1139 lines
393 KiB
Plaintext
1139 lines
393 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "43d948e5-c6ab-49bc-af02-cce09d448c0a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Первый взгляд на данные:\n",
|
||
" Имя Возраст Баллы\n",
|
||
"0 Анна 21 89\n",
|
||
"1 Борис 22 76\n",
|
||
"2 Виктор 23 95\n",
|
||
"3 Галина 24 82\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 4 entries, 0 to 3\n",
|
||
"Data columns (total 3 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 Имя 4 non-null object\n",
|
||
" 1 Возраст 4 non-null int64 \n",
|
||
" 2 Баллы 4 non-null int64 \n",
|
||
"dtypes: int64(2), object(1)\n",
|
||
"memory usage: 228.0+ bytes\n",
|
||
"None\n",
|
||
" Возраст Баллы\n",
|
||
"count 4.000000 4.000000\n",
|
||
"mean 22.500000 85.500000\n",
|
||
"std 1.290994 8.266398\n",
|
||
"min 21.000000 76.000000\n",
|
||
"25% 21.750000 80.500000\n",
|
||
"50% 22.500000 85.500000\n",
|
||
"75% 23.250000 90.500000\n",
|
||
"max 24.000000 95.000000\n",
|
||
"Имя 0\n",
|
||
"Возраст 0\n",
|
||
"Баллы 0\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
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"source": [
|
||
"import pandas as pd\n",
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||
"\n",
|
||
"# Создадим DataFrame\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"print(\"Первый взгляд на данные:\")\n",
|
||
"print(df.head())\n",
|
||
"print(df.info())\n",
|
||
"print(df.describe())\n",
|
||
"print(df.isnull().sum())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "22288d55-04b9-4e80-8120-2da41b1a32b0",
|
||
"metadata": {},
|
||
"source": [
|
||
"стандартный код, без изменений"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "4d8c96fc-1e83-4218-a96b-81575c28f0d7",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Первый взгляд на данные:\n",
|
||
" Имя Возраст Баллы\n",
|
||
"0 Анна 21 89\n",
|
||
"1 Борис 22 76\n",
|
||
"2 Виктор 23 95\n",
|
||
"3 Галина 24 82\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 4 entries, 0 to 3\n",
|
||
"Data columns (total 3 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 Имя 4 non-null object\n",
|
||
" 1 Возраст 4 non-null int64 \n",
|
||
" 2 Баллы 4 non-null int64 \n",
|
||
"dtypes: int64(2), object(1)\n",
|
||
"memory usage: 228.0+ bytes\n",
|
||
"None\n",
|
||
" Возраст Баллы\n",
|
||
"count 4.000000 4.000000\n",
|
||
"mean 22.500000 85.500000\n",
|
||
"std 1.290994 8.266398\n",
|
||
"min 21.000000 76.000000\n",
|
||
"25% 21.750000 80.500000\n",
|
||
"50% 22.500000 85.500000\n",
|
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"75% 23.250000 90.500000\n",
|
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"max 24.000000 95.000000\n",
|
||
"Имя 0\n",
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||
"Возраст 0\n",
|
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"Баллы 0\n",
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"dtype: int64\n"
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]
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},
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{
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"data": {
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"text/html": [
|
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"<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",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Анна</td>\n",
|
||
" <td>21</td>\n",
|
||
" <td>89</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Борис</td>\n",
|
||
" <td>22</td>\n",
|
||
" <td>76</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Виктор</td>\n",
|
||
" <td>23</td>\n",
|
||
