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17 changed files with 2 additions and 1117 deletions

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.idea/1xz.iml generated
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<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/.venv" />
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.idea/misc.xml generated
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<component name="Black">
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</project>

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# This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
def print_hi(name):
# Use a breakpoint in the code line below to debug your script.
print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint.
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
print_hi('PyCharm')
# See PyCharm help at https://www.jetbrains.com/help/pycharm/

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id,height,weight,age,chest_size,waist_size,hip_size,size,brand,category,price,rating,purchase_count
1,165,55,25,85,68,92,S,Zara,Dress,45.99,4.5,120
2,170,65,30,95,78,100,M,H&M,Jeans,59.99,4.2,85
3,175,75,28,100,85,105,L,Adidas,T-shirt,29.99,4.7,200
4,160,50,22,80,65,88,XS,Mango,Blouse,39.99,4.3,95
5,180,85,35,110,92,110,XL,Nike,Jacket,89.99,4.6,150
6,168,60,27,88,72,94,S,Uniqlo,Sweater,49.99,4.4,110
7,172,70,32,98,82,102,M,Zara,Pants,54.99,4.1,75
8,178,80,29,105,88,108,L,H&M,Hoodie,44.99,4.5,130
9,162,52,24,82,66,90,XS,Mango,Dress,64.99,4.8,90
10,175,72,31,102,86,106,L,Adidas,Shorts,34.99,4.3,105
11,167,58,26,87,70,93,S,Uniqlo,T-shirt,24.99,4.6,180
12,173,68,33,96,80,101,M,Nike,Leggings,39.99,4.4,95
13,182,90,36,112,95,112,XXL,Puma,Jacket,99.99,4.2,65
14,158,48,21,78,63,86,XXS,Zara,Blouse,42.99,4.7,88
15,169,62,29,90,74,96,S,H&M,Jeans,55.99,4.3,115
16,176,76,34,103,87,107,L,Mango,Coat,129.99,4.5,70
17,164,54,23,84,67,91,S,Adidas,Sneakers,79.99,4.8,220
18,171,66,30,94,77,99,M,Uniqlo,Polo,34.99,4.4,100
19,177,78,28,104,89,109,L,Nike,Tracksuit,89.99,4.6,85
20,163,53,25,83,68,90,XS,Zara,Skirt,37.99,4.2,92
1 id height weight age chest_size waist_size hip_size size brand category price rating purchase_count
2 1 165 55 25 85 68 92 S Zara Dress 45.99 4.5 120
3 2 170 65 30 95 78 100 M H&M Jeans 59.99 4.2 85
4 3 175 75 28 100 85 105 L Adidas T-shirt 29.99 4.7 200
5 4 160 50 22 80 65 88 XS Mango Blouse 39.99 4.3 95
6 5 180 85 35 110 92 110 XL Nike Jacket 89.99 4.6 150
7 6 168 60 27 88 72 94 S Uniqlo Sweater 49.99 4.4 110
8 7 172 70 32 98 82 102 M Zara Pants 54.99 4.1 75
9 8 178 80 29 105 88 108 L H&M Hoodie 44.99 4.5 130
10 9 162 52 24 82 66 90 XS Mango Dress 64.99 4.8 90
11 10 175 72 31 102 86 106 L Adidas Shorts 34.99 4.3 105
12 11 167 58 26 87 70 93 S Uniqlo T-shirt 24.99 4.6 180
13 12 173 68 33 96 80 101 M Nike Leggings 39.99 4.4 95
14 13 182 90 36 112 95 112 XXL Puma Jacket 99.99 4.2 65
15 14 158 48 21 78 63 86 XXS Zara Blouse 42.99 4.7 88
16 15 169 62 29 90 74 96 S H&M Jeans 55.99 4.3 115
17 16 176 76 34 103 87 107 L Mango Coat 129.99 4.5 70
18 17 164 54 23 84 67 91 S Adidas Sneakers 79.99 4.8 220
19 18 171 66 30 94 77 99 M Uniqlo Polo 34.99 4.4 100
20 19 177 78 28 104 89 109 L Nike Tracksuit 89.99 4.6 85
21 20 163 53 25 83 68 90 XS Zara Skirt 37.99 4.2 92

