{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Майнор по Анализу Данных, Группа ИАД-2\n",
"## 25/01/2017 Спасательная операция: Pandas, Matplotlib, NumPy. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Так как на предыдущем майноре вам не успели рассказать про `рandas`, `matplotlib`, `numpy`, то придется вас спасать.\n",
"\n",
"**Дисклеймер**
\n",
"Мы очень ограничены во времени и естественно стать гуру `pandas`, `numpy`, `matplotlib` и других модулей за такой краткий срок у вас не получится. Этому может способствовать только постоянная практика: наши домашние задания, онлайн курсы по анализу данных в `python`, [видео лекции](http://www.dataschool.io/easier-data-analysis-with-pandas/), ваша курсовая работа (?). Я постараюсь изложить основную суть. Для всего, что я не успею - можно спрашивать меня, google или [RTFM](http://pandas.pydata.org/).\n",
"\n",
"Начнем."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"\n",
"plt.style.use('ggplot')\n",
"plt.rcParams['figure.figsize'] = (16,8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NumPy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NumPy (short for Numerical Python) - это эффективная библиотека для работы с числовыми массивами и матрицами.\n",
"\n",
"Нам с вами очень важно уметь работать с этими структурами быстро по крайней мере по двум причинам:\n",
"* Данные любой природы в подавляющем числе случаем можно преставить в виде набора чисел (изображения, тексты, видео-клипы, простые табличные данные)\n",
"* Этих данных очень много и надо бы уметь их быстро обрабатывать, анализировать и модифицировать ( желательно избегая вложенных циклов, большого набора условных операторов!)\n",
"\n",
"Основа `numpy` - это `array` (массивы). В отличие от списков или кортежей, элементы массива должны быть одного типа. Такая жертва оправдывает скорость и низкие затраты на хранение информации, которую дает numpy. Массивы же, в свою очередь, используются во многих других библиотеках `python` для анализа данных."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Массивы"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Создание массива"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Массив можно создать из списка."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 3, 4, 7, 12])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.array([1,3,4,7,12])\n",
"arr"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 3., 4., 7., 12.])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.array([1,3,4,7,12.0])\n",
"arr"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['1', '3', '4', '7', '12.0'], \n",
" dtype='|S21')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.array([1,3,4,'7',12.0])\n",
"arr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Но наиболее распространены создания \"с нуля\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 0.7, 1.4, 2.1, 2.8, 3.5, 4.2, 4.9, 5.6,\n",
" 6.3, 7. , 7.7, 8.4, 9.1, 9.8, 10.5, 11.2, 11.9])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.arange(0, 12, 0.7) # расширение функции range()\n",
"arr"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 0.63157895, 1.26315789, 1.89473684,\n",
" 2.52631579, 3.15789474, 3.78947368, 4.42105263,\n",
" 5.05263158, 5.68421053, 6.31578947, 6.94736842,\n",
" 7.57894737, 8.21052632, 8.84210526, 9.47368421,\n",
" 10.10526316, 10.73684211, 11.36842105, 12. ])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.linspace(0, 12, 20) # равномерно распределенные 20 чисел от 0 до 12\n",
"arr"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1. 1. 1. 1. 1. 1. 1.]\n",
"[1 1 1 1 1 1 1]\n"
]
}
],
"source": [
"# Массив из \"1\"\n",
"arr = np.ones(7)\n",
"print arr\n",
"arr = np.ones(7, dtype=int)\n",
"print arr"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Массив из \"0\"\n",
"arr = np.zeros(7)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2.71828183, 2.71828183, 2.71828183, 2.71828183, 2.71828183,\n",
" 2.71828183, 2.71828183])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Массив из чего хотите\n",
"arr = np.full(7, np.exp(1), )\n",
"arr"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"?np.full"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Свойства массива"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[3, 1, 4],\n",
" [1, 5, 9],\n",
" [2, 6, 5],\n",
" [4, 1, 1]])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = np.array([[3, 1, 4], [1, 5, 9], [2, 6, 5], [4, 1, 1]])\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4, 3)\n",
"12\n",
"2\n"
]
}
],
"source": [
"print A.shape\n",
"print A.size\n",
"print A.ndim"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Форму массива (`shape`) можно менять, но так, чтобы это согласовывалось с его размером (`size`) "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[3, 1],\n",
" [4, 1],\n",
" [5, 9],\n",
" [2, 6],\n",
" [5, 4],\n",
" [1, 1]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = A.reshape((6,2))\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3 1 4 1 5 9 2 6 5 4 1 1]\n",
"(12,)\n",
"[[3 1 4 1 5 9 2 6 5 4 1 1]]\n",
"(1, 12)\n"
]
}
],
"source": [
"A = A.flatten()\n",
"print A\n",
"print A.shape\n",
"\n",
"#!!!\n",
"# Это не тоже самое, что A = A.reshape((1, 12))\n",
"\n",
"A = A.reshape((1,12))\n",
"print A\n",
"print A.shape"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[3, 1, 4],\n",
" [1, 5, 9],\n",
" [2, 6, 5],\n",
" [4, 1, 1]])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = A.reshape((4, -1)) \n",
"A\n",
"\n",
"# -1 как бы означает, \"сделай первую размерность равную 3, \n",
"# а все остальное запихни во вторую, если получится"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[3 1 4]\n",
" [1 5 9]\n",
" [2 6 5]\n",
" [4 1 1]]\n",
" \n",
"[[3 1 2 4]\n",
" [1 5 6 1]\n",
" [4 9 5 1]]\n"
]
}
],
"source": [
"print A\n",
"print ' '\n",
"print A.T # Транспонирование матрицы"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Индексация"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Довольно стандартная и интуитивно понятная"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 3, 6, 9, 12, 15, 18])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.arange(0, 19, 3)\n",
