diff --git a/matplotlib.ipynb b/matplotlib.ipynb new file mode 100644 index 0000000..ada2918 --- /dev/null +++ b/matplotlib.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "89d44664-3809-4ef2-a2e1-ad5a719c4880", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "x = np.linspace(0, 10, 100)\n", + "y1 = np.sin(x)\n", + "y2 = np.cos(x)\n", + "\n", + "# Рисуем синус красным цветом\n", + "plt.plot(x, y1, label='sin(x)', color='red') \n", + "\n", + "# Рисуем косинус синим цветом (второй график)\n", + "plt.plot(x, y2, label='cos(x)', color='blue', linestyle='--') \n", + "\n", + "plt.xlabel(\"X\")\n", + "plt.ylabel(\"Y\")\n", + "plt.title(\"График синуса и косинуса\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "33ea6b40-e74b-43e5-8c0a-e909a23c0810", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "plt.figure(figsize=(10, 3))\n", + "\n", + "# 1. Точечный график (Scatter)\n", + "plt.subplot(131)\n", + "plt.scatter(np.random.rand(20), np.random.rand(20), color='green')\n", + "plt.title(\"Scatter\")\n", + "\n", + "# 2. Столбчатая диаграмма (Bar)\n", + "plt.subplot(132)\n", + "plt.bar(['A', 'B', 'C'], [5, 10, 7]) # Добавили высоту столбиков [5, 10, 7]\n", + "plt.title(\"Bar\")\n", + "\n", + "# 3. Гистограмма (Hist)\n", + "plt.subplot(133)\n", + "data = np.random.randn(1000)\n", + "plt.hist(data, bins=20, color='orange')\n", + "plt.title(\"Histogram\")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "728fb98e-2555-44c2-9ed9-593f9dad13bc", + "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 +} diff --git a/numpy.ipynb b/numpy.ipynb new file mode 100644 index 0000000..2ee9db5 --- /dev/null +++ b/numpy.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a2f486bd-5b87-4e0f-9327-2ba680c00ebb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Сумма элементов массива: 15\n", + "Среднее значение: 3.0\n", + "Медиана: 3.0\n", + "Стандартное отклонение: 1.4142135623730951\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "arr = np.array([1, 2, 3, 4, 5])\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" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "896a8a44-ea62-4a5c-97ba-d1d4edc1d694", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Двумерный массив (матрица):\n", + "[[1 2]\n", + " [3 4]]\n" + ] + } + ], + "source": [ + "matrix = np.array([[1, 2], [3, 4]])\n", + "print(\"Двумерный массив (матрица):\")\n", + "print(matrix)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ce9bcbd6-a927-4e29-9206-32172f24299f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Массив через linspace: [ 0. 1.11111111 2.22222222 3.33333333 4.44444444 5.55555556\n", + " 6.66666667 7.77777778 8.88888889 10. ]\n", + "\n", + "Случайные числа:\n", + " [[-0.46721747 0.42148654]\n", + " [ 1.34381602 0.67923817]]\n", + "\n", + "Результат умножения векторов (dot): 11\n" + ] + } + ], + "source": [ + "# 1. Создаем 10 чисел от 0 до 10 с равным шагом\n", + "line = np.linspace(0, 10, 10)\n", + "print(\"Массив через linspace:\", line)\n", + "\n", + "# 2. Генерируем случайные числа (матрица 2x2)\n", + "rand_arr = np.random.randn(2, 2)\n", + "print(\"\\nСлучайные числа:\\n\", rand_arr)\n", + "\n", + "# 3. Скалярное произведение векторов (np.dot)\n", + "v1 = np.array([1, 2])\n", + "v2 = np.array([3, 4])\n", + "result = np.dot(v1, v2) # 1*3 + 2*4 = 11\n", + "print(\"\\nРезультат умножения векторов (dot):\", result)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1759f664-5a9e-46fc-9e16-1c7f935f465e", + "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 +} diff --git a/pandas.ipynb b/pandas.ipynb new file mode 100644 index 0000000..25204a6 --- /dev/null +++ b/pandas.ipynb @@ -0,0 +1,409 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "3013204a-5530-4de5-9362-c91dcf217fe8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Техническая информация:\n", + "\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 str \n", + " 1 Возраст 4 non-null int64\n", + " 2 Баллы 4 non-null int64\n", + "dtypes: int64(2), str(1)\n", + "memory usage: 228.0 bytes\n", + "None\n" + ] + } + ], + "source": [ + "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", + "}\n", + "df = pd.DataFrame(data)\n", + "\n", + "print(\"Техническая информация:\")\n", + "print(df.info())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b6287eb3-dcbe-4015-b86d-a4a8f42cbd03", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ИмяВозрастБаллы
0Анна2189
1Борис2276
2Виктор2395
3Галина2482
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ИмяВозрастБаллыНовый столбец
0Анна218997.9
1Борис227683.6
2Виктор2395104.5
3Галина248290.2
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" + ], + "text/plain": [ + " Имя Возраст Баллы Новый столбец\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" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n", + "df # Выводим результат, чтобы проверить\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5768c86e-9f44-466f-a23d-41465cfa6807", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e7738859-8f4f-44dc-9afc-a0dcf195b7d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Баллы
Имя
Анна89.0
Борис76.0
Виктор95.0
Галина82.0
