Students Performance Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bdc77d8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8785fe4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data into a pandas dataframe\n",
    "data = pd.read_csv('StudentsPerformance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9a6590c5",
   "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>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "541a5dc3",
   "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>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>518</td>\n",
       "      <td>319</td>\n",
       "      <td>226</td>\n",
       "      <td>645</td>\n",
       "      <td>642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.08900</td>\n",
       "      <td>69.169000</td>\n",
       "      <td>68.054000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.16308</td>\n",
       "      <td>14.600192</td>\n",
       "      <td>15.195657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.00000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.00000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.00000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        gender race/ethnicity parental level of education     lunch  \\\n",
       "count     1000           1000                        1000      1000   \n",
       "unique       2              5                           6         2   \n",
       "top     female        group C                some college  standard   \n",
       "freq       518            319                         226       645   \n",
       "mean       NaN            NaN                         NaN       NaN   \n",
       "std        NaN            NaN                         NaN       NaN   \n",
       "min        NaN            NaN                         NaN       NaN   \n",
       "25%        NaN            NaN                         NaN       NaN   \n",
       "50%        NaN            NaN                         NaN       NaN   \n",
       "75%        NaN            NaN                         NaN       NaN   \n",
       "max        NaN            NaN                         NaN       NaN   \n",
       "\n",
       "       test preparation course  math score  reading score  writing score  \n",
       "count                     1000  1000.00000    1000.000000    1000.000000  \n",
       "unique                       2         NaN            NaN            NaN  \n",
       "top                       none         NaN            NaN            NaN  \n",
       "freq                       642         NaN            NaN            NaN  \n",
       "mean                       NaN    66.08900      69.169000      68.054000  \n",
       "std                        NaN    15.16308      14.600192      15.195657  \n",
       "min                        NaN     0.00000      17.000000      10.000000  \n",
       "25%                        NaN    57.00000      59.000000      57.750000  \n",
       "50%                        NaN    66.00000      70.000000      69.000000  \n",
       "75%                        NaN    77.00000      79.000000      79.000000  \n",
       "max                        NaN   100.00000     100.000000     100.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "06c4f913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                         0\n",
       "race/ethnicity                 0\n",
       "parental level of education    0\n",
       "lunch                          0\n",
       "test preparation course        0\n",
       "math score                     0\n",
       "reading score                  0\n",
       "writing score                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63fe58ee",
   "metadata": {},
   "source": [
    "#### Check the male - female ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a471ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "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": [
    "data.groupby('gender').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "42755cd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pkENnuLfhKLAjycd47Ji7UyF1Nlg16gLmI8O9DR/o/khnHeezj4azZRqR5HHAD1XV7lHXImn0vLdMA5K8FNgBfLjbviaJF41I85jh3oY3AyuBRwCqagewbHTlSBo1w70NR6rq61PaHG+T5jF/UG3DfUleCSzoHmn2GuBTI65J0gh55n4OS3LsUWX/Qe/5qYeB9wLfoHcPbUnzlLNlzmFJdgEvpveAjhdMfd17y0jzl8My57a/pDdD5inARF978N4y0rzmmXsDkryjqn5j1HVIOnsY7pLUIH9QlaQGGe6S1CDDXTpJSd6V5OWjrkM6EcNdGrIkzkrTGeeXTk1L8kbgVcCDwMPAduAO4O3AGL2HnPxaVX0xybvoXQA2DjwJeENVvS9JgL8AXgjsozfV9NjxnwW8DbikO/6vVNXBJHfRu0r4OnrXIfzZ0D+s1MdwV7OSjAO/AFxL77t+D71w3wj8elXtSfJs4DZ6wQ2wiN6Tg55GL5TfB/w88FTgGcBCYBfwziQX0Av91VU1meQXgQ3AsQc/P7GqfmroH1SahuGulj0fuLOq/g8gyQeBi4DnAf/UOyEH4MK+fT5QVd8FdiVZ2LX9JPDeqjoKHEjy0a79qcDTga3dsRYAB/uO9Y9z/5GkwRjualmmaTsPeKSqrplhn8N96/37T3dBSICdVfXcGY71P7NWKA2JP6iqZZ8EXprkoiSXAC+hN8a+L8lNAOm5epbjfAJYk2RBkkV8/z4+u4GxJM/tjnVBkquG8kmkk2S4q1lV9Vl64+afB95P7/47X6f3A+u6JJ8HdgKrZznUHcAe4F7gHcDHu+N/G3g58EfdsXbQG/KRRs7bD6hpSS6pqkeTXEzvDHx9Vd0z6rqkYXPMXa3bmGQFvR9SNxnsmi88c5ekBjnmLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhr0/ypeEadNFYXvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('gender').size().plot(kind='bar', color=['green', 'orange'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "260fc6ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "female    518\n",
       "male      482\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('gender').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8aad1b",
   "metadata": {},
   "source": [
    "#### Check the ethnicity ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bfa294c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "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": [
    "data.groupby('race/ethnicity').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb24813",
   "metadata": {},
   "source": [
    "Group C makes the largest ethnicity group in the study followed by Group D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ad7eb01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='race/ethnicity'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('race/ethnicity').size().sort_values().plot(kind='barh', color=['orange', 'violet', 'yellow', 'green', 'red'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f1d268",
   "metadata": {},
   "source": [
