neznayhz/pandas.ipynb
2026-04-29 22:47:04 +03:00

201 lines
6.5 KiB
Plaintext
Raw Permalink Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

{
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"id": "e49a4fbc-f85f-47c0-b3a0-4af25468faa3",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Основная таблица с бонусами:\n",
" Имя Возраст Баллы Результат с бонусом Категория\n",
"0 Анна 21 89 102.35 Младше\n",
"1 Борис 22 76 87.40 Младше\n",
"2 Виктор 23 95 109.25 Старше\n",
"3 Галина 24 82 94.30 Старше\n",
"4 Дмитрий 21 91 104.65 Младше\n",
"\n",
"Статистика по группам:\n",
" Баллы Имя\n",
" mean max min count\n",
"Категория \n",
"Младше 85.33 91 76 3\n",
"Старше 88.50 95 82 2\n",
"\n",
"Отфильтрованные студенты:\n",
" Имя Возраст Баллы Результат с бонусом Категория\n",
"2 Виктор 23 95 109.25 Старше\n",
"4 Дмитрий 21 91 104.65 Младше\n",
"0 Анна 21 89 102.35 Младше\n",
"3 Галина 24 82 94.30 Старше\n",
"1 Борис 22 76 87.40 Младше\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>Имя</th>\n",
" <th>Возраст</th>\n",
" <th>Баллы</th>\n",
" <th>Результат с бонусом</th>\n",
" <th>Категория</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Анна</td>\n",
" <td>21</td>\n",
" <td>89</td>\n",
" <td>102.35</td>\n",
" <td>Младше</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Борис</td>\n",
" <td>22</td>\n",
" <td>76</td>\n",
" <td>87.40</td>\n",
" <td>Младше</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Виктор</td>\n",
" <td>23</td>\n",
" <td>95</td>\n",
" <td>109.25</td>\n",
" <td>Старше</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Галина</td>\n",
" <td>24</td>\n",
" <td>82</td>\n",
" <td>94.30</td>\n",
" <td>Старше</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Дмитрий</td>\n",
" <td>21</td>\n",
" <td>91</td>\n",
" <td>104.65</td>\n",
" <td>Младше</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Имя Возраст Баллы Результат с бонусом Категория\n",
"0 Анна 21 89 102.35 Младше\n",
"1 Борис 22 76 87.40 Младше\n",
"2 Виктор 23 95 109.25 Старше\n",
"3 Галина 24 82 94.30 Старше\n",
"4 Дмитрий 21 91 104.65 Младше"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"students_info = {\n",
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\", \"Дмитрий\"],\n",
" \"Возраст\": [21, 22, 23, 24, 21],\n",
" \"Баллы\": [89, 76, 95, 82, 91]\n",
"}\n",
"df = pd.DataFrame(students_info)\n",
"\n",
"df[\"Результат с бонусом\"] = df[\"Баллы\"].apply(lambda x: round(x * 1.15, 2))\n",
"\n",
"df[\"Категория\"] = df[\"Возраст\"].apply(lambda age: \"Младше\" if age < 23 else \"Старше\")\n",
"\n",
"grouped_stats = df.groupby(\"Категория\").agg({\n",
" \"Баллы\": [\"mean\", \"max\", \"min\"],\n",
" \"Имя\": \"count\"\n",
"}).round(2)\n",
"\n",
"filtered_df = df[(df[\"Возраст\"] > 21) | (df[\"Баллы\"] > 80)]\n",
"\n",
"filtered_df = filtered_df.sort_values(\"Баллы\", ascending=False)\n",
"\n",
"print(\"Основная таблица с бонусами:\")\n",
"print(df)\n",
"\n",
"print(\"\\nСтатистика по группам:\")\n",
"print(grouped_stats)\n",
"\n",
"print(\"\\nОтфильтрованные студенты:\")\n",
"print(filtered_df)\n",
"\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3edb05fc-37ae-44df-b4a2-9abdc9c8f541",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f808c922-f97c-4d19-aab9-5447d932cc71",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}