{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a83d3451",
   "metadata": {},
   "source": [
    "# Лабораторная работа №3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4626d84",
   "metadata": {},
   "source": [
    "Данная лабораторная предназначена для ознакомления с основными модулями Python используемыми в анализе данных.\n",
    "\n",
    "NumPy - модуль предназначенный для работы с многомерными массивами. Почитать можно [здесь](https://pythonworld.ru/numpy)\n",
    "\n",
    "Matplotlib - пакет модулей предназначенный для визуализации данных. Почитать можно [здесь](https://pythonworld.ru/novosti-mira-python/scientific-graphics-in-python.html) \n",
    "\n",
    "Pandas - модуль для анализа данных и поддерживающий их табличное представление. Почитать можно [здесь](https://pythonworld.ru/obrabotka-dannyx/pandas-cookbook-1-csv-reading.html)\n",
    "\n",
    "Для углубленного изучения можно почитать книгу Дж. Вандел Плас Python для сложных задач наука о данных:\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "044a3a41",
   "metadata": {},
   "source": [
    "## NumPy\n",
    "\n",
    "Данный модуль создан для ускорения работы с массивами больших размерностей. Для примера рассмотрим сравнение скорости подсчета суммы случайного ряда при помощи встроенных инструментов и инструментов numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb97f953",
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем модуль и создадим ему короткий псевдоним для удобства обращения к нему\n",
    "import numpy as np\n",
    "\n",
    "# импортируем этот модуль для генерации случайных данных\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4d629828",
   "metadata": {},
   "outputs": [],
   "source": [
    "# создадим список длиной 10_000 случайных целых чисел в диапазоне от -10_000 до 10_000\n",
    "arr = random.sample(range(-10_000, 10_000),k=10_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0701ca1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Подсчитаем время исполнения ячейки при помощи волшебного оператора %%time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "062a85f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101 µs ± 556 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sum(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86298baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Подсчитаем теперь преобразованный список в NumPy-массив при помощи np.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ee277123",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_2 = np.array(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e58e9290",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.18 µs ± 17.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.sum(arr_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48501958",
   "metadata": {},
   "source": [
    "Как видно алгоритм подсчет суммы ускоряется почти в 20 раз"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06dd75f1",
   "metadata": {},
   "source": [
    "NumPy позволяет создавать различного рода матрицы в одно действие: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9930b6c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#создать матрицу размерности 2х2 заполненную нулями\n",
    "np.zeros((2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55ed68ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#создать матрицу размерности 3х2 заполненную единицами\n",
    "np.ones((3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e86bd15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n",
      "(3, 2)\n"
     ]
    }
   ],
   "source": [
    "# размерность массива можно посмотреть вызвав поле shape у np.array\n",
    "\n",
    "a = np.array([[1, 2], [3, 4], [5, 6]])\n",
    "\n",
    "print(a)\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "166f7114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# len вернет нам вернет размер по первому измерению\n",
    "\n",
    "print(len(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885f11a7",
   "metadata": {},
   "source": [
    "### Задание\n",
    "\n",
    "Найдите в документации numpy функции для создания диагональной матрицы и заполнения матрицы пользовательским числом.\n",
    "\n",
    "1) Создайте диагональную единичную матрицу размерности 5х5\n",
    "\n",