" <td>95</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Галина</td>\n",
|
||
" <td>24</td>\n",
|
||
" <td>82</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Имя Возраст Баллы\n",
|
||
"0 Анна 21 89\n",
|
||
"1 Борис 22 76\n",
|
||
"2 Виктор 23 95\n",
|
||
"3 Галина 24 82"
|
||
]
|
||
},
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# Создадим DataFrame\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"print(\"Первый взгляд на данные:\")\n",
|
||
"print(df.head())\n",
|
||
"print(df.info())\n",
|
||
"print(df.describe())\n",
|
||
"print(df.isnull().sum())\n",
|
||
"df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e2b6ea08-81e6-4e0d-a9b6-be6559349c7e",
|
||
"metadata": {},
|
||
"source": [
|
||
"добавил df в конце кода"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "b1361266-d103-4766-9337-c9afe7c1873f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Первый взгляд на данные:\n",
|
||
" Имя Возраст Баллы\n",
|
||
"0 Анна 21 89\n",
|
||
"1 Борис 22 76\n",
|
||
"2 Виктор 23 95\n",
|
||
"3 Галина 24 82\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 4 entries, 0 to 3\n",
|
||
"Data columns (total 3 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 Имя 4 non-null object\n",
|
||
" 1 Возраст 4 non-null int64 \n",
|
||
" 2 Баллы 4 non-null int64 \n",
|
||
"dtypes: int64(2), object(1)\n",
|
||
"memory usage: 228.0+ bytes\n",
|
||
"None\n",
|
||
" Возраст Баллы\n",
|
||
"count 4.000000 4.000000\n",
|
||
"mean 22.500000 85.500000\n",
|
||
"std 1.290994 8.266398\n",
|
||
"min 21.000000 76.000000\n",
|
||
"25% 21.750000 80.500000\n",
|
||
"50% 22.500000 85.500000\n",
|
||
"75% 23.250000 90.500000\n",
|
||
"max 24.000000 95.000000\n",
|
||
"Имя 0\n",
|
||
"Возраст 0\n",
|
||
"Баллы 0\n",
|
||
"dtype: int64\n",
|
||
" Имя Возраст Баллы Баллы с коэффициентом\n",
|
||
"0 Анна 21 89 97.9\n",
|
||
"1 Борис 22 76 83.6\n",
|
||
"2 Виктор 23 95 104.5\n",
|
||
"3 Галина 24 82 90.2\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# Создадим DataFrame\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"print(\"Первый взгляд на данные:\")\n",
|
||
"print(df.head())\n",
|
||
"print(df.info())\n",
|
||
"print(df.describe())\n",
|
||
"print(df.isnull().sum())\n",
|
||
"\n",
|
||
"# Добавляем новый столбец с вычисляемыми значениями\n",
|
||
"df[\"Баллы с коэффициентом\"] = df[\"Баллы\"] * 1.1\n",
|
||
"\n",
|
||
"print(df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5d658850-1ef4-41cc-81c2-afda9213bd6d",
|
||
"metadata": {},
|
||
"source": [
|
||
"добавил новый столбец с вычисляемыми значениями"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "d27ebb99-d8b7-44b6-9d6f-59062c18eb81",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Первый взгляд на данные:\n",
|
||
" Имя Возраст Баллы Группа\n",
|
||
"0 Анна 21 89 A\n",
|
||
"1 Борис 22 76 B\n",
|
||
"2 Виктор 23 95 A\n",
|
||
"3 Галина 24 82 B\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 4 entries, 0 to 3\n",
|
||
"Data columns (total 4 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 Имя 4 non-null object\n",
|
||
" 1 Возраст 4 non-null int64 \n",
|
||
" 2 Баллы 4 non-null int64 \n",
|
||
" 3 Группа 4 non-null object\n",
|
||
"dtypes: int64(2), object(2)\n",
|
||
"memory usage: 260.0+ bytes\n",
|
||
"None\n",
|
||
" Возраст Баллы\n",
|
||
"count 4.000000 4.000000\n",
|
||
"mean 22.500000 85.500000\n",
|
||
"std 1.290994 8.266398\n",
|
||
"min 21.000000 76.000000\n",
|
||
"25% 21.750000 80.500000\n",
|
||
"50% 22.500000 85.500000\n",
|
||
"75% 23.250000 90.500000\n",
|
||
"max 24.000000 95.000000\n",
|
||
"Имя 0\n",
|
||
"Возраст 0\n",
|
||
"Баллы 0\n",
|
||
"Группа 0\n",
