Data Preprocessin basic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AgwgqvGj2G8v"
   },
   "source": [
    "###### Assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qV1S167Z2G8y"
   },
   "source": [
    "###### I am going to provide two .csv files , you are supposed to work on them and have to provide solutions to the following problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hicYGDi32G8y"
   },
   "source": [
    "###### import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "6G_jO5By2G8z"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q-o2aYxU2G8z"
   },
   "source": [
    "###### merge those two csv files (after getting as dataframes, get them as a single dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "KCvrFPTy2G8z"
   },
   "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>Name</th>\n",
       "      <th>python</th>\n",
       "      <th>mysql</th>\n",
       "      <th>Previous Geekions</th>\n",
       "      <th>CodeKata Score</th>\n",
       "      <th>Department</th>\n",
       "      <th>Rising</th>\n",
       "      <th>python_en</th>\n",
       "      <th>computational_thinking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A.Dharani</td>\n",
       "      <td>82.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>24500</td>\n",
       "      <td>24500</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>V.JEEVITHA</td>\n",
       "      <td>82.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>21740</td>\n",
       "      <td>21740</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HEMAVATHI.R</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>19680</td>\n",
       "      <td>19680</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mugunthan S</td>\n",
       "      <td>100.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>10610</td>\n",
       "      <td>10610</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sathammai.S</td>\n",
       "      <td>100.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8980</td>\n",
       "      <td>8980</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NIVEESHWAR S</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8200</td>\n",
       "      <td>8200</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KALAIARASAN K</td>\n",
       "      <td>85.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>7750</td>\n",
       "      <td>7880</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MOHAMED ZUBAIR AHMED</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7020</td>\n",
       "      <td>7020</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>J.SUGANTHI</td>\n",
       "      <td>27.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>5860</td>\n",
       "      <td>5860</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>thamizhpaana</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5500</td>\n",
       "      <td>5500</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Iyappan Samiraj</td>\n",
       "      <td>50.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>5400</td>\n",
       "      <td>5400</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ponniyamma.R</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11.25</td>\n",
       "      <td>5380</td>\n",
       "      <td>5380</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SABAPATHI</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5280</td>\n",
       "      <td>5280</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RAGHAVAN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5120</td>\n",
       "      <td>5120</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Melvin Dukles.R</td>\n",
       "      <td>56.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>4800</td>\n",
       "      <td>4800</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pavithra</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>4600</td>\n",
       "      <td>4600</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PRIYANKA .S</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4510</td>\n",
       "      <td>4510</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>v.vithyasri</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>4370</td>\n",
       "      <td>4370</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Abirami Anbazhagan</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4320</td>\n",
       "      <td>4320</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>S.srinivasan</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>4090</td>\n",
       "      <td>4090</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>s.janani</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3915</td>\n",
       "      <td>3915</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>kamalishwari</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>3665</td>\n",
       "      <td>3665</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Yuvasri</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>3460</td>\n",
       "      <td>3460</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>balaji</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3280</td>\n",
       "      <td>3280</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MEERA.M</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>3200</td>\n",
       "      <td>3200</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Tpavithra</td>\n",
       "      <td>100.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>3200</td>\n",
       "      <td>3200</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>sri dharshini</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>3110</td>\n",
       "      <td>3110</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>shankari</td>\n",
       "      <td>87.5</td>\n",
       "      <td>20.00</td>\n",
       "      <td>3000</td>\n",
       "      <td>3000</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>pooja priya.B</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>2570</td>\n",
       "      <td>2570</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>chithra</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2410</td>\n",