    "2) Создайте матрицу размерности 4х4 заполненную тройками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b85793e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d8cd415d",
   "metadata": {},
   "source": [
    "Массивы можно транспонировать и изменять размерности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c9ea473",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2],[3,4],[5,6]])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "058cd289",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "print(a.reshape((2,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0c2bd57d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 3 5]\n",
      " [2 4 6]]\n"
     ]
    }
   ],
   "source": [
    "print(a.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8a51aa3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "# можно так же расплющить массив в одномерный\n",
    "print(a.flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f356c97",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Создайте матрицу (любым известным вам способом) размерности 2х3х4 и транспонируйте ее. Попробуйте поменять очередность осей (по сути если представить трехмерную матрицу как куб, то это будет поворотом в пространстве на какой-то из боков) при помощи transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66d95b4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3e43e5ea",
   "metadata": {},
   "source": [
    "Массивы можно объединять и добавлять новые оси"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fff1f7a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[1,2],[3,4]])\n",
    "b = np.array([[5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7ae37c0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n"
     ]
    }
   ],
   "source": [
    "print(np.concatenate((a,b)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4aafd0ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "old matrix\n",
      "[[1 2]\n",
      " [3 4]]\n",
      "(2, 2) \n",
      "\n",
      "new matrix\n",
      "[[[1]\n",
      "  [2]]\n",
      "\n",
      " [[3]\n",
      "  [4]]]\n",
      "(2, 2, 1)\n"
     ]
    }
   ],
   "source": [
    "print('old matrix')\n",
    "print(a)\n",
    "print(a.shape, '\\n') # отступим строку \n",
    "\n",
    "print('new matrix')\n",
    "b = a[:,:,np.newaxis]\n",
    "print(b)\n",
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b9a5c9",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Создайте матрицу размерности 3х4 и продублируйте ее так, чтобы ее размерность стала 2х3х4 (понадобится newaxis и concatenate или при помощи squeeze)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90c9b02e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4a2fc50b",
   "metadata": {},
   "source": [
    "### Операции над матрицами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b799f6fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 2.]\n",
      " [3. 4.]] \n",
      "\n",
      "[[1. 0.]\n",
      " [0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# можно передавать тип данных, к которому требуется привести элементы матрицы, в данном случае float\n",
    "a = np.array([[1,2],[3,4]], float)\n",
    "# создадим единичную матрицу (на главной диагонали лежат единицы, все остальные элементы равны нулю)\n",
    "b = np.eye(2,2)\n",
    "\n",
    "print(a, '\\n')\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b91dd3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 2.],\n",
       "       [3., 5.]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c173c930",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 2.],\n",
       "       [3., 3.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a - b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab1e5d7",
   "metadata": {},
   "source": [
    "умножение матриц через операнд * работает как поэлементное умножение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a4b2adeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [0., 4.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * b"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a1aaf78",
   "metadata": {},
   "source": [
    "матрицу можно домножать на скаляр"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d080e165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10., 20.],\n",
       "       [30., 40.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642171cc",
   "metadata": {},
