Добавлен анализ данных в JupyterLab

movies.csv

@@ -0,0 +1,21 @@
+title,year,country,rating,duration,genre
+The Shawshank Redemption,1994,USA,9.3,142,Drama
+The Godfather,1972,USA,9.2,175,Crime
+The Dark Knight,2008,USA,9.0,152,Action
+Pulp Fiction,1994,USA,8.9,154,Crime
+Forrest Gump,1994,USA,8.8,142,Drama
+Inception,2010,USA,8.8,148,Sci-Fi
+Fight Club,1999,USA,8.8,139,Drama
+Interstellar,2014,USA,8.7,169,Sci-Fi
+The Matrix,1999,USA,8.7,136,Sci-Fi
+Gladiator,2000,USA,8.5,155,Action
+The Green Mile,1999,USA,8.6,189,Drama
+Whiplash,2014,USA,8.5,106,Drama
+Parasite,2019,South Korea,8.5,132,Thriller
+Joker,2019,USA,8.4,122,Drama
+Avengers Endgame,2019,USA,8.4,181,Action
+Django Unchained,2012,USA,8.5,165,Western
+The Departed,2006,USA,8.5,151,Crime
+The Prestige,2006,USA,8.5,130,Drama
+Coco,2017,USA,8.4,105,Animation
+WALL-E,2008,USA,8.4,98,Animation

requirements.txt

@@ -1,5 +1,102 @@
+anyio==4.13.0
+argon2-cffi==25.1.0
+argon2-cffi-bindings==25.1.0
+arrow==1.4.0
+asttokens==3.0.1
+async-lru==2.3.0
+attrs==26.1.0
+babel==2.18.0
+beautifulsoup4==4.14.3
+bleach==6.3.0
+certifi==2026.5.20
+cffi==2.0.0
+charset-normalizer==3.4.7
+colorama==0.4.6
+comm==0.2.3
+contourpy==1.3.3
+cycler==0.12.1
+debugpy==1.8.20
+decorator==5.3.1
+defusedxml==0.7.1
+executing==2.2.1
+fastjsonschema==2.21.2
+fonttools==4.63.0
+fqdn==1.5.1
+h11==0.16.0
+httpcore==1.0.9
+httpx==0.28.1
+idna==3.16
+ipykernel==7.2.0
+ipython==9.13.0
+ipython_pygments_lexers==1.1.1
+isoduration==20.11.0
+jedi==0.20.0
+Jinja2==3.1.6
+json5==0.14.0
+jsonpointer==3.1.1
+jsonschema==4.26.0
+jsonschema-specifications==2025.9.1
+jupyter-events==0.12.1
+jupyter-lsp==2.3.1
+jupyter_client==8.8.0
+jupyter_core==5.9.1
+jupyter_server==2.18.2
+jupyter_server_terminals==0.5.4
+jupyterlab==4.5.7
+jupyterlab_pygments==0.3.0
+jupyterlab_server==2.28.0
+kiwisolver==1.5.0
+lark==1.3.1
+MarkupSafe==3.0.3
+matplotlib==3.10.9
+matplotlib-inline==0.2.2
+mistune==3.2.1
+nbclient==0.10.4
+nbconvert==7.17.1
+nbformat==5.10.4
+nest-asyncio==1.6.0
+notebook_shim==0.2.4
 numpy==2.4.6
+packaging==26.2
 pandas==3.0.3
+pandocfilters==1.5.1
+parso==0.8.7
+pillow==12.2.0
+platformdirs==4.9.6
+prometheus_client==0.25.0
+prompt_toolkit==3.0.52
+psutil==7.2.2
+pure_eval==0.2.3
+pycparser==3.0
+Pygments==2.20.0
+pyparsing==3.3.2
 python-dateutil==2.9.0.post0
+python-json-logger==4.1.0
+pywinpty==3.0.3
+PyYAML==6.0.3
+pyzmq==27.1.0
+referencing==0.37.0
+requests==2.34.2
+rfc3339-validator==0.1.4
+rfc3986-validator==0.1.1
+rfc3987-syntax==1.1.0
+rpds-py==0.30.0
+seaborn==0.13.2
+Send2Trash==2.1.0
+setuptools==82.0.1
 six==1.17.0
+soupsieve==2.8.4
+stack-data==0.6.3
+terminado==0.18.1
+tinycss2==1.4.0
+tornado==6.5.6
+tqdm==4.67.3
+traitlets==5.15.0
+typing_extensions==4.15.0
 tzdata==2026.2
+uri-template==1.3.0
+urllib3==2.7.0
+wcwidth==0.7.0
+webcolors==25.10.0
+webencodings==0.5.1
+websocket-client==1.9.0

week2_analysis.ipynb

@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7297b19-5736-4a17-911d-645849a628a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from tqdm import tqdm\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "711ef140-61f6-4b64-8fcf-598247617d65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"movies.csv\")\n",
+    "\n",
+    "print(\"Размер таблицы:\", df.shape)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d269a73-b428-421b-a9e7-e1158368d099",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "965d5583-2cc3-409c-8db4-a7a00494a6ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c74ac668-6c80-49c8-8b72-eea13fdf5ded",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4940a598-773c-4f6e-8bce-e841404eebc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.histplot(df[\"rating\"], bins=8, kde=True)\n",
+    "plt.title(\"Распределение рейтингов фильмов\")\n",
+    "plt.xlabel(\"Рейтинг\")\n",
+    "plt.ylabel(\"Количество фильмов\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "906ffd6f-e7a2-402a-9a33-40a075cc5685",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.scatterplot(data=df, x=\"year\", y=\"rating\", hue=\"genre\")\n",
+    "plt.title(\"Связь года выпуска и рейтинга фильма\")\n",
+    "plt.xlabel(\"Год выпуска\")\n",
+    "plt.ylabel(\"Рейтинг\")\n",
+    "plt.legend(title=\"Жанр\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a05e677a-74e7-4a3a-adce-02f8dc04153e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "sns.boxplot(data=df, x=\"genre\", y=\"rating\")\n",
+    "plt.title(\"Распределение рейтингов по жанрам\")\n",
+    "plt.xlabel(\"Жанр\")\n",
+    "plt.ylabel(\"Рейтинг\")\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55e5ea86-f072-419c-b937-d752f8fec4e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ratings = df[\"rating\"].to_numpy()\n",
+    "\n",
+    "print(\"Средний рейтинг:\", np.mean(ratings))\n",
+    "print(\"Минимальный рейтинг:\", np.min(ratings))\n",
+    "print(\"Максимальный рейтинг:\", np.max(ratings))\n",
+    "print(\"Стандартное отклонение:\", np.std(ratings))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86546195-d108-4c55-9401-9d7017d96277",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in tqdm(range(100)):\n",
+    "    time.sleep(0.01)"
+   ]
+  }
+ ],
+ "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": "3.14.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}