.idea/1xz.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/.venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.13 (1xz)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.13 (2xz)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.13 (PythonProject)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13 (1xz)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13 (2xz)" project-jdk-type="Python SDK" />
 </project>

.ipynb_checkpoints/drygoe-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/main-checkpoint.py

@@ -0,0 +1,16 @@
+# 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/

.ipynb_checkpoints/matplotlib-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/numpy-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/pandas-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/seaborn-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/tqdm-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

clothing_sizes.csv

@@ -0,0 +1,21 @@
+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

numpy.ipynb

@@ -0,0 +1,109 @@
+{
+ "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",
+   "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
+}

pandas.ipynb

@@ -0,0 +1,200 @@
+{
+ "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
+}

tqdm.ipynb

@@ -0,0 +1,144 @@
+{
+ "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": {},
+   "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
+}