   "source": [
    "и проводить все базовые математические операции со скалярами поэлементно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "caf040de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a // 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca21353",
   "metadata": {},
   "source": [
    "Для матричного перемножения используется метод dot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "97af84bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2.],\n",
       "       [3., 4.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# т.к. при умножение на единичную матрицу исходная матрица не меняется\n",
    "a.dot(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf8a0f2",
   "metadata": {},
   "source": [
    "### Задание\n",
    "\n",
    "Создайте две матрицы размерности 2х3 и 3х2, перемножьте их"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ad3bc9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "71bc90d2",
   "metadata": {},
   "source": [
    "К массивам применимы те же операции, что и для списков. Их можно вызывать либо у массива, как метод, либо как функцию из numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ef0317fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2],[3,4]])\n",
    "\n",
    "print(a.max())\n",
    "print(np.max(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "448de690",
   "metadata": {},
   "source": [
    "### Задание\n",
    "\n",
    "Используя возможности numpy (БЕЗ SET) напишите функции, которые принимают заданный массив чисел и возвращают:\n",
    "\n",
    "1) список уникальных значений\n",
    "\n",
    "2) кортеж из среднего, максимального и минимального"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dff1fd72",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1,2,3,4,5,6,7,8,8,8,9,2,3,4,17])\n",
    "\n",
    "def unique_values(arr):\n",
    "    \"\"\"\n",
    "    напишите здесь свое решение\n",
    "    \"\"\"\n",
    "    pass\n",
    "\n",
    "def mean_max_min(arr):\n",
    "    \"\"\"\n",
    "    напишите здесь свое решение\n",
    "    \"\"\"\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "01a9aee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# тут менять ничего не нужно, просто исполнить ячейку\n",
    "print(unique_values(arr))\n",
    "print(mean_max_min(arr))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf2eea4",
   "metadata": {},
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5e0963",
   "metadata": {},
   "source": [
    "Данная библиотека используется для визуального представления данных (графики, гистограммы, изображения и т.д.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2bd5b586",
   "metadata": {},
   "outputs": [],
   "source": [
    "# импортируем модуль pyplot из пакета matplotlib и дадим общепринятое сокращенное имя plt\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "225c8dee",
   "metadata": {},
   "source": [
    "Рассмотрим для примера отрисовку графика синусоиды"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1bf1604a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x169302e1cd0>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# зададим функцию для подсчета значения синуса в точке\n",
    "def func(x):\n",
    "    return np.sin(x)\n",
    "\n",
    "# при помощи linspace создадим массив значений в 1000 шагов от минус 5Пи до плюс 5пи \n",
    "x = np.linspace(-np.pi * 5, np.pi * 5, 1000)\n",
    "\n",
    "# зададим параметр пропорций и размеров нашего графика (можете поменять эти значения или вообще удалить эту строчку для интереса)\n",
    "plt.figure(figsize=(10,3))\n",
    "# выведем график при помощи функции plot\n",
    "plt.plot(x, func(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ab380ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x169303e0850>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# в аргументы передается сначала значения оси Х (можно не задавать), а потом значения по оси Y\n",
    "\n",
    "# обратите внимание на значения оси Х, которые мы сами не задаем\n",
    "plt.figure(figsize=(10,3))\n",
    "plt.plot(func(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3a173a",
   "metadata": {},
   "source": [
    "графики можно накладывать друг на друга, менять цвета и тип линии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dd0fa23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x169304566a0>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "\n",
