From 5baaf657091a24f3f53efc78a66188fbe7ac5560 Mon Sep 17 00:00:00 2001 From: KardynovMikhail <85884247+KardynovMikhail@users.noreply.github.com> Date: Mon, 14 Jun 2021 16:57:40 +0300 Subject: [PATCH] Add files via upload --- ml/lb1/Lab_1.ipynb | 5149 ++++++++++++++++++++++++++++++++++++++++++++ ml/lb1/titanic.csv | 892 ++++++++ 2 files changed, 6041 insertions(+) create mode 100644 ml/lb1/Lab_1.ipynb create mode 100644 ml/lb1/titanic.csv diff --git a/ml/lb1/Lab_1.ipynb b/ml/lb1/Lab_1.ipynb new file mode 100644 index 00000000..7a033e6a --- /dev/null +++ b/ml/lb1/Lab_1.ipynb @@ -0,0 +1,5149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "purple-bottom", + "metadata": {}, + "source": [ + "# Markdown\n", + "\n", + "## Краткое руководство \n", + "\n", + "Абзаци создаются припомощи пустой строки. Если вокруг текста сверху и снизу есть пустые строки, то текст превращается в абзац.\n", + "\n", + "Чтобы сделать перенос строки, вместо абзаца, нужно поставить два пробела в конце предыдущей строки.\n", + "\n", + "Заголовки отмечаются диезом '#' в начале строки, от одного до шести. Например:\n", + "\n", + "# Заголовок первого уровня #\n", + "## Заголовок h2\n", + "### Заголовок h3\n", + "#### Заголовок h4\n", + "##### Заголовок h5\n", + "###### Заголовок h6\n", + "\n", + "В декоративных целях заголовки можно \"закрывать\" с обратной стороны." + ] + }, + { + "cell_type": "markdown", + "id": "boxed-iceland", + "metadata": {}, + "source": [ + "### Списки\n", + "\n", + "Для разметки неупорядоченных списков можно использовать или '*', или '-', или '+':\n", + "\n", + "- элемент 1\n", + "- элемент 2\n", + "- эелемент ...\n", + "\n", + "Вложенные пункты создаются четырьмя пробемелами перед меркером пункта:\n", + "\n", + "* элемент 1\n", + "* элемант 2\n", + " 1. вложенный элемент 2.1\n", + " 2. вложенный элемент 2.2\n", + "* элемент ...\n", + "\n", + "Упорядоченный список:\n", + "\n", + "1. элемент 1\n", + "2. элемент 2\n", + " 1. вложенный\n", + " 2. вложенный\n", + "3. элемент 3\n", + "4. Donec sit amet nisl. Aliquam semper ipsum sit amet velit. Suspendisse id sem consectetuer libero luctus adipisccing." + ] + }, + { + "cell_type": "markdown", + "id": "following-mirror", + "metadata": {}, + "source": [ + "# NumPy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "pharmaceutical-spider", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "copyrighted-moore", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vec = np.array([1, 2, 3])\n", + "vec.ndim # количество осей" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "unknown-contribution", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mat = np.array([[1, 2, 3], [4, 5, 6]])\n", + "mat.ndim" + ] + }, + { + "cell_type": "markdown", + "id": "conscious-jewelry", + "metadata": {}, + "source": [ + "##### Чтобы узнать длинну массива по каждой из осей" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "individual-burden", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3,)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vec.shape" + ] + }, + { + "cell_type": "markdown", + "id": "theoretical-insulation", + "metadata": {}, + "source": [ + "##### Чтобы узнать тип элементов и их размер в байтах" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "advance-karen", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'int32'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mat.dtype.name" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "compliant-arnold", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mat.itemsize" + ] + }, + { + "cell_type": "markdown", + "id": "presidential-indian", + "metadata": {}, + "source": [ + "## Создание массивов" + ] + }, + { + "cell_type": "markdown", + "id": "chief-gregory", + "metadata": {}, + "source": [ + "##### Передать итерируемый объект в качестве параметра функции array" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "several-resident", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 3])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array ([1, 2, 3])\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "another-ballet", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1., 2., 3.])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.array ([1, 2, 3], dtype = float)\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "stable-kelly", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3],\n", + " [4, 5, 6]])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "B = np.array ([(1, 2, 3), (4, 5, 6)])\n", + "B" + ] + }, + { + "cell_type": "markdown", + "id": "certain-mechanics", + "metadata": {}, + "source": [ + "##### Создание массива специального вида при помощи функций zeros, ones, empty, identity:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "deluxe-south", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0.])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.zeros ((3,))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "endangered-clothing", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 1., 1., 1.],\n", + " [1., 1., 1., 1.],\n", + " [1., 1., 1., 1.]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.ones((3, 4))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "fifteen-indian", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0.],\n", + " [0., 1., 0.],\n", + " [0., 0., 1.]