data_explore.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the necessary packages \n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "from pprint import pprint\n",
    "pd.set_option(\"display.max_rows\", 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Caution : This notebook is not exhaustive and is definitely prone to errors. Please take everything I say as a maybe wrong fact.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CompanyName</th>\n",
       "      <th>JobProfile</th>\n",
       "      <th>JobLocation</th>\n",
       "      <th>EligibleBranches</th>\n",
       "      <th>MinCgpa</th>\n",
       "      <th>IiitAllowed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ixambee</td>\n",
       "      <td>software developer</td>\n",
       "      <td>noida, delhi-ncr</td>\n",
       "      <td>cs, it, ece, ee, mech, civil, pie</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tummee.com</td>\n",
       "      <td>software development engineer</td>\n",
       "      <td>remote, gurgaon</td>\n",
       "      <td>cs, it, ece, ee, mech, civil, pie</td>\n",
       "      <td>na</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jcb india</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ballabgarh(delhi ncr)</td>\n",
       "      <td>mech,ee</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>smart energy water</td>\n",
       "      <td>software engineer</td>\n",
       "      <td>noida</td>\n",
       "      <td>cs, it, ece, ee, mech, civil, pie</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>escorts ltd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>faridabad</td>\n",
       "      <td>pie,ee,mech</td>\n",
       "      <td>7.7777</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>hindustan unilever ltd</td>\n",
       "      <td>supply chain trainee.</td>\n",
       "      <td>any factory in india</td>\n",
       "      <td>mech,pie,ee</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>oneplus</td>\n",
       "      <td>software engineer</td>\n",
       "      <td>hyderabad</td>\n",
       "      <td>cs,it,ece</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>pharmeasy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bangalore, mumbai</td>\n",
       "      <td>cs,it,ece</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>manglam electrical</td>\n",
       "      <td>get</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ece,ee</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>isie india</td>\n",
       "      <td>techno-management, technical-event-management,...</td>\n",
       "      <td>noida</td>\n",
       "      <td>mech,ee,ece,pie,civil</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                CompanyName  \\\n",
       "0                   ixambee   \n",
       "1                tummee.com   \n",
       "2                 jcb india   \n",
       "3        smart energy water   \n",
       "4               escorts ltd   \n",
       "..                      ...   \n",
       "109  hindustan unilever ltd   \n",
       "110                 oneplus   \n",
       "111               pharmeasy   \n",
       "112      manglam electrical   \n",
       "113              isie india   \n",
       "\n",
       "                                            JobProfile            JobLocation  \\\n",
       "0                                   software developer       noida, delhi-ncr   \n",
       "1                        software development engineer        remote, gurgaon   \n",
       "2                                                  NaN  ballabgarh(delhi ncr)   \n",
       "3                                    software engineer                  noida   \n",
       "4                                                  NaN              faridabad   \n",
       "..                                                 ...                    ...   \n",
       "109                              supply chain trainee.   any factory in india   \n",
       "110                                  software engineer              hyderabad   \n",
       "111                                                NaN      bangalore, mumbai   \n",
       "112                                                get                    NaN   \n",
