Student_Result_Analysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1b-B4hymre8wLjVMoFValNnGS19CE0LMM",
      "authorship_tag": "ABX9TyMpsoio0WUcCRnq0aqd33zr",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/student-Ranjan/Basic-calculator/blob/main/Student_Result_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "t_wKYdu6k95J",
        "outputId": "3dfc24f5-e81f-4391-a263-e418f25624df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Unnamed: 0  Gender EthnicGroup          ParentEduc     LunchType  \\\n",
            "0           0  female     group B   bachelor's degree      standard   \n",
            "1           1  female     group C        some college      standard   \n",
            "2           2  female     group B     master's degree      standard   \n",
            "3           3    male     group A  associate's degree  free/reduced   \n",
            "4           4    male     group C        some college      standard   \n",
            "\n",
            "    TestPrep  MathScore  ReadingScore  WritingScore  \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"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/student_scores.csv\")\n",
        "print(df.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "I52tkLDFynUD",
        "outputId": "137e10fb-40f6-4d4d-9beb-5715e1dfea65"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Unnamed: 0     MathScore  ReadingScore  WritingScore\n",
              "count  30641.000000  30641.000000  30641.000000  30641.000000\n",
              "mean     499.556607     66.749355     69.624980     68.468327\n",
              "std      288.747894     15.206049     14.671572     15.307814\n",
              "min        0.000000      0.000000     10.000000      5.000000\n",
              "25%      249.000000     56.000000     60.000000     58.000000\n",
              "50%      500.000000     67.000000     70.000000     69.000000\n",
              "75%      750.000000     78.000000     80.000000     79.000000\n",
              "max      999.000000    100.000000    100.000000    100.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4c8cb04d-ff4b-4564-9122-8cc60d85f9bf\" class=\"colab-df-container\">\n",
              "    <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>Unnamed: 0</th>\n",
              "      <th>MathScore</th>\n",
              "      <th>ReadingScore</th>\n",
              "      <th>WritingScore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>30641.000000</td>\n",
              "      <td>30641.000000</td>\n",
              "      <td>30641.000000</td>\n",
              "      <td>30641.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>499.556607</td>\n",
              "      <td>66.749355</td>\n",
              "      <td>69.624980</td>\n",
              "      <td>68.468327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>288.747894</td>\n",
              "      <td>15.206049</td>\n",
              "      <td>14.671572</td>\n",
              "      <td>15.307814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>249.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>58.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>500.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>69.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>750.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>79.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>999.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c8cb04d-ff4b-4564-9122-8cc60d85f9bf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4c8cb04d-ff4b-4564-9122-8cc60d85f9bf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4c8cb04d-ff4b-4564-9122-8cc60d85f9bf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a843c438-5a36-4225-bee0-b73cc2f0a2f0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a843c438-5a36-4225-bee0-b73cc2f0a2f0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a843c438-5a36-4225-bee0-b73cc2f0a2f0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10671.681928672424,\n        \"min\": 0.0,\n        \"max\": 30641.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          499.5566071603407,\n          500.0,\n          30641.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MathScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10813.936448488956,\n        \"min\": 0.0,\n        \"max\": 30641.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          66.74935543879116,\n          67.0,\n          30641.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ReadingScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10812.853721041434,\n        \"min\": 10.0,\n        \"max\": 30641.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          69.6249796024934,\n          70.0,\n          30641.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WritingScore\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10813.33679939841,\n        \"min\": 5.0,\n        \"max\": 30641.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          68.46832675173786,\n          69.0,\n          30641.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "j9W20nH-y3YX",
        "outputId": "ab200df5-4d7c-44d4-bee3-7925a6308814"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 30641 entries, 0 to 30640\n",
            "Data columns (total 9 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Unnamed: 0    30641 non-null  int64 \n",
            " 1   Gender        30641 non-null  object\n",
            " 2   EthnicGroup   30641 non-null  object\n",
            " 3   ParentEduc    30641 non-null  object\n",
            " 4   LunchType     30641 non-null  object\n",
            " 5   TestPrep      30641 non-null  object\n",
            " 6   MathScore     30641 non-null  int64 \n",
            " 7   ReadingScore  30641 non-null  int64 \n",
            " 8   WritingScore  30641 non-null  int64 \n",
            "dtypes: int64(4), object(5)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "o75ylFk8zGSD",
        "outputId": "1c53d78b-9a48-4851-dc03-10021211da59"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0      0\n",
              "Gender          0\n",