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.identity(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "editorial-quantity", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[4.00528573e-307, 1.89144519e-307, 4.67265511e-307,\n", + " 3.33774999e-307, 1.61323712e-307],\n", + " [1.64352612e-288, 4.67265516e-307, 3.56039658e-307,\n", + " 2.78146116e-307, 2.11392372e-307]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.empty((2, 5))" + ] + }, + { + "cell_type": "markdown", + "id": "miniature-illness", + "metadata": {}, + "source": [ + "##### Создание последовательностей при помощи функций arange и linspace" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "assisted-overview", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 5, 8, 11, 14, 17])" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange (2, 20, 3) # аналагично стандартной функции range python, правая граница не включается" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "unlike-payday", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2.5, 3.4, 4.3, 5.2, 6.1, 7. , 7.9])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange (2.5, 8.7, 0.9) #нш может работать и с вещественными числами" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "supposed-ticket", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2. , 3.23076923, 4.46153846, 5.69230769, 6.92307692,\n", + " 8.15384615, 9.38461538, 10.61538462, 11.84615385, 13.07692308,\n", + " 14.30769231, 15.53846154, 16.76923077, 18. ])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.linspace (2, 18, 14) #правая граница включается (по умолчанию)" + ] + }, + { + "cell_type": "markdown", + "id": "headed-april", + "metadata": {}, + "source": [ + "##### Для изменения размеров существующего массива можно воспользоваться функцией reshape" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "smaller-douglas", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2],\n", + " [3, 4, 5],\n", + " [6, 7, 8]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(9).reshape(3, 3)" + ] + }, + { + "cell_type": "markdown", + "id": "formal-tennis", + "metadata": {}, + "source": [ + "##### Вместо значения длинны массива по одному из измерений можно указать -1 - в этом случае значение будет рассчитано автоматически" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "arranged-signal", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2, 3],\n", + " [4, 5, 6, 7]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.arange(8).reshape(2, -1)" + ] + }, + { + "cell_type": "markdown", + "id": "banner-armstrong", + "metadata": {}, + "source": [ + "##### Транспортирование существующего массива" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "disturbed-greensboro", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2],\n", + " [3, 4, 5]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C = np.arange(6).reshape(2, -1)\n", + "C" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "urban-guitar", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 3],\n", + " [1, 4],\n", + " [2, 5]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "C. T" + ] + }, + { + "cell_type": "markdown", + "id": "packed-chest", + "metadata": {}, + "source": [ + "##### Объединение существующих массиво по заданной оси" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "overhead-party", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 0, 1, 4],\n", + " [ 3, 4, 5, 9, 16, 25]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.arange (6).reshape (2, -1)\n", + "np.hstack((A, A**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "infrared-freedom", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2],\n", + " [ 3, 4, 5],\n", + " [ 0, 1, 4],\n", + " [ 9, 16, 25]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.vstack((A, A**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "honey-creek", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 0, 1, 4],\n", + " [ 3, 4, 5, 9, 16, 25]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.concatenate ((A, A**2), axis =1)" + ] + }, + { + "cell_type": "markdown", + "id": "cooperative-watch", + "metadata": {}, + "source": [ + "##### Повторение сущствующего массива " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "personalized-korean", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2, 0, 1, 2],\n", + " [0, 1, 2, 0, 1, 2]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.arange(3)\n", + "np.tile (a, (2, 2))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "alleged-riverside", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2],\n", + " [0, 1, 2],\n", + " [0, 1, 2],\n", + " [0, 1, 2]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.tile(a, (4, 1))" + ] + }, + { + "cell_type": "markdown", + "id": "proprietary-daniel", + "metadata": {}, + "source": [ + "## Базовые операции" + ] + }, + { + "cell_type": "markdown", + "id": "accessible-aberdeen", + "metadata": {}, + "source": [ + "##### Базовые арифметические оперции над массивами выполняется поэлементно" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "seventh-flooring", + "metadata": {}, + "outputs": [], + "source": [ + "A = np.arange(9).reshape(3, 3)\n", + "B = np.arange (1, 10).reshape (3, 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "friendly-insight", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 2]\n", + " [3 4 5]\n", + " [6 7 8]]\n", + "[[1 2 3]\n", + " [4 5 6]\n", + " [7 8 9]]\n" + ] + } + ], + "source": [ + "print (A)\n", + "print (B)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "bottom-ranking", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1, 3, 5],\n", + " [ 7, 9, 11],\n", + " [13, 15, 17]])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A + B" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "inappropriate-landing", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0. , 0.5 , 0.66666667],\n", + " [0.75 , 0.8 , 0.83333333],\n", + " [0.85714286, 0.875 , 0.88888889]])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A * 1.0/ B" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "prescription-carol", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 2, 3],\n", + " [4, 5, 6],\n", + " [7, 8, 