       "113  techno-management, technical-event-management,...                  noida   \n",
       "\n",
       "                      EligibleBranches MinCgpa  IiitAllowed  \n",
       "0    cs, it, ece, ee, mech, civil, pie       0          NaN  \n",
       "1    cs, it, ece, ee, mech, civil, pie      na          0.0  \n",
       "2                              mech,ee     6.5          NaN  \n",
       "3    cs, it, ece, ee, mech, civil, pie       7          1.0  \n",
       "4                          pie,ee,mech  7.7777          0.0  \n",
       "..                                 ...     ...          ...  \n",
       "109                        mech,pie,ee       7          0.0  \n",
       "110                          cs,it,ece       0          0.0  \n",
       "111                          cs,it,ece       7          0.0  \n",
       "112                             ece,ee       0          NaN  \n",
       "113              mech,ee,ece,pie,civil     6.5          0.0  \n",
       "\n",
       "[114 rows x 6 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../../data.csv\")\n",
    "df.reset_index(drop=True, inplace=True)\n",
    "del df[df.columns.values[0]]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. How many different companies came to campus this year ? <br /> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Total Companies : 114'\n",
      "'The list of these companies are : '\n",
      "{'accenture',\n",
      " 'accolite',\n",
      " 'airtel x labs & airtel paymentbank',\n",
      " 'amazon',\n",
      " 'amdocs',\n",
      " 'analog devices',\n",
      " 'anglo eastern ship management ltd. hong kong (merchant navy)',\n",
      " 'apisero inc',\n",
      " 'avalara technologies',\n",
      " 'bajaj auto ltd',\n",
      " 'bigbasket',\n",
      " 'brigosha',\n",
      " 'brillio technologies',\n",
      " 'cadence',\n",
      " 'capgemini',\n",
      " 'carwale',\n",
      " 'cdac',\n",
      " 'cgi',\n",
      " 'claritrics india private limited',\n",
      " 'codenation innovation labs',\n",
      " 'cognizant',\n",
      " 'cubastion consulting',\n",
      " 'cvent india pvt ltd',\n",
      " 'decision point pvt ltd',\n",
      " 'delhivery',\n",
      " 'deloitte consulting india pvt ltd',\n",
      " 'dmi finance',\n",
      " 'escorts ltd',\n",
      " 'evalueserve',\n",
      " 'evosys',\n",
      " 'exl services',\n",
      " 'expertlancing research services llp',\n",
      " 'fiapl',\n",
      " 'fiftyfive technologies',\n",
      " 'flipkart internet pvt ltd',\n",
      " 'gameskraft technologies',\n",
      " 'godrej & boyce',\n",
      " 'helium consulting private limited',\n",
      " 'hexaviewtech',\n",
      " 'hindustan unilever ltd',\n",
      " 'hpcl',\n",
      " 'ihs markit',\n",
      " 'incedo',\n",
      " 'infoedge',\n",
      " 'interra systems india pvt. ltd.',\n",
      " 'ion trading',\n",
      " 'isie india',\n",
      " 'ixambee',\n",
      " 'jcb india',\n",
      " 'jio platforms ltd.',\n",
      " 'jivox india',\n",
      " 'larsen and toubro infotech',\n",
      " 'larsen and toubro limit',\n",
      " 'larsen and toubro technology services',\n",
      " 'lava international',\n",
      " 'learning routes pvt ltd.',\n",
      " \"lowe's india\",\n",
      " 'mahindra comviva',\n",
      " 'manglam electrical',\n",
      " 'maruti suzuki',\n",
      " 'mentor graphics',\n",
      " 'naflon pharmaceutical',\n",
      " 'namekart',\n",
      " 'natwest group',\n",
      " 'nokia',\n",
      " 'nykaa',\n",
      " 'octro inc (sgm software pvt ltd)',\n",
      " 'onebanc technologies',\n",
      " 'oneplus',\n",
      " 'oracle financial services software',\n",
      " 'oracle india pvt. ltd',\n",
      " 'paxcom-india',\n",
      " 'paytm',\n",
      " 'perceptive analytics',\n",
      " 'pharmeasy',\n",
      " 'phrogapp labs pvt ltd',\n",
      " 'pindexx',\n",
      " 'planetspark',\n",
      " 'pranav vikas (part of vikas group)',\n",
      " 'publicis sapient',\n",
      " 'q3 technologies',\n",
      " 'quovantis technologies pvt. ltd',\n",
      " 'raam group',\n",
      " 'reliance jio platforms',\n",
      " 'samsung r&d delhi',\n",
      " 'samsung sds',\n",
      " 'schneider electric',\n",
      " 'siemens',\n",
      " 'signalchip innovations',\n",
