Добавлен анализ с визуализацией
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week2_analysis.ipynb
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153
week2_analysis.ipynb
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6ea73c34-1679-48e5-a566-8b1f4cea9652",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Анализ данных - Неделя 2\n",
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"**Автор:** Александр Писцов \n",
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"Этот блокнот содержит базовый анализ с pandas и matplotlib."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4b73c60f-cd31-402a-b3a8-97e9fa0eacb7",
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'pandas'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# Создаем таблицу\u001b[39;00m\n\u001b[32m 4\u001b[39m data = {\n\u001b[32m 5\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mИмя\u001b[39m\u001b[33m\"\u001b[39m: [\u001b[33m\"\u001b[39m\u001b[33mАнна\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mБорис\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mВиктор\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mГалина\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m 6\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mВозраст\u001b[39m\u001b[33m\"\u001b[39m: [\u001b[32m21\u001b[39m, \u001b[32m22\u001b[39m, \u001b[32m23\u001b[39m, \u001b[32m24\u001b[39m],\n\u001b[32m 7\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mБаллы\u001b[39m\u001b[33m\"\u001b[39m: [\u001b[32m89\u001b[39m, \u001b[32m76\u001b[39m, \u001b[32m95\u001b[39m, \u001b[32m82\u001b[39m]\n\u001b[32m 8\u001b[39m }\n",
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"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'pandas'"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Создаем таблицу\n",
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"data = {\n",
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" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
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" \"Возраст\": [21, 22, 23, 24],\n",
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" \"Баллы\": [89, 76, 95, 82]\n",
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"}\n",
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"df = pd.DataFrame(data)\n",
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"\n",
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"# Добавляем столбец\n",
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"df[\"Бонусные баллы\"] = df[\"Баллы\"] * 1.1\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c2e25c6c-bbda-4bb7-8b27-56579b481174",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Произведение матриц:\n",
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" [[ 7 10]\n",
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" [15 22]]\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"\n",
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"# Создаем матрицу 2x2\n",
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"matrix = np.array([[1, 2], [3, 4]])\n",
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"print(\"Произведение матриц:\\n\", np.dot(matrix, matrix))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "d8412aa4-fb69-4da4-9d5e-ce6e1924c7ae",
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'matplotlib'",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m 3\u001b[39m plt.figure(figsize=(\u001b[32m8\u001b[39m, \u001b[32m4\u001b[39m))\n\u001b[32m 4\u001b[39m plt.bar(df[\u001b[33m\"\u001b[39m\u001b[33mИмя\u001b[39m\u001b[33m\"\u001b[39m], df[\u001b[33m\"\u001b[39m\u001b[33mБаллы\u001b[39m\u001b[33m\"\u001b[39m], color=\u001b[33m'\u001b[39m\u001b[33mskyblue\u001b[39m\u001b[33m'\u001b[39m)\n",
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"\u001b[31mModuleNotFoundError\u001b[39m: No module named 'matplotlib'"
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]
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"plt.figure(figsize=(8, 4))\n",
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"plt.bar(df[\"Имя\"], df[\"Баллы\"], color='skyblue')\n",
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"plt.title(\"Баллы студентов\")\n",
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"plt.xlabel(\"Имя\")\n",
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"plt.ylabel(\"Баллы\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2b5c128b-f9a8-408e-b128-6360630b4f9f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns\n",
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"\n",
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"sns.scatterplot(data=df, x=\"Возраст\", y=\"Баллы\", hue=\"Имя\", s=100)\n",
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"plt.title(\"Зависимость баллов от возраста\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "652d456d-8a78-40a2-84ea-b76caf74224f",
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"metadata": {},
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"outputs": [],
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"source": [
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"from tqdm import tqdm\n",
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"import time\n",
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"\n",
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"print(\"Обработка данных:\")\n",
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"for i in tqdm(range(100), desc=\"Прогресс\"):\n",
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" time.sleep(0.02) # Имитация работы"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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