9]])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A +1" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "round-acoustic", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 3, 6],\n", + " [ 9, 12, 15],\n", + " [18, 21, 24]])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "3*A" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "peaceful-stations", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 4],\n", + " [ 9, 16, 25],\n", + " [36, 49, 64]], dtype=int32)" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A ** 2" + ] + }, + { + "cell_type": "markdown", + "id": "urban-junior", + "metadata": {}, + "source": [ + "##### Отдельно обратим внимание на то, что умножение массива также является поэлементным, а не матричным " + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "aboriginal-wheel", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 2, 6],\n", + " [12, 20, 30],\n", + " [42, 56, 72]])" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A * B" + ] + }, + { + "cell_type": "markdown", + "id": "plastic-command", + "metadata": {}, + "source": [ + "##### Для выполнения метричного умножения необходимо использоовать функцию dot" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "coastal-robinson", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 18, 21, 24],\n", + " [ 54, 66, 78],\n", + " [ 90, 111, 132]])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.dot(B)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "married-rendering", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2],\n", + " [10, 11, 12],\n", + " [20, 21, 22],\n", + " [30, 31, 32]])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.tile(np.arange(0, 40, 10), (3, 1)).T + np.array([0, 1, 2])" + ] + }, + { + "cell_type": "markdown", + "id": "upper-gabriel", + "metadata": {}, + "source": [ + "##### Универсальные функции так же применяются поэлементно" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "broad-wallpaper", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.00000000e+00, 2.71828183e+00, 7.38905610e+00],\n", + " [2.00855369e+01, 5.45981500e+01, 1.48413159e+02],\n", + " [4.03428793e+02, 1.09663316e+03, 2.98095799e+03]])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.exp(A)" + ] + }, + { + "cell_type": "markdown", + "id": "victorian-particular", + "metadata": {}, + "source": [ + "##### Некоторые оперции над массивами выполняются над всеми элементами вне зависимости отформы массива =, однако при указании оси выполняются вдоль нее" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "thick-mauritius", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2],\n", + " [3, 4, 5],\n", + " [6, 7, 8]])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "premium-parent", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.min()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "german-arcade", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6, 7, 8])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.max(axis = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "trained-spyware", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3, 12, 21])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A.sum (axis =1)" + ] + }, + { + "cell_type": "markdown", + "id": "consolidated-customer", + "metadata": {}, + "source": [ + "## Индексация" + ] + }, + { + "cell_type": "markdown", + "id": "purple-psychiatry", + "metadata": {}, + "source": [ + "##### Для индексации могут исопльзовать ся конкретные значения индесксов и среды. Для многомерных массивов индексы для различных осей разделяются запятой. Если для иногомерного массива указаны индексы не для всех измерений, недостающие заполняются полным срезом" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "toxic-controversy", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.arange(10)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "divided-interpretation", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2, 3, 4])" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[2:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "aboriginal-banana", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 5, 7])" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[3:8:2]" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "arbitrary-assurance", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8],\n", + " [ 9, 10, 11, 12, 13, 14, 15, 16, 17],\n", + " [18, 19, 20, 21, 22, 23, 24, 25, 26],\n", + " [27, 28, 29, 30, 31, 32, 33, 34, 35],\n", + " [36, 37, 38, 39, 40, 41, 42, 43, 44],\n", + " [45, 46, 47, 48, 49, 50, 51, 52, 53],\n", + " [54, 55, 56, 57, 58, 59, 60, 61, 62],\n", + " [63, 64, 65, 66, 67, 68, 69, 70, 71],\n", + " [72, 73, 74, 75, 76, 77, 78, 79, 80]])" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.arange(81).reshape(9, -1)\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "focal-native", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[18, 19, 20, 21, 22, 23, 24, 25, 26],\n", + " [27, 28, 29, 30, 31, 32, 33, 34, 35]])" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[2:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "welsh-edmonton", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2, 3],\n", + " [11, 12],\n", + " [20, 21],\n", + " [29, 30],\n", + " [38, 39],\n", + " [47, 48],\n", + " [56, 57],\n", + " [65, 66],\n", + " [74, 75]])" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[:, 2:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "approximate-start", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[20, 21],\n", + " [29, 30]])" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[2:4, 2:4]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "after-longitude", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([72, 73, 74, 75, 76, 77, 