    "#### Lets compute the average math, reading and writing scores for male and female students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f5dd810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        math score  reading score  writing score\n",
      "gender                                          \n",
      "female   63.633205      72.608108      72.467181\n",
      "male     68.728216      65.473029      63.311203\n"
     ]
    }
   ],
   "source": [
    "mean_scores_by_gender = data.groupby('gender').aggregate('mean')\n",
    "print (mean_scores_by_gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "56be98f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_scores_by_gender.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8278628c",
   "metadata": {},
   "source": [
    "From the above plot we infer that female students have performed better than the male students in reading and writing but not so in math. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac328ea3",
   "metadata": {},
   "source": [
    "#### Average score for female and male students"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ee46da9d",
   "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>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "      <th>average score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "      <td>82.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "      <td>92.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "      <td>49.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "      <td>76.333333</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>995</th>\n",
       "      <td>female</td>\n",
       "      <td>group E</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>88</td>\n",
       "      <td>99</td>\n",
       "      <td>95</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>57.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>high school</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>completed</td>\n",
       "      <td>59</td>\n",
       "      <td>71</td>\n",
       "      <td>65</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>68</td>\n",
       "      <td>78</td>\n",
       "      <td>77</td>\n",
       "      <td>74.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>female</td>\n",
       "      <td>group D</td>\n",
       "      <td>some college</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>77</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     gender race/ethnicity parental level of education         lunch  \\\n",
       "0    female        group B           bachelor's degree      standard   \n",
       "1    female        group C                some college      standard   \n",
       "2    female        group B             master's degree      standard   \n",
       "3      male        group A          associate's degree  free/reduced   \n",
       "4      male        group C                some college      standard   \n",
       "..      ...            ...                         ...           ...   \n",
       "995  female        group E             master's degree      standard   \n",
       "996    male        group C                 high school  free/reduced   \n",
       "997  female        group C                 high school  free/reduced   \n",
       "998  female        group D                some college      standard   \n",
       "999  female        group D                some college  free/reduced   \n",
       "\n",
       "    test preparation course  math score  reading score  writing score  \\\n",
       "0                      none          72             72             74   \n",
       "1                 completed          69             90             88   \n",
       "2                      none          90             95             93   \n",
       "3                      none          47             57             44   \n",
       "4                      none          76             78             75   \n",
       "..                      ...         ...            ...            ...   \n",
       "995               completed          88             99             95   \n",
       "996                    none          62             55             55   \n",
       "997               completed          59             71             65   \n",
       "998               completed          68             78             77   \n",
       "999                    none          77             86             86   \n",
       "\n",
       "     average score  \n",
       "0        72.666667  \n",
       "1        82.333333  \n",
       "2        92.666667  \n",
       "3        49.333333  \n",
       "4        76.333333  \n",
       "..             ...  \n",
       "995      94.000000  \n",
       "996      57.333333  \n",
       "997      65.000000  \n",
       "998      74.333333  \n",
       "999      83.000000  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['average score'] = data.apply('mean', axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "36ec2608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby('gender')['average score'].aggregate('mean').plot(kind='bar', color=['yellow', 'green'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be037372",
   "metadata": {},
   "source": [
    "On average, girls performed better than boys"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b9cc8d",
   "metadata": {},
   "source": [
    "#### Compute average scores against each race/ethnicity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "17f1653c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                math score  reading score  writing score\n",
      "race/ethnicity                                          \n",
      "group A          61.629213      64.674157      62.674157\n",
      "group B          63.452632      67.352632      65.600000\n",
      "group C          64.463950      69.103448      67.827586\n",
      "group D          67.362595      70.030534      70.145038\n",
      "group E          73.821429      73.028571      71.407143\n"
     ]
    }
   ],
   "source": [
    "mean_scores_by_race = data.groupby('race/ethnicity').aggregate('mean')\n",
    "print (mean_scores_by_race)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "043575b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='race/ethnicity'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_scores_by_race.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "613b4c8b",
   "metadata": {},
   "source": [