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "3af2cc9d-8792-4968-972a-cdfed0d85dce",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Моя матрица 2x2:\n",
"[[ 5 7]\n",
" [ 9 10]]\n",
"\n",
"Тестирую linspace:\n",
"Массив от 0 до 10 с 5 элементами:\n",
"[ 0. 2.5 5. 7.5 10. ]\n",
"\n",
"Случайные числа из randn:\n",
"[ 0.16121834 0.8534192 -1.17185391]\n",
"\n",
"Умножаю матрицу на саму себя через dot:\n",
"Результат:\n",
"[[ 88 105]\n",
" [135 163]]\n",
"\n",
"Сумма всех элементов матрицы:\n",
"31\n",
"Среднее арифметическое:\n",
"7.75\n"
]
},
{
"data": {
"text/plain": [
"array([[ 5, 7],\n",
" [ 9, 10]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"matrix = np.array([[5, 7], [9, 10]])\n",
"\n",
"print(\"Моя матрица 2x2:\")\n",
"print(matrix)\n",
"\n",
"print(\"\\nТестирую linspace:\")\n",
"arr = np.linspace(0, 10, 5)\n",
"print(\"Массив от 0 до 10 с 5 элементами:\")\n",
"print(arr)\n",
"\n",
"print(\"\\nСлучайные числа из randn:\")\n",
"random_arr = np.random.randn(3)\n",
"print(random_arr)\n",
"\n",
"print(\"\\nУмножаю матрицу на саму себя через dot:\")\n",
"result = np.dot(matrix, matrix)\n",
"print(\"Результат:\")\n",
"print(result)\n",
"\n",
"print(\"\\nСумма всех элементов матрицы:\")\n",
"summa = np.sum(matrix)\n",
"print(summa)\n",
"\n",
"print(\"Среднее арифметическое:\")\n",
"srednee = np.mean(matrix)\n",
"print(srednee)\n",
"\n",
"matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2bbf5089-c335-4e5a-8c8f-5b7858176c6d",
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{
"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",
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" 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": {},
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"cell_type": "code",
"execution_count": null,
"id": "f808c922-f97c-4d19-aab9-5447d932cc71",
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},
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "1b768852-5883-4ca4-86e7-d4a93c9a1a55",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Таблица создана:\n",
" ID Data\n",
"0 0 -1.082938\n",
"1 1 -0.374716\n",
"2 2 0.609893\n",
"3 3 0.032177\n",
"4 4 1.138623\n",
"\n",
"Запускаю цикл с прогресс-баром...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Загрузка: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 19.56it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Первый цикл готов!\n",
"\n",
"Обрабатываю строки таблицы...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Обработка: 100it [00:00, 20892.13it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Обработано строк: 100\n",
"\n",
"Запускаю второй прогресс-бар...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Вычисления: 100%|\u001b[34m████████████████████████████████████████████████████████████████████████████████████████████████████████\u001b[0m| 20/20 [00:02<00:00, 9.86it/s]\u001b[0m"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Все вычисления завершены!\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from tqdm import tqdm\n",
"import time\n",
"\n",
"df = pd.DataFrame({\n",
" 'ID': range(100),\n",
" 'Data': np.random.randn(100)\n",
"})\n",
"\n",
"print(\"Таблица создана:\")\n",
"print(df.head())\n",
"\n",
"print(\"\\nЗапускаю цикл с прогресс-баром...\")\n",
"for i in tqdm(range(50), desc='Загрузка'):\n",
" time.sleep(0.05)\n",
"\n",
"print(\"Первый цикл готов!\")\n",
"\n",
"print(\"\\nОбрабатываю строки таблицы...\")\n",
"schetchik = 0\n",
"for index, row in tqdm(df.iterrows(), desc=\"Обработка\"):\n",
" vremya = row['Data'] ** 2\n",
" schetchik = schetchik + 1\n",
"\n",
"print(f\"Обработано строк: {schetchik}\")\n",
"\n",
"print(\"\\nЗапускаю второй прогресс-бар...\")\n",
"for i in tqdm(range(20), desc='Вычисления', colour='blue'):\n",
" result = i * 2\n",
" time.sleep(0.1)\n",
"\n",
"print(\"Все вычисления завершены!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe6502c6-7248-4a62-9d1c-38fd51c85549",
"metadata": {},
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"source": []
}
],
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