"arr"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr[3]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 3, 6])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr[:3]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 9, 12])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr[3:5]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 9, 18])"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr[::3] #?!"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([15, 18])"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr[-2:] #?!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Задание**
\n",
"Догадайтесь, как вывести массив в обратном порядке?"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([18, 15, 12, 9, 6, 3, 0])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Your code here\n",
"arr[::-1]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[False False False False True True True]\n",
"[12 15 18]\n"
]
}
],
"source": [
"print arr>10\n",
"print arr[arr>10]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 9, 6])"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr[[1,3,2]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"На многомерные массивы (матрицы) все распространяется точно также."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[14, 7, 8, 9, 2, 17],\n",
" [ 3, 5, 6, 11, 3, 18],\n",
" [ 9, 1, 13, 1, 6, 14],\n",
" [ 7, 4, 0, 0, 0, 10],\n",
" [19, 14, 16, 12, 8, 16]])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = np.random.randint(0, 20, (5,6))\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 8 6 13 0 16]\n",
"[ 9 1 13 1 6 14]\n"
]
}
],
"source": [
"# Небольшое дополнение\n",
"print A[:, 2]\n",
"print A[2, :]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([14, 7, 8, 9, 17, 6, 11, 18, 9, 13, 6, 14, 7, 10, 19, 14, 16,\n",
" 12, 8, 16])"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A[A>5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Задание**\n",
"Задайте матрица размера `(5,6)` c числами от 5 до 34, такую что на каждой строчке числа поледовательно возрастают на 1. Первая строчка начинается с 5 и заканчивается 10, вторая начинается с 11 и тп. "
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 5, 6, 7, 8, 9, 10],\n",
" [11, 12, 13, 14, 15, 16],\n",
" [17, 18, 19, 20, 21, 22],\n",
" [23, 24, 25, 26, 27, 28],\n",
" [29, 30, 31, 32, 33, 34]])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Your code here\n",
"a = np.arange(5, 35)\n",
"a = a.reshape((5,-1))\n",
"a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Склейка массивов"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[4 9 6 9 1]\n",
" [5 5 6 3 5]]\n",
" \n",
"[[9 6 8 5 2]\n",
" [0 2 6 2 1]]\n"
]
}
],
"source": [
"a = np.random.randint(0, 10, (2, 5))\n",
"b = np.random.randint(0, 10, (2, 5))\n",
"\n",
"print a\n",
"print ' '\n",
"print b"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[4, 9, 6, 9, 1],\n",
" [5, 5, 6, 3, 5],\n",
" [9, 6, 8, 5, 2],\n",
" [0, 2, 6, 2, 1]])"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = np.r_[a,b]\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[4, 9, 6, 9, 1],\n",
" [5, 5, 6, 3, 5],\n",
" [9, 6, 8, 5, 2],\n",
" [0, 2, 6, 2, 1]])"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = np.concatenate((a,b), axis=0)\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[4, 9, 6, 9, 1, 9, 6, 8, 5, 2],\n",
" [5, 5, 6, 3, 5, 0, 2, 6, 2, 1]])"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = np.c_[a,b]\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[4, 9, 6, 9, 1, 9, 6, 8, 5, 2],\n",
" [5, 5, 6, 3, 5, 0, 2, 6, 2, 1]])"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = np.concatenate((a,b), axis=1)\n",
"A"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Операции и функции на массивах"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Тут все тоже довольно просто"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 2., 3., 4., 5.])"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.arange(1, 6, dtype=float)\n",
"arr"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 0.5 , 0.33333333, 0.25 , 0.2 ])"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1/arr"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2., 4., 6., 8., 10.])"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr * 2"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 1., 1., 2., 2.])"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr // 2"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([6, 5, 4, 3, 2])"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bar = np.arange(6,1,-1)\n",
"bar"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 7., 7., 7., 7., 7.])"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr + bar"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6., 10., 12., 12., 10.])"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr * bar"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 32., 81., 64., 25.])"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr ** bar"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"50.0"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Матричное умножение (скалярное произведение)\n",
"arr.dot(bar)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В `numpy` реализовано много математических функций"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 0.69314718, 1.09861229, 1.38629436, 1.60943791])"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(arr)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 1.41421356, 1.73205081, 2. , 2.23606798])"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sqrt(arr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Задание**
\n",
"Задайте два случайных массива $a$ и $b$ одинаковой длины. \n",
"\n",
"Вычислите следующие расстояния между массивами:\n",
"\n",
"* Euclidean Distance\n",
"$$ d(a, b) = \\sqrt{\\sum_i (a_i - b_i)^2} $$\n",
"* Manhattan Distance\n",
"$$ d(a, b) = \\sum_i |a_i - b_i| $$\n",
"* Cosine Distance\n",
"$$ d(a, b) = 1 - \\frac{a^\\top b}{||a||_2\\cdot||b||_2}$$\n"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.02390982409\n",
"1.02390982409\n",
"1.99847446939\n"
]
}
],
"source": [
"a = np.random.rand(5)\n",
"b = np.random.rand(5)\n",
"\n",
"# Евклидово расстояние\n",
"c = a-b\n",
"euclid = np.sqrt(c.dot(c))\n",