78, 79, 80])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[-1]" + ] + }, + { + "cell_type": "markdown", + "id": "visible-links", + "metadata": {}, + "source": [ + "##### Также может использоваться индексация при помощи списков индексов " + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "american-press", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8],\n", + " [ 9, 10, 11, 12, 13, 14, 15, 16, 17],\n", + " [18, 19, 20, 21, 22, 23, 24, 25, 26],\n", + " [27, 28, 29, 30, 31, 32, 33, 34, 35],\n", + " [36, 37, 38, 39, 40, 41, 42, 43, 44],\n", + " [45, 46, 47, 48, 49, 50, 51, 52, 53],\n", + " [54, 55, 56, 57, 58, 59, 60, 61, 62],\n", + " [63, 64, 65, 66, 67, 68, 69, 70, 71],\n", + " [72, 73, 74, 75, 76, 77, 78, 79, 80]])" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.arange(81).reshape (9, -1)\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "committed-typing", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([18, 37, 48])" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[[2, 4, 5], [0, 1, 3]]" + ] + }, + { + "cell_type": "markdown", + "id": "protective-identifier", + "metadata": {}, + "source": [ + "##### Может так же применяться логическая индексация" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "successful-paintball", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = np.arange(11)\n", + "A" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "systematic-filename", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 4, 5, 6, 7, 9, 10])" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[A % 5 != 3]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "otherwise-admission", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 4, 5, 6, 9, 10])" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A[np.logical_and(A != 7, A % 5 !=3)]" + ] + }, + { + "cell_type": "markdown", + "id": "liberal-cooking", + "metadata": {}, + "source": [ + "### Cкорость" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "helpful-tradition", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "A_quick_arr = np.random.normal(size=(1000000,))\n", + "B_quick_arr = np.random.normal(size=(1000000,))\n", + "\n", + "A_slow_list, B_slow_list = list(A_quick_arr), list(B_quick_arr)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "departmental-rocket", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.259075899997697\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "ans = 0\n", + "for i in range(len(A_slow_list)):\n", + " ans += A_slow_list[i] * B_slow_list[i]\n", + "print (time.perf_counter() - start)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "infectious-moscow", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2270256999981939\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "ans = sum ([A_slow_list[i] * B_slow_list[i] for i in range(1000000)])\n", + "print (time.perf_counter() - start)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "operating-offering", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.007141000001865905\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "ans = np.sum(A_quick_arr * B_quick_arr)\n", + "print (time.perf_counter() - start)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "wound-movie", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.06732900000133668\n" + ] + } + ], + "source": [ + "start = time.perf_counter()\n", + "ans = A_quick_arr.dot(B_quick_arr)\n", + "print (time.perf_counter() - start)" + ] + }, + { + "cell_type": "markdown", + "id": "front-flesh", + "metadata": {}, + "source": [ + "# Pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "fresh-september", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 5\n", + "1 6\n", + "2 7\n", + "3 8\n", + "4 9\n", + "5 10\n", + "dtype: int64" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Series\n", + "my_series = pd.Series([5, 6, 7, 8, 9, 10])\n", + "my_series" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "suspected-sheet", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=6, step=1)" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series.index" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "measured-scholar", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 5, 6, 7, 8, 9, 10], dtype=int64)" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series.values" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "qualified-burner", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series[4]" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "desperate-bedroom", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series2 = pd.Series([5, 6, 7, 8, 9, 10], index=['a', 'b', 'c', 'd', 'e', 'f'])\n", + "my_series2['f']" + ] + }, + { + "cell_type": "markdown", + "id": "western-tattoo", + "metadata": {}, + "source": [ + "##### Делать выборку по нескольких индексам и осуществлять групповое присваивание" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "fresh-firewall", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 5\n", + "b 6\n", + "f 10\n", + "dtype: int64" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series2[['a', 'b', 'f']]" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "trying-ottawa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 0\n", + "b 0\n", + "c 7\n", + "d 8\n", + "e 9\n", + "f 0\n", + "dtype: int64" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series2[['a', 'b', 'f']] = 0\n", + "my_series2" + ] + }, + { + "cell_type": "markdown", + "id": "indoor-painting", + "metadata": {}, + "source": [ + "##### Фильтровать series и применять математические операции" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "fixed-richmond", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "c 7\n", + "d 8\n", + "e 9\n", + "dtype: