lect3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лекция №3. Организация программ\n",
    "\n",
    "П.Н. Советов, РТУ МИРЭА"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Модули\n",
    "\n",
    "Модули позволяют разбить программу на независимые части. В Питоне каждый модуль располагается в отдельном файле. Уже знакомое нам ключевое слово `import` используется для загрузки и исполнения модуля.\n",
    "\n",
    "Обратиться к элементам пространства имен модуля можно, как мы тоже уже знаем, с использованием точки:"
   ]
  },
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   ],
   "source": [
    "import math\n",
    "\n",
    "math.sin(math.pi / 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С помощью функции `dir` можно узнать содержимое пространства имен загруженного модуля:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__doc__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'comb', 'copysign', 'cos', 'cosh', 'degrees', 'dist', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'isqrt', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'perm', 'pi', 'pow', 'prod', 'radians', 'remainder', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'tau', 'trunc']\n"
     ]
    }
   ],
   "source": [
    "print(dir(math))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для импортирования конкретных функций из модуля используется `from`:"
   ]
  },
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     "execution_count": 3,
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   ],
   "source": [
    "from math import sin, pi\n",
    "\n",
    "sin(pi / 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С помощью звездочки можно импортировать все пространство имен модуля в текущее пространство имен:"
   ]
  },
  {
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     "execution_count": 4,
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   ],
   "source": [
    "from math import *\n",
    "\n",
    "sin(pi / 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При этом следует по возможности избегать бесконтрольного импорта всего содержимого модуля.\n",
    "\n",
    "Хорошим тоном считается описание импорта каждого модуля отдельной строкой. То есть:\n",
    "\n",
    "```Python\n",
    "import sys\n",
    "import math\n",
    "```\n",
    "\n",
    "вместо\n",
    "\n",
    "```Python\n",
    "import sys, math\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Каждый модуль импортируется и исполняется только один раз. Повторный импорт приводит к ссылке на уже загруженный ранее модуль.\n",
    "\n",
    "Модули не могут взаимно импорировать друг друга. В большинстве случаев полезнее не пытаться искать обходные пути для решения этой проблемы, а попробовать реорганизовать программу так, чтобы взаимные связи между модулями исчезли.\n",
    "\n",
    "Имя текущего модуля хранится в глобальной переменной `__name__`. Этим фактом можно воспользоваться, чтобы менять поведение модуля в зависимости от того, каким образом он был загружен: с помощью `import` или как главный скрипт:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Запуск в режиме скрипта.\n"
     ]
    }
   ],
   "source": [
    "def test_module():\n",
    "    print('Запуск в режиме скрипта.')\n",
    "    \n",
    "if __name__ == '__main__':\n",
    "    test_module()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Пакеты\n",
    "\n",
    "Сложные иерархии из модулей строятся с помощью пакетов. Пакет представляет собой пространство имен для коллекции модулей.\n",
    "\n",
    "Например, пакет sly из проекта [SLY](https://github.com/dabeaz/sly) представляет собой одноименный каталог, в котором содержатся следующие файлы:\n",
    "\n",
    "```\n",
    "sly/\n",
    "    __init__.py\n",
    "    ast.py\n",
    "    docparse.py\n",
    "    lex.py\n",
    "    yacc.py\n",
    "```\n",
    "\n",
    "Помимо коллекции модулей здесь имеется специальный файл `__init__.py`, определяющий, что каталог является пакетом.\n",
    "\n",
    "Импортирование отдельных модулей из пространства имен пакета осуществляется традиционным образом: `import sly.lex` или `from sly import lex`.\n",
    "\n",
    "Импортирование модулей внутри пакета происходит в особом относительном режиме с предваряющей точкой, обозначающей текущий пакет: `from . import lex` (импорт всего модуля `lex`) или `from .lex import Lex` (импорт класса `Lex` из `lex`).\n",