"print euclid\n",
"\n",
"euclid = np.sqrt(((a - b) ** 2).sum())\n",
"print euclid\n",
"\n",
"# Расстояние таксиста\n",
"manhat = abs(a-b).sum()\n",
"print manhat\n",
"\n",
"# Косунус - самиии\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Задание**
\n",
"Выполните загрузку данных, как указано ниже (может занять время).\n",
"\n",
"Выберите случайную строчку из данных. Найдите другую \"ближайшую\" (по евклидовому расстоянию) к ней строчку и выведите её."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_olivetti_faces\n",
"faces = fetch_olivetti_faces()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = faces.data"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(400, 4096)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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MKqF8GbGH/s/KsXUuqoGNQ+uxb8+nO5sZVcch4X29/07Ty6I4yExbM+udkdmXOsKpyKci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MDj2TfriOr1bMksZqSuvfbadscdd5hlIBsY+626GP9mpXnlWHMzOo+umee89uh7FgTInxtvvLG9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\n1V784hdfncbcb+IXig5R3XhPTk6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Re7MJUn2uxm0lCA5tPAtHkxSJ2s5dtDurw9pIP3X3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\nHMdxnEr5P+xYN1NKV8msAAAAAElFTkSuQmCC\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = X[0, :].reshape(64,64)\n",
"\n",
"plt.imshow(x, cmap=plt.cm.Greys_r)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Выбираем случайную фотку\n",
"idx = np.random.randint(0, X.shape[0])\n",
"\n",
"x = X[idx, :]\n",
"\n",
"# Считaем евклидово расстояние..\n",
"d = X - x"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4096,)\n",
"(400, 4096)\n"
]
}
],
"source": [
"print x.shape\n",
"print X.shape"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"euclidean = (d ** 2).sum(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(400,)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"euclidean.shape"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 22.74522018]\n"
]
}
],
"source": [
"print np.sort(euclidean)[:2]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"324\n"
]
}
],
"source": [
"# Ближайшая, отличная от исходной фотка находится на позиции...\n",
"idx = np.argsort(euclidean)[1]\n",
"print idx"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MuHXKd/WxeBXnDIIyhE7Rs7xqjmebAOUgzBxamA5/rFmtPdfmCjJSwQxGvN+p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\n3mperH+GZznP0mY7M5Yun4urGIrYnyxcwRZiUsmH/XMuJyErr0KcbnI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PCqr91i3THbE8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\nN9Q7V67+W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+Mv+wblLyY6s7u5Pjgh6JJtOBe2B5Vg5OQdXqv86/l8vpeYVDib+1YJyR15\nuHkhOSyafBDrSrLHCSW8xW2SPu7UJZtjOXQYT/Hv5Or6rmM3X67bIXW9GhvtU42fxk4YE+/DKymY\n/s246K1MEOLxpWCMaQISkrPo+kv8TozQOSM3jjNGVIzkfJUTc8RjpX64bnIuzVslSzg4MG8qmCdQ\ntAOCUv/ns/+TEiwjAEnl7hMNno8KOjy2khE7NXRKWMf9q0Cn5Ozktivb0gcev/RF2RKT0vvurT9u\n73RTlSegZJK4S3PB40vHWmttJQiVXKc0AWrKT7LuTnwY9qHqlJ525yfnVLsutnU2gXPvyPls1adr\nj+MpIMZ9Yz87+jDhfSIbJsUjtk9gE8fF7QgYe6Q3ky7Bol1/yT6df8eyqps8+JrwmPAT85B4Svu1\n9O9+11jsc1zMcTxM4gKP5eTXvf2nxp9gANem+EIeU1nt1RuEdW4H+07qpgnAqru6urp9QI3tHIbs\nsCVu/PZgbVhXY2FZHXdyKf1NNoM6hnV4jHbvrk16XrxM9Ez1gXXqDTz+TT9FbNcqcYdbR+fzB58Y\nJ4zNvLlkYWeHzs/wcfLf3TpVndJthy+mxPrleFbXcdnZ+sQvKB2fjt+dSzhNYU5ncwmfqn7ZNt0e\n++f1nhLqQOlNSgp+JBOEWHaKmcgpj2qjDJodYOcwHAhRCsqK5frtgM0k4GB5ApQSKcV3PHcAyl2n\nyultJwzOuFd1HeDd7YP35/P5zifGaWyXGGSgpYAXvvU3AU/MCx47Z8dvEGJQc28wdna7K1fleDFB\nyGM5R6z0PiX0FJiofhzwSgAmXZv0WyUIO3+RgI/js+NfyUWNneStqJNrnWO9UUF5h6b23vmIHd+g\neHB22vH6KmLhTn/VNq3zpbHG+So8l+aR7ITru3kr/zdNtDzSm0WdPe6Q8l14TtUxjuNyHXfxJJV5\nvhP+FW/Kf6tjN59JXFU87cpFteX5dzLtsEA3j+k5jrHIo+K7K9/cvPzZG4WZ8JpJ/EpllRS8vr6+\nc4xJQUwO3tzc3LZjHhO2dO1UcrC2GqN0Wn0tw/N3MkE9TbjUyY7tC/fcLtnYjl5N9FYl2/B4Et8n\nuLbr53x+mSCseTj8mhKRuwnCCTn7mKxT6Z3qb2fs7py675riX9xP7vlqfpfGz87fOVI+k/npYlrX\nP9so8zWZv2rDMsWXc7BumiCcrsFrnyBcq79Bu7TPteYKoPZdH1zvFHJCLqC4/pXxu/OK1zROx6O6\nEXuVxMbCySJHikdl5B0Q6hxqBSnkldsgaKgyJ/zqmBOCXDcBSsoRsTxRFvyEFYOZ4r37JCTJYrpH\nnl2CULXFtWbq3pBziT2uQ8CiAEwCIhPAwu15Tlw3HacDjE5uinbbd8QBPOmE0jfV11QHJ+Vpv12d\n4nOnjaNL4o/rj8FXF6MmvLljtbbc3sk1xcxp7GP9m4z7SI+0c8PgdMZhyS5mTG6AJ+Tsyc2B/YDz\nC+yTsN59YtrJjftM246NujEQ/zg5KErJKuYdj6eE81MYoTATzkGV1b6rw3LCIrW2qlzJQffg+8g2\nwVT8BqGqS7Kp/fX1dYxbas924mIqn+90xvmDIw+9+XcI+bgjdQ3jZaWvrEPqT/PSQ27V/0QfdmgX\nD9T64R5fACl9SS+O4OZ00/GWyvzWrtsr3lxSHWmKuxS5/qbr2flm1XeHFbuYOOlfzUf10enCR+oN\nwhS4uMx9KFJCd8qCjqT2qq27nsGKU2zlKLq+uG4qk05ZUSZTg+V5VD+dEe6252tRXuy0FH+4d2Oq\neWB75oHLXFfBDHl2Zd444efeGlQgOm3dvDAJiMBJgSjlqBhIqbGdjSveki8oQNDJlsnZcZckTABj\nrbufc6RE4QSMKWDjAF23V2Myv0f7Z5l27XBtJ8T2mGJCZ1M716a6aT9pr8rJx7j6ie+vc8mv7wJi\n16/ynZM+1LGzY7Wm3E8nuzTfybXM4+Mnxo+kyN0AJEq2g9dyGeOHiltuU+MlfOP2Cs91fSu6urqy\nvntyvRrPxTdFPA83duEiNdZk/BRzXcxmHl2dO4+YCefzkHGM67o5q2Qh4luFM48kDru1Z3zrvppJ\n8lDy3cEMNYayJZYrytaVlV9Qx906uaRg9wYh6yT3kXhxe/XQ3SUJ3dwnlLCr8pvJRtz1tUeddvup\n3jsenA5yufxvmldni+k+vPPlu/5917cm++I2aq9k4mTV8c1+Eq9VcaRki74y5QGYr45e+wQhU6f0\n3NbV4WKww+2cm3Jcqp3q042/awQ8n0lZKcp0DDakSXsud+dSP1M+cc+fenVOAMtON9R40+O11nrx\n4oV14F0dbu6tQXYMXQBRvLKTQnCEx3VevT3o3iiczJn5cAFJnVMJQr6mWyMFIl7FG4ST4OWAWfck\ntMqqLpWPgjSUHZfVeCx/BRbUWjlQ4XRjx9d1+jUpT/pR43Vtdmiq567vnXikfLuLd924E35dLOt8\nCJbZv01I+ctXBXAf6e0hZUtKb5xfVOcmY9Z+NznoeEaeJj4Mzyusi5hAzbmOHYZx1OEYjkHFi4th\nSEou6ecE0EdM6iY4AI+RV+5X4XVXxgRW9cXlSYzrrnV4xn0OV+f497VYL/CLmd2H40jMm3trkDFv\nks/5fL7zBmEXw5Rs3RqzvHEOrsyyV3WuvcOh7i3AjiZv902wq3qDkPeXvj2YfDD7zanvrGP2j1Wv\nEuIq8TO5xzuyr/LV1dWo7a697eImF0eZOr1luStf6YivmfDeybobR82VZeuSsm/9J8YuAHXCnh47\n43QLoxyGUizVlyorpVfKq2jH6JyRdtTxoCgFtKPU9aXWcy39VBdlm5yMG2NSr+oqmHaOfHKcPi3G\nIMJvHuL1zKeyJQWYVB07KQZRaV6XBC/cVwBw53fWL701+CreINwFKwnkYT9dmfeuT9eHIuUb1bjs\nD+ucsyd3ndOL3W3SH/I6Oc/n1Hl3DZ9LNNHtST9O/rt+PI01jSdOD1JcOyq/KaF/U/2mhMEjfTSp\ns6mkn+5ch1tUjOjeEHK8HvVTaCfJr7s9YpWEiSZ9pRiKbbg8oS5eMQ+uTvG5Vp8gTPiWx1BlvGl2\n85rGOdUe+WK9REzpkoXq6xhXrjl3Gz7gVjKvc5wQxHVyn3FyO/dVi8IeeB5txukky1etsZtnh/U6\n3KneHFRvEDpbcom76ZcstdV4ytelNws7HPsQ5DAit+FYgHrtdN75yE63dvfTZHiyN5yrk5M75rLy\nd3ys9HWtuz/noOJTiqvFh2ub/OEOJf/N47EOpLcHMRcy5e2NTBB2C5GUC4kX2imIcyadE1RgIPHg\nFo3PHZmvMuJEymk5Hp2hqPqHdsjcZzevbp3SNROjSm0qmE0ADPblnPYEPLkgkj4rwTI7WnwKgXWo\nw/zEVQXJI0EMr1dlBGPqvFofp5Pp7UEMOAm4KECy+7nw5Jr01Bfrq+z2kw2J61Jw5WvY5yX/p9ZL\nraNb9w60pD6U/kz0a6LXO3VOFu54GgORXHzC69wa47pwPHUxY7LePAaWu7VK5Usp9cUPpR4/MX6k\nHX1nW8LrJvboYkTFD7xOxQbFb7IxPnY+QM0p+eqq4+SMGz/5XuZNbYrvCZW9F/bguMh9YRtun+K3\nOlZ9VRse18VzfqAxiY1JzqlO8Y4Ylo8dtnV11W+3TbANJgd57/Sf9+dzj/mVLJHPiS4qPeK98wtT\n7Kf8SdoUsT5O3yBM/KYEY0oMTjfmfeLHGWPyeqv2SEq31XFtOwnC5EvVOfzEOO0THkvxouNBtV/L\n51+UfqiXW5xtKT+KY3Cc2NlPScVKFUPS+ruE4Vpv8W8QOsV313IZhb3jgIu6oKLa8zi88N01O/Pr\nyu6c4l0ZhuM19VPXTmS8sxbMp+trZ63c+Z3gkCiBBQYeyVGmJ6x1zP3ik8zJ08+at9vwKSyOgyBq\nAthqvGnAStfsJghdnQMaCqQoORUvClDt9NOBIQdopmCH99O3Bzt7UuNxP7wGnd9T/rrTIwVOsD/V\nZtJnlVOdO+fKnd5PyPl4N3aRknkHjhIPeH0Xx3Zosi7IA5f5eDfWJL6OnHukjw45DOGwl8JWbI9u\nHBc38Fq3Vzwyb64NnmOfoeY0wQb1iVvixfWH43NM4nqOLbukvlZJ/bFcuFzHGJP5OPllF3+VbnQ0\niYG4V3U4v8KoePOOOJa/hElJQpdMTFvCRWgPKUmIupzm7b7iUTgFdYKPeU0VJdtO69/hTudP3JuE\nDuexnFVfHX98rN4gVOU0RidT1hFec0cOgyZcupZOEE7eEOt0DPme2i5+YjztYxeX7fC2lo5ZStdV\ncpAf5GBZkYrDEx4VdW3Z1hVv7BNqn/QDH6Ds0GufIFwrg4DUjutZIRm0OBCDxE4lBWXmoRsvjevm\nkoxscn5CU2XmeXLbI6SAz4TPDjDVsVpL53QupQpmzrE7wKHm5pKCeFx9c2Kwrp84/AlYcInBCVCr\nsY4EHC6j80/XsFzV2naAQ21KXi4xiE821XXMQweSphvOj8uufzzn5OQojb3j89w1bBcJmCjd43Gc\nbk50akefXV2qd7LpziUe1polB9UaqPEYyKQxdmniA1R7d4x1nX9n3VCg0c3v0nk/0ttFrGud3ii7\nQx2c+nJMBLlY4PhIvjPx7/x2lRGTKExU5cRnhzFq7BQPsY3zb7uk+uF1U+vY4QrVhsdJsV7hGibl\nx3f8r7omzZsThqgDKVlydXUlZbGjD2rOa71M+OLezc3Nv+bmMIWzLcWvWx9V7/bpK5QdHMkJwdrS\nG4RqDuoNv45H3mOC0M2nezjf+Yfi9xJMk7AKt5skwKdJQjV20lksn06n+BuEyeZTn5Nzrn2n47jG\nKjmIiTK2Lef7sT3XMU38g5IP9stzTdcpn5GShWu9pW8Q1nFqM92nYNUpSSrzMfalxnE0aTcxOFfe\ndWyOP1e+FGDt9jGRaTI6Bl9c565T5Nrd3NysFy9e3AO/xbf6dMHNI705iAEEE4Wn0+kOAEs2grJK\nQbPa7CYJcZyJ/TJfqoxgzM1nx0krMOGePqq6IyDFydyBnq5NB3i43PHF/O0QXqd87cSGsR0eJ73i\n8+o61Waqn0fOTcvqWMnDXZN4cuT8+cQnKx+r4oQ6Vnxw38n+eTx1jL5iSs5nTGKzu/6RHmlKrEtO\nd5WfVjfd7jo1pvOHO7zzzVXZDW8pMeTmp8bjPpGPtGEb5n0Sl9y8eXyuY5/ZYQI+5mSM6lP1W9d1\niePO5+7sEe/WHpODav25rhKDmLjjZGHCAGotHBbi5KCqU3PF9Z3+rBDz19mbw3PKPhwm3cGO3Nb9\nOYl6W9nJmcdNyUGVTHQ+zs2rw7ZKxtxuB8M4fNlhlOlbsupNMTdesl3HE/eJ7SZ9uPNp35XVunFd\nlxzkda0xnB4U8fmp73PycG1UzHTEOQT39iD6q7cyQeicabpW7avMgQr33B7JORUXcCZ9Ot67c1Nj\nnZR3+OA5qXJHqZ0LdikIOt5d8K+92vjclFRbrKsAlRJq03EQ6LDzQ+DFgAPLJSfnqCeBEwETzwn7\nmthx0mPXBwZpThDyedUv2yqu0xQ8sUwUsJn2xf0defKpxuP1c2vq5tKdS9ckSoDG8c1+SbVT8QFt\npEDsJKZwbFA8TGyqO7dW/5tQnf0o20Ab4LKj6dqpdsgzjje51p13c+Uyj6/qeP7quMMXitdu3Ed6\ns2miZ5f2Px3f2fdaPea5hK/pnJWv5ljcbWqeuzx3fntKl/ThfMPOdclXpWPVp4vral99dvir2691\n93Nb7KfaqAQi95X4rPMTveqoSw7ymrr4kj4xThvPw2E2dZz2R3EkXsfJukmCUPHaPeROD9kxwe2u\nT31yO5TPFLt2hP2qtXSYjHW9qNNj7DeN0VGn12zDk35UH6qvKb5L61MPHLo67OuSuOhkhLyn+Thy\n9qOu5QcqnEhWDzQ6eu0ThEdoxwgcwHKL6vrvHOFRUNIZquNrUp6Q493NdwpKuL0L9nitKqsxUx8p\n2KWgWMSySPJUfFUdPi1FADFxUtXOGXydQ73AOry2c8hTR5YCh3OOaWysYxvlMs6RZcllXg9VV/sE\nUlQ7Pn7IbfIJBuu9C3qpbtLPhN80XgeM1Rsjzge4+eBY06exaVNyYl6cjrs5q/qbm5v1/Pnz23M8\nF95P+nXHHSl/pGR9NLa56x0QQv3o1inxVnVs/5fE6Uf66BLq00P0o8r8kEfhh2rr4upD89uR8ptH\n8JPDee4aNb8UL7i9O1a8KUK/mfwL4ho3l9onPOLiPOoC+1TWExXPqpzWLPnMLtYonuuhdpWVjk/6\nTrii+lbzYf74oTvWuXjK/bk3CI/gDic3rE97hy2PYFGXiOO3lRXvtZ+MvfOlzORezu2T7NzaqnVX\n13a6q/z91dXV7Vux6ndYnZ/fxZ2KJ5wL668aw8lHyWAXZzkc2sUFdd0l+K76Y5+b5KBsnd+M5/sd\nReh/cM/8cf+8Fb2VbxDicedUuwCC/SVhT/sp6pyD46HbT5zKTnnCV9HUoDkQJPCSgobrZ7dfx0sK\naOnpVVoPF8yZEADVsQMFjhR44Kec1a6OVbkLGOVk1FMXxRP2z7Lpxulkq9rjHFg/8BpcO9ZldvJY\nr3Sg+ywYy27r2kwBEfLPfSqa2uHufNIcmXjdOKDhnm2R+VR7ruNAWZ8mpQShCuYTXXB6qjY1Ro3z\n/Pnz6KtdvJvUYx33q8Z5CFIxFutYT9y4SZ7p+tTfWtoHTH3xI320KOkF69Nuv25fm4rDjB8cnxwn\nq+4Ir9O5OAzs7C2dTxiRaSf+Jb4dZugIr3d9pb3qS+EBru/mpnSAx3Jx391QqjU8IiukhJHTWClG\n8oP4CV/8tiAnC90DeuTtfH6ZYGGcwThItdmJQ1NctIMxu43fGlT6mXjaHaezgWlf3Kfyrc5/pTqW\nN/ejrle6g5/Lc3JwB7t1ODThwtJR99av0k01V/Z9E7/axaakV04mR6j4UD42+R7cu+Sgkv9Eb7At\n+xF1L8U/wbAjl9c+Qcg0cZo7xpzaJwNC2gEv3L/jJTkP5snNxZWPEBpzp8Td/F3gUud443OTMSeB\n0AUNDh7JuXbzrXL3ycGEVEJQlcuBcGJQ/a7ERNcdKVmkwOPK3TkOMhxwSrYMvuo8nkNix3nJJ8ZY\nPgJcUt3k7UGcU3deUZrDdG5pDFwX9yp8ncc3Z6qMgXSyLpUUvLq6ur354EQh2oT6ND99SoN8ORCl\ngraa/83NB79TirJi2bn+XX1qw32qfceLoonvcLaIhPrTza2Ld2pePL4CceIkagAAIABJREFUgZfG\nzkd6uwn90e51kxhY/XNMUzrv6hyvR3l383HHPKbjF2kHTydS8QjjPccUxgu7PLjrHQ7p+k7xX80J\neUD9cXy6MTGucdKuu17xwf1XveKNMTK/VZgeWiPeTfbVUfWj9kpfVNlhUpcIVLxP46zDoVg3wZwT\nnOreIEx4seNH1Xf4F6+Zvj2o5sBvqCZK2GIyP6UnvGccjW1rm764wXY0xYZVV+NMcJeSkZqzwn3J\nFyp/uRsXdnGc4gfXhufg1pRlr94g5HFcbHB+B8fiewu81ylSb6Qqeu0ThMlBdsqaruvGXOtuxtsZ\nQVLSpOxc7hzG5NqunTueklJid05dqxwllpPhpwA04TsFOQ4UalvL65t7uqrkxfOeONtEHThRSUIG\nKw8FpJw+qr7V+ckx847ltfTTZm5TdSwzrJ+AFGzH1/DxBHQlMDMBYFPbmJxT+515KWL9nH7yi0+K\nGSCwPXN5rZdvJGBysD7hwDFVYrD2Nzc3d/6pj/+5Dz/DSCAsvcVYQEF9YsxlZxuqrtv4GjWGW8dJ\nveLfgcbuum6eE544vuP4u3N+pI8WJT1Dne6widKvtE2wg+pL8Yj6/1DU2ceu/TgMOG27Q0ouzkc4\nUmuvytyv05kOK3ByYKJvSqe4D4U5ijBJyPNR403qWA6O3Juyql/kUcWL9FUMysAlBasPjmEqXp/P\nZ4kNsB+1LtMkoVpDnIeqe6i3B3c2lm13jHU7ib9uc2874pgJdynieu6TXzhROqPwEa/t6XS6o0tq\nfVknWO/ccYo70z/ZUXrIc+r0WV2T5OLK3A+ef4ix2Xer8aqM8nZvEDp/nOZTbdi/Vf8fiU+M19KJ\nhE7JdgEJXzs1HiZ2djt8pKDaOa2pU7tELhNyoKNr64JZ5+zX6h33TvBw/8zFQVu9gTchBXR2HSfK\n7NK3Bx2I2uHFBTvuc3LO2ZnjE8u1hjt+wjl5BTwUEKm2qTwFTQ5QTPpJ81PAwckBy1O7UVunOy6Y\nVfKM1w11gOdWNsq2W8AGE4SYHCzbUEnCGq/avHjxYl1fX9t9lyCsOnybUe3P5/ufGDt/72xnrWVt\nHPnh6zqfwOu3c6zWX4HI7hrmR9l44sWVlc7u+mO8rrPLR3pzSekJ+ovd67He+Q324+ijXKxztuxu\n3o5Ssj0mvuly59k2XR/TcbprsI0qJ36QX3cd6odr281nGveVDkz6V+MUnkJsqWSSSGErx5PTabQH\nTu45PVIYnWP85E1ExM08fxebsY/r6+v4oN4lA51duznXeA6LrrVsYmyKN6uPydbhyI5fxxsn9I5i\nVMUrr6da5+kadC+bpL2y8eID30jtZMpxxN3DquMaw91TYZIp+cQ6fwRLJZrGWrW202tUHyXfLu5V\nnUsOujcImd+pr08vXODvWe7I4o1IECZyjpXbdH24PqekFhjLyVi6IJPq0lx2gNsuuflinQJ2yplx\nP1yXggW2V8499cNOHBMM+GZQOXV+w4jfmpuAY+SZHfGuzqn1RVCzA0pwDrv63wU7ZZuuzQQosbzQ\nnnjNU79IrEM74KmuT2BsCmg4OXikD27LclHzVmU1VhobgRaS82UVJDmI1fH19fU9fmpNkZf0dl8B\nqroxwOSgeosQbxyqfD6fb5OBL168WM+fP79XdglCLtcceUMeMUGYZOfKCsjxsQN96hq3lml9U/vJ\nOSQGu6q8wwfHYqxLAEpd90iPtNY8yeb0SvlFLKP/U8lBvNbFOIyV7EcvmXeaW2qfrkn4MBHa7+7c\n0AdwGftinnkcJWfXvuMzYdbOR7k5Oh1x8X2tdS8eKlnx+Ek3FGZTslLtJ3Oexgn3NiHbmSt3cRpx\nkeqn2kzwOPbt4tQEt02+VuHrlf5NMWjCplzmuu5LmsRn2lQyE+1VravTNaU77j6ycCLridqzDiCG\nLV7d+mGsQF0rjKtiDN7foq5igtvpqJKP8ovVryPlazs/zudV+84nT/jhPlQ/6pjlz7LvYqDq1/HK\nm3uD8K39xHgCgtKxa6/GmIyHxGAmBdLE9zS4Tc5x+SHIgTYF6lS71H4SnDiQdTJ2fbJjRafOyYa1\n1p3gjg64zrl5Mi9OL3adlnp7kMFJShIqh7Kj72ls3k/11zk5lJXrt9Zx6ngnupLAFLZT107BkdPt\n9CQ2jTMh1477mAIylouSL68bgxF+i5DXgftTtotv9NVv+T19+vQOuOJEoXvqhvOr/p4/fy63