       "      <td>2410</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Poojitha Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>7210</td>\n",
       "      <td>8970</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>1760</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Gowtham Chandrasekaran</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7250</td>\n",
       "      <td>8950</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>1700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Gowtham R</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>8650</td>\n",
       "      <td>8650</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>shifak N</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5180</td>\n",
       "      <td>8320</td>\n",
       "      <td>Electronics and Electrical Engineering</td>\n",
       "      <td>3140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Balaji</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>6170</td>\n",
       "      <td>8160</td>\n",
       "      <td>Electronics and Electrical Engineering</td>\n",
       "      <td>1990</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Rushitha</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>7470</td>\n",
       "      <td>8090</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>620</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Balaraman Muthupandi</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7670</td>\n",
       "      <td>8050</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>380</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>JPradhap</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7340</td>\n",
       "      <td>8030</td>\n",
       "      <td>Electronics and Electrical Engineering</td>\n",
       "      <td>690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>SRIRAMKUMAR B</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6400</td>\n",
       "      <td>7940</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>1540</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Praneetha</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>7170</td>\n",
       "      <td>7730</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>560</td>\n",
       "      <td>23.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Rahul Raj K</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6280</td>\n",
       "      <td>7640</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>1360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Guhan S</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>7310</td>\n",
       "      <td>7630</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>320</td>\n",
       "      <td>72.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Suryarajan S</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>6710</td>\n",
       "      <td>7550</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>840</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Narasimhan Y L</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4800</td>\n",
       "      <td>6800</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>2000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Leelakrishna</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5300</td>\n",
       "      <td>6640</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>1340</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>PremsagarReddy</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5290</td>\n",
       "      <td>6290</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>1000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>pradeeban</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>6060</td>\n",
       "      <td>6090</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>30</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>santhoshini</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3910</td>\n",
       "      <td>5740</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>1830</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>MUKESH S M</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5200</td>\n",
       "      <td>5200</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>P.GOVARDHANAN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5050</td>\n",
       "      <td>5050</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>SHAIK DANISH</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4050</td>\n",
       "      <td>4820</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>770</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>sridhar.s</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3860</td>\n",
       "      <td>4440</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>580</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Vimalesh Fernando</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4170</td>\n",
       "      <td>4170</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>M.Karthikeyan</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4020</td>\n",
       "      <td>4020</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Abishak s</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>3220</td>\n",
       "      <td>3220</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>praveen raj j</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2380</td>\n",
       "      <td>2380</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>AMARNATH D</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1890</td>\n",
       "      <td>1890</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>bala</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1720</td>\n",
       "      <td>1720</td>\n",
       "      <td>Electronics and Communication Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>XY Z</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Hariharan</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name  python   mysql  Previous Geekions  \\\n",