    "\n",
    "Файл `__init__.py` может содержать код для импорта отдельных модулей из пакета. В примере с пакетом sly организовано автоматическое подключение модулей при вызове `import sly`. Содержимое `__init__.py` имеет следующий вид:\n",
    "\n",
    "```\n",
    "from .lex import *\n",
    "from .yacc import *\n",
    "...\n",
    "```\n",
    "\n",
    "Выполнение отдельного модуля из пакета производится с помощью опции `-m`:\n",
    "\n",
    "```\n",
    "python -m пакет.модуль\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Установка пакетов\n",
    "\n",
    "Сторонние пакеты устанавливаются с помощью менеджера пакета `pip`:\n",
    "\n",
    "```\n",
    "pip install sly\n",
    "```\n",
    "\n",
    "или\n",
    "\n",
    "```\n",
    "python -m pip install sly\n",
    "```\n",
    "\n",
    "Иногда полезно использовать виртуальное окружение с минимумом предустановленных пакетов. Это позволяет предотвратить конфликты между устанавливаемыми пакетами, а также дает возможность экспериментировать с новыми пакетами без замусоривания основного хранилища пакетов.\n",
    "\n",
    "Создать новое виртуальное окружение:\n",
    "\n",
    "```\n",
    "python -m venv мое_окружение\n",
    "```\n",
    "\n",
    "Далее переход в новое окружение совершается с помощью запуска скрипта `activate`, находящегося внутри созданного каталога `мое_окружение`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Создание пакетов для других пользователей\n",
    "\n",
    "Для получения дистрибутива пакета, пригодного для установки с помощью pip, необходимо создать файл setup.py рядом с каталогом пакета.\n",
    "\n",
    "Пример содержимого setup.py:\n",
    "\n",
    "```Python\n",
    "# setup.py\n",
    "import setuptools\n",
    "\n",
    "setuptools.setup(\n",
    "    name='mypackage', # Имя пакета\n",
    "    version='0.0.1',  # Версия пакета\n",
    "    author='Your Name',\n",
    "    author_email='your@domain.com',\n",
    "    description='My Package',\n",
    "    packages=setuptools.find_packages(),\n",
    ")\n",
    "```\n",
    "\n",
    "Если в состав пакета входят дополнительные файлы данных, то их необходимо перечислить в файле MANIFEST.in. Описание работы с этим файлом имеется в [официальной документации](https://packaging.python.org/guides/using-manifest-in/). В проекте sly содержимое этого файла имеет следующий вид:\n",
    "\n",
    "```\n",
    "recursive-include example *\n",
    "recursive-include docs *\n",
    "```\n",
    "\n",
    "Создание дистрибутва производится с помощью следующей команды:\n",
    "\n",
    "```\n",
    "python setup.py sdist\n",
    "```\n",
    "\n",
    "Результат в виде архива с расширением .tar.gz можно найти в каталоге dist. Далее архив можно установить с помощью команды pip:\n",
    "\n",
    "```\n",
    "pip install mypackage-0.0.1.tar.gz\n",
    "```\n",
    "\n",
    "Пакет можно опубликовать в официальном репозитории, см. сайт [pypi.org](https://pypi.org/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Исключения\n",
    "\n",
    "Исключения в Питоне, как и в С++ или Java, используются для обработки ошибок и других нештатных ситуаций, возникающих в процессе выполнения программы.\n",
    "\n",
    "Например, при делении на нуль возникает исключение:\n",
    "\n",
    "```Python\n",
    ">>> def f():\n",
    "...     return 1 / 0\n",
    "...\n",
    ">>> f()\n",
    "Traceback (most recent call last):\n",
    "  File \"<stdin>\", line 1, in <module>\n",
    "  File \"<stdin>\", line 2, in f\n",
    "ZeroDivisionError: division by zero\n",
    "```\n",
    "\n",
    "Здесь указан класс исключения (`ZeroDivisionError`), сообщение исключения (`division by zero`), а также результат трассировки со стеком последних вызовов (`Traceback`), приведших к возникновению исключения.\n",
    "\n",
    "Сгенерировать исключение можно с помощью `raise`:\n",
    "\n",
    "```Python\n",
    ">>> raise ZeroDivisionError('Деление на нуль')\n",
    "Traceback (most recent call last):\n",
    "  File \"<stdin>\", line 1, in <module>\n",
    "ZeroDivisionError: Деление на нуль\n",
    "```\n",
    "\n",
    "Для обработки исключений используется конструкция `try/except`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Деление на нуль!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    1 / 0\n",
