Basic Examples Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Введение в Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Это блокнот - удобное средство для проведения экспериментов и анализа данных.\n",
    "Так можно оформлять текст, создавать заголовки, делить программу на небольшие автономные блоки\n",
    "Благодарим за создание данного блокнота Виктора Харламова и Елену Викторовну Хиль"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-1-a79c0a35f353>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-a79c0a35f353>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    pip install seaborn\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# так можно устанавливать библиотеки, если у вас установлен pip\n",
    "pip install seaborn\n",
    "#под анакондой это можно делать через более привычный интерфейс меню"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Стандартные классы в Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "нулевой элемент списка равен  0\n",
      "нулевой элемент кортежа равен  0\n",
      "букве а в словаре соответствует символ  0\n"
     ]
    }
   ],
   "source": [
    "#Переменные в Python не требуется объявлять, просто положите в них что-то\n",
    "int_var = 5 # целое число\n",
    "float_var = 0.53 # вещественное число\n",
    "str_var = \"string\" # строка\n",
    "bool_var = True # логический тип\n",
    "list_var = [0, 1, 2] # список\n",
    "print(\"нулевой элемент списка равен \", list_var[0]) #так мы напечатаем элемент списка \n",
    "tuple_var = (0, 1, 2) # кортеж\n",
    "print(\"нулевой элемент кортежа равен \", tuple_var[0]) #точно также кортежа\n",
    "set_var = {0, 1, 2} # множество \n",
    "dict_var = {'a': 0, 'b': 1, 'c': 2} # словарь\n",
    "print(\"букве а в словаре соответствует символ \", dict_var['a']) #посмотрим на что словарь заменяет букву a."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно перегонять переменные из одного типа в другой"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "1\n",
      "True\n",
      "(0, 1, 2)\n",
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(int(\"5\")) #строка перешла в число\n",
    "print(int(True)) #True - это единица, False - это ноль\n",
    "print(str(True)) #Из boolean True получаем строку из букв True\n",
    "print(tuple([0,1,2])) #переводим list в кортеж\n",
    "print(list((1,2,3))) #кортеж в list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Важно, что в Python существуют изменяемые и неизменяемые переменные. К неизменяемым относятся int, str, bool, tuple (в каком-то смысле это и суть tuple - незизменяемый список). Иными словами, когда вы присваиваете их значение другим переменным, создается новый объект. Изменить вы неизменяемые переменные не можете."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'string'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str_var2 = str_var\n",
    "str_var += \" another\" # ~ str_var = str_var + \" another\"\n",
    "str_var2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Изменяемые же объекты ведут себя иначе. Их можно изменять. При присваивании их значение другим переменным объекты не копируются, а копируются только ссылки на объект. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# например\n",
    "\n",
    "list_var2 = list_var\n",
    "list_var2 += [3] # однако лучше использовать list_var2.append(3)\n",
    "list_var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Вывод данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выводить данные можно самыми раными методами, остановимся на 2х стандартных. Результат выполнения последней операции выводится на экран как в примере выше (если вам этого не хочется, поставьте в самом конце ';'), а вывести переменную из середины кода в ячейке можно с помощью функции print."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42 is a magic number\n"
     ]
    }
   ],
   "source": [
    "print(\"42 is a magic number\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'42 is a magic number'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"42 is a magic number\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Но последний метод не будет работать, если в конце будет не просто объект, а объект внутри условия или цикла."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1):\n",
    "    \"42 is a magic number\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Функции"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь функции не требуют объявления типов входных и выходных значений в силу динамической типизации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def product(a, b):\n",
    "    return a * b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Важно!!! Отступы являются частью синтаксиса (возможно, это даже приятнее чем видеть бесконечные {})."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product(7, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'777777'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product('7', 6) # да, тут можно умножить строку на число :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Циклы и генераторы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Есть 2 типа циклов: прохождение по итерируемому объекту (с помощью for) или совершение действий пока выполнено какое-то условие (цикл while)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = []\n",
    "for i in range(5): # это специальный итерируемый объект, выдающий числа 0, 1, 2, 3, 4\n",
    "    lst.append(i) # добавляем элемент в список\n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# иной способ генерации такого списка\n",
    "lst = [i for i in range(5)]\n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst = []\n",
    "i = 0\n",
    "while i < 5:\n",
    "    lst.append(i)\n",
    "    i += 1\n",
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "К\n",
      "о\n",
      "л\n",
      "м\n",
      "о\n",
      "г\n",
      "о\n",
      "р\n",
      "о\n",
      "в\n"
     ]
    }
   ],
   "source": [
    "# к итерируемым объектам относятся список, кортеж, словарь, множество, строка\n",
    "\n",
    "for c in \"Колмогоров\":\n",
    "    print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно увидеть, что именно отступы определяют то, где начинается тело цикла и где оно заканчивается.\n",
    "При этом отступы должны состоять либо из знаков табуляции, либо из пробелов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Условия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1\n",
    "if a == 1:\n",
    "    b = 2\n",
    "else:\n",
    "    b = 3\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# можно писать условия в одну строчку\n",
    "b = 2 if a == 1 else 3\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разберемся в том, что тут происходит.\n",
    "\"a == 1\" - условие\n",
    "Если оно верно, то возвращается 2,\n",
    "иначе возвращается 3.\n",
    "То есть общая конструкция такова:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'условие истинно'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"условие истинно\" if 5 < 6 else \"условие ложно\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'условие ложно'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"условие истинно\" if 5 > 6 else \"условие ложно\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# множественное условие\n",
    "a = 1\n",
    "b = 2\n",
    "\n",
    "if a < 1:\n",
    "    c = 4\n",
    "elif b > 2.5:\n",
    "    c = 3\n",
    "else:\n",
    "    c = 2\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ошибки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы часто ошибаемся, поэтому нужно знать, как исправляться или как игнорировать ошибки)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-20-60c5ce86bae6>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-20-60c5ce86bae6>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    print(\"a is more then 2\")\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "a = 3\n",
    "if a > 2:\n",
    "print(\"a is more then 2\")\n",
    "# TypeError - класс ошибки, позволяет понять о сути самой проблемы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-21-03490d863ae1>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-21-03490d863ae1>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    print(\"a is more then 2\")\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    a = 3\n",
    "    if a > 2:\n",
    "    print(\"a is more then 2\")\n",
    "except Exception as er: # отлавливаем ошибку\n",
    "    print(er)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Увы, такие ошибки вы просто должны не совершать)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "must be str, not int",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-c83c0461db39>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"3\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: must be str, not int"
     ]
    }
   ],
   "source": [
    "a = \"3\" + 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "must be str, not int\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    a = \"3\" + 4\n",
    "except Exception as er: # отлавливаем ошибку\n",
    "    print(er)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Векторы и матрицы. Библиотека numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Инициализация векторов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обычно для работы с векторами используют библиотеку numpy. Она позволяет делать многие базовые операции из линейной алгебры, и не только."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем библиотеку\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# создаем вектор из списка\n",
    "a = np.array([1, 2, 3])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n",
      "[0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# генерируем с помощью имеющихся библиотечных средств\n",
    "b = np.arange(3) #здесь элементы целые\n",
    "print(b)\n",
    "c = np.zeros(2) #здесь вещественные\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# обращение к элементам вектора\n",
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:-1] #не включая последний"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Арифметические операции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# скалярное произведение векторов\n",
    "\n",
    "a.dot(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4, 16])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# поэлементные действия над элементами вектора\n",
    "\n",
    "(2 * b)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# агрегирующие функции\n",
    "\n",
    "a.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Инициализация матриц"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  0,  3],\n",
       "       [ 3,  5, -1]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# создаем матрицу из списка списков\n",
    "A = np.array([[4, 0, 3], \n",
    "              [3, 5, -1]])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [0, 1, 2]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = np.vstack([a, b])\n",
    "B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Операции из линейной алгебры"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  0,  9],\n",
       "       [ 0,  5, -2]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# поэлементное умножение\n",
    "A * B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0],\n",
       "       [2, 1],\n",
       "       [3, 2]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#транспонирование\n",
    "B.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13,  6],\n",
       "       [10,  3]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# матричное умножение\n",
    "A.dot(B.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 2, 6],\n",
       "       [3, 6, 1]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0., -1.],\n",
       "       [ 1.,  0.]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# обратная матрица\n",
