1061 lines
194 KiB
Plaintext
1061 lines
194 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3f62aa08-c686-41e2-81c8-055906edc3ef",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Самостоятельное задание\n",
|
||
"\n",
|
||
"Для самостоятельного анализа был выбран набор данных StudentsPerformance.csv. В нём содержится информация о студентах, их поле, группе, уровне образования родителей, типе питания, прохождении подготовительного курса и результатах по математике, чтению и письму.\n",
|
||
"\n",
|
||
"Цель анализа — загрузить данные с помощью pandas, изучить структуру таблицы, выполнить базовый статистический анализ и построить графики для визуального сравнения результатов студентов."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "453fa1dd-608a-4715-b014-06a38d6e9727",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import seaborn as sns\n",
|
||
"from tqdm import tqdm\n",
|
||
"import time"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "b301e0e8-fbc1-4a6f-ab4a-22df31e942c8",
|
||
"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": 27,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"students_df = pd.read_csv(\"data/students_performance.csv\")\n",
|
||
"\n",
|
||
"students_df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "b2b7c4a9-d6fa-4c99-b07b-ccafb600aac4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
|
||
" 'test preparation course', 'math score', 'reading score',\n",
|
||
" 'writing score'],\n",
|
||
" dtype='str')"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"students_df.columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e2ab113-a2d9-4fc2-be28-bce87336a656",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Базовый анализ данных\n",
|
||
"\n",
|
||
"На данном этапе выполняется просмотр структуры таблицы, типов данных, статистического описания и количества пропущенных значений."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "8049bfe4-27cf-44e9-94be-b415dc0436bd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Первые строки таблицы:\n"
|
||
]
|
||
},
|
||
{
|
||
"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 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Информация о таблице:\n",
|
||
"<class 'pandas.DataFrame'>\n",
|
||
"RangeIndex: 1000 entries, 0 to 999\n",
|
||
"Data columns (total 8 columns):\n",
|
||
" # Column Non-Null Count Dtype\n",
|
||
"--- ------ -------------- -----\n",
|
||
" 0 gender 1000 non-null str \n",
|
||
" 1 race/ethnicity 1000 non-null str \n",
|
||
" 2 parental level of education 1000 non-null str \n",
|
||
" 3 lunch 1000 non-null str \n",
|
||
" 4 test preparation course 1000 non-null str \n",
|
||
" 5 math score 1000 non-null int64\n",
|
||
" 6 reading score 1000 non-null int64\n",
|
||
" 7 writing score 1000 non-null int64\n",
|
||
"dtypes: int64(3), str(5)\n",
|
||
"memory usage: 62.6 KB\n",
|
||
"Статистическое описание:\n"
|
||
]
|
||
},
|
||
{
|
||
"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>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>1000.00000</td>\n",
|
||
" <td>1000.000000</td>\n",
|
||
" <td>1000.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\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>15.16308</td>\n",
|
||
" <td>14.600192</td>\n",
|
||
" <td>15.195657</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\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>57.00000</td>\n",
|
||
" <td>59.000000</td>\n",
|
||
" <td>57.750000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\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>77.00000</td>\n",
|
||
" <td>79.000000</td>\n",
|
||
" <td>79.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\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": [
|
||
" math score reading score writing score\n",
|
||
"count 1000.00000 1000.000000 1000.000000\n",
|
||
"mean 66.08900 69.169000 68.054000\n",
|
||
"std 15.16308 14.600192 15.195657\n",
|
||
"min 0.00000 17.000000 10.000000\n",
|
||
"25% 57.00000 59.000000 57.750000\n",
|
||
"50% 66.00000 70.000000 69.000000\n",
|
||
"75% 77.00000 79.000000 79.000000\n",
|
||
"max 100.00000 100.000000 100.000000"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Количество пропущенных значений:\n"
|
||
]
|
||
},
|
||
{
|
||
"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"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"Первые строки таблицы:\")\n",
|
||
"display(students_df.head())\n",
|
||
"\n",
|
||
"print(\"Информация о таблице:\")\n",
|
||
"students_df.info()\n",
|
||
"\n",
|
||
"print(\"Статистическое описание:\")\n",
|
||
"display(students_df.describe())\n",
|
||
"\n",
|
||
"print(\"Количество пропущенных значений:\")\n",
|
||