|
||
"dtype: int64\n",
|
||
"\n",
|
||
"Результат группировки и агрегации:\n",
|
||
" Возраст Баллы \n",
|
||
" mean min max sum mean count\n",
|
||
"Группа \n",
|
||
"A 22.0 21 23 184 92.0 2\n",
|
||
"B 23.0 22 24 158 79.0 2\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# Создадим DataFrame\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82],\n",
|
||
" \"Группа\": [\"A\", \"B\", \"A\", \"B\"] # Новый столбец для группировки\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"print(\"Первый взгляд на данные:\")\n",
|
||
"print(df.head())\n",
|
||
"print(df.info())\n",
|
||
"print(df.describe())\n",
|
||
"print(df.isnull().sum())\n",
|
||
"\n",
|
||
"# Группировка по столбцу \"Группа\" и агрегация данных\n",
|
||
"grouped = df.groupby(\"Группа\").agg({\n",
|
||
" \"Возраст\": [\"mean\", \"min\", \"max\"], # Средний, минимальный и максимальный возраст\n",
|
||
" \"Баллы\": [\"sum\", \"mean\", \"count\"] # Сумма, среднее и количество баллов\n",
|
||
"})\n",
|
||
"\n",
|
||
"print(\"\\nРезультат группировки и агрегации:\")\n",
|
||
"print(grouped)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0a2f358c-75f5-4b5a-a8dd-949a72c614de",
|
||
"metadata": {},
|
||
"source": [
|
||
"Применил .groupby() и .agg(), чтобы сгруппировать данные"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "07c7b265-837c-4df7-a7f7-500bf4559183",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Первый взгляд на данные:\n",
|
||
" Имя Возраст Баллы\n",
|
||
"0 Анна 21 89\n",
|
||
"1 Борис 22 76\n",
|
||
"2 Виктор 23 95\n",
|
||
"3 Галина 24 82\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 4 entries, 0 to 3\n",
|
||
"Data columns (total 3 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 Имя 4 non-null object\n",
|
||
" 1 Возраст 4 non-null int64 \n",
|
||
" 2 Баллы 4 non-null int64 \n",
|
||
"dtypes: int64(2), object(1)\n",
|
||
"memory usage: 228.0+ bytes\n",
|
||
"None\n",
|
||
" Возраст Баллы\n",
|
||
"count 4.000000 4.000000\n",
|
||
"mean 22.500000 85.500000\n",
|
||
"std 1.290994 8.266398\n",
|
||
"min 21.000000 76.000000\n",
|
||
"25% 21.750000 80.500000\n",
|
||
"50% 22.500000 85.500000\n",
|
||
"75% 23.250000 90.500000\n",
|
||
"max 24.000000 95.000000\n",
|
||
"Имя 0\n",
|
||
"Возраст 0\n",
|
||
"Баллы 0\n",
|
||
"dtype: int64\n",
|
||
"\n",
|
||
"Отфильтрованные данные (Возраст > 21):\n",
|
||
" Имя Возраст Баллы\n",
|
||
"1 Борис 22 76\n",
|
||
"2 Виктор 23 95\n",
|
||
"3 Галина 24 82\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# Создадим DataFrame\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"print(\"Первый взгляд на данные:\")\n",
|
||
"print(df.head())\n",
|
||
"print(df.info())\n",
|
||
"print(df.describe())\n",
|
||
"print(df.isnull().sum())\n",
|
||
"\n",
|
||
"# Фильтрация записей по условию (Возраст > 21)\n",
|
||
"filtered_df = df[df[\"Возраст\"] > 21]\n",
|
||
"\n",
|
||
"print(\"\\nОтфильтрованные данные (Возраст > 21):\")\n",
|
||
"print(filtered_df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f654990d-8e34-4b92-8bd5-c0724834cc2e",
|
||
"metadata": {},
|
||
"source": [
|
||
"Добавил фильтр по условиям (df[df[\"Возраст\"] > 21])."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "5e5033d4-60df-415b-adbc-25b6db7c1bbc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Одномерный массив:\n",
|
||
"[1 2 3 4 5]\n",
|
||
"Сумма элементов массива: 15\n",
|
||
"Среднее значение: 3.0\n",
|
||
"Медиана: 3.0\n",
|
||
"Стандартное отклонение: 1.4142135623730951\n",
|
||
"\n",
|
||
"Двумерный массив:\n",
|
||
"[[1 2]\n",
|
||
" [3 4]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"# Одномерный массив\n",
|
||