int64" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series2[my_series2 > 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "educational-walker", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "c 14\n", + "d 16\n", + "e 18\n", + "dtype: int64" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series2[my_series2 > 0] * 2" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "genetic-angola", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 5\n", + "b 6\n", + "c 7\n", + "d 8\n", + "dtype: int64" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series3 = pd.Series({'a': 5, 'b': 6, 'c': 7, 'd': 8})\n", + "my_series3" + ] + }, + { + "cell_type": "markdown", + "id": "asian-plate", + "metadata": {}, + "source": [ + "##### у объекта Series можно задать имя индексам и объектам" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "sweet-thermal", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "letters\n", + "a 5\n", + "b 6\n", + "c 7\n", + "d 8\n", + "Name: numbers, dtype: int64" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series3.name = 'numbers'\n", + "my_series3.index.name = 'letters'\n", + "my_series3" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "stainless-carol", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "A 5\n", + "B 6\n", + "C 7\n", + "D 8\n", + "Name: numbers, dtype: int64" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "my_series3.index = ['A', 'B', 'C', 'D']\n", + "my_series3" + ] + }, + { + "cell_type": "markdown", + "id": "reliable-variance", + "metadata": {}, + "source": [ + "## DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "dependent-comparison", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrypopulationsquare
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" + ], + "text/plain": [ + " country population square\n", + "0 Kazakhstan 17.04 2724902\n", + "1 Russia 143.50 17125191\n", + "2 Belarus 9.50 207600\n", + "3 Ukraine 45.50 603628" + ] + }, + "execution_count": 113, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame({\n", + " 'country': ['Kazakhstan', 'Russia', 'Belarus', 'Ukraine'],\n", + " 'population': [17.04, 143.5, 9.5, 45.5],\n", + " 'square': [2724902, 17125191, 207600, 603628],\n", + "})\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "ethical-grocery", + "metadata": {}, + "source": [ + "##### Чтобы убедиться, что в столбец это series извлекаем любой" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "macro-chassis", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Kazakhstan\n", + "1 Russia\n", + "2 Belarus\n", + "3 Ukraine\n", + "Name: country, dtype: object" + ] + }, + "execution_count": 114, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['country']" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "packed-schedule", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['country', 'population', 'square'], dtype='object')" + ] + }, + "execution_count": 115, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "suspended-racing", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RangeIndex(start=0, stop=4, step=1)" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.index" + ] + }, + { + "cell_type": "markdown", + "id": "active-declaration", + "metadata": {}, + "source": [ + "##### Доступ по индексу по строкам можно задать разными способами, например при формировании самого объекта" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "light-transmission", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrypopulationsquare
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indexcountrypopulationsquare
0KZKazakhstan17.042724902
1RURussia143.5017125191
2BYBelarus9.50207600
3UAUkraine45.50603628
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" + ], + "text/plain": [ + " index country population square\n", + "0 KZ Kazakhstan 17.04 2724902\n", + "1 RU Russia 143.50 17125191\n", + "2 BY Belarus 9.50 207600\n", + "3 UA Ukraine 45.50 603628" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.reset_index()" + ] + }, + { + "cell_type": "markdown", + "id": "unlimited-brother", + "metadata": {}, + "source": [ + "##### Добавим новый столбец, в котором население поделим на площадь страны" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "micro-height", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrypopulationsquaredensity
KZKazakhstan17.0427249026.253436
RURussia143.50171251918.379469
BYBelarus9.5020760045.761079
UAUkraine45.5060362875.377550
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" + ], + "text/plain": [ + " country population square density\n", + "KZ Kazakhstan 17.04 2724902 6.253436\n", + "RU Russia 143.50 17125191 8.379469\n", + "BY Belarus 9.50 207600 45.761079\n", + "UA Ukraine 45.50 603628 75.377550" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['density'] = df['population'] / df['square'] * 1000000\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "passive-characterization", + "metadata": {}, + "source": [ + "##### Теперь удалим его" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "elect-enforcement", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrypopulationsquare
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RURussia143.5017125191
BYBelarus9.50207600
UAUkraine45.50603628
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" + ], + "text/plain": [ + " country population square\n", + "KZ Kazakhstan 17.04 2724902\n", + "RU Russia 143.50 17125191\n", + "BY Belarus 9.50 207600\n", + "UA Ukraine 45.50 603628" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.drop(['density'], axis='columns')" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "sustained-amber", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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countrypopulationsquaredensity