"display(students_df.isnull().sum())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "af23ae64-e9a7-48b7-a657-49874b0c114b",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Добавление среднего балла\n",
|
||
"\n",
|
||
"Для дальнейшего анализа был добавлен новый столбец average score, который показывает среднее значение результатов по математике, чтению и письму."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "be454756-e447-4cb6-9843-c0e3f3d4ede4",
|
||
"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",
|
||
" </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 \n",
|
||
"\n",
|
||
" average score \n",
|
||
"0 72.666667 \n",
|
||
"1 82.333333 \n",
|
||
"2 92.666667 \n",
|
||
"3 49.333333 \n",
|
||
"4 76.333333 "
|
||
]
|
||
},
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"students_df[\"average score\"] = (\n",
|
||
" students_df[\"math score\"] +\n",
|
||
" students_df[\"reading score\"] +\n",
|
||
" students_df[\"writing score\"]\n",
|
||
") / 3\n",
|
||
"\n",
|
||
"students_df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f697b4f1-61f5-4376-a71c-b3d9defaadf5",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Фильтрация данных\n",
|
||
"\n",
|
||
"Для примера были отобраны студенты, у которых средний балл выше 85."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "981410c2-230b-493e-8156-322fee0b1ea5",
|
||
"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>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>6</th>\n",
|
||
" <td>female</td>\n",
|
||
" <td>group B</td>\n",
|
||
" <td>some college</td>\n",
|
||
" <td>standard</td>\n",
|
||
" <td>completed</td>\n",
|
||
" <td>88</td>\n",
|
||
" <td>95</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>91.666667</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>male</td>\n",
|
||
" <td>group C</td>\n",
|
||
" <td>high school</td>\n",
|
||
" <td>standard</td>\n",
|
||
" <td>none</td>\n",
|
||
" <td>88</td>\n",
|
||
" <td>89</td>\n",
|
||
" <td>86</td>\n",
|
||
" <td>87.666667</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>34</th>\n",
|
||
" <td>male</td>\n",
|
||
" <td>group E</td>\n",
|
||
" <td>some college</td>\n",
|
||
" <td>standard</td>\n",
|
||
" <td>none</td>\n",
|
||
" <td>97</td>\n",
|
||
" <td>87</td>\n",
|
||
" <td>82</td>\n",
|
||
" <td>88.666667</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>94</th>\n",
|
||
" <td>female</td>\n",
|
||
" <td>group B</td>\n",
|
||
" <td>some college</td>\n",
|
||
" <td>standard</td>\n",
|
||
" <td>none</td>\n",
|
||
" <td>79</td>\n",
|
||
" <td>86</td>\n",
|
||
" <td>92</td>\n",
|
||
" <td>85.666667</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" gender race/ethnicity parental level of education lunch \\\n",
|
||
"2 female group B master's degree standard \n",
|
||
"6 female group B some college standard \n",
|
||
"16 male group C high school standard \n",
|
||
"34 male group E some college standard \n",
|
||
"94 female group B some college standard \n",
|
||
"\n",
|
||
" test preparation course math score reading score writing score \\\n",
|
||
"2 none 90 95 93 \n",
|
||
"6 completed 88 95 92 \n",
|
||
"16 none 88 89 86 \n",
|
||
"34 none 97 87 82 \n",
|
||
"94 none 79 86 92 \n",
|
||
"\n",
|
||
" average score \n",
|
||
"2 92.666667 \n",
|
||
"6 91.666667 \n",
|
||
"16 87.666667 \n",
|
||
"34 88.666667 \n",
|
||
"94 85.666667 "
|
||
]
|
||
},
|
||
"execution_count": 31,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"high_score_students = students_df[students_df[\"average score\"] > 85]\n",
|
||
"\n",
|
||
"high_score_students.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "076c2030-2c95-4089-9a9c-dcc4d5e4669f",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Группировка данных\n",
|
||
"\n",
|
||
"Далее данные были сгруппированы по полу студентов для сравнения средних результатов."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "dae33048-48c6-4420-9128-25c554b414ef",
|
||
"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>math score</th>\n",
|
||
" <th>reading score</th>\n",
|
||
" <th>writing score</th>\n",
|
||
" <th>average score</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>gender</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>female</th>\n",
|
||
" <td>63.633205</td>\n",
|
||
" <td>72.608108</td>\n",
|
||
" <td>72.467181</td>\n",
|
||
" <td>69.569498</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>male</th>\n",
|
||
" <td>68.728216</td>\n",
|
||
" <td>65.473029</td>\n",
|
||
" <td>63.311203</td>\n",
|
||
" <td>65.837483</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" math score reading score writing score average score\n",
|
||
"gender \n",
|
||
"female 63.633205 72.608108 72.467181 69.569498\n",
|
||
"male 68.728216 65.473029 63.311203 65.837483"