"arr = np.array([1, 2, 3, 4, 5])\n",
|
||
"print(\"Одномерный массив:\")\n",
|
||
"print(arr)\n",
|
||
"print(\"Сумма элементов массива:\", np.sum(arr))\n",
|
||
"print(\"Среднее значение:\", np.mean(arr))\n",
|
||
"print(\"Медиана:\", np.median(arr))\n",
|
||
"print(\"Стандартное отклонение:\", np.std(arr))\n",
|
||
"\n",
|
||
"# Создаем двумерный массив 2x2\n",
|
||
"arr_2d = np.array([[1, 2], [3, 4]])\n",
|
||
"\n",
|
||
"print(\"\\nДвумерный массив:\")\n",
|
||
"print(arr_2d)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "719a4d8a-98a6-4074-a2a2-0de2814c7666",
|
||
"metadata": {},
|
||
"source": [
|
||
"Создал двумерный массив (np.array([[1, 2], [3, 4]]))."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "825af5cf-872c-4853-81fa-9df9b890a374",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Одномерный массив:\n",
|
||
"[1 2 3 4 5]\n",
|
||
"Сумма элементов массива: 15\n",
|
||
"Среднее значение: 3.0\n",
|
||
"Медиана: 3.0\n",
|
||
"Стандартное отклонение: 1.4142135623730951\n",
|
||
"\n",
|
||
"1. Создание массива с np.linspace():\n",
|
||
"linspace(0, 10, 5): [ 0. 2.5 5. 7.5 10. ]\n",
|
||
"\n",
|
||
"2. Генерация случайных чисел с np.random.randn():\n",
|
||
"Случайный массив 3x2:\n",
|
||
" [[ 0.57976378 -0.30290431]\n",
|
||
" [-1.88318021 0.87022824]\n",
|
||
" [-0.59744352 -0.50314146]]\n",
|
||
"\n",
|
||
"3. Матричное умножение с np.dot():\n",
|
||
"Матрица A:\n",
|
||
" [[1 2]\n",
|
||
" [3 4]]\n",
|
||
"Матрица B:\n",
|
||
" [[5 6]\n",
|
||
" [7 8]]\n",
|
||
"Результат умножения A.dot(B):\n",
|
||
" [[19 22]\n",
|
||
" [43 50]]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"# Исходный одномерный массив\n",
|
||
"arr = np.array([1, 2, 3, 4, 5])\n",
|
||
"print(\"Одномерный массив:\")\n",
|
||
"print(arr)\n",
|
||
"print(\"Сумма элементов массива:\", np.sum(arr))\n",
|
||
"print(\"Среднее значение:\", np.mean(arr))\n",
|
||
"print(\"Медиана:\", np.median(arr))\n",
|
||
"print(\"Стандартное отклонение:\", np.std(arr))\n",
|
||
"\n",
|
||
"# 1. Использование np.linspace()\n",
|
||
"print(\"\\n1. Создание массива с np.linspace():\")\n",
|
||
"lin_arr = np.linspace(0, 10, 5) # 5 чисел от 0 до 10 (равномерно распределенных)\n",
|
||
"print(\"linspace(0, 10, 5):\", lin_arr)\n",
|
||
"\n",
|
||
"# 2. Использование np.random.randn()\n",
|
||
"print(\"\\n2. Генерация случайных чисел с np.random.randn():\")\n",
|
||
"rand_arr = np.random.randn(3, 2) # Массив 3x2 со случайными числами из стандартного нормального распределения\n",
|
||
"print(\"Случайный массив 3x2:\\n\", rand_arr)\n",
|
||
"\n",
|
||
"# 3. Использование np.dot() для матричного умножения\n",
|
||
"print(\"\\n3. Матричное умножение с np.dot():\")\n",
|
||
"matrix_a = np.array([[1, 2], [3, 4]])\n",
|
||
"matrix_b = np.array([[5, 6], [7, 8]])\n",
|
||
"dot_product = np.dot(matrix_a, matrix_b)\n",
|
||
"print(\"Матрица A:\\n\", matrix_a)\n",
|
||
"print(\"Матрица B:\\n\", matrix_b)\n",
|
||
"print(\"Результат умножения A.dot(B):\\n\", dot_product)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "74b4b0b0-8b27-4acd-9b16-b9f58add3788",
|
||
"metadata": {},
|
||
"source": [
|
||
"Использовалnp.linspace(), np.random.randn(), np.dot()."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "b2c852ac-8539-4416-978f-5f4002adc7ac",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"x = np.linspace(0, 10, 100)\n",
|
||
"y = np.sin(x)\n",
|
||
"\n",
|
||
"plt.plot(x, y, color='red', label='sin(x)')\n",
|
||
"plt.xlabel(\"X\")\n",
|
||
"plt.ylabel(\"Y\")\n",
|
||
"plt.title(\"График синуса\")\n",
|
||
"plt.legend()\n",
|
||
"plt.grid()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3befe6cd-1c59-4e76-8254-ceea8a1f614d",