KZKazakhstan17.0427249026.253436
RURussia143.50171251918.379469
BYBelarus9.5020760045.761079
UAUkraine45.5060362875.377550
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" + ], + "text/plain": [ + " country population square density\n", + "KZ Kazakhstan 17.04 2724902 6.253436\n", + "RU Russia 143.50 17125191 8.379469\n", + "BY Belarus 9.50 207600 45.761079\n", + "UA Ukraine 45.50 603628 75.377550" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.rename(columns={'Country Code': 'country code'})\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "fewer-rabbit", + "metadata": {}, + "source": [ + "##### Группировка и агрегирование в panadas" + ] + }, + { + "cell_type": "markdown", + "id": "interested-messenger", + "metadata": {}, + "source": [ + "##### Группировка данных один из самых часто используемых методов при анализе данных. В pandas за группировку отвечает метод .groupby." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "numeric-growing", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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891 rows × 12 columns

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" + ], + "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 \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", + ".. ... ... ... ... ... \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "888 2 W./C. 6607 23.4500 NaN S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "titanic_df = pd.read_csv('titanic.csv')\n", + "titanic_df" + ] + }, + { + "cell_type": "markdown", + "id": "chronic-worth", + "metadata": {}, + "source": [ + "##### Необходимо подсчитать, сколько женщин и мужчин выжило, а сколько нет. В этом нам поможет метод .groupby." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "thick-project", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sex Survived\n", + "female 0 81\n", + " 1 233\n", + "male 0 468\n", + " 1 109\n", + "Name: PassengerId, dtype: int64\n" + ] + } + ], + "source": [ + "print(titanic_df.groupby(['Sex', 'Survived'])['PassengerId'].count())" + ] + }, + { + "cell_type": "markdown", + "id": "existing-rabbit", + "metadata": {}, + "source": [ + "##### А теперь проанализируем в разрезе класса каюты:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bottom-plain", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pclass Survived\n", + "1 0 80\n", + " 1 136\n", + "2 0 97\n", + " 1 87\n", + "3 0 372\n", + " 1 119\n", + "Name: PassengerId, dtype: int64\n" + ] + } + ], + "source": [ + "print(titanic_df.groupby(['Pclass', 'Survived'])['PassengerId'].count())" + ] + }, + { + "cell_type": "markdown", + "id": "reduced-livestock", + "metadata": {}, + "source": [ + "##### Pandas (Python Data Analysis Library) - библиотека для [удобных] обработки и анализа данных.\n", + "\n", + "##### Рассмотрим данную библиотеку на примере данных соревнования о предсказании судьбы пассажиров лайнера \"Титаник\". Имеется некоторая информация о пассажирах, по которой необходимо предсказать выживаемость каждого из них.\n", + "\n", + "##### Какого типа данная задача? Что является объектами? Что является ответами? Какие могут быть признаки? Какие у них типы? Загрузка данных в pandas происходит в специальный объект типа DataFrame:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "forced-resort", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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891 rows × 12 columns

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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
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3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.100C123S
4503Allen, Mr. William Henrymale35.0003734508.050NaNS
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" + ], + "text/plain": [ + " Survived Pclass\n", + "1 1 1\n", + "2 1 3\n", + "3 1 1\n", + "4 0 3" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data.iloc[1:5, 1:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "appointed-drawing", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Braund, Mr. Owen Harris\n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th...\n", + "2 Heikkinen, Miss. Laina\n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel)\n", + "4 Allen, Mr. William Henry\n", + "Name: Name, dtype: object" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data['Name'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "accurate-amendment", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameSexAge
0Braund, Mr. Owen Harrismale22.0
1Cumings, Mrs. John Bradley (Florence Briggs Th...female38.0
2Heikkinen, Miss. Lainafemale26.0
3Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.0
4Allen, Mr. William Henrymale35.0
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" + ], + "text/plain": [ + " Name Sex Age\n", + "0 Braund, Mr. Owen Harris male 22.0\n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0\n", + "2 Heikkinen, Miss. Laina female 26.0\n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0\n", + "4 Allen, Mr. William Henry male 35.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data[['Name', 'Sex', 'Age']].head()" + ] + }, + { + "cell_type": "markdown", + "id": "severe-candy", + "metadata": {}, + "source": [ + "##### Также действует и логическая индексация, которая позволяет рассматривать определенные группы объектов:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "hired-heath", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
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" + ], + "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", + " 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", + " 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 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data[pass_data['Sex'] == 'female'].head() # женщины на борту" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "dramatic-factory", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