    "plt.plot(func(x), color='red', linestyle=':')\n",
    "plt.plot(func(x)*5, color='green', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e7a25d0",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Постройте графики функции x^2+2x-1 и ее производной синего и оранжевого цвета соответственно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf87e95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6183ff76",
   "metadata": {},
   "source": [
    "При помощи imshow можно смотреть на тепловые (высотные) карты двумерных данных. Например значения функций двух переменных или изображения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fd9bad72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x169304b67c0>"
      ]
     },
     "execution_count": 33,
     "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": [
    "arr = np.array([[0,1,0],[1,2,1],[0,1,0]])\n",
    "plt.imshow(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "420a9abe",
   "metadata": {},
   "source": [
    "При помощи hist можно отобразить сравнительное графическое представление данных в виде гистограммы.\n",
    "\n",
    "Указывая количество бинов, мы можем регулировать промежутки на которых будут формироваться столбцы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f2c4d922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQ9UlEQVR4nO3dXYxcZ33H8e+vxlYLRFDwJiC/4Fz4goDiEK1MUFDjoBI5vNRFopItCggRWUGJBBWlMlwQld5QIaEKErAssAIqSVQJAlYxJBGlDSUK9ToNSUwSujJps3KEDaHhVY1M/73YYzrZzHqOvbO7zrPfjzSac56XM8954vz27LNnZlJVSJLa9XvLPQBJ0uIy6CWpcQa9JDXOoJekxhn0ktS45y33AIZZu3Ztbdq0abmHIUnPGYcPH/5JVU0Mqzsng37Tpk1MTU0t9zAk6TkjyX/OV+fSjSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWrcyKBPsiHJt5M8nORIkvcPaZMkn0oyneSBJJcO1G1P8mhXt2fcJyBJOr0+V/QngQ9W1SuBy4Drklw0p83VwObusRv4LECSVcBNXf1FwK4hfSVJi2hk0FfVE1V1X7f9C+BhYN2cZjuAL9ase4EXJ3k5sBWYrqqjVfU0cFvXVpK0RM7onbFJNgGvAb43p2od8PjA/kxXNqz8tfMcezezvw2wcePGMxnWM2za8/Wz7rsQj338zcvyupI0Su8/xiZ5IfBl4ANV9fO51UO61GnKn11Yta+qJqtqcmJi6Mc1SJLOQq8r+iSrmQ35L1XVV4Y0mQE2DOyvB44Ba+YplyQtkT533QT4PPBwVX1ynmYHgHd1d99cBjxVVU8Ah4DNSS5MsgbY2bWVJC2RPlf0lwPvBB5Mcn9X9hFgI0BV7QUOAm8CpoFfA+/p6k4muR64A1gF7K+qI+M8AUnS6Y0M+qr6V4avtQ+2KeC6eeoOMvuDQJK0DHxnrCQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcSO/eCTJfuAtwPGqevWQ+g8B7xg43iuBiap6MsljwC+A3wInq2pyXAOXJPXT54r+ZmD7fJVV9YmquqSqLgE+DPxLVT050OTKrt6Ql6RlMDLoq+pu4MlR7Tq7gFsXNCJJ0liNbY0+yfOZvfL/8kBxAXcmOZxk97heS5LU38g1+jPwVuC7c5ZtLq+qY0nOB+5K8kj3G8KzdD8IdgNs3LhxjMOSpJVtnHfd7GTOsk1VHeuejwO3A1vn61xV+6pqsqomJyYmxjgsSVrZxhL0SV4EXAF8baDsBUnOO7UNXAU8NI7XkyT11+f2yluBbcDaJDPADcBqgKra2zV7G3BnVf1qoOsFwO1JTr3OLVX1zfENXZLUx8igr6pdPdrczOxtmINlR4EtZzswSdJ4+M5YSWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJatzIoE+yP8nxJEO/7zXJtiRPJbm/e3x0oG57kkeTTCfZM86BS5L66XNFfzOwfUSb71TVJd3jYwBJVgE3AVcDFwG7kly0kMFKks7cyKCvqruBJ8/i2FuB6ao6WlVPA7cBO87iOJKkBRjXGv3rknw/yTeSvKorWwc8PtBmpisbKsnuJFNJpk6cODGmYUmSxhH09wGvqKotwKeBr3blGdK25jtIVe2rqsmqmpyYmBjDsCRJMIagr6qfV9Uvu+2DwOoka5m9gt8w0HQ9cGyhrydJOjMLDvokL0uSbntrd8yfAoeAzUkuTLIG2AkcWOjrSZLOzPNGNUhyK7ANWJtkBrgBWA1QVXuBtwPvS3IS+A2ws6oKOJnkeuAOYBWwv6qOLMpZSJLmNTLoq2rXiPobgRvnqTsIHDy7oUmSxsF3xkpS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjRgZ9kv1Jjid5aJ76dyR5oHvck2TLQN1jSR5Mcn+SqXEOXJLUT58r+puB7aep/xFwRVVdDPwNsG9O/ZVVdUlVTZ7dECVJC9HnO2PvTrLpNPX3DOzeC6wfw7gkSWMy7jX69wLfGNgv4M4kh5PsPl3HJLuTTCWZOnHixJiHJUkr18gr+r6SXMls0L9+oPjyqjqW5HzgriSPVNXdw/pX1T66ZZ/Jycka17gkaaUbyxV9kouBzwE7quqnp8qr6lj3fBy4Hdg6jteTJPW34KBPshH4CvDOqvrhQPkLkpx3ahu4Chh6544kafGMXLpJciuwDVibZAa4AVgNUFV7gY8CLwU+kwTgZHeHzQXA7V3Z84Bbquqbi3AOkqTT6HPXza4R9dcA1wwpPwpseXYPSdJS8p2xktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1LiRQZ9kf5LjSYZ+32tmfSrJdJIHklw6ULc9yaNd3Z5xDlyS1E+fK/qbge2nqb8a2Nw9dgOfBUiyCripq78I2JXkooUMVpJ05kYGfVXdDTx5miY7gC/WrHuBFyd5ObAVmK6qo1X1NHBb11aStIRGfjl4D+uAxwf2Z7qyYeWvne8gSXYz+xsBGzduHMOwltamPV9fttd+7ONvXpbX9ZyXznKd70rV2n/ncfwxNkPK6jTlQ1XVvqqarKrJiYmJMQxLkgTjuaKfATYM7K8HjgFr5imXJC2hcVzRHwDe1d19cxnwVFU9ARwCNie5MMkaYGfXVpK0hEZe0Se5FdgGrE0yA9wArAaoqr3AQeBNwDTwa+A9Xd3JJNcDdwCrgP1VdWQRzkGSdBojg76qdo2oL+C6eeoOMvuDQJK0THxnrCQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDWuV9An2Z7k0STTSfYMqf9Qkvu7x0NJfpvkJV3dY0ke7Oqmxn0CkqTT6/OdsauAm4A3AjPAoSQHquoHp9pU1SeAT3Tt3wr8RVU9OXCYK6vqJ2MduSSplz5X9FuB6ao6WlVPA7cBO07Tfhdw6zgGJ0lauD5Bvw54fGB/pit7liTPB7YDXx4oLuDOJIeT7J7vRZLsTjKVZOrEiRM9hiVJ6qNP0GdIWc3T9q3Ad+cs21xeVZcCVwPXJfmjYR2ral9VTVbV5MTERI9hSZL66BP0M8CGgf31wLF52u5kzrJNVR3rno8DtzO7FCRJWiJ9gv4QsDnJhUnWMBvmB+Y2SvIi4ArgawNlL0hy3qlt4CrgoXEMXJLUz8i7bqrqZJLrgTuAVcD+qjqS5Nqufm/X9G3AnVX1q4HuFwC3Jzn1WrdU1TfHeQKSpNMbGfQAVXUQODinbO+c/ZuBm+eUHQW2LGiEkqQF8Z2xktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TGGfSS1LheQZ9ke5JHk0wn2TOkfluSp5Lc3z0+2revJGlxjfwqwSSrgJuANwIzwKEkB6rqB3Oafqeq3nKWfSVJi6TPFf1WYLqqjlbV08BtwI6ex19IX0nSGPQJ+nXA4wP7M13ZXK9L8v0k30jyqjPsS5LdSaaSTJ04caLHsCRJffQJ+gwpqzn79wGvqKotwKeBr55B39nCqn1VNVlVkxMTEz2GJUnqo0/QzwAbBvbXA8cGG1TVz6vql932QWB1krV9+kqSFlefoD8EbE5yYZI1wE7gwGCDJC9Lkm57a3fcn/bpK0laXCPvuqmqk0muB+4AVgH7q+pIkmu7+r3A24H3JTkJ/AbYWVUFDO27SOciSRpiZNDD75ZjDs4p2zuwfSNwY9++kqSl4ztjJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXG9gj7J9iSPJplOsmdI/TuSPNA97kmyZaDusSQPJrk/ydQ4By9JGm3kVwkmWQXcBLwRmAEOJTlQVT8YaPYj4Iqq+lmSq4F9wGsH6q+sqp+McdySpJ76XNFvBaar6mhVPQ3cBuwYbFBV91TVz7rde4H14x2mJOls9Qn6dcDjA/szXdl83gt8Y2C/gDuTHE6ye75OSXYnmUoydeLEiR7DkiT1MXLpBsiQshraMLmS2aB//UDx5VV1LMn5wF1JHqmqu591wKp9zC75MDk5OfT4kqQz1+eKfgbYMLC/Hjg2t1GSi4HPATuq6qenyqvqWPd8HLid2aUgSdIS6RP0h4DNSS5MsgbYCRwYbJBkI/AV4J1V9cOB8hckOe/UNnAV8NC4Bi9JGm3k0k1VnUxyPXAHsArYX1VHklzb1e8FPgq8FPhMEoCTVTUJXADc3pU9D7ilqr65KGciSRqqzxo9VXUQODinbO/A9jXANUP6HQW2zC2XJC0d3xkrSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9JjesV9Em2J3k0yXSSPUPqk+RTXf0DSS7t21eStLhGBn2SVcBNwNXARcCuJBfNaXY1sLl77AY+ewZ9JUmLqM8V/VZguqqOVtXTwG3AjjltdgBfrFn3Ai9O8vKefSVJi6jPl4OvAx4f2J8BXtujzbqefQFIspvZ3wYAfpnk0R5ja8Fa4CcLOUD+dkwjOff9bq5W0DkDZ3y+C/43tUKcc/O0wH/Xr5ivok/QZ0hZ9WzTp+9sYdU+YF+P8TQlyVRVTS73OJ4LnKt+nKd+VtI89Qn6GWDDwP564FjPNmt69JUkLaI+a/SHgM1JLkyyBtgJHJjT5gDwru7um8uAp6rqiZ59JUmLaOQVfVWdTHI9cAewCthfVUeSXNvV7wUOAm8CpoFfA+85Xd9FOZPnrhW3XLUAzlU/zlM/K2aeUjV0yVyS1AjfGStJjTPoJalxBv0SSrI/yfEkDw2UvSTJXUn+o3v+w+Uc47kgyYYk307ycJIjSd7flTtXA5L8fpJ/S/L9bp7+uit3noZIsirJvyf5x25/xcyTQb+0bga2zynbA3yrqjYD3+r2V7qTwAer6pXAZcB13UdnOFfP9D/AG6pqC3AJsL276815Gu79wMMD+ytmngz6JVRVdwNPzineAXyh2/4C8KdLOaZzUVU9UVX3ddu/YPZ/znU4V8/QfeTIL7vd1d2jcJ6eJcl64M3A5waKV8w8GfTL74LuPQd0z+cv83jOKUk2Aa8Bvodz9SzdcsT9wHHgrqpynob7O+CvgP8dKFsx82TQ65yV5IXAl4EPVNXPl3s856Kq+m1VXcLsu863Jnn1Mg/pnJPkLcDxqjq83GNZLgb98vtx90mfdM/Hl3k854Qkq5kN+S9V1Ve6YudqHlX138A/M/s3IOfpmS4H/iTJY8x+gu4bkvw9K2ieDPrldwB4d7f9buBryziWc0KSAJ8HHq6qTw5UOVcDkkwkeXG3/QfAHwOP4Dw9Q1V9uKrWV9UmZj+G5Z+q6s9ZQfPkO2OXUJJbgW3Mfjzqj4EbgK8C/wBsBP4L+LOqmvsH2xUlyeuB7wAP8v9rqh9hdp3eueokuZjZPyKuYvai7R+q6mNJXorzNFSSbcBfVtVbVtI8GfSS1DiXbiSpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJatz/AXhVSugcd0FYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.array([10,4,26,17,8,45])\n",
    "\n",
    "plt.hist(data, bins=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "913b220c",
   "metadata": {},
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af08b78f",
   "metadata": {},
   "source": [
    "Pandas позволяет обрабатывать и анализировать данные. Можно провести аналогию с Excel.\n",
    "В данном разделе рассмотрим основные возможности (т.к. библиотека крайне объемная и всю документацию будет физически невозможно сюда уместить)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7ebddb89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Импортируем библотеку pandas и дадим ей сокращенное общепринятое имя pd \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4199517",
   "metadata": {},
   "source": [
    "Pandas позволяет считывать табличные данные из файлов в форматах .csv, .xml, .xlsx, .json и т.д.\n",
    "\n",
    "Скачаем для примера классический датасет с информацией о пассажирах титаника.\n",
    "\n",
    "Линк на датасет: https://github.com/datasciencedojo/datasets/blob/master/titanic.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bbf38d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Загрузим данный датасет. \n",
    "т.к. он в формате .csv то воспользуемся функцией read_csv() \n",
    "в которой укажем относительный путь к этому файлу \n",
    "\"\"\"\n",
    "df = pd.read_csv('lab_3_titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "268d6ec7",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим первые 10 записей\n",
    "df[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "044a069c",