|
||
"metadata": {},
|
||
"source": [
|
||
"добавлен красный цвет"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "08c3bed5-88b0-4161-b374-7c401bb30367",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"x = np.linspace(0, 10, 100)\n",
|
||
"y_sin = np.sin(x)\n",
|
||
"y_cos = np.cos(x)\n",
|
||
"\n",
|
||
"# Рисуем оба графика на одном поле\n",
|
||
"plt.plot(x, y_sin, label='sin(x)', color='blue')\n",
|
||
"plt.plot(x, y_cos, label='cos(x)', color='red')\n",
|
||
"\n",
|
||
"# Оформление графика\n",
|
||
"plt.xlabel(\"X\")\n",
|
||
"plt.ylabel(\"Y\")\n",
|
||
"plt.title(\"Графики sin(x) и cos(x)\") # Обновили заголовок\n",
|
||
"plt.legend()\n",
|
||
"plt.grid()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4d94101a-a675-4996-8a2b-79a149cde9c0",
|
||
"metadata": {},
|
||
"source": [
|
||
"Добавление графика с косинусом"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "0725474f-0598-4fc0-9ba5-539748df8c4c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"# Общие данные\n",
|
||
"x = np.linspace(0, 10, 100)\n",
|
||
"y = np.sin(x)\n",
|
||
"\n",
|
||
"# 1. Линейный график (исходный)\n",
|
||
"plt.figure(figsize=(15, 10))\n",
|
||
"\n",
|
||
"plt.subplot(2, 2, 1)\n",
|
||
"plt.plot(x, y, label='sin(x)', color='blue')\n",
|
||
"plt.xlabel(\"X\")\n",
|
||
"plt.ylabel(\"Y\")\n",
|
||
"plt.title(\"1. Линейный график синуса\")\n",
|
||
"plt.legend()\n",
|
||
"plt.grid()\n",
|
||
"\n",
|
||
"# 2. Столбчатая диаграмма (bar)\n",
|
||
"plt.subplot(2, 2, 2)\n",
|
||
"x_bar = np.arange(5)\n",
|
||
"y_bar = np.random.rand(5)\n",
|
||
"plt.bar(x_bar, y_bar, color='green')\n",
|
||
"plt.xlabel(\"Категории\")\n",
|
||
"plt.ylabel(\"Значения\")\n",
|
||
"plt.title(\"2. Столбчатая диаграмма\")\n",
|
||
"plt.grid(axis='y')\n",
|
||
"\n",
|
||
"# 3. Точечный график (scatter)\n",
|
||
"plt.subplot(2, 2, 3)\n",
|
||
"x_scatter = np.random.rand(50) * 10\n",
|
||
"y_scatter = np.sin(x_scatter) + np.random.normal(0, 0.1, 50)\n",
|
||
"plt.scatter(x_scatter, y_scatter, color='red', alpha=0.6)\n",
|
||
"plt.xlabel(\"X\")\n",
|
||
"plt.ylabel(\"sin(X) с шумом\")\n",
|
||
"plt.title(\"3. Точечный график\")\n",
|
||
"plt.grid()\n",
|
||
"\n",
|
||
"# 4. Гистограмма (hist)\n",
|
||
"plt.subplot(2, 2, 4)\n",
|
||
"data = np.random.randn(1000)\n",
|
||
"plt.hist(data, bins=30, color='purple', alpha=0.7)\n",
|
||
"plt.xlabel(\"Значения\")\n",
|
||
"plt.ylabel(\"Частота\")\n",
|
||
"plt.title(\"4. Гистограмма распределения\")\n",
|
||
"plt.grid(axis='y')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "63385d43-70f5-484d-b5a7-0a771fab4181",
|
||
"metadata": {},
|
||
"source": [
|
||
"Использовалbar, scatter, hist."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "caa56920-85bd-4cf0-be03-6997c68a3955",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 1500x500 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82],\n",
|
||
" \"Категория\": [\"A\", \"B\", \"A\", \"B\"]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(15, 5))\n",
|
||
"\n",
|
||
"plt.subplot(1, 3, 1)\n",
|
||
"sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
|
||
"\n",
|
||
"plt.subplot(1, 3, 2)\n",
|
||
"sns.histplot(data=df, x=\"Баллы\", bins=10, kde=True, color='skyblue')\n",
|
||
"\n",
|
||
"plt.subplot(1, 3, 3)\n",
|
||
"sns.scatterplot(data=df, x=\"Возраст\", y=\"Баллы\", hue=\"Категория\", palette=[\"red\", \"blue\"], s=100)\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "732ae00c-fd19-4926-9afb-99859a9a5958",
|
||
"metadata": {},
|
||
"source": [
|
||