    "except ZeroDivisionError:\n",
    "    print('Деление на нуль!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Следует стараться сократить до минимума блок, находящийся внутри `try/except`, что позволяет лучше локализовать ошибку. Это же касается обработки конкретного класса исключения в `except`. В примере ниже код обработки исключений излишне универсален:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Деление на нуль!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    1 / 0\n",
    "except: # Слишком общий случай\n",
    "    print('Деление на нуль!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "При необходимости можно узнать подробности о полученном исключении:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'ZeroDivisionError'> division by zero\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    1 / 0\n",
    "except ZeroDivisionError as e: # e это объект класса ZeroDivisionError\n",
    "    print(type(e), e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Блок `finally` полезен, если необходимо осуществить некоторые завершающие действия (закрыть файл, разорвать соединение и так далее)  даже при возникновании исключений:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Уборка.\n",
      "Деление на нуль!\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    try:\n",
    "        1 / 0\n",
    "    finally:\n",
    "        print('Уборка.')\n",
    "except ZeroDivisionError:\n",
    "    print('Деление на нуль!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Использование `with` в большинстве случаев позволяет обойтись без `finally`.\n",
    "\n",
    "В блоке except можно повторно вызывать обрабатываемое исключение с помощью `raise` без параметров. Это используется, в частности, для ведения лога работы программы.\n",
    "\n",
    "Легковесной альтернативой исключениям являются возвращаемые коды или специальные значения, сигнализирующие об ошибках. В Питоне принято использовать значение `None`, возвращение которого соответствует некоторой нестандартной ситуации. Преимуществами исключений, по сравнению с кодами ошибок, являются:\n",
    "\n",
    "* Детальная информация об ошибке, включая стек вызовов.\n",
    "* Отсутствие необходимости отслеживания возвращаемых значений функций, необязательность явной обработки ошибки.\n",
    "\n",
    "При этом исключениями легко злоупотребить, использовать их в духе изощренного варианта GOTO. В частности, исключения иногда используются не для обработки ошибок, а в реализации алгоритмов, как в примере ниже:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_freq(text):\n",
    "    freq = {}\n",
    "    for w in text.split():\n",
    "        try:\n",
    "            freq[w] += 1\n",
    "        except KeyError:\n",
    "            freq[w] = 1\n",
    "    return freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь можно было бы воспользоваться методом `get` и избавиться от обработки исключения:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_freq(text):\n",
    "    freq = {}\n",
    "    for w in text.split():\n",
    "        freq[w] = freq.get(w, 0) + 1\n",
    "    return freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Или, еще лучше, использовать мощные возможности стандартной библиотеки Питона:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "def get_freq(text):\n",
    "    return Counter(text.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оформление программ\n",
    "\n",
    "Стиль оформления программ на Питоне определен в [PEP 8](https://www.python.org/dev/peps/pep-0008/). Правил, изложенных в этом документе, желательно придерживаться. Особенно, если вы планируете показать свою программу другим разработчикам. Существует ряд инструментов, позволяющих автоматически проверить код на соответствие PEP 8, см., например, [pycodestyle](https://pypi.org/project/pycodestyle/). В развитых IDE такие инструменты уже встроены.\n",
    "\n",
    "Файл модуля должен содержать текст в кодировке UTF-8. Общая структура модуля имеет следующий вид:\n",
    "\n",
    "1. Комментарий-docstring модуля.\n",
    "1. Импорты из стандартной библиотеки.\n",
    "1. Импорты из сторонних библиотек.\n",
    "1. Локальные импорты из приложения.\n",
    "1. Глобальные переменные и константы.\n",
    "1. Классы и функции.\n",
    "\n",
    "Для отступов используется 4 пробела, а максимальная длина строки не должна превышать 79 символов.\n",
    "\n",
    "Функции и классы в глобальном пространстве имен отделяются друг от друга 2 пустыми строками. Методы отделяются друг от друга 1 пустой строкой.\n",
    "\n",
    "Для назначения имен используются следующие правила:\n",
    "\n",
    "1. Пакеты и модули именуются с помощью коротких слов в нижнем регистре. Примеры: sys, math.\n",
    "1. Для классов используются слова, начинающиеся с заглавной буквы и соединенные слитно. Примеры: MyClass, PathLike.\n",
    "1. Имена функций, методов, аргументов и локальных переменных пишутся в нижнем регистре, слова разделяются символом подчеркивания. Примеры: read_all, generate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Профилирование программ\n",
    "\n",
    "Для сбора характеристик выполненной программы используется встроенный профилировщик. Его можно вызвать следующим образом:\n",
    "\n",
    "```\n",
    "python -m cProfile программа.py\n",
    "```\n",
    "\n",
    "Результат профилирования:\n",
    "\n",
    "```\n",
    "         1395459 function calls (1392906 primitive calls) in 3.226 seconds\n",
    "\n",
    "   Ordered by: standard name\n",
    "\n",
    "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
    "     13/3    0.000    0.000    0.027    0.009 <frozen importlib._bootstrap>:1002(_find_and_load)\n",
    "      3/2    0.000    0.000    0.001    0.001 <frozen importlib._bootstrap>:1033(_handle_fromlist)\n",
    "       15    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:112(release)\n",
    "       13    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:152(__init__)\n",
    "       13    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:156(__enter__)\n",
    "       13    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:160(__exit__)\n",
    "       15    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:166(_get_module_lock)\n",
    "       13    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:185(cb)\n",
    "        2    0.000    0.000    0.000    0.000 <frozen importlib._bootstrap>:203(_lock_unlock_module)\n",
    "     18/3    0.000    0.000    0.023    0.008 <frozen importlib._bootstrap>:220(_call_with_frames_removed)\n",
    "\n",
    "```\n",
    "\n",
    "Здесь строка `Ordered by: standard name` означает, что вывод данных для отдельных функций/методов отсортирован по имени (см. последнюю колонку).\n",
    "\n",
    "Удобной является сортировка по общему затраченному времени (`cumulative`) на выполнение:\n",
    "\n",
    "```\n",
    "python -m cProfile -s cumulative программа.py\n",
    "```\n",
    "\n",
    "```\n",
    "         1315617 function calls (1313064 primitive calls) in 3.115 seconds\n",
    "\n",
    "   Ordered by: cumulative time\n",
    "\n",
    "   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n",
    "     11/1    0.000    0.000    3.115    3.115 {built-in method builtins.exec}\n",
    "        1    0.000    0.000    3.115    3.115 terrain.py:4(<module>)\n",
    "        1    0.000    0.000    2.084    2.084 __init__.py:593(mainloop)\n",
    "        1    0.042    0.042    2.084    2.084 {method 'mainloop' of '_tkinter.tkapp' objects}\n",
    "       18    0.000    0.000    2.042    0.113 __init__.py:1880(__call__)\n",
    "       17    0.000    0.000    2.035    0.120 __init__.py:804(callit)\n",
    "       17    0.001    0.000    2.035    0.120 terrain.py:73(update_screen)\n",
    "       17    1.146    0.067    1.929    0.113 terrain.py:51(render)\n",
    "        1    0.004    0.004    0.911    0.911 terrain.py:37(__init__)\n",
    "        1    0.002    0.002    0.835    0.835 terrain.py:18(load_data)\n",
    "```\n",
    "\n",
    "Описание колонок приведено в таблице:\n",
    "\n",
    "| Имя колонки | Описание                                                |\n",
    "|-------------|---------------------------------------------------------|\n",
    "| ncalls      | Количество вызовов                                      |\n",
    "| tottime     | Общее время работы функции, не включая время подфункций |\n",
    "| percall     | tottime/ncalls                                          |\n",
    "| cumtime     | Общее время работы функции                              |\n",
    "| percall     | cumtime/ncalls                                          |\n",