       "0                 A.Dharani    82.0   20.00              24500   \n",
       "1               V.JEEVITHA     82.0   20.00              21740   \n",
       "2               HEMAVATHI.R   100.0  100.00              19680   \n",
       "3               Mugunthan S   100.0   47.00              10610   \n",
       "4               Sathammai.S   100.0    8.00               8980   \n",
       "5              NIVEESHWAR S   100.0    0.00               8200   \n",
       "6             KALAIARASAN K    85.0   47.00               7750   \n",
       "7      MOHAMED ZUBAIR AHMED    82.0    4.00               7020   \n",
       "8                J.SUGANTHI    27.0   50.00               5860   \n",
       "9              thamizhpaana    29.0    0.00               5500   \n",
       "10          Iyappan Samiraj    50.0   24.00               5400   \n",
       "11             Ponniyamma.R   100.0   11.25               5380   \n",
       "12                SABAPATHI   100.0    0.00               5280   \n",
       "13                 RAGHAVAN   100.0    0.00               5120   \n",
       "14          Melvin Dukles.R    56.0   20.00               4800   \n",
       "15                 Pavithra   100.0  100.00               4600   \n",
       "16              PRIYANKA .S    85.0    4.00               4510   \n",
       "17              v.vithyasri   100.0   12.00               4370   \n",
       "18       Abirami Anbazhagan   100.0    0.00               4320   \n",
       "19             S.srinivasan   100.0   12.00               4090   \n",
       "20                 s.janani   100.0    0.00               3915   \n",
       "21             kamalishwari    32.0   20.00               3665   \n",
       "22                  Yuvasri   100.0  100.00               3460   \n",
       "23                   balaji    82.0    0.00               3280   \n",
       "24                  MEERA.M   100.0  100.00               3200   \n",
       "25                Tpavithra   100.0   24.00               3200   \n",
       "26            sri dharshini   100.0  100.00               3110   \n",
       "27                 shankari    87.5   20.00               3000   \n",
       "28            pooja priya.B   100.0  100.00               2570   \n",
       "29                  chithra   100.0    0.00               2410   \n",
       "..                      ...     ...     ...                ...   \n",
       "89               Poojitha Y     0.0   35.00               7210   \n",
       "90   Gowtham Chandrasekaran    -1.0    0.00               7250   \n",
       "91                Gowtham R    -1.0    4.00               8650   \n",
       "92                 shifak N    58.0    0.00               5180   \n",
       "93                  Balaji     -1.0   20.00               6170   \n",
       "94                 Rushitha    -1.0   62.00               7470   \n",
       "95     Balaraman Muthupandi    -1.0    0.00               7670   \n",
       "96                 JPradhap   100.0    0.00               7340   \n",
       "97            SRIRAMKUMAR B    -1.0    0.00               6400   \n",
       "98                Praneetha    -1.0  100.00               7170   \n",
       "99              Rahul Raj K    -1.0    0.00               6280   \n",
       "100                 Guhan S    -1.0   35.00               7310   \n",
       "101            Suryarajan S    -1.0   31.00               6710   \n",
       "102          Narasimhan Y L    -1.0    0.00               4800   \n",
       "103            Leelakrishna    -1.0    4.00               5300   \n",
       "104          PremsagarReddy    -1.0    0.00               5290   \n",
       "105               pradeeban    16.0   24.00               6060   \n",
       "106             santhoshini    -1.0    0.00               3910   \n",
       "107              MUKESH S M    -1.0    0.00               5200   \n",
       "108           P.GOVARDHANAN    -1.0    0.00               5050   \n",
       "109            SHAIK DANISH    -1.0    0.00               4050   \n",
       "110               sridhar.s    -1.0    0.00               3860   \n",
       "111       Vimalesh Fernando    -1.0    0.00               4170   \n",
       "112           M.Karthikeyan    -1.0    4.00               4020   \n",
       "113               Abishak s    -1.0   35.00               3220   \n",
       "114           praveen raj j    24.0    0.00               2380   \n",
       "115              AMARNATH D    -1.0   12.00               1890   \n",
       "116                    bala    32.0    0.00               1720   \n",
       "117                    XY Z    -1.0   -1.00                  0   \n",
       "118               Hariharan    -1.0   -1.00                  0   \n",
       "\n",
       "     CodeKata Score                                 Department  Rising  \\\n",
       "0             24500           Computer Science and Engineering       0   \n",
       "1             21740           Computer Science and Engineering       0   \n",
       "2             19680           Computer Science and Engineering       0   \n",
       "3             10610           Computer Science and Engineering       0   \n",
       "4              8980           Computer Science and Engineering       0   \n",
       "5              8200           Computer Science and Engineering       0   \n",
       "6              7880  Electronics and Communication Engineering     130   \n",
       "7              7020           Computer Science and Engineering       0   \n",
       "8              5860  Electronics and Communication Engineering       0   \n",
       "9              5500           Computer Science and Engineering       0   \n",
       "10             5400  Electronics and Communication Engineering       0   \n",
       "11             5380           Computer Science and Engineering       0   \n",
       "12             5280           Computer Science and Engineering       0   \n",
       "13             5120           Computer Science and Engineering       0   \n",
       "14             4800  Electronics and Communication Engineering       0   \n",