              "EthnicGroup     0\n",
              "ParentEduc      0\n",
              "LunchType       0\n",
              "TestPrep        0\n",
              "MathScore       0\n",
              "ReadingScore    0\n",
              "WritingScore    0\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EthnicGroup</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ParentEduc</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LunchType</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TestPrep</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MathScore</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ReadingScore</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WritingScore</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(\"Unnamed: 0\", axis = 1)\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "IhrIB6efzVgw",
        "outputId": "ef625d74-b932-4570-b9d1-35769b778c56"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Gender EthnicGroup          ParentEduc     LunchType   TestPrep  MathScore  \\\n",
            "0  female     group B   bachelor's degree      standard       none         72   \n",
            "1  female     group C        some college      standard  completed         69   \n",
            "2  female     group B     master's degree      standard       none         90   \n",
            "3    male     group A  associate's degree  free/reduced       none         47   \n",
            "4    male     group C        some college      standard       none         76   \n",
            "\n",
            "   ReadingScore  WritingScore  \n",
            "0            72            74  \n",
            "1            90            88  \n",
            "2            95            93  \n",
            "3            57            44  \n",
            "4            78            75  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#gender distribution\n",
        "plt.figure(figsize= (4,5))\n",
        "ax = sns.countplot(data = df, x = \"Gender\")\n",
        "ax.bar_label(ax.containers[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "XzxtQhYb13fp",
        "outputId": "a9d7141c-a0dc-4a3a-d1fb-aa794fe5b3e8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#From the above chart we are analysed that:\n",
        "#the number of females in the data is greater than the number of males"
      ],
      "metadata": {
        "id": "MR3jsqTZ33Vq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gb = df.groupby(\"ParentEduc\").agg({\"MathScore\":\"mean\", \"ReadingScore\":\"mean\", \"WritingScore\":\"mean\"})\n",
        "print(gb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "mkl_tX5j4n58",
        "outputId": "6f57c73f-1b58-4b95-baf1-21aabf5c1d48"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    MathScore  ReadingScore  WritingScore\n",
            "ParentEduc                                               \n",
            "associate's degree  68.527787     71.368350     70.374619\n",
            "bachelor's degree   70.635000     73.375556     73.423611\n",
            "high school         64.670038     67.522467     65.565909\n",
            "master's degree     72.168137     75.202044     75.246168\n",
            "some college        66.603434     69.528093     68.653661\n",
            "some high school    62.926359     65.826816     63.815473\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(gb, annot = True)\n",
        "plt.title(\"Relationship between Parent's Education and Student's Score\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "Tpv6XduU5m5H",
        "outputId": "81a271f7-ed21-4281-c42f-ceeb1c7ca9c5"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#from the above chart we have concluded that the education of the parents have a good impact on their scores."
      ],
      "metadata": {
        "id": "jfHDb76aGDik"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data = df, y = \"MathScore\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "lbWG_cq1RI5V",
        "outputId": "4915351e-213a-4e0e-9a34-7399682d3277"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data = df, y = \"ReadingScore\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "960fslekWiT-",
        "outputId": "ca7f613d-8827-4fe9-ab72-09e7eb4fd0dc"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#From this boxplot we are analysed that Math is comparitivily difficult subject instead of Reading and Writing to scores good marks."
      ],
      "metadata": {
        "id": "siC0JtnuWon9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df[\"EthnicGroup\"].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "HTvcy6oNYrB3",
        "outputId": "ab5a0cb4-06e7-4795-d796-803d4806a024"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['group B' 'group C' 'group A' 'group D' 'group E']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Distribution of Eithic groups\n",
        "groupA =df.loc[(df['EthnicGroup'] == \"group A\")].count()\n",
        "groupB =df.loc[(df['EthnicGroup'] == \"group B\")].count()\n",
        "groupC =df.loc[(df['EthnicGroup'] == \"group C\")].count()\n",
        "groupD =df.loc[(df['EthnicGroup'] == \"group D\")].count()\n",
        "groupE =df.loc[(df['EthnicGroup'] == \"group E\")].count()\n",
        "\n",
        "lab = [\"group A\", \"group B\", \"group C\", \"group D\", \"group E\"]\n",
        "mlist = [groupA[\"EthnicGroup\"], groupB[\"EthnicGroup\"], groupC[\"EthnicGroup\"], groupD[\"EthnicGroup\"], groupE[\"EthnicGroup\"]]\n",
        "print(mlist)\n",
        "plt.pie(mlist, labels =lab, autopct= \"%1.2f%%\")\n",
        "plt.title(\"Distribution of Ethnic Groups\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "WdFgrbeLZwp6",
        "outputId": "85a7591f-f373-4a99-f240-d7708a39b5bf"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2359, 6192, 9816, 7986, 4288]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.countplot(data = df, x = \"EthnicGroup\")\n",
        "ax.bar_label(ax.containers[0])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "876h37tReRkF",
        "outputId": "8439545a-ccac-487f-d97d-76dc7aeabccc"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}