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" + ], + "text/plain": [ + " Баллы\n", + "Имя \n", + "Анна 89.0\n", + "Борис 76.0\n", + "Виктор 95.0\n", + "Галина 82.0" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Посчитаем средний балл (в данном случае он будет равен самому баллу)\n", + "grouped = df.groupby(\"Имя\").agg({\"Баллы\": \"mean\"})\n", + "grouped\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b20b87a-4a84-4fc7-8ff8-73853ece191c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "155692d0-03dc-4802-b976-f422eb01b8c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ИмяВозрастБаллыНовый столбец
1Борис227683.6
2Виктор2395104.5
3Галина248290.2
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" + ], + "text/plain": [ + " Имя Возраст Баллы Новый столбец\n", + "1 Борис 22 76 83.6\n", + "2 Виктор 23 95 104.5\n", + "3 Галина 24 82 90.2" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_df = df[df[\"Возраст\"] > 21]\n", + "filtered_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4a5a6b8-fa2e-448e-8584-577f9134c876", + "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 +} diff --git a/seaborn.ipynb b/seaborn.ipynb new file mode 100644 index 0000000..855b0c6 --- /dev/null +++ b/seaborn.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "id": "7b7b699b-0841-478f-9252-32a2ee79852c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# 1. Подготовка данных (если вдруг таблица стерлась)\n", + "data = {\n", + " \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\", \"Дмитрий\", \"Елена\"],\n", + " \"Возраст\": [21, 22, 23, 24, 21, 23],\n", + " \"Баллы\": [89, 76, 95, 82, 90, 85],\n", + " \"Категория\": [\"A\", \"B\", \"A\", \"B\", \"A\", \"B\"]\n", + "}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Устанавливаем стиль графиков\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "# 2. Создаем фигуру с двумя графиками (1 строка, 2 колонки)\n", + "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "# --- Первый график: Гистограмма баллов ---\n", + "sns.histplot(df[\"Баллы\"], kde=True, color=\"skyblue\", ax=ax[0])\n", + "ax[0].set_title(\"Распределение баллов (Histplot)\") # Исправили опечатку здесь\n", + "\n", + "# --- Второй график: Связь возраста и баллов ---\n", + "sns.scatterplot(x=\"Возраст\", y=\"Баллы\", hue=\"Категория\", style=\"Категория\", s=100, data=df, ax=ax[1])\n", + "ax[1].set_title(\"Связь возраста и баллов (Scatterplot)\") # И здесь\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# --- 3. Тепловая карта корреляции (Heatmap) ---\n", + "plt.figure(figsize=(6, 4))\n", + "# Выбираем только числовые колонки, чтобы корреляция не выдавала ошибку\n", + "numeric_df = df.select_dtypes(include=[np.number]) \n", + "sns.heatmap(numeric_df.corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n", + "plt.title(\"Корреляция числовых признаков\")\n", + "plt.show()\n", + "\n", + "# --- 4. Обзор всех зависимостей (Pairplot) ---\n", + "sns.pairplot(df, hue=\"Категория\", palette=\"husl\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7776c6f-fa66-4526-8b46-daf3f8c09616", + "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 +} diff --git a/tqdm.ipynb b/tqdm.ipynb new file mode 100644 index 0000000..9bb56a6 --- /dev/null +++ b/tqdm.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "bd70558d-d516-46a7-956d-d85685f46b96", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Простой процесс:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Базовая загрузка: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 93.30it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Обработка строк таблицы:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Анализ данных: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00, 3.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Стилизованная загрузка:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Сохранение отчета: 100%|████████████████████| 100/100 [00:02<00:00, 48.43it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Все процессы завершены успешно!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "import time\n", + "import pandas as pd\n", + "\n", + "# 0. Создаем данные (чтобы не было ошибки \"df is not defined\")\n", + "data = {\n", + " \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\", \"Дмитрий\", \"Елена\"],\n", + " \"Возраст\": [21, 22, 23, 24, 25, 26],\n", + " \"Баллы\": [89, 76, 95, 82, 90, 88]\n", + "}\n", + "df = pd.DataFrame(data)\n", + "\n", + "# 1. Базовый прогресс-бар\n", + "print(\"Простой процесс:\")\n", + "for i in tqdm(range(100), desc='Базовая загрузка'):\n", + " time.sleep(0.01)\n", + "\n", + "# 2. Использование tqdm для обработки данных таблицы\n", + "print(\"\\nОбработка строк таблицы:\")\n", + "# Здесь добавили .shape[0], чтобы tqdm знал общее количество строк\n", + "for index, row in tqdm(df.iterrows(), total=df.shape[0], desc='Анализ данных'):\n", + " time.sleep(0.3) # Симулируем обработку каждой строки\n", + "\n", + "# 3. Кастомная стилизация\n", + "print(\"\\nСтилизованная загрузка:\")\n", + "for i in tqdm(range(100), desc='Сохранение отчета', bar_format='{l_bar}{bar:20}{r_bar}'):\n", + " time.sleep(0.02)\n", + "\n", + "print(\"\\nВсе процессы завершены успешно!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1beae766-f7ad-4e74-b6fa-a44ca8672667", + "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 +}