Fy9e\nWDCA87u6ulpPnz69kxzE4/P5fMv3xFaw3Mm3K+Mx+wu0Q+RpEnMujT/Jr6h9N66LF3y865cf6e2n\nLi5ibJ344hT70I+wv8V6xdORWH4JXWLvyq+j/HZiGl6n1kDxhv4Ar0u4UsU5HD/tXT9qPrV3eCT5\nKKUDU3yKcdXpoMPbzEM3v3Se9Te98cfjYgxDXlKcQFyv7Cwl57mvKuOflDhMzvG2rucxGbuynuIc\nsFzHLpl0JOHn3hpM2NDhSbd31+98XaN4Zv7ZjyY/kUiNhQ+s8aH3BMdwPMCHu918ENtyPOEyPqBG\nnUSdUQlFxoXKpllueI+WkoV4ffLp3XUPEfucP69z7pq15m8Qsm/r5plivlpjlPVUJq99grAjBYKm\nk08G6sBV1zc6pjruAOW0nILQpJ+HJp6na8PtUnBQdSlATZxAF7RUclA5dbzJVzSRteL5iN6qNUZg\n40AIOqjJ1sm0S1ImflUbx0OVMZBzGXWCnaWbg5O7AiHpCSbKBMu7AEbppOqb69TaOD4nNOVfbSqg\nO53gtwcrYVbXYRKPk4TOduvtvkq03dzcrKdPn1p+8R/j3IbJwffff/92q+Pnz59HIMAJwtrq7cHi\nr94gZFm5vbKftfI/p+EeASH7juqXH34oe9mJSe66CR0ZRwEpjhl4rPQVydkR6v0jfTSIY1Fa/07X\n2V7RdyRfuxO3mdcp79P5XGLPSB2mc5TmwnGJx2f7Vzzw9ThOJ9sjMlY4wb1ByJjoCE0xSiLFVxrP\n1fN6TDBp7TGGqQQI3ozjOIxnGTcjL4x3sYx91htY1aeKsV2Z+1bySWvF2LIrpz66ZKA73+FnXkM1\nF+5DtZmcUw/hE9bZsSccgzGpSxC6cfg+Bjd8M9Ela12iSNXhW8J4T8X6y9iQE1vJZ/K9WI3J17Nf\nOIqt2OfvxJWJP1fXqDqHwd3ad3FMHSu8z/ceRR+JNwhd+6Pj8PXKWU8C1Vr6bSfV52RM1TY5FXa4\nrHiK19rjP2i69olUMHHOGdvx8VrrnrNTDrCTyel0kk60c6y8qX45udHR6XSK7SayZr1i0KFACAY/\ndZw2NT4CKebB8YGyYznWnoFXEQYRdvjYn9rUHBUpG0EZu+NLAxm3Rf13bY6S403ZXtcPU601tnF2\nWeui3qCrMutXt3VvjqbzCKTchr85iGAPjxPQqjcWT6e7/4g8edjAYIGPnV2pJKEDK6mM46mHAOhT\nsDwBUbvx2l3rQFO6lvV1LZ8cTNc+0iMdoaT7KmZ1sdrFP4Vl1N6N3eErno/DDYoYq6rzjEmTzDqe\nEv8qDnYJBodxuT/VfxfP+CsWxq3sw2s+vG4VkxBH1/7Jkyfr2bNnt/2eTqfbJEa6ma7rUc5dzJ8S\nj8v6OcEBOG+13ujvGadiHcoYMbXTMWVXfA+hrpts6jo1vpInj7sbv5x+TzAjt1HXpXJaa9eGx3V8\nTvxrWnPu6/r6+nYsTKa5uTkc0uHRGov9BO6Rh4Tv0lYPrLukopujkrWT7VE74DESod2rsloPFzMn\npOaqfLbiTelHN18nX8TyO/TaJwgT7QaeIrXQKijxOCj0SZDngJIUfhpg3R7n5o474HJzc7Pef/99\nex2T4qHKR15ZVwDMJfH4KWqSpUsCdslB7quMzH0iiLqk1uB0Oq2nT5+O1qIrs0PjpBzqN+osOg0+\n5rmW7HBcBFElQ5UM4Tq1RliHfXPQUX0oJ7jW/de5U5B3xHpYcylZqKdmam3w2Dl9FQAmQWEnSBUf\nqm81jgNSqE9Vx+1ZN10Z7aZLEE7AAgNBtGXVNgHMWnNOEOLny+pBBc+fZYFzqQQpJ0rLv6Q15Drl\nO5yNKH/mwB8e81uW6obU6QeSihW7MTzF6ck1XOcA4aRfpF3w9UhvPj3Emnf4UOGatCE+LR5dWdWl\n2Kl8b2crO5iU625uPvjZiAkWSrwoP8xtGPt1uBQxJNZN5ZzWhZOB6oE2xo/z+XyLQSu+Jl2o7erq\nan3sYx+798db1Q/+3AX6S8TeKeZz3c4aoexcrHCxu0sM4h5xM+M0xNV4TY3hsBhjvBR308O7Kqc5\n4HjdxnJRskK5Myl7ZUI8gP0yBsU2HbHNp021Yf5UUkslcJT/wzni/Lhfp5vK5ovwHiNtay37u9u1\noQ4rX15l/h1uPL65uVnvvfee/K1u5qeLNzvxK/Gd/MtOXErn2LcxrsdzHaUcEM9j1y7cPJJ8i6cJ\nvfYJQhVclDPuqLsJ4P7wZkcpMDtb56ycgrvF5Dk6ReK9A1jJYSoHxglCtU+KjkY0DVipnN70479f\nZ1kiL+4JKp5TN/zYJ9/MY4KjPotkXUO5PXnywdNaF7Qna8g6isk+TMqhDnNykAMH9lP1+ISaAQSP\npYAV17m1wWNMDDKYwHIKJgiSXZBhGSMhz5wcRFlgoE8OnWWi5qPapz5cXSLVTtl20jVl53Wsxkv+\napIg7HylWku0KX6KinqpkoO1xqUrWOY3B/mBQvU1iUXYP/uT0+l0+7kxr4Pz26o+AQS0B5UYVFvJ\nkZOFbp14vq6O91Oa6N8uL2hbrv0un4/00aEdUP8q9EjZN/M18fkT3+FsZBJf1fjJnz1//ryNUYkP\nPE5+Z4JTFWbkeON8suNf7buH2efz+TZWFjHW6W4Cy39//OMfX8+fP19XV1d3/iCL+cU6PNfJVuE9\nd54p2QljTcRoiGEdTwk/4x7jn+Kb9U7FEW7PyZiEV5PN8XzUixn4MJvlo/rCNe18R1ovFVcd9p3g\n2AnuSXU4DvvIOpeSg5wkdpiK+S2bxetZd7H9Wuv2/tYlk9dadx5YY4IfE4cTDIgPGnhfOYGunfKV\nXDfFpIxHGatO4pLyM1xmHWVbYn3hdk5f1Rgu2Vwb6qSzkQk5mSKOVzw6eqMShLzQaeGLHEjhIJgC\nFAoagyIeq+umN2pKsZWi4zyVTBLASaAHN0wQOpAzCWQ7PHQJu/Q0tXPYzD/PWSUlOUmIRsY39E+f\nPl3Pnj2TCULen06nO781huNwwkLJndeg5IwgRzkd1leVGMR5cnKQHadKRCpgxXXdWiF4UbJjO2UH\n6Bwxl9leFQhyyUEEVsiXIpZR5/xTXepj4uy7QOP8mOJNAeXaJ/+EdSpBiHtO5ib7Rv6V71Bt17r7\ne6Du5qL44OQgfsZRfXYxhfVR+ZSSDc6JbQnnkHxXF2O6pCCCwLJ7fFulxmb74jl38piS0iV3POlH\n2RrWpz6T3T/SR4telR4kbJhiaNlk8aZuWlO5G6PaMK+KfxVbp3i0/HclCB0uUuOq47RPvHRJQa5P\nG8ohlbsH2tXm2bNndzBGzad8tdIdPK43CK+urtb7779/p43CwmoOSqZOzhP9dWup4ivzo+K3w7CI\nnxSmxfs7TiSt5d/CcbaAa+MSP4wPnN0puaBM1MNLlxB0e5YxylnZHq+zwrrc3yVxNNmX8g+43mvd\n/9PJdO/Aa+GOUWfYR1TCT70Qwr6jdIKThKgL+MYv/87hixcv7mFfpUuc6Kutjq+vr2/fIKzt+vr6\nNnmIONA9MGEcnmJawqAqSdgdsy6jnird5WO8zpUTTXhT9nKJXbjxlR/p6LVPEDLhJOtYldd6mOQg\njokL3QGBKdjqDHgSGKrsbhodyFDnOEGo5tRl+VG2znl3QEslB91fxnfbZK3S+tVe3dC/884769mz\nZ7dATc21yk+efPAGIfKuEoNJ/kqvENiwjnIQxH1K9GCyZK27v+HG4xVfPAYm2bo14uSgShR2QeV0\nuvsGoUsSooxxr8oqccT2z4Tz5TLbqrJfV4f9sUx4fEVqDDWHdP2uP8NrsTxJEE7GdTbCDxDUnFmf\nOCmIGz+h5ZtC17eToXvgcDq9fIjA82F95bnihmOh/LGu+2yEQSAmLtW82GewzrMMlG50lK7b6Qfb\nK7vrxk70EMDukV5Pcj5ftTtKbB/qXBdLHR8Om3CcZd/BdVXPvKmxuG6CBdHnYYKww2lOZpO9e1CN\ndemLlp0koZKLkpN7oM1rhVipjvHtNJf0qAQh4yOcP/4moVtHXgNVTjfzmNDG61IsdbhNJQdZlwsb\nK5zmEoNubi5RqK7DMTAZ47Aqr4uSK68ZrovDr5w0ZXknTOjKjj/XZ5KTswdlS+5YnWP++Fy3Dnyv\nq+rQVp8+fXonOcj3NMwj+hk1Fuqn+y1srHe+DueLyT/ebm5efmKMX/ooP4f35ur+XdkhltlvuX1a\nC7bjaexKulhrpMrTPpzv4/nv2Ekaz+GCSbxEeu0ThLywWL9LbJipH7d4nHjptg5sqXp1zDxxnXI2\nfPPYHePTWgd01tK/j8DG2znvDmypZKAqO2NQBpeCSNqXnPmGvhKDz549W++88869gKN0oX4Q+nS6\n/2PQuK47eqV0B4Oy0lsEZJwoVEAi6SwCER5X8auAISfeUAbJRjFIFO8qoLNTTmteMkngqiOcM8+f\n9UoFBaxLZeQdr3U8qTLOO/GBdU7OrEuKn9NpniBMwY75Rv9RIIbni3PhRFptSuddglDZZSKeA/uU\n0+l0CyrRDxfvnPTEeaN/ZNmrfYFQlShUT4hrU3+sgr4D+U3yUDGuI6VLlxL3MbWnR3okJgfuJzqk\nbIHjLdd3G/tRh8ewrGK84i/FZRVf+FjhT/Zl5/PdBKHyuZ1Muez2U5zsvmbBPyjoMHRHDkPyGpW/\nxjiBWFX5dz7+2Mc+dmdti3f891LFD87PyRyPVSIF+ecb2Ek8LeKYrbBr9atwmUoWOtljTMSxOVGo\n7NXdOzmsyglC7Jfrig/coy4xpleJQpZLhxkVsS6xTJV/qOtUPfPgfJjzaY4/5on1R60FJ6jUxr4M\nE8Eod+Ydr3ny5OXbg5wsPJ1Ot2/yVTLw+fPn98qYIEzr5H5THxOET58+XdfX17cPslnm6R69ysn/\n1qbeZuQ3G9NWPgX7xnuFFLNUWd1rdT5J3d+5TbW5hFyf3D/7KkevfYKQqXOUjqaBmcdSAlc3pymQ\nu754HDVWmmeatwNc3ZPP999/3z61xADJ4KMcIRpqctgOaLnEoPtBVufUlcGpYDfRCw4G/HnxO++8\ns955550781bgsK5lnqq941P1mRyMC8gqua0ShWu9TBJ2Dq2uRUClQBaukXPiOE+XgMc1wT45aHNg\nx73SyQQ2Sl93k4TI5xQQufZYh3PAY6ZOt5Vc3RyQuiDtrsO6DyNBiDdsKI/ORytdV77H3SB1hHrK\nbxCyTz2dXgJ9lYTHuSNvaf1wDQqAou9WyUE1BwYcSqYTQLULinbi4M6NyFF+HumRdnzABMMmjJjq\n0TdiG04WpJtqxZfiM9mJwl0qnnf4zz20PuJ7HRYv6vDxBKsq3Md1SVYc213sQkyDb/Sste7Flm77\n+Mc/fjsuzvHFixe3MUA9qMJNzYXr+N6A8SZjvy6GpLWfYFeFl7muWy+cA9uPw3a8dilBiPbc2aCy\nc5QFY3rkiWXjZLuDGdW9AO6rv248LjMPyrdhO+ZLleuY7yF5HbqEe60B2hG3VziefaFKEPP6YEKw\nkoJVfv78+T0cqORa96QOh1eCsJKD7n7s5ubm3m8g8kP1Lu6xz1KJS5Y3l9m34D0bY1PHR/LHKq4x\nuVju1tK1P4pDVczGe1/Fo6M3IkG4c0NQpBxLlY/ckCgA1iUJEshSZR5Lncd+sKyc5wR8ceIQE4QO\nkCmQgecmDtslAVVSMJ1Ta5OM78ha1Z5v6PHtwXffffcOqHAJwvoUuajaMp8OZNZeBXU+Ln4YlDE4\n4+Mi9eSFx8Lz6IBVHY+tnHONq24mcK3cujPYUvvuJgmBau3XOpYkdAFBgaIElFjeSm4JDKV2Vefk\n7ebEQRqfkib+15olCJ3cMBGpfJ3yS+mJ7cRPqxtEZ+ed3Fh26hNjBvJFrHPFA/rH+ufJRKfT3TcI\nEciifrN9qLV0/adYq/zWDqn2zqaYd3d9d9PzSI/0YZDCg+jz04ZJAPSRda3yVS7Gcnni14uSLSls\n43Df+Xy+kyBkvzt9UIeyc3NSPt4lBl25w86M9RI/SZa1vvVmDybwCgdhXOE/1MOb74997GP31uLF\nixfr6dOn6/nz5zJhx3FnInt1I8+ksJ+TifL3VZ/sQt0fTPAZzqX2iA/ZzpQdIz+IUxjHpgShK+Pc\n1Zrg/FOykGWzQyi7Ds/ukLuXVPhE7TvZqfVJa8a4F7Ev+gXVJt3voa9RelDlq6urewlB3sp/Jtlw\nghC3+h3D9957T/peXNvyGeqzZ8Skbh3qWPmqKleSEjH+zc3NHayvfAve8/F4bs+4Ua3X1D5SvFZt\nFI+75Hzgbr9vRIIQKS1qR7s3Ok7ACMDUjbUKbpO9C1qKz3Tcga8EcjhByOW17t/cs+PggKn2KRGY\nNn5K0QXWziG5tVZrgwAH3yBUnxgrgFgOEGWAT544oDog7fRK8VvXujKulyLmKwETnBPWYRl1A5/y\nJBvqeMOEEcqTy85mlaxRX9UTWOZbAbIdcKTAlNIJlDuPoXzZlAcla8cP2wJvE198NEGIa1o8oo/D\n33wpsFB+gv1Psicuq2sUUHKyxD5ZP/FmrnyE+zxE3aihPy1QNln3GlslvvGtFKVjCegoO3JyVaDI\nkWrXxcF0retrajPKNh/pkXYp2Ybbq3iPx/wgrPbTmHfpjYqzDR6bE3CM825uPniDkHGoSxAmv+v2\niF3cw/Pu4TUnFDE+MBbEcVM5zaNu7OsnazhBWOfxT/TUHhOEOL/6V+OKAyxnXrfODxZPV1f6d2wx\nbnT9FCGu5GO8Xyt+0z3W7hqcTqc78ZLtLeE5fJusZML3LFxO+KT2KVFbPDGOTX6Ex6t+3DrxfQKX\nec/tOkp+LO15/m6+k4fS3du4pRdoSy9evLh3n+ewJd7XPnnyRL4EUzLDhFwlBd9///3bPX5Bw7Kr\nMvoJtV1fX9/mBBwePJ1Od3hRnz3jQ2tnW4V9HR+F7dEn8ctJ3B/qmNLbFEMVry4+qjYsI7WlsXdJ\n+S013s4Yr32CMC1a58STg5oEAbyer1VvELEiuhsrVacWVpWLWAkVeFGv/HZPPvkzCQY5avwUYFAu\nVe8Aao3jDInH6NaxA7vMtwvOCMbSVvN0ALEcoJo3Jy9Kv2rD43Tjq9Zid3NyVPUVEBkouXJtyZEx\neFLHvOekS/cGobPTovQmRpUR8LvEI8tqGijUE94OlCnCNeC5qXasWw7wJZ05Avh4r0BI0hF8yogP\nLmpueAPHx0ofa9wi9kscCwpsKb2tsgI+CC5RR5Os1CfADjSnNVH2pWynAxeqb3ezlMqOurXhY9fn\nZKwJORmrNl27R/ro0lQPXbx3fhj9O8dl9hOMWS+1DeQXb3xVWeFULiMmVbgqJUWQOM5OfP4OTa5z\n8Zt5dL4W+Z8kK7qHcJXQSAmCwquKv1286BIwiTjm78QJx4dafxWTlJ9X66XiJ86V47xbB3XvoWTu\n5ow4VeGlIsQyVZ/uu5ydsF6oMj9UZ9mrMu+T7N16Kn6PbO5at+b8hiHi0norF+fixii9YH0qOTms\n52wr4a40l9PpNPIzfA+v+ks4sYgfPPA80O+nDd8o7GSUbErpUUcs37Qurn+l3934GMOTPk/nsdYb\nkCBkmk6s2irBThwDG5NymNjWJRrU2HxOKQVSd7OHRsNAi//2nJOCuC8wNkmgdA6zm4N74woNGjdX\nn9YQnWoKYthWJZtOp9Od3xqsfy7mfemCA8Xl+LsbzASqOUHo5NzNKSUBkhNTusdPT7nsdN7JPgVb\nBFUquVJBbTdByFsFuVoPvNGqubkbL04a1rh1XQdEODnJPuxIwgH5ZL7RFtmf4bHTD7X2HbFMa74I\nVNMTS6cvlRhEm1MJd3wKOeFZ3ZhiXdmBS14Xb/iv55gkREDGCekJsGKZpJhRZZXMdPanbnwU8OBx\ncP1UXFXUxfgO6HCdu8FN/St5Jep8+iO93YQ4Efd1DtulPnbGqz3HjbX0b7qVb+2wXUcT3zK5kXMP\ns7F8Pp9vE4Xsgzv+kzzdww+eg6rrsD7qAMc1l4hROEDdb9T+yZMn7QNr9XmxeuMJcZX63BAxjMOS\nTlYsGxdHeM0SRuRx3Bp0Mp7SBL+qWJoSsLweHc+TOIcyntwbraWThROMmu6JVVLQYUccD8sObya7\nSPJRusTH3VgdBsI2KiH87NmzO7ge29dbhuqzZH4JQumjwm4oX7ZJxID8pjGW2c+oF2X4d/UdOT5U\nPEHMfn19fa8Pdc/FG57ntwuVP2GdYdrxGXyN83PdOM72j/AxHR/ptU8Q7jhHd31RcnzuWuew1vLf\nticelXFP2zojLwNRyUFOFCI4Y7B2Pp9vjVEFeRW83Y2l4rubG78urBwBJwongVXxnYK82vBTYtzz\nG4ScRMDtdDrdzsvJGNfTJSPqJqADZS7ZycFmApYTsePnxJID1CpQugCLSRZVX3qREoMIntLmEoJu\nr+aJ/dQxju/0k8dFoMU3nJxsmcyrZMRzZf6xTy4rXcH+lC/j9VdJQayrMV2SEOVUOlCJP3xLt/rH\nhCD7v8RnkXt7BetYJ3mP4Eq9RVhrzjqHVHJBH6vsCK9XfgKvUwlAlyRUoFqtdQKlSc4ca9X5rqzm\n6froCHmZ+MJHeiSkqb521OEwxKTo01TSUPGo4nSn78muVTJQ1aW3B9NDa44Lysb5nMN9E/vmeK9k\nhP1yHMO6WosjuLWOr66u2qRg97a6e0MIr3n27Nktv7xhgtf5eyw7TJbuiVR7tcZqPY6Q8/eTewaF\nSTEB45KEShfUuqNsHCFvPCes50Q1Yy/EqdgvjsPnec99Yj8Ky3K7D1rJAAAgAElEQVTZje/KLCO+\nF1K6hLiGr+e67h4RMSm/PYg+EPvCz4/Rnlz/aq6sj4V1Uc5uX/e27icInjx5st5555172JXLDn+x\nr+w2bKuwOfp/98Cfk4PKjnmdGTN3mDHZYWcX3IfqS+n1EeL57vrH1z5BqGhnsVQ75eyUg1YLnW5e\nqp1SzMRnUqIiDrocfNXnxbx1oA2NMt3AceBWzrMjduJqfu6tQXWDnIJrt3GQV3v1BiEnCk+n0z0Z\nc4IQf4PQydglIJTjYz3A42mC0Ol/IuX4FQ9unt1aTF5rV8kLLrOucjKMg1DJjZOEbq9uXPhGrMBx\n+Q6VJHTjYh8K+OwQJwa5jjf0ZQrUIbmA7IjfbEEwWeXSdyYHiG5ubu59wo99OUDBMlVltD1nl+4G\nAcEiv0Go3oLt/Cc/gEE94k8+cA68d/bCtpSAauczcCwnY5yHujnA81051U3PKd6Sr562faS3mzr9\nxXZcTpjP9cFxk336xMdP/XWn/yr+M85Ue/4ZHPeJccc3Y3UXqxR+Vf42+U6uc/0XT5gk4SSMw9MJ\nl5Wf54fU/BaQemOQ481aKyYH67fDusSIWn+uUzrr4ofDhQ4DJWyi1qhr260tx0gn48L8/MUArw/2\nzzxO5qR4RF6LVGJQ1Tu9U74LbY8xrOtDXevKanzHC8uiKGGaCeZysmX95HvJWmu+D8R26mfA2D6w\nXPJRfOHYqi2X1YNrThC6cylByONxXOB7L157dR7jWmFclCvfNyg/jTLk+zjmA69xZecn1PHE76i+\ndv2U62d37LXegAThxBF016c+XYDCa5WzcmOxMu7y2SmD2wpwpURhSl6VUdUbhJ2RuGCJN7ppnnxj\nXvNQbw6q16URdCBPrqzAF9a5N9Zq42QgJwo5QaiemD958uROAkPJpvpQCYhUZl1wQIbLDgw4/VPH\nCgwmwNgFV/5MY7qpeSo5oJxSkk8FDzVv11+1qTqVGCyeyh5c8ELfUmUXoFCfFI/VvwrEfA0e89qh\nHnC7RCULTgii/ifdQRmy3rBNVHvl6zgJ2a1zShKWn0o+hD/P4E+MC6QmsKr8Gs4f/5zF2SeumQOh\nVUbenM9g3nCdFUhTx9jHLkhz4ztysd7pL+ue4x/rXPmR3mxSMbLzw9XmyLlkJymOrKV/f/YIqVjD\nZbZ19TDa/dFHShIWJu38WbJpPq+wB8/LxQD2zYzbXTIQ43vytwmXMV51ycH0JqHDTBWf3BuEDqfx\nG4Rpc/hcrVuSgcM+U1vpKMWfhF3Vm5ink//EGDelS11sU/NNsmO+1/KfGCucyrw421PJQZcwZHm7\nspO/kw1Tl+B2faCMu+tRH5Qu4H2g28qmnK10fgyxn8JjSi7pE+LT6XTnDUL3KXKnn4ib0R9incLb\nfMzJQXzQjwlCJqfLGDP5XnCCQSe6N7Hnzl5ZnopcTJz6E0WvfYKwIzfR6eI6A0SFVQ5LjYcLqAIY\nE/c3CQjuZpXBl3qDsAK5Sw4WGEsO2IEWdnJuHixfdgoFwlSisG7ClfzcGq617jlw3FcZARI7dkwQ\nqsQgOtNah3oyhAB5rXU7r04vXBLCJQ9xj/NXCTMOiskOkCcuO8CODnZy48Tr4AKsS74gyC/Qq9a4\n9iUnlyTj5FxKGOL1HGCwjUsOKl1NAYvlmeocIJjwzX3gOChX1gkO9o5ccpDBg5oHykoBI7ym+mcb\nUbaFY/L4yfeib0qb+sQYdRt1lMkBCgScCJjcPPCYbURtaJMqSah4qzFKD3ZjY4q3O/EyyW5CPL7j\nudP1R/rokNL5IzqLxLbDfaMtqqQg+/fp5uwX+Uqbe2jtHmSr4/P5ZYIQZYF75S+Qd14f9G0uQZAw\njiI1LsYyPHZYzGFThbfVQ+uUHFQ4CvtSbxDWVjqEsaYSGVXmB3Pq4bWSl5NjWqu0Fs4u0jh4bRdH\n1FowbsW4fjqd2k+/lf5NfIOKTSmWI7ZQiUHGYW6b8Djp28me7djhjYQ/uB51iF9AQPmjXrEPULLk\nrXBgPagtPUCbcEl2vIfvSPGE45fMO2yCDxrU7wtiAjH9ViHHCIU3VYIY6/Aavo8tnUDcXn6n9oj1\neS1RVoyvO5/ClPyKa5+Od/vZwZtsR7tY9bVPELJj2BWucmiqP27HDovbdGMmYFV9Omfrglft1Q1v\nAl31l+Pu7RkuTwJC5yidvFlGeHPNgMIlCfE88+bW24Gw4pdv1vlpKicI3W8QlpMq8MS/O1GAS+kM\nGnECXO5NKH46g/NT5WmgVTrIey7zTQbb0CTYMqgtMKUShPg0HPeqrJKD7tglBXmufIxrjrJQiUKW\nPd/koQyTb2FZcx3yxmOpja914M7pR9IjnCPrAfKXbi7qGpccxP6c30z+Ra2pKqM/Sklt9CWcJOwS\nhDh3lhXrK/PG82D9ULqY7FIlCd06Kz3dAWKTc1P/5Xju4vQO7bZ/pDeb+CZm97pLxsVYwP2pB0FH\nCf1pshPlK9VbhCoh6JKE5/P59jNX5ofrnF0rnKMwUYrxiAF4DD5WGFrFOfavDqMpLMNvEKqb+GfP\nnrUPWKsvFbs4QYi/jVYxpxIcLj6qWJTWEeWoZJXujdyapDXrsAzy4nhjvIryK8zfvUW4Q1O5IY9F\nRxKDaoyOJn2zzeIxy1/NTfHj+OWEIN9DdPbP/TAGYn2opBX/5E3JxtlR+cnuPkPNT+HfjhQuxQcP\nT568/A1C9wCCPzFmW0efwH84Unte0zqn3qZUSUH1oAJlgGvDX0nyfU7y84yTJ3Q01rOupzjc+QQu\nT3l67ROED0EKIKS9uz71z4vHeyQFZiZjMQBjEOb+pIQ/MXY3y2utWxCAAcQ5aBUk0XniNdyPekvI\nPY1xMnCOsAvmXHZPT7FOJQW5rvrF5CCDQwUGWEYq+eAShLXuKvi6ZGgCWx0Ac0FUvYmWHC7O2yUG\nXbLFBXsFJrnMwZSTK2v5f4BU5XRcsuExlU1wYgz5QD/ifAv7GWyPesX98g0kB0vXF+5TcHLn1Q1T\n8ZF0EM+hnyg9wHbVr0vsTd56UJtrlxKDVVYAi9+CTQnCmhsnutO8Sn68TeJhB4hR1mqtFJhN/qCb\nN7dzsTv11Z1De0BKoMyVH+mjR+ifuV61vaT/FD+QXGxO1/AclF47H6MwisKnXZLwfD7fual08kh4\nmuMi4wLGQmzLOL+Oh9pcknCt/HtoLkHI5aur+79B6H6+IiUJE/599uzZHQyLMadu0utNKfeyAdY5\nfWHZqtiC85/qr1ufRMpu3Ron3FoyPJ1O92K9SiLi+Im34sO1Zf7Wmv/u4CSpp8ZTdZM+lJ/k44RN\nXJ+Kv+7+gnGvw0vJZhG/YcKK8Vj1g/aDdsQJ95RsVxga69MarXX3DUL3u/qYIHRbrZ3CSLWxH2cc\nivW4Z77R/7BPUjJQ66Z8DPPvdJHle5Q6P7ODnSdjJTtx9FYnCJ2jcU7H1e2MV0rFAe/Sm4Z0s9q9\nPZgShK6Mzo5vJtno+LiSYMoIsXz0Jnx3TRyfKbDzExP892L3RyUlL0wOnk6nO2V+g3AqF5YHPm2q\ngMxPZzqgeUT/WBfXuvuHE2njOastBfICU5gUxGO11jh/lm9KANZa8jknA3XM9R3g6mRYsut8CbbB\n+SZw7ZKRXFd9Kv1QZUdKvzswynNEvvlt4toUQLv0WJ1b6+XTWPXmq/IvDqjW3m1uPXc3B6ZZjuqm\ndbJOSg+cbqiY2VGK46mfnb7Xmr1B9UgfLVI+NunHEXA+GR/76vyBSqbwgy2mpO8qxqFPVMnBSZJQ\nfWKM82Y5dHLiY4dhcS48rzpOfpk3lGfCOZNNYaPJPxirGMNvrrm3B6+vr2+TXPXQGxOD+CaP2lAH\nuhjq1k7JDK9Rflr10ekFkoqNzI9K2NaePzF2Lx3gw0E1By6jjhY/WOa3A48mBvFaliHvJ7Jym5M3\n48zpPtV1CULUK+fPpnZfYzA+rH3JVyXZscz3d7VGSdZ8n8ty4TL7Eb6vffLkifUtuLEOTPAmY39e\nf7XHtaz7Mj5mHKl8LtZP8TH3x7I8Qio+of7jWA5jcLxy/B7Br29EgnAKrnBReRGVU0kOxwUbpUg1\ndu15IbuF7ci1Y4VQhsBP91KQPp1Ot07BAZNyGO4TOZed5zLz6Ay0A1Y4d16rog5sPXny8jOABLQw\noKMz3iU3r/P57qcsKAsG/5WELPlxYEFn2gUP1imlYxzsXPs034kMuOzWzelClxx0wCb5GJZ92TLK\nGW2Mk21pUw49ydGBQ+VzeM+buqF0bXGsSZI+6cVELlNZMH/cZuL7WL932mHZ3ZCpepXkxnmzb2M/\nsbNurr2TK4/L/q5LFnZr222qX+XrlZ50OqOO2RfgMduVwwZK/lj/SG8HqVjG51FnnG6rvbuBcf6w\nboqmpNozflS62vlA7oflgfPDm7rymSwfbvvuu+/e6Y/LE3+CPtc9mFGf0Dp8pRJgKTGmcJnb8GGn\nwzz4BqHD4G79MEG01rq3lrxu6PvU2mLb7q2nLiapBBon09zbihXvmTdFaU5pjiledTiVy3jscF3x\n6uKRszVM9vGc+RrlE/h+BN/SYhm5+JnkVe0YuyCf1Q+/IYaJ6SdPPvgEtltDZf8KeyG+4hcF2G67\nOap7W1wfTBKyLuPv1+Om6pMfcoTzdW8id2/Aqpc02N5xvPKh9fCn/Go9jKgHQU4XeA2cH1U/saDW\nO8Vx1ukkw91zqq2yWyw7XOkwLLaZ4H5Hb0SCcEoMptSNRFeHZRXEVP1a9xODyZEnmrRXi+1AUbqB\n5Lqrq6v17rvvtsHNfcLATpP5wmNUfnYyyB87XARx/NYQl9EJpvmUU3FP+fBTgM7JpJtHJgZhFfB4\njVUZgyUGGQRlDsgw3+5mlkFl1WFZ6R5vTkd5bVXZBYEEuBIPSl/YubJDdlvpsQpmnCjkp7aso44X\n5NeBSPQ1GDC43x1f4M6XrbgxmFQbtaZuzVygxfMIgvF89+ZCN1e1pRsgd/OJwJRv5tQDB3XDgKCh\nW8/pWjqZIinelK3V9cn/d/XONzh9SDoy0Ru2l65O2V8nZyfrR3q7yPnkOlY6vRPHqh8m9AlTPotY\nr5mcP1E+VY3h4nz5Sm6DN//4Z28qQbjjcxAvoi/GMo6Pe5ccnCQC3V7FP3UDm3wvJwgxrijMwzik\n9moN1Xrx+ioZM+bZjbFrrXsP4/nte4zruCEvbHu453mmti4OKbzC5zHpNO0D/QfbKJZTXMJ1WGvd\nKat1VL6jcJ5KzCndwr6Z17S5dcEyrzPbYSW4EikfMEkWsS53a6jWQN2n4v2e8xtuwwRh8j+cIHRY\nvezK/VTB6XS6d1+s8Kt6yafmiz4LE4P8x6r8oMJhVncPX8f1ViS+8ONyFEmXE25T103OufZOf9bS\nb40yr6qvh6A3OkGoBOGcfKpTAQGd7ySwsSNJZSQHrhJoS8SJNuxXGRvuC4x1yRqVSHM3+yzXOq62\n7EgLPJa8ENDgNeVkUnDHsbvEE4MSVXZPaRUYc5QCJgfytO/AqArQai04iPBYNUYRAssdUuN3IEut\nFa8bbmute+vibB3n3Dl7Z+v81FoFFvVECPnpxsd2qYwy5vlxmeeB5Q7MF2Bwuslld07dEE9virGO\nk4N1LQOlydw62aSbwPKf6uYTy+kJbM2rAKSKHXjDlnh1a8ptnFxRvsk+u1i6W1YAzulD0pEJJftD\nmfMer1XE8t31lY/0ZhLrB9Yr+9lJCKU4sZMkxLiv9LrI+ZCJP8U5c+zDm+5qh7iutqdPn95LEHYY\nL+Ed9dY2Y7knT57c+ZSWsWa6mecyy4pxWUpYqPXn47oJxrd9sC9eR1xL1BXmTems81/IW8Um1hk8\n7s6tdfdzZ/XGEsZ2TJbgz/h0/pZ1n+eUdMqth8Iw6T6Kyzg2rtvUXpn3tdad9cA9j8HrWWtZa8/Y\nNemEw6ksJzW+4qvGdvjryZM+QVj9uPsHXAPknTe1xmr9UEYqOaiwI5fV5s5xvbJpt6/7efdP6Hy/\n7x5+11xTHECbdfsOu6Ic3V69hezeIOS1d7bCfsLFY+dPEikdwhiE8nBxfooxk/9Q9EYmCNMiJBBR\nexcEuI6dnVNeVcfXTCi1TX2xI3bOSV3PzpgThGrvgre7uVOyxoQf8or8YNDHJ0b4NGm6vilAlxNU\nwCQlB9m5TNcS10QF3S6Qd6C9nIiTCe4TiOM1YB6mjibZYrcuCWi5cjd3x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Vrr/me07jjV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hFax5NVgFwnpbyve5YJ+2V155pdVHLeWvt3LFB4O2ZIeHw1tvLvHtX4GeKvMKQrWSEPtx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\nY+J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TfA8Oxk+LneSrObYKR2BQ3CbjOx37zewDzissH4FBwjjv2MrCzgpCn3coF8od00bQ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\nhH0CqAl06GZl754AAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,2)\n",
"\n",
"ax[0].imshow(x.reshape(64,64), cmap=plt.cm.Greys_r)\n",
"ax[1].imshow(X[idx,:].reshape(64,64), cmap=plt.cm.Greys_r)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Аггрегация"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Массивы можно аггрегировать - считать среднее значение, медиану, моду, максимум, минимум, сумму и тп"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.36575967, 0.37234193, 0.14970974, 0.9461111 , 0.53290026,\n",
" 0.31879417],\n",
" [ 0.56842278, 0.97844321, 0.78435445, 0.6571982 , 0.56716522,\n",
" 0.91125909],\n",
" [ 0.73045639, 0.0338118 , 0.35913168, 0.13991442, 0.39521841,\n",
" 0.36330705],\n",
" [ 0.91820868, 0.16336231, 0.46958397, 0.36622901, 0.70162311,\n",
" 0.46877314],\n",
" [ 0.59470251, 0.90434144, 0.04637567, 0.16406337, 0.44744451,\n",
" 0.79758306]])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = np.random.rand(5,6)\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.63551001 0.49046014 0.3618311 0.45470322 0.5288703 0.5719433 ]\n",
"\n",
"[ 0.44760281 0.74447383 0.33697329 0.51463004 0.49241843]\n"
]
}
],
"source": [
"print A.mean(axis=0) # среднее по столбцам\n",
"print \n",
"print A.mean(axis=1) # среднее по строкам"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.13322254, 0.57411707, 0.26534129, 0.72223775, 0.78302364,\n",
" 0.40246901, 0.30582463, 0.43780534, 0.10431622, 0.93445869,\n",
" 0.46749456])"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.random.rand(11)\n",
"arr"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.466391885991\n",
"0.466391885991\n"
]
}
],
"source": [
"print np.mean(arr)\n",
"print arr.mean()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.1303107458965549"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr.sum()"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"максимальное значение 0.9345 находится на 9 позиции\n"
]
}
],
"source": [
"print 'максимальное значение %.4f находится на %d позиции' % (arr.max(), arr.argmax())\n",
"# аналогично argmax, есть argmin и argsort"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.43780534034250507"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.median(arr)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.10431622, 0.13322254, 0.26534129, 0.30582463, 0.40246901,\n",
" 0.43780534, 0.46749456, 0.57411707, 0.72223775, 0.78302364,\n",
" 0.93445869])"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sort(arr)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.13322254, 0.19928192, 0.7526307 ])"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.percentile(arr, [10, 15, 85])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Задание**
\n",
"Сгенерируйте такой случайный вектор (`np.random.rand()`) длины 10, что сумма его элементов равна 2."
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.0"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Your code here\n",