    "Group E seems to have performed better on average than the rest of the groups"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558ef669",
   "metadata": {},
   "source": [
    "#### Check male-female ratio in Group E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5e9caf64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 32,
     "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": [
    "data[data['race/ethnicity']=='group E'].groupby('gender').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aad623ca",
   "metadata": {},
   "source": [
    "Equal number of boys and girls in group E"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f36163",
   "metadata": {},
   "source": [
    "#### Check ratio of parental education in Group E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd1dc927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "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": [
    "data[data['race/ethnicity']=='group E'].groupby('parental level of education').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1319ccbb",
   "metadata": {},
   "source": [
    "#### Check the ratio in group E receiving subsidised food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3407413e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "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": [
    "data[data['race/ethnicity']=='group E'].groupby('lunch').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd109dfd",
   "metadata": {},
   "source": [
    "Almost 3/4th of Group E students can afford food at standard pricing (not receiving at a subsidised rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a0b9b76",
   "metadata": {},
   "source": [
    "#### Check ratio of students who received test prep course "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6cda0dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "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": [
    "data[data['race/ethnicity']=='group E'].groupby('test preparation course').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f489b1bb",
   "metadata": {},
   "source": [
    "more than half of group E students didn't take any special test prep course and yet outperformed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b0c992b",
   "metadata": {},
   "source": [
    "#### Check male-female ratio in Group A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "64e2fed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "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": [
    "data[data['race/ethnicity']=='group A'].groupby('gender').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de63a073",
   "metadata": {},
   "source": [
    "male students outnumber female students in Group A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8114b27b",
   "metadata": {},
   "source": [
    "#### Check ratio of parental education in Group A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "948e1e1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "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": [
    "data[data['race/ethnicity']=='group A'].groupby('parental level of education').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4200bc8",
   "metadata": {},
   "source": [
    "Parents of most students in Group A are high school passouts without a college degree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a34fc4",
   "metadata": {},
   "source": [
    "#### Check the ratio in group E receiving subsidised food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "78bfe94d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "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": [
    "data[data['race/ethnicity']=='group A'].groupby('lunch').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cfa873",
   "metadata": {},
   "source": [
    "#### Check ratio of students who received test prep course"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0ede9169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "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": [
    "data[data['race/ethnicity']=='group A'].groupby('test preparation course').size().plot(kind='pie', label='')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7845b332",
   "metadata": {},
   "source": [
    "#### Check correlation between math score and reading score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "708640ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='math score', ylabel='reading score'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='math score', y='reading score', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1664b717",
   "metadata": {},
   "source": [
    "#### Check correlation between math score and writing score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "14472c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='math score', ylabel='writing score'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='math score', y='writing score', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8715ed61",
   "metadata": {},
   "source": [
    "#### Check correlation between reading score and writing score "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a51d3959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='reading score', ylabel='writing score'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='reading score', y='writing score', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4123cf4d",
   "metadata": {},
   "source": [
    "#### Distribution for math, reading and writing score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "db546355",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_distribution_plot(df, column, title):\n",
    "    plt = df[column].plot(kind='hist', title=title)\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ba14974b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "math_score_dist_plot = get_distribution_plot(data,'math score','math score distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "73b79198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reading_score_dist_plot = get_distribution_plot(data,'reading score','reading score distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "50aebc9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "writing_score_dist_plot = get_distribution_plot(data,'writing score','writing score distribution')"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}