   "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",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# то же самое можно сделать и так\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09d489ac",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Выведите последние 10 строк таблицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "beb0df55",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ca5614f8",
   "metadata": {},
   "source": [
    "Как видим, в таблице имеются столбцы, означающие некоторые признаки:\n",
    "- PassengerId: уникальный идентификатор пассажира в данном наборе данных\n",
    "- Survived: 0 - погиб, 1 - выжил\n",
    "- Pclass: класс обслуживания пассажира\n",
    "- Name: ФИО пассажира (как был записан в документах)\n",
    "- Sex: пол пассажира\n",
    "- Age: возраст пассажира\n",
    "- SibSp: сколько братьев/сестер или супругов на борту\n",
    "- Parch: сколько детей/родителей на борту\n",
    "- Ticket: номер билета\n",
    "- Fare: пассажирский тариф\n",
    "- Cabin: номер каюты\n",
    "- Embarked: порт погрузки. C - Cherbourg, Q - Queenstown, S - Southampton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "14180f18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PassengerId',\n",
       " 'Survived',\n",
       " 'Pclass',\n",
       " 'Name',\n",
       " 'Sex',\n",
       " 'Age',\n",
       " 'SibSp',\n",
       " 'Parch',\n",
       " 'Ticket',\n",
       " 'Fare',\n",
       " 'Cabin',\n",
       " 'Embarked']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Получить список столбцов можно при помощи следующей функции\n",
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0701d6db",
   "metadata": {},
   "source": [
    "Краткую статистику по всем данным можно получить при помощи метода describe().\n",
    "\n",
    "Она будет посчитана только для тех признаков, которые представлены численно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d0cde577",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c1c6b5",
   "metadata": {},
   "source": [
    "Информацию по типам данных и количеству пропусков можно посмотреть при помощи info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "964c9588",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a039cc",
   "metadata": {},
   "source": [
    "Можно применять различные встроенные методы к определенным признакам, обращаясь двумя равнозначными способами:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "375462f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Survived.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a7fbc32b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3838383838383838"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33c70db",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Выведите максимальный и минимальный возраст пассажиров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ef929a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bd46e44e",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Воспользуйтесь встроенным методом value_counts для признака Pclass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b38010e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "858da916",
   "metadata": {},
   "source": [
    "Для получения конкретной строки можно использовать индексацию:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "890e3a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId                   6\n",
       "Survived                      0\n",
       "Pclass                        3\n",
       "Name           Moran, Mr. James\n",
       "Sex                        male\n",
       "Age                         NaN\n",
       "SibSp                         0\n",
       "Parch                         0\n",
       "Ticket                   330877\n",
       "Fare                     8.4583\n",
       "Cabin                       NaN\n",
       "Embarked                      Q\n",
       "Name: 5, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d365deb",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Выведите номер билета у 120-го пассажира "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c8bea19",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d024a508",
   "metadata": {},
   "source": [
    "Возможно применение фильтрации для данных по какому-либо условию:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a9f9500d",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
       "      <td>female</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Wheadon, Mr. Edward H</td>\n",