"Построил histplot, scatterplot"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "2068340c-9012-4e7d-9291-0e1becb9d49c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 500x500 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82],\n",
|
||
" \"Категория\": [\"A\", \"B\", \"A\", \"B\"]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"# Boxplot\n",
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Pairplot\n",
|
||
"sns.pairplot(df)\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Heatmap\n",
|
||
"plt.figure(figsize=(8, 5))\n",
|
||
"sns.heatmap(df.select_dtypes(include='number').corr(), annot=True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1a1d75cb-3a53-4936-a828-d1cafc3da15f",
|
||
"metadata": {},
|
||
"source": [
|
||
"Добавил sns.pairplot(df), sns.heatmap(df.corr(), annot=True"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "82c71eb9-2f4a-4abb-9e2c-1174530a050a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Обработка данных: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:02<00:00, 1.99it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Результат обработки:\n",
|
||
" Имя Возраст Баллы\n",
|
||
"0 Анна 22 89\n",
|
||
"1 Борис 23 76\n",
|
||
"2 Виктор 24 95\n",
|
||
"3 Галина 25 82\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from tqdm import tqdm\n",
|
||
"import pandas as pd\n",
|
||
"import time\n",
|
||
"\n",
|
||
"# Создаем DataFrame\n",
|
||
"data = {\n",
|
||
" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
|
||
" \"Возраст\": [21, 22, 23, 24],\n",
|
||
" \"Баллы\": [89, 76, 95, 82]\n",
|
||
"}\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"# Обработка данных с прогресс-баром\n",
|
||
"for index, row in tqdm(df.iterrows(), total=len(df), desc=\"Обработка данных\"):\n",
|
||
" # Здесь может быть ваша обработка каждой строки\n",
|
||
" time.sleep(0.5) # Имитация долгой обработки\n",
|
||
" # Пример: увеличение возраста на 1\n",
|
||
" df.at[index, 'Возраст'] = row['Возраст'] + 1\n",
|
||
"\n",
|
||
"print(\"\\nРезультат обработки:\")\n",
|
||
"print(df)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f578d83f-2972-463c-bd61-81cded8eb8c4",
|
||
"metadata": {},
|
||
"source": [
|
||
"Использовал tqdm для обработки данных (tqdm(df.iterrows()))."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "21a2f3cf-a6d2-4af7-862c-0a4806063876",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Загрузка данных: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 92.97it/s]\n",
|
||
"Обработка: 100%|\u001b[32m>>>>>>>>>>>>>>>>>>>>\u001b[0m| 100/100 [00:01<00:00, 93.91it/s]\u001b[32m \u001b[0m\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from tqdm import tqdm\n",
|
||
"import time\n",
|
||
"\n",
|
||
"# Базовый вариант с описанием\n",
|
||
"for i in tqdm(range(100), desc='Загрузка данных'):\n",
|
||
" time.sleep(0.01)\n",
|
||
"\n",
|
||
"# С дополнительными параметрами стилизации\n",
|
||
"for i in tqdm(range(100),\n",
|
||
" desc='Обработка',\n",
|
||
" bar_format='{l_bar}{bar:20}{r_bar}{bar:-20b}',\n",
|
||
" colour='green',\n",
|
||
" ncols=80,\n",
|
||
" ascii='->'):\n",
|
||
" time.sleep(0.01)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ac7229ec-c541-48a2-a8c9-c01fa9d8aca2",
|
||
"metadata": {},
|
||
"source": [
|
||
"Добавил кастомные стилизации (tqdm(range(100), desc='Загрузка'))."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "d3c7fc47-ad57-4bee-bea6-b3f4e628eb79",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"=== Информация о данных ===\n",
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 20 entries, 0 to 19\n",
|
||
"Data columns (total 10 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 class 20 non-null object\n",
|
||
" 1 image_count 20 non-null int64 \n",
