Functions using apply, applymap and map.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(np.random.rand(4, 3)*100, \n",
    "                    columns=['Physics','Çhemistry','Maths'],\n",
    "                    index = ['Student 1', 'Student 2','Student 3','Student 4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Physics</th>\n",
       "      <th>Çhemistry</th>\n",
       "      <th>Maths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Student 1</th>\n",
       "      <td>11.849391</td>\n",
       "      <td>28.713003</td>\n",
       "      <td>79.864810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student 2</th>\n",
       "      <td>16.650692</td>\n",
       "      <td>84.210567</td>\n",
       "      <td>92.854690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student 3</th>\n",
       "      <td>72.804809</td>\n",
       "      <td>40.681747</td>\n",
       "      <td>35.292184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student 4</th>\n",
       "      <td>37.716594</td>\n",
       "      <td>56.268743</td>\n",
       "      <td>52.028996</td>\n",
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      "text/plain": [
       "             Physics  Çhemistry      Maths\n",
       "Student 1  11.849391  28.713003  79.864810\n",
       "Student 2  16.650692  84.210567  92.854690\n",
       "Student 3  72.804809  40.681747  35.292184\n",
       "Student 4  37.716594  56.268743  52.028996"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "RoundUpto2Decimal = lambda x: round(x,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Physics</th>\n",
       "      <th>Çhemistry</th>\n",
       "      <th>Maths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Student 1</th>\n",
       "      <td>11.85</td>\n",
       "      <td>28.71</td>\n",
       "      <td>79.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student 2</th>\n",
       "      <td>16.65</td>\n",
       "      <td>84.21</td>\n",
       "      <td>92.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student 3</th>\n",
       "      <td>72.80</td>\n",
       "      <td>40.68</td>\n",
       "      <td>35.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student 4</th>\n",
       "      <td>37.72</td>\n",
       "      <td>56.27</td>\n",
       "      <td>52.03</td>\n",
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      "text/plain": [
       "           Physics  Çhemistry  Maths\n",
       "Student 1    11.85      28.71  79.86\n",
       "Student 2    16.65      84.21  92.85\n",
       "Student 3    72.80      40.68  35.29\n",
       "Student 4    37.72      56.27  52.03"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.applymap(RoundUpto2Decimal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "AverageMarks = lambda x: np.mean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Physics      34.755372\n",
       "Çhemistry    52.468515\n",
       "Maths        65.010170\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(AverageMarks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student 1    40.142401\n",
       "Student 2    64.571983\n",
       "Student 3    49.592914\n",
       "Student 4    48.671444\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(AverageMarks,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "SquareOfMarks = lambda x: x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Student 1     140.408063\n",
       "Student 2     277.245559\n",
       "Student 3    5300.540214\n",
       "Student 4    1422.541461\n",
       "Name: Physics, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Physics'].map(SquareOfMarks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Physics</th>\n",
       "      <th>Çhemistry</th>\n",
       "      <th>Maths</th>\n",
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       "      <th>Student 1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.774334</td>\n",
       "    </tr>\n",
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       "      <th>Student 2</th>\n",
       "      <td>0.078767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>Student 3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.215663</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Student 4</th>\n",
       "      <td>0.424363</td>\n",
       "      <td>0.496522</td>\n",
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      "text/plain": [
       "            Physics  Çhemistry     Maths\n",
       "Student 1  0.000000   0.000000  0.774334\n",
       "Student 2  0.078767   1.000000  1.000000\n",
       "Student 3  1.000000   0.215663  0.000000\n",
       "Student 4  0.424363   0.496522  0.290759"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.apply(lambda x: (x-np.min(x))/(np.max(x)-np.min(x)))"
   ]
  }
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