      " 'signify innovations india ltd',\n",
      " 'smart energy water',\n",
      " 'splashlearn',\n",
      " 'tcs (tata consultancy service)',\n",
      " 'tejas networks',\n",
      " 'thcm',\n",
      " 'truminds',\n",
      " 'tummee.com',\n",
      " 'udaan',\n",
      " 'undostres',\n",
      " 'urban company',\n",
      " 'varroc engineering ltd.',\n",
      " 'varun beverages',\n",
      " 'vedanta limited',\n",
      " 'verizon',\n",
      " 'wapcos limited',\n",
      " 'wells fargo',\n",
      " 'whitehat jr',\n",
      " 'wipro limited',\n",
      " 'wns global services',\n",
      " 'xenonstack',\n",
      " 'zs associates'}\n"
     ]
    }
   ],
   "source": [
    "total_companies = len(df)\n",
    "pprint(f\"Total Companies : {total_companies}\")\n",
    "pprint(f\"The list of these companies are : \")\n",
    "pprint(set(df[\"CompanyName\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. How many different Profiles do students do into ? \n",
    "\n",
    "##### Note : A lot of overlapping is here, example : Sde, Software Developer, Software Engineer all are same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Total Different Profiles are 79'\n",
      "'All the different profiles are here'\n",
      "{' analog design engineer',\n",
      " ' analyst',\n",
      " ' associate systems engineer',\n",
      " ' business technology analyst (bta)',\n",
      " ' business-development-executive',\n",
      " ' circuit simulation.',\n",
      " ' data analyst',\n",
      " ' data scientists',\n",
      " ' decision analytics associate (daa)',\n",
      " ' digital design engineer',\n",
      " ' ev-engineer',\n",
      " ' firmware design engineer',\n",
      " ' hardware engineering',\n",
      " ' hardware-engineer-r&d',\n",
      " ' jr. data engineer',\n",
      " ' jr. data scientist',\n",
      " ' machine learning engineering',\n",
      " ' management',\n",
      " ' production',\n",
      " ' schematic design & pcb layout design & development',\n",
      " ' software developer (base+)',\n",
      " ' technical-event-management',\n",
      " ' testing',\n",
      " 'analyst – deloitte consulting',\n",
      " 'associate consultant',\n",
      " 'associate software engineer',\n",
      " 'business analyst',\n",
      " 'business development counsellor',\n",
      " 'business operations associate (boa)',\n",
      " 'data analyst',\n",
      " 'data scientist',\n",
      " 'deputy manager',\n",
      " 'development',\n",
      " 'digital marketer',\n",
      " 'dsp engineer',\n",
      " 'engineer',\n",
      " 'engineer trainee',\n",
      " 'engineer trainees',\n",
      " 'engineering lead',\n",
      " 'full stack developer',\n",
      " 'game developer',\n",
      " 'get',\n",
      " 'graduate engineer trainee',\n",
      " 'graduate trainee engineer (gte)',\n",
      " 'hardware engineer',\n",
      " 'it associate systems engineer',\n",
      " 'jr. software engineer',\n",
      " 'knowledge associates',\n",
      " 'management trainee(pfa)',\n",
      " 'member technical staff',\n",
      " 'mobile application developer',\n",
      " 'officer-engineering',\n",
      " 'oracle functional consulting',\n",
      " 'patent research associate trainee.',\n",
      " 'project engineer.',\n",
      " 'research associate(intellectual property)',\n",
      " 'rnd engineer',\n",
      " 'rotational product developer',\n",
      " 'sales associate',\n",
      " 'sales manager',\n",
      " 'sde',\n",
      " 'sde-1',\n",
      " 'senior analyst',\n",
      " 'software developer',\n",
      " 'software developers',\n",
      " 'software development',\n",
      " 'software development engineer',\n",
      " 'software development engineering',\n",
      " 'software engineer',\n",
      " 'software engineer and cloud engineer',\n",
      " 'software engineer trainee',\n",
      " 'software engineer)',\n",
      " 'software engineering associate',\n",
      " 'software-developer-(pathfinder)',\n",
      " 'software-engineer-r&d',\n",
      " 'supply chain trainee.',\n",
      " 'supply-chain',\n",
      " 'techno-management',\n",
      " 'trainee analyst'}\n"
     ]
    }
   ],
   "source": [
    "total_different_profiles = 0\n",
    "set_of_profiles = set()\n",