5603Moran, Mr. JamesmaleNaN003308778.4583NaNQ
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
7803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name Sex Age \\\n", + "0 1 0 3 Braund, Mr. Owen Harris male 22.0 \n", + "4 5 0 3 Allen, Mr. William Henry male 35.0 \n", + "5 6 0 3 Moran, Mr. James male NaN \n", + "6 7 0 1 McCarthy, Mr. Timothy J male 54.0 \n", + "7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 \n", + "\n", + " SibSp Parch Ticket Fare Cabin Embarked \n", + "0 1 0 A/5 21171 7.2500 NaN S \n", + "4 0 0 373450 8.0500 NaN S \n", + "5 0 0 330877 8.4583 NaN Q \n", + "6 0 0 17463 51.8625 E46 S \n", + "7 3 1 349909 21.0750 NaN S " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# женщины старше 60 и мужчины на борту\n", + "pass_data[(pass_data['Sex'] == 'female') & (pass_data['Age'] >= 60) | (pass_data['Sex'] == 'male')].head()" + ] + }, + { + "cell_type": "markdown", + "id": "exposed-tomorrow", + "metadata": {}, + "source": [ + "### Пример" + ] + }, + { + "cell_type": "markdown", + "id": "happy-thomas", + "metadata": {}, + "source": [ + "##### Посмотрим, сколько на борту было относительно молодых женщин, путешествующих в одиночку. Скорее всего, довольно мало потому что в такое длительное путешествие молодых девушек одних не отпустили бы опекающие родственники." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "polish-science", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(49, 12)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data[(pass_data.Sex == 'female') & (pass_data.Age > 18) & (pass_data.Age < 25)].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "balanced-french", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PassengerIdSurvivedPclassNamesexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "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", + " 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", + " 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 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data.rename(columns = {'Sex' : 'sex'}, inplace = True)\n", + "pass_data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "engaged-person", + "metadata": {}, + "source": [ + "##### Применение преобразования к существующему признаку. Например, выделим фамилию:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "constitutional-margin", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Braund\n", + "1 Cumings\n", + "2 Heikkinen\n", + "3 Futrelle\n", + "4 Allen\n", + "Name: Name, dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_last_name(name):\n", + " return name.split(',')[0].strip()\n", + "\n", + "last_names = pass_data['Name'].apply(get_last_name)\n", + "last_names.head()" + ] + }, + { + "cell_type": "markdown", + "id": "roman-variable", + "metadata": {}, + "source": [ + "##### Добавление признака" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "alternate-white", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNamesexAgeSibSpParchTicketFareCabinEmbarkedLast_name
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNSBraund
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85CCumings
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNSHeikkinen
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123SFutrelle
4503Allen, Mr. William Henrymale35.0003734508.0500NaNSAllen
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" + ], + "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", + " 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", + " Parch Ticket Fare Cabin Embarked Last_name \n", + "0 0 A/5 21171 7.2500 NaN S Braund \n", + "1 0 PC 17599 71.2833 C85 C Cumings \n", + "2 0 STON/O2. 3101282 7.9250 NaN S Heikkinen \n", + "3 0 113803 53.1000 C123 S Futrelle \n", + "4 0 373450 8.0500 NaN S Allen " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data['Last_name'] = last_names\n", + "pass_data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "excited-finish", + "metadata": {}, + "source": [ + "##### Удаление признака" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "dressed-tucson", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNamesexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "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", + " 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", + " 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 " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data.drop('Last_name', axis = 1, inplace = True)\n", + "pass_data.head()" + ] + }, + { + "cell_type": "markdown", + "id": "lasting-anime", + "metadata": {}, + "source": [ + "##### Работа с пропущенными данными Методы isnull() и notnull() позволяют получить бинарный массив, отражающий отсутствие или наличие данных для каждого из объектов соответсвенно" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "false-percentage", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "Name: SibSp, dtype: bool" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data['SibSp'].isnull().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "foster-disco", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNamesexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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" + ], + "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", + " 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", + " 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 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data[pass_data['Age'].notnull()].head() # пассажиры с известным возрастом" + ] + }, + { + "cell_type": "markdown", + "id": "constitutional-scale", + "metadata": {}, + "source": [ + "##### Сортировка объектов/признаков" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "limiting-authentication", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNamesexAgeSibSpParchTicketFareCabinEmbarked