|
||
" 2 avg_width 20 non-null int64 \n",
|
||
" 3 avg_height 20 non-null int64 \n",
|
||
" 4 min_width 20 non-null int64 \n",
|
||
" 5 min_height 20 non-null int64 \n",
|
||
" 6 max_width 20 non-null int64 \n",
|
||
" 7 max_height 20 non-null int64 \n",
|
||
" 8 formats 20 non-null object\n",
|
||
" 9 corrupt_files 20 non-null int64 \n",
|
||
"dtypes: int64(8), object(2)\n",
|
||
"memory usage: 1.7+ KB\n",
|
||
"None\n",
|
||
"\n",
|
||
"=== Описательная статистика ===\n",
|
||
" image_count avg_width avg_height min_width min_height max_width \\\n",
|
||
"count 20.000000 20.000000 20.000000 20.000000 20.000000 20.0 \n",
|
||
"mean 185.550000 138.100000 125.100000 82.000000 68.700000 162.0 \n",
|
||
"std 16.391509 6.356927 4.037978 10.682007 11.220751 0.0 \n",
|
||
"min 158.000000 129.000000 119.000000 54.000000 50.000000 162.0 \n",
|
||
"25% 174.750000 133.000000 121.000000 78.000000 62.000000 162.0 \n",
|
||
"50% 195.500000 137.500000 125.000000 82.500000 68.000000 162.0 \n",
|
||
"75% 200.000000 144.250000 128.250000 91.250000 77.500000 162.0 \n",
|
||
"max 200.000000 147.000000 131.000000 93.000000 90.000000 162.0 \n",
|
||
"\n",
|
||
" max_height corrupt_files \n",
|
||
"count 20.0 20.0 \n",
|
||
"mean 140.0 0.0 \n",
|
||
"std 0.0 0.0 \n",
|
||
"min 140.0 0.0 \n",
|
||
"25% 140.0 0.0 \n",
|
||
"50% 140.0 0.0 \n",
|
||
"75% 140.0 0.0 \n",
|
||
"max 140.0 0.0 \n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1500x1000 with 3 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Обработка данных с прогресс-баром:\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Анализ классов: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 19.53it/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import pandas as pd\n",
|
||
"import seaborn as sns\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"from tqdm import tqdm\n",
|
||
"\n",
|
||
"# 1. Загрузка данных\n",
|
||
"df = pd.read_csv('dataset_stats.csv')\n",
|
||
"\n",
|
||
"# 2. Базовый анализ\n",
|
||
"print(\"=== Информация о данных ===\")\n",
|
||
"print(df.info())\n",
|
||
"print(\"\\n=== Описательная статистика ===\")\n",
|
||
"print(df.describe())\n",
|
||
"\n",
|
||
"# 3. Визуализация данных\n",
|
||
"plt.figure(figsize=(15, 10))\n",
|
||
"\n",
|
||
"# Гистограмма распределения количества изображений\n",
|
||
"plt.subplot(2, 2, 1)\n",
|
||
"sns.histplot(data=df, x='image_count', bins=15, kde=True)\n",
|
||
"plt.title('Распределение количества изображений по классам')\n",
|
||
"\n",
|
||
"# Scatter plot: средняя ширина vs средняя высота\n",
|
||
"plt.subplot(2, 2, 2)\n",
|
||
"sns.scatterplot(data=df, x='avg_width', y='avg_height', s=100)\n",
|
||
"plt.title('Средняя ширина vs средняя высота изображений')\n",
|
||
"\n",
|
||
"# Boxplot размеров изображений\n",
|
||
"plt.subplot(2, 2, 3)\n",
|
||
"melted_df = df.melt(id_vars=['class'], \n",
|
||
" value_vars=['avg_width', 'avg_height'],\n",
|
||
" var_name='dimension',\n",
|
||
" value_name='pixels')\n",
|
||
"sns.boxplot(data=melted_df, x='dimension', y='pixels')\n",
|
||
"plt.title('Распределение средних размеров изображений')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# 4. Пример использования tqdm с данными\n",
|
||
"print(\"\\nОбработка данных с прогресс-баром:\")\n",
|
||
"for idx, row in tqdm(df.iterrows(), total=len(df), desc=\"Анализ классов\"):\n",
|
||
" # Имитация обработки данных\n",
|
||
" time.sleep(0.05)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "a4675194-683f-4625-8664-45d2a3bf7139",
|
||
"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": "3.13.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|