05AEpA2uC+NYYB14ExIdwsEEONlay0mkgp18zDt8yofAXVqsX/pdkvGPBXKa8rowRgDfB7KBtYDSjtaMZDydOdDXAjOdAuv4mLksYjcvAM/aZdDGAjnNSUA6AufiwvlrYCcA+zGE/RlHAd18LC8z7WID7/IOX/EB8LAWq62daIAMDmQR2aGqbfyuIxG81vKhwEVAPm7McjWCcDBDGcxYWtHB1yIzQTWVLOF93uNjqnkCeN0u9DC1WSBnEG/M8hm4S6/Lcf3LSg7ZHMpwBjCKXFr7WWNaqqKcNXzEByxnB28Dj2mxfu13WSb5pH0gi8gzuLmE84HbVfV+7/4dwH3AWGAL8F1V3SAiw3DLJxUCS3Ctyh7ANFU9wtt3ABBW1YNFZDhwK9AGdwnzhaq6tk4NU4FS3MUb/XH9uhcAI4H5qnqht92JQAho5T3391V1h4hcj5tQqAB4G/iRqqqIzALme6+hA/ADVX2z0d9JQHrjZo4bgTvh58IhnzyGM5I+jLQr/WKgijJW8x7vs5xdbAKmAh94K4wb8w2ZEMidVHWziBQA7wCjVXWTiChwnqo+6gVeN1X9iYgsBH6qqrNF5Aagnar+TEQ+BM5U1aUicjWQC9wCzAZO88J8InCSql5Up4apuD8Ik4Ag8AhwDLDYq+kHuNnbZgLjVXWn9xytVPWGmtfgHesR4J+q+pwXyO+p6lUicgrwc1UdF/XvJiD7AGcD++PGLW8GoC2FDOc4enA4WeQ05fdtgEpKWcW7fMBKdrMdeB6YpcW60+/STHLLhDfb5SJyhvd9X2AIsAmoBp7w7v8HMFNE2gMdVHW2d/804Env+38C5wA3AxO9WwA4CHhFRGDPaIb6POe1ahcB61V1EYCILAYG4JZpOgCY4x0rD5jr7TtWRH6Fa7V3wgV5zerCM72v73nHiZoW61IJyJ9wgTwRGAhsYjvbmMVLtGY2B3IIvTmcAro05dgZqYJdrOQdPmQVZWwDngXe0mK1dRBNVNI6kEVkDDAOGKmqu7wWZX6EzRv7qPAE8KSIzARUVb8QkaHAYlUdGUU5NfPXVtf6vubnHKAKeEVVJ9V5DfnA3cDhqrpKRH5X5zXUHKuKZvx7eh+fP5GAhIBhwHdxwb6NnWxhAfOB+QyiP4MYTicOIIvspj5P2lKU7SxnFZ/xCZuooAR4GphrE8ibpkrrQAbaA1u8MN4POKrWY1m4yd5n4C6ieEtVt4rIFhE5zuuL/R6uSwJVXSIiVcB17GlZFwNdRWSkqs4VkVxgX1Vd3Ixa5wF3ichgVf1SRApxreaakz8bRaSNV/NTzTh+g7wrw96XgCwChuIuww7ggv5rlrCCJaygLS9yIMPoxXDy6RTrOlJGKRtYx0d8ynJKyAG24j6tzLfJ401zpXsgvwhc6vULF+NCr8ZO4EAReQ/3Zpro3X8BcK8XiEtxJ+BqPAH8GffRHlUtF5GzgDu87o4c4DZcl0KTeH3QFwKPi0jNCbVrVfVzEZkCLAKW4/qc48YbhvU+Lpx74vq6T8C1yrexnS3M423gbQYzkAEcTEcGk0v6j1ipYCcbWMQXfM5qKnF/1L/CrXn4nk0cb1oq7U/qmZaTgLTCtZpPBgZR02p2Q+ecPvSgH0PowhBa0ydtJskvZxslLGEVn/MF26kmBzcq5zXgXWC9jZowsWKBbKLmXWDSG9dqHoM78QhQAuyZmaw1+ezDIHowhA4MTqmxzZWUsp2VbGQJK1nOesANNywH5uBOtC61yX9MPFggm2aRgGQD/XAjNI7C9XcD7Ma1IPdcgdaHHnSnNx3oSRt6UED3pBhOpyhlbGYbq9jMKtawknXsxo3prmnhL8SdR/jU+oZNvFkgm5iQgHQABgPDcZdp53oP7fBuewI6iyy605nOdKUdXWhDFwrpQh4dyaZVTLs7qqmmnBJ2s5lSNrOTzWxjM1vYzEa2UkUh0A4XwIK7rPxd4DNguRbr7pjVYkwjLJBNzElAcnCt5/1wLehBuJOCigu9UtwVgqW4YX+1dkYoJJ/WFFBAAfkU0Ip8WlFAHgXkUoAgVFFJFRXf+FpJJVVUUkkFW9nKJkqoJgvX7VDAN4c9LsedMF0CrNRi3Rqv34sxjbFANnHn9T13BHoCvXDjnPvjLkkX9owBz/J+rsK1qCvrfFVvm5pbdoSf87xta7avwM3bsRpYibvEfROwxrohTDKxQDa+8fqhO+KuPqxpvRbgxo+3x3UltK11y8adXKu5ldXzdTduBMhG3KXgm4EdNhLCpAILZGOMSRJZfhdgjDHGsUA2xpgkYYFsjDFJwgLZGGOShAWyMcYkCQtkY4xJEhbIxhiTJCyQjTEmSVggG2NMkrBANsaYJGGBbIwxScIC2RhjkoQFsjHGJAkLZGOMSRIWyMYYkyQskI0xJklYIBtjTJKwQDbGmCTx/1GX9G94DhLQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "roll_and_cgpa = []\n",
+    "for (k, v) in data.items():\n",
+    "    try:\n",
+    "        roll_and_cgpa.append((k,float(v['cgpa'][2])))\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "    \n",