    "for i in range(len(df)):\n",
    "    profile_here = df.loc[i, \"JobProfile\"]\n",
    "    try:\n",
    "        for x in profile_here.split(','):\n",
    "            set_of_profiles.add(x)\n",
    "    except Exception:\n",
    "        pass\n",
    "\n",
    "pprint(f\"Total Different Profiles are {len(set_of_profiles)}\")\n",
    "pprint(\"All the different profiles are here\")\n",
    "pprint(set_of_profiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. How many different places in India are students placed ? \n",
    "##### Note : We also have overlapping places here : Like Bangalore, Bengaluru etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Total Different Job Locations are 49'\n",
      "'All the different Job Locations are here'\n",
      "{' bangalore',\n",
      " ' bengaluru',\n",
      " ' chennai',\n",
      " ' delhi',\n",
      " ' delhi-ncr',\n",
      " ' goregaon',\n",
      " ' gurgaon',\n",
      " ' haryana',\n",
      " ' hyderabad',\n",
      " ' jaipur',\n",
      " ' kolkata',\n",
      " ' mohali',\n",
      " ' mumbai',\n",
      " ' navi mumbai',\n",
      " ' noida',\n",
      " ' pune',\n",
      " ' remote',\n",
      " 'ahmedabad',\n",
      " 'any factory in india',\n",
      " 'ballabgarh(delhi ncr)',\n",
      " 'bangalore',\n",
      " 'bangalore/hyderabad/noida/pune',\n",
      " 'banglore',\n",
      " 'bengaluru',\n",
      " 'chandigarh',\n",
      " 'chennai',\n",
      " 'delhi-ncr',\n",
      " 'faridabad',\n",
      " 'gurgaon',\n",
      " 'gurugram',\n",
      " 'hyderabad',\n",
      " 'hyderabad/mumbai/gurgaon/benagaluru',\n",
      " 'kharagpur',\n",
      " 'mexico city',\n",
      " 'mohali',\n",
      " 'mohali/delhi/gurgaon',\n",
      " 'mumbai',\n",
      " 'mumbai and bangalore',\n",
      " 'mumbai/bangalore/pune',\n",
      " 'navi mumbai',\n",
      " 'noida',\n",
      " 'noida sector-126',\n",
      " 'on company managed ships.',\n",
      " 'pan india',\n",
      " 'pan india.',\n",
      " 'pan-india',\n",
      " 'pune',\n",
      " 'remote',\n",
      " 'vashi'}\n"
     ]
    }
   ],
   "source": [
    "total_diff_places = 0\n",
    "set_of_places = set()\n",
    "for i in range(len(df)):\n",
    "    place_here = df.loc[i, \"JobLocation\"]\n",
    "    try:\n",
    "        for x in place_here.split(','):\n",
    "            set_of_places.add(x)\n",
    "    except Exception :\n",
    "        pass\n",
    "pprint(f\"Total Different Job Locations are {len(set_of_places)}\")\n",
    "pprint(\"All the different Job Locations are here\")\n",
    "pprint(set_of_places)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. A lot of myth in college is around this \"People in core branches are less placed.\" <br />\n",
    "#### a) My hold in this is that there is direct correlation between the `number of companies` coming and `number of people getting placed`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mech', 'it', 'pie', 'cs', 'civil', 'ece', 'ee'}\n",
      "'Total Companies that allowed CSE : 86'\n",
      "'Total Companies that allowed IT : 81'\n",
      "'Total Companies that allowed ECE : 82'\n",
      "'Total Companies that allowed EE : 65'\n",
      "'Total Companies that allowed CIVIL : 29'\n",
      "'Total Companies that allowed MECH : 47'\n",
      "'Total Companies that allowed PIE : 38'\n"
     ]
    }
   ],
   "source": [
    "#These are the count of companies that allow 'variable_name' branch to sit. \n",
    "#Example if cs = 50, then 50 companies allowed \"CSE\" to sit for the test.\n",
    "cs = 0; it = 0; ece = 0; civil = 0; mech = 0; pie = 0; ee = 0\n",
    "total_diff_branches = 0\n",
    "set_of_branches = set()\n",
    "for i in range(len(df)):\n",
    "    branches_here = df.loc[i, \"EligibleBranches\"]\n",
    "    try:\n",
    "        for x in branches_here.split(','):\n",
    "            branch = x.strip()\n",
    "            set_of_branches.add(branch)\n",
    "            if(branch == \"cs\"):\n",
    "                cs += 1\n",
    "            elif(branch == \"it\"):\n",
    "                it += 1\n",
    "            elif(branch == \"pie\"):\n",
    "                pie += 1\n",
    "            elif(branch == \"mech\"):\n",
    "                mech += 1\n",
    "            elif(branch == \"ece\"):\n",