       "      <td>male</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A. 24579</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Ostby, Mr. Engelhart Cornelius</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>113509</td>\n",
       "      <td>61.9792</td>\n",
       "      <td>B30</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hays, Mrs. Charles Melville (Clara Jennings Gr...</td>\n",
       "      <td>female</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12749</td>\n",
       "      <td>93.5000</td>\n",
       "      <td>B69</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>858</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Daly, Mr. Peter Denis</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113055</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>E17</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "6              7         0       1   \n",
       "11            12         1       1   \n",
       "15            16         1       2   \n",
       "33            34         0       2   \n",
       "54            55         0       1   \n",
       "..           ...       ...     ...   \n",
       "820          821         1       1   \n",
       "829          830         1       1   \n",
       "851          852         0       3   \n",
       "857          858         1       1   \n",
       "879          880         1       1   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "15                    Hewlett, Mrs. (Mary D Kingcome)   female  55.0      0   \n",
       "33                               Wheadon, Mr. Edward H    male  66.0      0   \n",
       "54                      Ostby, Mr. Engelhart Cornelius    male  65.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "820  Hays, Mrs. Charles Melville (Clara Jennings Gr...  female  52.0      1   \n",
       "829          Stone, Mrs. George Nelson (Martha Evelyn)  female  62.0      0   \n",
       "851                                Svensson, Mr. Johan    male  74.0      0   \n",
       "857                             Daly, Mr. Peter Denis     male  51.0      0   \n",
       "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
       "\n",
       "     Parch      Ticket     Fare Cabin Embarked  \n",
       "6        0       17463  51.8625   E46        S  \n",
       "11       0      113783  26.5500  C103        S  \n",
       "15       0      248706  16.0000   NaN        S  \n",
       "33       0  C.A. 24579  10.5000   NaN        S  \n",
       "54       1      113509  61.9792   B30        C  \n",
       "..     ...         ...      ...   ...      ...  \n",
       "820      1       12749  93.5000   B69        S  \n",
       "829      0      113572  80.0000   B28      NaN  \n",
       "851      0      347060   7.7750   NaN        S  \n",
       "857      0      113055  26.5500   E17        S  \n",
       "879      1       11767  83.1583   C50        C  \n",
       "\n",
       "[64 rows x 12 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# посмотрим все данные пассажиров, которые старше 50\n",
    "df[df.Age > 50]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd091601",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Выведите имена всех мужчин в виде списка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b52fd60b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c2a8a119",
   "metadata": {},
   "source": [
    "Можно использовать встроенные графики для отображения данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d1066e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "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": [
    "df.Parch.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e95229",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Постройте гистограмму на которой будет отображено количество людей в каждом из классов обслуживания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3b54a84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1ed5afe4",
   "metadata": {},
   "source": [
    "Из датафрейма можно выделять подвыборки и делать их самостоятельными датафреймами:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b7c07681",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>314 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "8              9         1       3   \n",
       "9             10         1       2   \n",
