lect3.ipynb

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   "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', 'cbrt', 'ceil', 'comb', 'copysign', 'cos', 'cosh', 'degrees', 'dist', 'e', 'erf', 'erfc', 'exp', 'exp2', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'isqrt', 'lcm', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'nextafter', 'perm', 'pi', 'pow', 'prod', 'radians', 'remainder', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'tau', 'trunc', 'ulp']\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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   ],
   "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()"
   ]
  },
  {
   "attachments": {},
   "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 модуля `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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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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      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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."
   ]
  }
 ],
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