    "C = np.array([[0, 1],\n",
    "              [-1, 0]])\n",
    "np.linalg.inv(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Таблицы. Библиотека pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Классическая библиотека для работы с таблицами. Позволяет делать многие естественные операции с таблицами (наподобие SQL'ных)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Инициализация таблиц"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Score</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Petr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Andrey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score    Name\n",
       "0      1    Petr\n",
       "1      2    Ivan\n",
       "2      2  Andrey"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# из словаря\n",
    "df = pd.DataFrame(data={\"Score\": [1, 2, 2], \"Name\": [\"Petr\", \"Ivan\", \"Andrey\"]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Score</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Petr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score  Name\n",
       "0      1  Petr\n",
       "1      2  Ivan"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# из списка списков\n",
    "df2 = pd.DataFrame([[1, \"Petr\"], [2, \"Ivan\"]], columns=[\"Score\", \"Name\"])\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Строки и стоблцы таблицы являются объектами класса Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Score\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# так можно посмотреть на тип объекта\n",
    "type(df[\"Score\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размеры (3, 2)\n",
      "RangeIndex(start=0, stop=3, step=1)\n",
      "Названия строк: [0, 1, 2]\n",
      "['Score', 'Name']\n",
      "статистика по Score:           Score\n",
      "count  3.000000\n",
      "mean   1.666667\n",
      "std    0.577350\n",
      "min    1.000000\n",
      "25%    1.500000\n",
      "50%    2.000000\n",
      "75%    2.000000\n",
      "max    2.000000\n",
      "   Score    Name\n",
      "0      1    Petr\n",
      "1      2    Ivan\n",
      "2      2  Andrey\n",
      "нулевая строка: Score       1\n",
      "Name     Petr\n",
      "Name: 0, dtype: object\n",
      "нулевая и вторая строка, столбец Name      Name\n",
      "0    Petr\n",
      "2  Andrey\n",
      "нулевая и вторая строка, нулевой столбец    Score\n",
      "0      1\n",
      "2      2\n"
     ]
    }
   ],
   "source": [
    "print(\"Размеры\", df.shape) #Размеры\n",
    "print(df.index) #это массив типа индекс с названиями строк, в нашем случае их специально не задавали \n",
    "print(\"Названия строк:\", df.index.tolist()) #если перевести его в list, то будет более наглядно\n",
    "print(df.columns.tolist()) #названия столбцов\n",
    "print(\"статистика по Score:\", df.describe()) #описательная статистика для числовых переменных\n",
    "print(df.head()) #первые 5 строк массива, полезно для больших наборов данных\n",
    "#print(df.loci[0])\n",
    "print(\"нулевая строка:\", df.loc[0]) #печатаем нулевую строку\n",
    "print(\"нулевая и вторая строка, столбец Name\",df.loc[[0,2],[\"Name\"]]) #печатаем 0 и 2 строки из cтолбца Name\n",
    "print(\"нулевая и вторая строка, нулевой столбец\",df.iloc[[0,2],[0]]) #печатаем 0 и 2 строки из нулевого столбца\n",
    "#loc дает доступ к строке по метке, столбцу по названию, iloc ко всему по номеру"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получение данных из таблицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Score</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Andrey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score    Name\n",
       "1      2    Ivan\n",
       "2      2  Andrey"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Выделение строк по условию\n",
    "df[df[\"Score\"] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# аггрегирующие функции\n",
    "df[\"Score\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Score\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Методы apply, assign"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Достаточно часто приходится сталкиваться с тем, что нам нужно из какого-то стоблца сделать другой, или же использовать информацию из всей таблицы сразу. Для этого можно использовать циклы, но это крайне нежелательно, так как есть методы, позволяющие ускорить такого рода действия."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    4\n",
       "Name: Score, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# лямбда-функции позволяют создавать безымянные функции прямо внутри методов\n",
    "df[\"Score\"].apply(lambda x: x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Score</th>\n",
       "      <th>Name</th>\n",
       "      <th>cnt</th>\n",
       "      <th>is_Petr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Petr</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ivan</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Score  Name  cnt  is_Petr\n",
       "0      1  Petr    1     True\n",
       "1      2  Ivan    1    False"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.assign(cnt=1, is_Petr=lambda _df: _df[\"Name\"] == \"Petr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Группировка и агрегирующие функции"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В большинстве случаев мы имеем большой набор данных, который нам нужно сгруппировать по определенному признаку, а затем получить по нему статистику некоторыми агрегирующими функциями."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Went to store</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Bought something</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>M</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>W</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>M</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>W</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Went to store Sex  Bought something\n",