       "15             4600           Computer Science and Engineering       0   \n",
       "16             4510           Computer Science and Engineering       0   \n",
       "17             4370  Electronics and Communication Engineering       0   \n",
       "18             4320  Electronics and Communication Engineering       0   \n",
       "19             4090           Computer Science and Engineering       0   \n",
       "20             3915           Computer Science and Engineering       0   \n",
       "21             3665  Electronics and Communication Engineering       0   \n",
       "22             3460           Computer Science and Engineering       0   \n",
       "23             3280           Computer Science and Engineering       0   \n",
       "24             3200           Computer Science and Engineering       0   \n",
       "25             3200  Electronics and Communication Engineering       0   \n",
       "26             3110           Computer Science and Engineering       0   \n",
       "27             3000  Electronics and Communication Engineering       0   \n",
       "28             2570           Computer Science and Engineering       0   \n",
       "29             2410           Computer Science and Engineering       0   \n",
       "..              ...                                        ...     ...   \n",
       "89             8970           Computer Science and Engineering    1760   \n",
       "90             8950           Computer Science and Engineering    1700   \n",
       "91             8650  Electronics and Communication Engineering       0   \n",
       "92             8320     Electronics and Electrical Engineering    3140   \n",
       "93             8160     Electronics and Electrical Engineering    1990   \n",
       "94             8090           Computer Science and Engineering     620   \n",
       "95             8050           Computer Science and Engineering     380   \n",
       "96             8030     Electronics and Electrical Engineering     690   \n",
       "97             7940           Computer Science and Engineering    1540   \n",
       "98             7730  Electronics and Communication Engineering     560   \n",
       "99             7640           Computer Science and Engineering    1360   \n",
       "100            7630  Electronics and Communication Engineering     320   \n",
       "101            7550           Computer Science and Engineering     840   \n",
       "102            6800           Computer Science and Engineering    2000   \n",
       "103            6640           Computer Science and Engineering    1340   \n",
       "104            6290           Computer Science and Engineering    1000   \n",
       "105            6090           Computer Science and Engineering      30   \n",
       "106            5740           Computer Science and Engineering    1830   \n",
       "107            5200           Computer Science and Engineering       0   \n",
       "108            5050  Electronics and Communication Engineering       0   \n",
       "109            4820           Computer Science and Engineering     770   \n",
       "110            4440           Computer Science and Engineering     580   \n",
       "111            4170           Computer Science and Engineering       0   \n",
       "112            4020           Computer Science and Engineering       0   \n",
       "113            3220  Electronics and Communication Engineering       0   \n",
       "114            2380           Computer Science and Engineering       0   \n",
       "115            1890  Electronics and Communication Engineering       0   \n",
       "116            1720  Electronics and Communication Engineering       0   \n",
       "117               0           Computer Science and Engineering       0   \n",
       "118               0           Computer Science and Engineering       0   \n",
       "\n",
       "     python_en  computational_thinking  \n",
       "0          NaN                     NaN  \n",
       "1          NaN                     NaN  \n",
       "2          NaN                     NaN  \n",
       "3          NaN                     NaN  \n",
       "4          NaN                     NaN  \n",
       "5          NaN                     NaN  \n",
       "6          NaN                     NaN  \n",
       "7          NaN                     NaN  \n",
       "8          NaN                     NaN  \n",
       "9          NaN                     NaN  \n",
       "10         NaN                     NaN  \n",
       "11         NaN                     NaN  \n",
       "12         NaN                     NaN  \n",
       "13         NaN                     NaN  \n",
       "14         NaN                     NaN  \n",
       "15         NaN                     NaN  \n",
       "16         NaN                     NaN  \n",
       "17         NaN                     NaN  \n",
       "18         NaN                     NaN  \n",
       "19         NaN                     NaN  \n",
       "20         NaN                     NaN  \n",
       "21         NaN                     NaN  \n",
       "22         NaN                     NaN  \n",
       "23         NaN                     NaN  \n",
       "24         NaN                     NaN  \n",
       "25         NaN                     NaN  \n",
       "26         NaN                     NaN  \n",
       "27         NaN                     NaN  \n",
       "28         NaN                     NaN  \n",
       "29         NaN                     NaN  \n",
       "..         ...                     ...  \n",
       "89        78.0                     0.0  \n",
       "90         0.0                     3.0  \n",
       "91        60.0                     6.0  \n",
       "92         0.0                     0.0  \n",
       "93       100.0                     0.0  \n",
       "94        15.0                     9.0  \n",
       "95         9.0                     0.0  \n",
       "96         0.0                     0.0  \n",
       "97         0.0                     0.0  \n",
