Добавили все файлы

2026-05-07 16:38:13 +03:00 · 2026-05-07 16:38:13 +03:00 · 41cbaf4130
commit 41cbaf4130
9 changed files with 1070 additions and 0 deletions
--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1,164 @@
+# ---> Python
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+.idea
+.venv
+.ipynb_checkpoints
--- a/22lb/Untitled.ipynb
+++ b/22lb/Untitled.ipynb
@ -0,0 +1,250 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "75d19e99-7e7a-47f6-9e31-537e645d8f3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Первый взгляд на данные:\n",
+      "      Имя  Возраст  Баллы\n",
+      "0    Анна       21     89\n",
+      "1   Борис       22     76\n",
+      "2  Виктор       23     95\n",
+      "3  Галина       24     82\n",
+      "<class 'pandas.DataFrame'>\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",
+      "         Возраст      Баллы\n",
+      "count   4.000000   4.000000\n",
+      "mean   22.500000  85.500000\n",
+      "std     1.290994   8.266398\n",
+      "min    21.000000  76.000000\n",
+      "25%    21.750000  80.500000\n",
+      "50%    22.500000  85.500000\n",
+      "75%    23.250000  90.500000\n",
+      "max    24.000000  95.000000\n",
+      "Имя        0\n",
+      "Возраст    0\n",
+      "Баллы      0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Создадим DataFrame\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(\"Первый взгляд на данные:\")\n",
+    "print(df.head())\n",
+    "print(df.info())\n",
+    "print(df.describe())\n",
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5d78faf3-86e3-4af2-938a-9c49db11ca0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Анна</td>\n",
+       "      <td>21</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Борис</td>\n",
+       "      <td>22</td>\n",
+       "      <td>76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Виктор</td>\n",
+       "      <td>23</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Галина</td>\n",
+       "      <td>24</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Имя  Возраст  Баллы\n",
+       "0    Анна       21     89\n",
+       "1   Борис       22     76\n",
+       "2  Виктор       23     95\n",
+       "3  Галина       24     82"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1bc4b60e-2c46-4cf7-8da7-08770555c62f",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Новый столбец</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Борис</td>\n",
+       "      <td>22</td>\n",
+       "      <td>76</td>\n",
+       "      <td>83.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Виктор</td>\n",
+       "      <td>23</td>\n",
+       "      <td>95</td>\n",
+       "      <td>104.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Галина</td>\n",
+       "      <td>24</td>\n",
+       "      <td>82</td>\n",
+       "      <td>90.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
+    "df\n",
+    "df.groupby(\"Имя\").agg({\"Баллы\": \"mean\"})\n",
+    "df[df[\"Возраст\"] > 21]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "306c6886-f00d-4317-ad69-f4d9f11220a4",
+   "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": "3.14.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/22lb/Untitled1.ipynb
+++ b/22lb/Untitled1.ipynb
@ -0,0 +1,86 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fcb09a3f-8f09-442f-9dc0-05087980216c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Матрица 2x3:\n",
+      " [[1 2 3]\n",
+      " [4 5 6]]\n",
+      "Форма (shape): (2, 3)\n",
+      "------------------------------\n",
+      "Равномерная шкала (linspace):\n",
+      " [0.         0.55555556 1.11111111 1.66666667 2.22222222 2.77777778\n",
+      " 3.33333333 3.88888889 4.44444444 5.        ]\n",
+      "------------------------------\n",
+      "Случайная матрица 2x2:\n",
+      " [[-0.95758367  0.58589362]\n",
+      " [-0.25530405  1.71658099]]\n",
+      "------------------------------\n",
+      "Результат матричного умножения A и B:\n",
+      " [[19 22]\n",
+      " [43 50]]\n",
+      "------------------------------\n",
+      "Сумма по столбцам матрицы: [5 7 9]\n",