+    "\n",
+    "def sortbycg(l):\n",
+    "   return l[1]\n",
+    "\n",
+    "def sortbyroll(l):\n",
+    "    return int(l[0][7:9])\n",
+    "\n",
+    "\n",
+    "sorted_cgpa = roll_and_cgpa[:]\n",
+    "sorted_cgpa.sort(key=sortbycg)\n",
+    "\n",
+    "\n",
+    "print(\"Top 5 Students:\\n\")\n",
+    "for element in sorted_cgpa[:-6:-1]:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "    \n",
+    "roll_and_cgpa.sort(key=sortbyroll)\n",
+    "\n",
+    "# for individual cgpa \n",
+    "\n",
+    "print(\"Students with their cgpa :\\n\")\n",
+    "for element in roll_and_cgpa:\n",
+    "    print('%s '%(element[0]),'%s ' % (data[element[0]]['name']),'%s' %(data[element[0]]['cgpa'][2]))\n",
+    "\n",
+    "cgpa = np.array([element[1] for element in roll_and_cgpa], dtype='float')\n",
+    "\n",
+    "roll = np.array([element[0][7:9] for element in roll_and_cgpa],dtype='int')\n",
+    "\n",
+    "\n",
+    "cgpa_average = round(np.mean(cgpa), 2)\n",
+    "cgpa_median = round(np.median(cgpa), 2)\n",
+    "cgpa_highest = round(np.max(cgpa), 2)\n",
+    "cgpa_lowest = round(np.min(cgpa),2)\n",
+    "cgpa_standard_deviation = round(np.std(cgpa),2)\n",
+    "\n",
+    "print(\"\\nCGPA:\")\n",
+    "print(\"Highest: %s\" % cgpa_highest)\n",
+    "print(\"lowest: %s\"% cgpa_lowest)\n",
+    "print(\" Median: %s\" % cgpa_median)\n",
+    "print(\"Average: %s\" % cgpa_average)\n",
+    "print(\"Standard Deviation: %s \\n\"% cgpa_standard_deviation)\n",
+    "\n",
+    "print(\"   9.5+: %s\" % len([cg for cg in cgpa if cg >= 9.5]))\n",
+    "print(\"  9-9.5: %s\" % len([cg for cg in cgpa if cg >= 9 and cg < 9.5]))\n",
+    "print(\"  8.5-9: %s\" % len([cg for cg in cgpa if cg >= 8.5 and cg < 9]))\n",
+    "print(\"  8-8.5: %s\" % len([cg for cg in cgpa if cg >= 8 and cg < 8.5]))\n",
+    "print(\"  7.5-8: %s\" % len([cg for cg in cgpa if cg >= 7.5 and cg < 8]))\n",
+    "print(\"  7-7.5: %s\" % len([cg for cg in cgpa if cg >= 7 and cg < 7.5]))\n",
+    "print(\"     7-: %s\" % len([cg for cg in cgpa if cg < 7]))\n",
+    "\n",
+    "\n",
+    "unit_array = np.ones(len(roll),dtype='int')\n",
+    "fig,cgpa_plot = plt.subplots()\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "label=['above mean','below mean']\n",
+    "cgpa_plot.plot(roll,cgpa,label='above mean cgpa',color='green')\n",
+    "cgpa_plot.plot(roll,cgpa_median*unit_array,label='below mean cgpa',color='red')\n",
+    "cgpa_plot.grid(b = True)\n",
+    "cgpa_plot.legend()\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa >= cgpa_median*unit_array,facecolor = 'green',interpolate = True )\n",