       "98        23.0                    -1.0  \n",
       "99         0.0                     0.0  \n",
       "100       72.0                    39.0  \n",
       "101       63.0                     0.0  \n",
       "102       -1.0                     0.0  \n",
       "103        6.0                     0.0  \n",
       "104       20.0                     0.0  \n",
       "105       20.0                     0.0  \n",
       "106       43.0                     0.0  \n",
       "107       46.0                     0.0  \n",
       "108       40.0                    -1.0  \n",
       "109       43.0                     0.0  \n",
       "110       52.0                     0.0  \n",
       "111        0.0                     0.0  \n",
       "112        0.0                     6.0  \n",
       "113       55.0                     6.0  \n",
       "114       -1.0                     0.0  \n",
       "115       52.0                    -1.0  \n",
       "116       49.0                    -1.0  \n",
       "117       20.0                    -1.0  \n",
       "118       -1.0                     0.0  \n",
       "\n",
       "[119 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.read_csv('C:/Users/Praveen/Desktop/college_1.csv')\n",
    "data2 = pd.read_csv('C:/Users/Praveen/Desktop/college_2.csv')\n",
    "df = pd.concat([data1, data2], sort = False, ignore_index = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BmTgoMCp2G80"
   },
   "source": [
    "###### Take each csv file , split that csv file into multiple categories (example csv files are added in the repo) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gYCNL7pS2G80"
   },
   "source": [
    "###### consider if the codekata score exceeds 15000 points(present week) then make a csv on those observations as Exceeded expectations.csv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AF4J6t3B2G81"
   },
   "source": [
    "###### if  10000<codekata score<15000   (Reached_expectations.csv)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NGYNNikv2G81"
   },
   "source": [
    "###### if  7000<codekata score<10000   (Needs_Improvement.csv)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YMSdMYyV2G82"
   },
   "source": [
    "###### if  codekate score < 7000        (Unsatisfactory.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "nHmZG-s22G82"
   },
   "outputs": [],
   "source": [
    "df[(df['CodeKata Score'] > 15000)].to_csv(\"Exceeded expectations.csv\", index=False)\n",
    "df[(df['CodeKata Score'] > 10000) & df['CodeKata Score'] < 15000].to_csv(\"Reached_expectations.csv\", index=False)\n",
    "df[(df['CodeKata Score'] > 7000) & df['CodeKata Score'] < 10000].to_csv(\"Needs_Improvement.csv\", index=False)\n",
    "df[(df['CodeKata Score'] < 7000)].to_csv(\"Unsatisfactory.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6IZY99642G82"
   },
   "source": [
    "###### Average of previous week geekions vs this week geekions (i.e Previous Geekions vs CodeKata Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "UayQl-kT2G83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of Previous Geekions and CodeKata Score is -209.41176470588243\n"
     ]
    }
   ],
   "source": [
    "avgpgcs = df['Previous Geekions'].mean() - df['CodeKata Score'].mean()\n",
    "print(f'Average of Previous Geekions and CodeKata Score is {avgpgcs}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fsljZtaW2G83"
   },
   "source": [
    "###### No of students participated "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "u9Phc7wP2G83"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of Students participated is 119\n"
     ]
    }
   ],
   "source": [
    "nosp = df[df.columns[0]].count()\n",
    "print(f'No of Students participated is {nosp}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LSkiGNlB2G83"
   },
   "source": [
    "###### #Average completion of python course or my_sql or python english or computational thinking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "6RZjGcs02G83",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Completion of Python is 0.3445378151260504\n",
      "Average Completion of MySQl is 0.11764705882352941\n",
      "Average Completion of Python English is 0.01680672268907563\n",
      "Average Completion of Computational Thinking is 0.0\n"
     ]
    }
   ],
   "source": [
    "pca = (df['python'] == 100).mean()\n",
    "msca = (df['mysql'] == 100).mean()\n",
    "peca = (df['python_en'] == 100).mean()\n",
    "cta = (df['computational_thinking'] == 100).mean()\n",
    "print(f'Average Completion of Python is {pca}')\n",
    "print(f'Average Completion of MySQl is {msca}')\n",
    "print(f'Average Completion of Python English is {peca}')\n",
    "print(f'Average Completion of Computational Thinking is {cta}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pNEG4aEk2G83"
   },
   "source": [
    "###### rising star of the week (top 3 candidate who performed well in that particular week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "Ncviz2ZT2G84"
   },
   "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>Name</th>\n",
       "      <th>python</th>\n",
       "      <th>mysql</th>\n",
       "      <th>Previous Geekions</th>\n",
       "      <th>CodeKata Score</th>\n",
       "      <th>Department</th>\n",
       "      <th>Rising</th>\n",
       "      <th>python_en</th>\n",
       "      <th>computational_thinking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>shifak N</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5180</td>\n",
       "      <td>8320</td>\n",
       "      <td>Electronics and Electrical Engineering</td>\n",
       "      <td>3140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Ganesh Ramkumar R</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>8790</td>\n",
       "      <td>10790</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>2000</td>\n",
       "      <td>55.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Narasimhan Y L</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4800</td>\n",