       "0           True   M             False\n",
       "1          False   W             False\n",
       "2           True   M              True\n",
       "3           True   W             False"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[True, \"M\", False],\n",
    "                   [False, \"W\", False],\n",
    "                   [True, \"M\", True],\n",
    "                   [True, \"W\", False]],\n",
    "                  columns=[\"Went to store\", \"Sex\", \"Bought something\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "M    0.5\n",
       "W    0.0\n",
       "Name: conversion, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# с помощью слэшей можно переносить код со строчки на строчку\n",
    "# 1 оставляем только те строки, где в столбце стоит True\n",
    "# 2 добавляем новый столбец тождественную единицу для удобства подсчёта\n",
    "# 3 группируем по полу\n",
    "# 4 суммируем значения некоторых столбцов в каждой группе\n",
    "# 5 добавляем новый столбец, означающий эмперическую вероятность покупки для каждой группы\n",
    "# 6 выбираем определенный столбец\n",
    "df.loc[df[\"Went to store\"]] \\\n",
    "  .assign(cnt=1) \\\n",
    "  .groupby(\"Sex\") \\\n",
    "  .agg({\"Bought something\": \"sum\", \"cnt\": \"sum\"}) \\\n",
    "  .assign(conversion=lambda _df: _df[\"Bought something\"] / _df[\"cnt\"]) \\\n",
    "  [\"conversion\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Графики. Библиотека matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем модуль библиотеки\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe030dfb748>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# обычный график\n",
    "a = np.linspace(0, 1)\n",
    "plt.plot(a, a**2, '^')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# гистограмма\n",
    "data = pd.DataFrame(data={\"Name\": [\"Maria\", \"Petr\", \"Maria\", \"Ivan\", \"Ivan\"],\n",
    "                          \"Point\": [5, 2, 3, 4, 5]})\n",
    "avg_points = data.groupby(\"Name\") \\\n",
    "                 .agg({\"Point\": \"mean\"})\n",
    "plt.bar(avg_points.index, avg_points[\"Point\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fe030d4f4a8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# круговая диаграмма\n",
    "data.assign(cnt=1) \\\n",
    "    .groupby(\"Point\") \\\n",
    "    .agg({\"cnt\": \"sum\"}) \\\n",
    "    .plot(kind=\"pie\", subplots=True) \n",
    "# можно получать графики из pandas не прибегая явно к matplotlib\n",
    "# это быстрее и проще, но не всегда достаточно"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Визуализация данных. Библиотека seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем модуль библиотеки\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В библиотеке seaborn есть набор массивов данных, которыми удобно пользоваться для обучения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = sb.load_dataset(\"iris\")\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим гистограмму для sepal_length с помощью distplot()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3b7d622288>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "set1 = iris['sepal_length']\n",
    "sb.distplot(set1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно построить несколько гистограмм на одном графике."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "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": [
    "set2 = iris['sepal_width']\n",
    "for dataset in [set1,set2]:\n",
    "    sb.distplot(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Диаграмма рассеяния"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для построения диаграмм рассеяния будем использовать seaborn.scatterplot\n",
    "https://seaborn.pydata.org/generated/seaborn.scatterplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3b041b1a88>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.scatterplot(x='sepal_length', y='sepal_width', data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3b02f74548>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.scatterplot(x='sepal_length', y='sepal_width', hue='species', data=iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3b0410c2c8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.scatterplot(x='sepal_length', y='sepal_width', hue = 'petal_length', style='species', data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ядерная оценка плотности\n",
    "https://seaborn.pydata.org/generated/seaborn.kdeplot.html?highlight=kdeplot#seaborn.kdeplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "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": [
    "mean, cov = [0, 2], [(1, .5), (.5, 1)]\n",
    "x, y = np.random.multivariate_normal(mean, cov, size=50).T\n",
    "sb.kdeplot(x, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "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": [
    "sb.kdeplot(x, y, n_levels=30, cmap=\"Purples_d\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "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": [
    "sb.kdeplot(x, bw=.15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Диаграмма размаха"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3b042200c8>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.boxplot(x=\"species\", y=\"sepal_length\", data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Параллельные координаты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3b026d13c8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import parallel_coordinates\n",
    "plt.figure(figsize=(6, 5)) #зададим размеры картинки (попробуйте поменять числа)\n",
    "parallel_coordinates(iris, 'species', colormap='gist_rainbow')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "colormape отвечает за цвета, бывает еще, например,'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink','spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia','hot', 'afmhot', 'gist_heat', 'copper'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}