+    "cgpa_plot.fill_between(roll,cgpa,cgpa_median*unit_array,where= cgpa< cgpa_median*unit_array,facecolor = 'red',interpolate = True )\n",
+    "plt.xlabel('roll number')\n",
+    "plt.ylabel('cgpa (0-10)', horizontalalignment='right',rotation =0)\n",
+    "plt.title('cgpa distribution wrt roll number')\n",
+    "plt.show()\n",
+    "\n",
+    "print('\\n')\n",
+    "#plt.fill(cgpa,cgpa_median*median,'r')g\n",
+    "sorted_cgpa=cgpa[:]\n",
+    "sorted_cgpa.sort()\n",
+    "fig,histo_cgpa = plt.subplots()\n",
+    "histo_cgpa.hist(sorted_cgpa,bins=40)\n",
+    "#histo_cgpa.fill_between(roll,histo_cgpa,where =sorted_cgpa >= 8.0,facecolor = 'green',interpolate = True )\n",
+    "#histo_cgpa.fill(histo_cgpa, where = sorted_cgpa >= cgpa_median)\n",
+    "plt.title('frequency distribution of class cgpa')\n",
+    "\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' number\\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.show()\n",
+    "#plt.plot(norm.pdf(cgpa_in_seq,cgpa_median,cgpa_standard_deviation))\n",
+    "\n",
+    "print('\\n')\n",
+    "fig = norm.pdf(sorted_cgpa, np.mean(sorted_cgpa), np.std(sorted_cgpa))\n",
+    "plt.plot(sorted_cgpa,fig,'-o')\n",
+    "plt.title('standard distribution of class cgpa')\n",
+    "plt.xlabel('cgpa (0-10)')\n",
+    "plt.ylabel(' fraction \\nof students ', horizontalalignment='right',rotation =0)\n",
+    "plt.grid(b = True)\n",
+    "plt.show()\n",
+    "\n",
+    "above_mean = [cg for cg in cgpa if cg >= cgpa_median]\n",
+    "below_mean = [cg for cg in cgpa if cg < cgpa_median]\n",
+    "\n",
+    "print('\\n')\n",
+    "labels = 'above mean','below mean'\n",
+    "sizes = [len(above_mean),len(below_mean)]\n",
+    "#print(len(above_mean),len(below_mean))\n",
+    "explode = (0.1,0)\n",
+    "colors = ['green','red']\n",
+    "# Plot\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
+    "        autopct='%2.1f%%', shadow=True, startangle=140)\n",
+    " \n",
+    "plt.axis('equal')\n",
+    "plt.title('cgpa distribution wrt mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# results plot \n",
+    "this class posses second maximum deviation from mean,only after cse, compare to other department batches. Special care is required for some students to improve their academic performance.\n",
+    "\n",
+    "\n",
+    "https://analytics4all.org/2016/05/05/python-histograms-and-frequency-distribution/\n",
+    "\n",
+    "https://etav.github.io/python/count_basic_freq_plot.html for histogram frequency distribution\n",
+    "\n",
+    "https://stackoverflow.com/questions/20011494/plot-normal-distribution-with-matplotlib for ploting the standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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