       "      <td>6800</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>2000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Balaji</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6170</td>\n",
       "      <td>8160</td>\n",
       "      <td>Electronics and Electrical Engineering</td>\n",
       "      <td>1990</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  python  mysql  Previous Geekions  CodeKata Score  \\\n",
       "92            shifak N    58.0    0.0               5180            8320   \n",
       "86   Ganesh Ramkumar R    -1.0   24.0               8790           10790   \n",
       "102     Narasimhan Y L    -1.0    0.0               4800            6800   \n",
       "93             Balaji     -1.0   20.0               6170            8160   \n",
       "\n",
       "                                 Department  Rising  python_en  \\\n",
       "92   Electronics and Electrical Engineering    3140        0.0   \n",
       "86         Computer Science and Engineering    2000       55.0   \n",
       "102        Computer Science and Engineering    2000       -1.0   \n",
       "93   Electronics and Electrical Engineering    1990      100.0   \n",
       "\n",
       "     computational_thinking  \n",
       "92                      0.0  \n",
       "86                      6.0  \n",
       "102                     0.0  \n",
       "93                      0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nlargest(n=4, columns='Rising') # n = 4, because two persons having the same score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yVbuGZVX2G84"
   },
   "source": [
    "###### Shining stars of the week (top 3 candidates who has highest geekions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "2Z4LB0Cn2G84"
   },
   "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>Name</th>\n",
       "      <th>python</th>\n",
       "      <th>mysql</th>\n",
       "      <th>Previous Geekions</th>\n",
       "      <th>CodeKata Score</th>\n",
       "      <th>Department</th>\n",
       "      <th>Rising</th>\n",
       "      <th>python_en</th>\n",
       "      <th>computational_thinking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A.Dharani</td>\n",
       "      <td>82.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>24500</td>\n",
       "      <td>24500</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>V.JEEVITHA</td>\n",
       "      <td>82.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21740</td>\n",
       "      <td>21740</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HEMAVATHI.R</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>19680</td>\n",
       "      <td>19680</td>\n",
       "      <td>Computer Science and Engineering</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name  python  mysql  Previous Geekions  CodeKata Score  \\\n",
       "0    A.Dharani    82.0   20.0              24500           24500   \n",
       "1  V.JEEVITHA     82.0   20.0              21740           21740   \n",
       "2  HEMAVATHI.R   100.0  100.0              19680           19680   \n",
       "\n",
       "                         Department  Rising  python_en  computational_thinking  \n",
       "0  Computer Science and Engineering       0        NaN                     NaN  \n",
       "1  Computer Science and Engineering       0        NaN                     NaN  \n",
       "2  Computer Science and Engineering       0        NaN                     NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nlargest(n=3, columns = 'Previous Geekions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YJBXQC1V2G84"
   },
   "source": [
    "###### Department wise codekata performence (pie chart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "CkUg7sqV2G84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c65d717860>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['Department']).sum().plot(kind = 'pie', y = 'CodeKata Score', autopct = '%1.lf%%',\n",
    "                                            colors = ['red', 'green', 'blue'], shadow = True, startangle = 120,\n",
    "                                     title = 'Department vs CodeKata Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "psZ0pLfL2G84"
   },
   "source": [
    "###### Department wise toppers (horizantal bar graph or any visual representations of your choice)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "dvvvWhji2G85"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Computer Science Department\n",
    "df[df['Department'] == 'Computer Science and Engineering'].groupby(['Name']).max().plot(kind = 'bar', y = 'CodeKata Score',\n",
    "                                            color = ['pink', 'silver', 'steelblue'], figsize = (16, 6))\n",
    "\n",
    "plt.title('Computer Science Engineering\\n CodeKata Score vs Name')\n",
    "plt.xlabel('Name')\n",
    "plt.ylabel('CodeKata Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "e2SL31Ii2G84"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Electronics and Communication Department\n",
    "df[df['Department'] == 'Electronics and Communication Engineering'].groupby(['Name']).max().plot(kind = 'bar', y = 'CodeKata Score',\n",
    "                                            color = ['pink', 'silver', 'steelblue'], figsize = (12, 5))\n",
    "\n",
    "plt.title('Electronics and Communication Engineering\\n CodeKata Score vs Name')\n",
    "plt.xlabel('Name')\n",
    "plt.ylabel('CodeKata Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "Zu5HZFei2G85"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Electronics and Electrical Department\n",
    "df[df['Department'] == 'Electronics and Electrical Engineering'].groupby(['Name']).max().plot(kind = 'bar', y = 'CodeKata Score',\n",
    "                                            color = ['pink', 'silver', 'steelblue'], figsize = (10, 5))\n",
    "\n",
    "plt.title('Electronics and Electrical Engineering\\n CodeKata Score vs Name')\n",
    "plt.xlabel('Name')\n",
    "plt.ylabel('CodeKata Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "I053r7_52G85"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}