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
272801Fortune, Mr. Charles Alexandermale19.03219950263.0000C23 C25 C27S
303101Uruchurtu, Don. Manuel Emale40.000PC 1760127.7208NaNC
343501Meyer, Mr. Edgar Josephmale28.010PC 1760482.1708NaNC
353601Holverson, Mr. Alexander Oskarmale42.01011378952.0000NaNS
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name sex Age \\\n", + "6 7 0 1 McCarthy, Mr. Timothy J male 54.0 \n", + "27 28 0 1 Fortune, Mr. Charles Alexander male 19.0 \n", + "30 31 0 1 Uruchurtu, Don. Manuel E male 40.0 \n", + "34 35 0 1 Meyer, Mr. Edgar Joseph male 28.0 \n", + "35 36 0 1 Holverson, Mr. Alexander Oskar male 42.0 \n", + "\n", + " SibSp Parch Ticket Fare Cabin Embarked \n", + "6 0 0 17463 51.8625 E46 S \n", + "27 3 2 19950 263.0000 C23 C25 C27 S \n", + "30 0 0 PC 17601 27.7208 NaN C \n", + "34 1 0 PC 17604 82.1708 NaN C \n", + "35 1 0 113789 52.0000 NaN S " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data.sort_values(by = ['Pclass', 'Survived'], ascending = True).head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ranging-welcome", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNamesexAgeSibSpParchTicketFareCabinEmbarked
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
232411Sloper, Mr. William Thompsonmale28.00011378835.5000A6S
313211Spencer, Mrs. William Augustus (Marie Eugenie)femaleNaN10PC 17569146.5208B78C
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "1 2 1 1 \n", + "3 4 1 1 \n", + "11 12 1 1 \n", + "23 24 1 1 \n", + "31 32 1 1 \n", + "\n", + " Name sex Age SibSp \\\n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "11 Bonnell, Miss. Elizabeth female 58.0 0 \n", + "23 Sloper, Mr. William Thompson male 28.0 0 \n", + "31 Spencer, Mrs. William Augustus (Marie Eugenie) female NaN 1 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "1 0 PC 17599 71.2833 C85 C \n", + "3 0 113803 53.1000 C123 S \n", + "11 0 113783 26.5500 C103 S \n", + "23 0 113788 35.5000 A6 S \n", + "31 0 PC 17569 146.5208 B78 C " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pass_data.sort_values(by = ['Pclass', 'Survived'], ascending = [True, False]).head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "radical-kitchen", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ml/lb1/titanic.csv b/ml/lb1/titanic.csv new file mode 100644 index 00000000..63b68ab0 --- /dev/null +++ b/ml/lb1/titanic.csv @@ -0,0 +1,892 @@ +PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked +1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S +2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C +3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S +4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S +5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S +6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q +7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S +8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S +9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S +10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C +11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S +12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S +13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S +14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S +15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S +16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S +17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q +18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S +19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S +20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C +21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S +22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S +23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q +24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S +25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S +26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S +27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C +28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S +29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q +30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S +31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C +32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C +33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q +34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S +35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C +36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S +37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C +38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S +39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S +40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C +41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S +42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S +43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C +44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C +45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q +46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S +47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q +48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q +49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C +50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S +51,0,3,"Panula, Master. 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