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"gender_group = students_df.groupby(\"gender\").agg({\n",
|
||
" \"math score\": \"mean\",\n",
|
||
" \"reading score\": \"mean\",\n",
|
||
" \"writing score\": \"mean\",\n",
|
||
" \"average score\": \"mean\"\n",
|
||
"})\n",
|
||
"\n",
|
||
"gender_group"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1652e49b-16c4-45d1-b770-c22846a5f9fc",
|
||
"metadata": {},
|
||
"source": [
|
||
"## График 1. Распределение баллов по математике\n",
|
||
"\n",
|
||
"Гистограмма показывает, как распределяются результаты студентов по математике."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "1bd5f9b8-98ac-4f87-9e9b-2f5e9a8b1c71",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"sns.histplot(students_df[\"math score\"], bins=10, kde=True)\n",
|
||
"plt.title(\"Распределение баллов по математике\")\n",
|
||
"plt.xlabel(\"Балл по математике\")\n",
|
||
"plt.ylabel(\"Количество студентов\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "10536826-d3b3-417f-98d5-d38cc55e51bf",
|
||
"metadata": {},
|
||
"source": [
|
||
"## График 2. Связь результатов по чтению и письму\n",
|
||
"\n",
|
||
"Диаграмма рассеяния позволяет увидеть связь между баллами по чтению и письму."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"id": "ded78249-058f-4ae7-a264-42374263a218",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"sns.scatterplot(\n",
|
||
" data=students_df,\n",
|
||
" x=\"reading score\",\n",
|
||
" y=\"writing score\",\n",
|
||
" hue=\"gender\"\n",
|
||
")\n",
|
||
"plt.title(\"Связь баллов по чтению и письму\")\n",
|
||
"plt.xlabel(\"Балл по чтению\")\n",
|
||
"plt.ylabel(\"Балл по письму\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9d28b2b1-44d2-4d04-b766-1262ccb876c8",
|
||
"metadata": {},
|
||
"source": [
|
||
"## График 3. Сравнение среднего балла по полу\n",
|
||
"\n",
|
||
"Boxplot позволяет сравнить распределение среднего балла между группами студентов."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"id": "9a02b0e2-0479-4d0a-94d3-3c3a2c4e93b6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"sns.boxplot(\n",
|
||
" data=students_df,\n",
|
||
" x=\"gender\",\n",
|
||
" y=\"average score\"\n",
|
||
")\n",
|
||
"plt.title(\"Распределение среднего балла по полу\")\n",
|
||
"plt.xlabel(\"Пол\")\n",
|
||
"plt.ylabel(\"Средний балл\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5f3fbdf8-8d7c-4e3f-8bc9-9a87032a88ef",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Использование tqdm\n",
|
||
"\n",
|
||
"Для демонстрации прогресс-бара была выполнена обработка строк таблицы с использованием tqdm."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "b51c33c4-9fee-4454-846d-d6a1dd5aeb4f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Обработка студентов: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:00<00:00, 19955.68it/s]\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"[{'gender': 'female', 'average score': 72.66666666666667},\n",
|
||
" {'gender': 'female', 'average score': 82.33333333333333},\n",
|
||
" {'gender': 'female', 'average score': 92.66666666666667},\n",
|
||
" {'gender': 'male', 'average score': 49.333333333333336},\n",
|
||
" {'gender': 'male', 'average score': 76.33333333333333}]"
|
||
]
|
||
},
|
||
"execution_count": 36,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"processed_students = []\n",
|
||
"\n",
|
||
"for _, row in tqdm(students_df.iterrows(), total=len(students_df), desc=\"Обработка студентов\"):\n",
|
||
" processed_students.append({\n",
|
||
" \"gender\": row[\"gender\"],\n",
|
||
" \"average score\": row[\"average score\"]\n",
|
||
" })\n",
|
||
"\n",
|
||
"processed_students[:5]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c8f1ed28-4e62-4d85-a40b-2ee22930803b",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Вывод по самостоятельному заданию\n",
|
||
"\n",
|
||
"В самостоятельной части работы был выполнен анализ набора данных StudentsPerformance.csv. Данные были загружены с помощью pandas, после чего была изучена структура таблицы, типы данных, статистические показатели и наличие пропущенных значений.\n",
|
||
"\n",
|
||
"Для анализа был добавлен новый столбец со средним баллом студента. Также была выполнена фильтрация студентов с высоким средним баллом и группировка данных по полу.\n",
|
||
"\n",
|
||
"С помощью seaborn были построены три графика: гистограмма распределения баллов по математике, диаграмма рассеяния результатов по чтению и письму, а также boxplot для сравнения среднего балла по полу. Дополнительно был использован tqdm для отображения прогресса обработки строк таблицы."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"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": "3.14.4"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|