    "                ece += 1\n",
    "            elif(branch == \"ee\"):\n",
    "                ee += 1\n",
    "            else:\n",
    "                civil += 1\n",
    "    except:\n",
    "        pass\n",
    "pprint(set_of_branches)\n",
    "pprint(f\"Total Companies that allowed CSE : {cs}\")\n",
    "pprint(f\"Total Companies that allowed IT : {it}\")\n",
    "pprint(f\"Total Companies that allowed ECE : {ece}\")\n",
    "pprint(f\"Total Companies that allowed EE : {ee}\")\n",
    "pprint(f\"Total Companies that allowed CIVIL : {civil}\")\n",
    "pprint(f\"Total Companies that allowed MECH : {mech}\")\n",
    "pprint(f\"Total Companies that allowed PIE : {pie}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Lets talk about the CGPA crtiteria here. Usually seniors tell juniors to maintain a good enough 7 an you will be good to go. Let's check if that thing follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'The Maximum CGPA required is 8.0'\n",
      "('Note : 0 means that there is no requirement on CGPA but it maybe that you '\n",
      " 'have no active backlogs')\n",
      "'The Minimum CGPA required is 0.0'\n",
      "'Note : Here it should be provided that you are in EligibleBranches'\n",
      "'If you want to sit for 100% of companies. 8.0'\n",
      "'If you want to sit for 75% of companies. 7.0'\n",
      "'If you want to sit for 50% of companies. 6.5'\n",
      "'If you want to sit for 40% of companies. 6.5'\n"
     ]
    }
   ],
   "source": [
    "total_diff_cgpa = []\n",
    "s = set()\n",
    "for i in range(len(df)):\n",
    "    cgpa_here = df.loc[i, \"MinCgpa\"]\n",
    "    if(pd.isnull(cgpa_here)):\n",
    "        cgpa_here = str(0)\n",
    "    cgpa_here = cgpa_here.split(',')[0].split(' ')\n",
    "    cgpa_here = cgpa_here[0]\n",
    "    # Note some companies have no cgpa criteria. \n",
    "        ## In here there are 2 cases.\n",
    "            ### a) That donot allow active backlogs\n",
    "            ### b) That allow active backlogs\n",
    "        # While gathering this data, I have found that type(a) companies are much great\n",
    "        # er than type(b) companies. So please don't have any active backlog while sitting for placements.     \n",
    "    if(cgpa_here == \"all\" or cgpa_here == \"backlogs\" or cgpa_here == \"na\" or cgpa_here == \"nb\" or cgpa_here == \"pwd\"):\n",
    "        cgpa_here = str(0)\n",
    "    s.add(cgpa_here)\n",
    "    total_diff_cgpa.append(np.float(cgpa_here))\n",
    "\n",
    "\n",
    "    # Sort this CGPA list\n",
    "total_diff_cgpa = sorted(total_diff_cgpa)\n",
    "total_diff_cgpa\n",
    "\n",
    "assert(len(total_diff_cgpa) == len(df))\n",
    "\n",
    "pprint(f\"The Maximum CGPA required is {total_diff_cgpa[-1]}\")\n",
    "pprint(\"Note : 0 means that there is no requirement on CGPA but it maybe that you have no active backlogs\")\n",
    "pprint(f\"The Minimum CGPA required is {total_diff_cgpa[0]}\")\n",
    "\n",
    "# Lets see some Percentiles\n",
    "pprint(f\"Note : Here it should be provided that you are in EligibleBranches\")\n",
    "pprint(f\"If you want to sit for 100% of companies. {total_diff_cgpa[-1]}\")\n",
    "pprint(f\"If you want to sit for 75% of companies. {total_diff_cgpa[int(0.75*len(total_diff_cgpa))]}\")\n",
    "pprint(f\"If you want to sit for 50% of companies. {total_diff_cgpa[int(0.50*len(total_diff_cgpa))]}\")\n",
    "pprint(f\"If you want to sit for 40% of companies. {total_diff_cgpa[int(0.40*len(total_diff_cgpa))]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now the part you were all waiting for. The Average and Median Package possible\n",
    "##### Note I will not provide the CTC Details as it may-be confidential and should not be made public.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-82-6804a603e2f5>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-82-6804a603e2f5>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    average_package_offered =\u001b[0m\n\u001b[0m                              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "average_package_offered = 0\n",
    "median_package_offered = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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