    "\n",
    "Для оценки производительности отдельной функции используется функция `timeit` из одноименного модуля:\n",
    "\n",
    "```Python\n",
    ">>> from timeit import timeit\n",
    ">>> def f1(): return 2**64 - 1\n",
    "\n",
    ">>> def f2(): math.pow(2, 64) - 1\n",
    "\n",
    ">>> timeit(f1, number=1000000) # параметр number определяет число запусков функции\n",
    "0.06563599999992675\n",
    ">>> timeit(f2, number=1000000)\n",
    "0.2985565000000179\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Во многих случаях самым производительным кодом на Питоне будет \"немногословный\" код. Тот, в котором используются преимущественно высокоуровневые конструкции, библиотечные функции и структуры данных. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter-блокноты\n",
    "\n",
    "Вычислительный блокнот (или просто \"блокнот\") является одним из способов организовать документацию и программу в одном документе. Блокноты традиционно используются в математических пакетах, таких, как Mathcad и Mathematica. Блокноты предназначены, в первую очередь, для создания специфического вида документированных программ — журналов или отчетов о проведенных вычислениях из области компьютерной алгебры, статистики, анализа данных, машинного обучения и других областей наук.\n",
    "\n",
    "[Jupyter](https://jupyter.org/)-блокнот является веб-реализацией концепции интерактивных блокнотов. Поддерживается Питон, а также ряд других языков программирования. \n",
    "\n",
    "Для начала работы с Jupyter-блокнотами необходимо запустить сервер блокнотов, после чего просмотр и редактирование блокнотов можно осуществить в прямо в веб-бразуере.\n",
    "\n",
    "Документ в формате Jupyter-блокнота имеет расширение .ipynb и содержит последовательность ячеек. Используется два основных вида ячеек:\n",
    "\n",
    "1. Ячейки кода.\n",
    "1. Ячейки текста.\n",
    "\n",
    "Ячейки кода содержат текст на Питоне. В блокноте есть средства для выполнения отдельной ячейки, а также для последовательного выполнения всех ячеек документа.\n",
    "\n",
    "Ячейки текста поддерживают формат Markdown с дополнительной возможностью записи формул в формате LaTeX.\n",
    "\n",
    "При работе с блокнотом полезно держать под рукой перечень \"горячих клавиш\", с помощью которых осуществляются основные действия с документом. См. верхнее меню, последний пункт: Help->Keyboard Shortcuts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Библиотека Matplotlib\n",
    "\n",
    "[Matplotlib](https://matplotlib.org/stable/index.html) — библиотека для визуализации данных. Поддерживается множество видов визуализации: линейные, столбчатые, точечные и круговые диаграммы, гистограммы, тепловые карты и другие."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим простейший график."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = range(0, 10)\n",
    "# Символ ; здесь используется, чтобы не выводить служебную информацию на экран\n",
    "plt.plot(xs, [random.random() for _ in xs]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавим второй график к первому, а также добавим надписи."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xs, [random.random() for _ in xs], label='$f_1$') # Нотация LaTeX\n",
    "plt.plot(xs, [2 * random.random() for _ in xs], label='$f_2$')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('График')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Еще несколько примеров."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = [random.choice(['red', 'blue']) for _ in xs]\n",
    "fig, axs = plt.subplots(2, 2, figsize=(15, 5))\n",
    "axs[0, 0].plot([random.random() for _ in xs])\n",
    "axs[0, 1].scatter([random.random() for _ in xs], [random.random() for _ in xs], c=colors)\n",
    "axs[1, 0].bar(xs, [random.random() for _ in xs])\n",
    "axs[1, 1].imshow([[random.random() for _ in xs] for _ in xs]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
    "axs[0].pie([random.random() for _ in xs])\n",
    "axs[1].boxplot([random.random() for _ in xs]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В [галерее](https://matplotlib.org/3.3.4/gallery/) официального сайта Matplotlib представлены примеры различных видов графиков, которые можно взять за основу для своих задач."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}