       "..           ...       ...     ...   \n",
       "880          881         1       2   \n",
       "882          883         0       3   \n",
       "885          886         0       3   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "8        2            347742  11.1333   NaN        S  \n",
       "9        0            237736  30.0708   NaN        C  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "880      1            230433  26.0000   NaN        S  \n",
       "882      0              7552  10.5167   NaN        S  \n",
       "885      5            382652  29.1250   NaN        Q  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "\n",
       "[314 rows x 12 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Выделим в отдельную таблицу всех женщин\n",
    "df_2 = df[df.Sex == 'female']\n",
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "15eef0ed",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Name   Age\n",
       "0                              Braund, Mr. Owen Harris  22.0\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0\n",
       "2                               Heikkinen, Miss. Laina  26.0\n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0\n",
       "4                             Allen, Mr. William Henry  35.0\n",
       "..                                                 ...   ...\n",
       "886                              Montvila, Rev. Juozas  27.0\n",
       "887                       Graham, Miss. Margaret Edith  19.0\n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"   NaN\n",
       "889                              Behr, Mr. Karl Howell  26.0\n",
       "890                                Dooley, Mr. Patrick  32.0\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Выделим датафрейм хранящий только имена и возраст\n",
    "df_3 = df[['Name', 'Age']]\n",
    "df_3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "985f01cf",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Создайте датафрейм где будут поля Name и Survived для пассажиров без детей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b462754",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c73eba53",
   "metadata": {},
   "source": [
    "Для преобразования значений столбца можно применять метод .apply с указанием в нем функции, применяемой к значению каждой строки. В данном примере применяется анонимная функция (лямбда-функция)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4d58666a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Embarked</th>\n",
       "      <th>isAdult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>PC 17599</td>\n",
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       "      <td>True</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
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       "      <td>S</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  isAdult  \n",
       "0        0         A/5 21171   7.2500   NaN        S     True  \n",
       "1        0          PC 17599  71.2833   C85        C     True  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S     True  \n",
       "3        0            113803  53.1000  C123        S     True  \n",
       "4        0            373450   8.0500   NaN        S     True  \n",
       "..     ...               ...      ...   ...      ...      ...  \n",
       "886      0            211536  13.0000   NaN        S     True  \n",
       "887      0            112053  30.0000   B42        S     True  \n",
       "888      2        W./C. 6607  23.4500   NaN        S    False  \n",
       "889      0            111369  30.0000  C148        C     True  \n",
       "890      0            370376   7.7500   NaN        Q     True  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['isAdult'] = df['Age'].apply(lambda x: x > 18)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51e5b6be",
   "metadata": {},
   "source": [
    "### Задание\n",
    "Посчитайте среднее значение цены билета на корабле. Введите новый столбец, который будет показывать дешевле или дороже средней цены был купленный билет у каждого человека. Можете заполнить поля значениями True/False или строчными представлениями \"дешевле\"/\"дороже\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "167386f6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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