+      "Среднее по строкам матрицы: [2. 5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "matrix = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "print(\"Матрица 2x3:\\n\", matrix)\n",
+    "print(\"Форма (shape):\", matrix.shape)\n",
+    "print(\"-\" * 30)\n",
+    "linear_space = np.linspace(0, 5, 10)\n",
+    "print(\"Равномерная шкала (linspace):\\n\", linear_space)\n",
+    "print(\"-\" * 30)\n",
+    "random_matrix = np.random.randn(2, 2)\n",
+    "print(\"Случайная матрица 2x2:\\n\", random_matrix)\n",
+    "print(\"-\" * 30)\n",
+    "A = np.array([[1, 2], [3, 4]])\n",
+    "B = np.array([[5, 6], [7, 8]])\n",
+    "dot_product = np.dot(A, B)\n",
+    "print(\"Результат матричного умножения A и B:\\n\", dot_product)\n",
+    "print(\"-\" * 30)\n",
+    "print(\"Сумма по столбцам матрицы:\", matrix.sum(axis=0))\n",
+    "print(\"Среднее по строкам матрицы:\", matrix.mean(axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d0670c4-4ce2-4024-b803-456646dbb94f",
+   "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": "3.14.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/22lb/Untitled2.ipynb
+++ b/22lb/Untitled2.ipynb
--- a/22lb/Untitled3.ipynb
+++ b/22lb/Untitled3.ipynb
@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f3b57c0d-557d-41fd-9dcb-5bd23450d4d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "========== ЭТАП 1: ОБРАБОТКА ==========\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ba14952aa5b44cab19264f1070e2952",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Анализ строк:   0%|          | 0/100 [00:00<?, ?строк/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "======================================\n",
+      "\n",
+      "========== ЭТАП 2: НЕЙРОСЕТЬ ==========\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3e5da2b4604645d18fa78063105cbc7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Загрузка весов:   0%|          | 0/100 [00:00<?, ? слоев/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm.auto import tqdm \n",
+    "import time\n",
+    "\n",
+    "df = pd.DataFrame({\n",
+    "    'ID': range(1, 101),\n",
+    "    'Data': np.random.randn(100)\n",
+    "})\n",
+    "\n",
+    "print(f\"{'='*10} ЭТАП 1: ОБРАБОТКА {'='*10}\")\n",
+    "# Использование tqdm напрямую для итерации по строкам\n",
+    "for _ in tqdm(df.values, desc=\"Анализ строк\", unit=\"строк\"):\n",
+    "    time.sleep(0.01) # Ускорил для теста\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*38 + \"\\n\")\n",
+    "print(f\"{'='*10} ЭТАП 2: НЕЙРОСЕТЬ {'='*10}\")\n",
+    "for i in tqdm(range(100), \n",
+    "              desc='Загрузка весов', \n",
+    "              unit=' слоев', \n",
+    "              colour='green'):\n",
+    "    time.sleep(0.02)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fdc92940-7e90-4489-948f-0141f7441baa",
+   "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": "3.14.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--- a/22lb/Untitled4.ipynb
+++ b/22lb/Untitled4.ipynb
--- a/22lb/Untitled5.ipynb
+++ b/22lb/Untitled5.ipynb
--- a/22lb/golden_age_literature.csv
+++ b/22lb/golden_age_literature.csv
@ -0,0 +1,26 @@
+title,popularity,vote_average,vote_count,release_year
+Евгений Онегин,95.2,9.8,15000,1833
+Герой нашего времени,88.4,9.5,12000,1840
+Война и мир,120.1,9.7,18000,1869
+Преступление и наказание,110.5,9.6,17500,1866
+Мертвые души,75.3,9.2,10500,1842
+Отцы и дети,65.8,8.8,9000,1862
+Анна Каренина,105.2,9.4,14000,1877
+Идиот,92.7,9.3,13000,1868
+Капитанская дочка,55.4,8.9,8500,1836
+Шинель,45.1,8.7,7000,1842
+Братья Карамазовы,115.8,9.8,16000,1880
+Ревизор,70.2,9.1,9500,1836
+Руслан и Людмила,40.5,8.5,6000,1820
+Обломов,58.9,8.6,8200,1859
+Горе от ума,62.4,9.0,8800,1825
+Демон,35.7,8.8,5400,1839
+Муму,30.2,8.2,4500,1854
+Бедные люди,25.4,8.4,3800,1846
+Гроза,48.6,8.3,7200,1859
+Левша,32.1,8.5,5100,1881
+Нос,42.8,8.9,6800,1836
+Пиковая дама,50.3,9.1,7400,1834
+Вишневый сад,82.5,9.2,11000,1903
+Борис Годунов,44.2,8.7,5900,1831
+Бесприданница,38.9,8.4,6100,1878
--- a/README.md
+++ b/README.md
@ -0,0 +1,3 @@
+2 лаба
+
+Работа с JupyterLab, анализ и визуализация данных