"a = np.random.rand(10)\n",
"res = 2. * a / a.sum()\n",
"\n",
"res.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Задание**
\n",
"Сгенерируйте случайный вектор (`np.random.rand()`) длины 100. Выполните такое преобразование массива, что \n",
"* Максимальному элементу(-ам) соответствовало число 1\n",
"* Минимальному элементу(-ам) соответствовало число 0\n",
"* Остальные элементы находились на интервале 0-1 с сохранением порядка"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Your code here\n",
"a = np.random.rand(100)\n",
"res = (a - a.min()) / (a.max() - a.min())"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n",
"1.0\n"
]
}
],
"source": [
"print res.min()\n",
"print res.max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Задание**
\n",
"Сгенерируйте случайный вектор длины 20 из целых чисел на интервале [0,50]. Оставьте в нем только те элементы что меньше 5 персентиля и больше 95 персентиля"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Your code here\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Что касается матриц - то в них все примерно тоже самое."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"A = np.random.rand(3,5)\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"A.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Задание**
\n",
"Сгенерируйте случайную матрицу размера $5 \\times 6$ из целых чисел на интервале [0,50]. Выведите столбец с содержащий максимальное значение во всей матрице."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Пропущенные значения"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В `numpy` есть специальные обозначения для бесконечности и пропущенных значений.\n",
"\n",
"В реальном мире приходится работать с очень \"грязными\" данными и частенько бывает, что какие-то измерения, значения признаков и тп просто отсутствуют. К этому надо быть готовым"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/andrey.shestakov/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/plain": [
"-inf"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/andrey.shestakov/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in log\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(-1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nan # not a number"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.74610106, nan, 0.82777625, 0.31485978, nan,\n",
" 0.09422482, 0.34026536, nan, nan, 0.17418014])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr = np.random.rand(10)\n",
"idx = np.random.randint(0, 10, 4)\n",
"arr[idx] = np.nan\n",
"arr"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([False, True, False, False, True, False, False, True, True, False], dtype=bool)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# проверяем, является ли значение пропущенным\n",
"is_nan = np.isnan(arr)\n",
"is_nan"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# проверяем, есть ли хотя бы одно пропущенное\n",
"np.any(is_nan)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# проверяем, есть ли хотя бы одно пропущенное\n",
"np.all(is_nan)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Аггрегация массивов с пропущенными значениями может выполняться без учета np.nan"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.416234568324\n",
"nan\n"
]
}
],
"source": [
"print np.nanmean(arr)\n",
"print np.mean(arr)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"nan"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.nan / 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Задание**
\n",
"Замените все пропущенные значение средним"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"idx = np.isnan(arr)\n",
"arr[idx] = np.nanmean(arr)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.74610106, 0.41623457, 0.82777625, 0.31485978, 0.41623457,\n",
" 0.09422482, 0.34026536, 0.41623457, 0.41623457, 0.17418014])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Линейная регрессия (Пока бездумно)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Загрузите [файл 1](https://www.dropbox.com/s/kg9px9v3xfysak9/tutorial_dataset.csv?dl=0) и [файл 2](https://www.dropbox.com/s/f87gm612o144emx/tutorial_dataset_2.csv?dl=0) в папку с тетрадкой. С помощью функции `loadtxt` в модуле `numpy` загрузите табличные данные одного из файлов. Присвойте y = D[:,0] а X = D[:, 1:].\n",
"\n",
"Сейчас мы воспользуемся одной магической формулой и построим модель линейной регрессии. Откуда эта формула берется мы узнаем на следующих занятиях.\n",
"\n",
"Модель линейной регрессии в матричном виде выглядит так: $\\hat{y} = X\\hat{\\beta}$, где\n",
"\n",
"$$ \\hat{\\beta} = (X^T X)^{-1} X^T y $$\n",
"Остатки модели рассчитываются как\n",
"$$ \\text{res} = y - \\hat{y} $$\n",
"\n",
"Итак, еще раз:\n",
"\n",
"1. Загрузите данные\n",
"2. Оцените веса $\\beta$ с помощью формулы\n",
"3. Постройте график, на котором по оси Y: остатки, а по оси X: $\\hat{y}$"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# load data\n",
"D = np.loadtxt('tutorial_dataset_2.csv', \n",
" skiprows=1, \n",
" delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2209, 6)"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D.shape"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = D[:, 0]\n",
"X = D[:, 1:]"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6.85976194e-04, 1.17459428e+00, 1.08903184e+00,\n",
" 8.87441294e-01, 1.00928516e+00])"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Теперь правильно примените формулу выше\n",
"Beta = np.linalg.inv((X.T).dot(X)).dot(X.T).dot(y)\n",
"Beta"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_hat = X.dot(Beta)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"res = y - y_hat"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Mw4cjLT8rl324884CEomSZdvVq3Gn5Sc/GcPhwxG8+moA0agfn/pU6+uDByOIRHz49rfX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\n7RyrAEupNKRaLaZ4fPMG98orLzo6C2BnFemNjdiu2TBOMq1KXVDbEFW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ggBGwiSrSAAAA\nAACTWEVedt6nzsxwnwrvkYMRsIkq0gAAAAAA01DkBSZggBGwiSrSAAAAAACTUOQFpuAVacCmTlWk\nC4Wkz5EBAAAAAKKIIi8wBQOMgE2cuAEAAAAAJqHIC0zBACNgEyduAAAAAIBJKPICU3iSg/ELX/iC\n/uZv/kbLy8t6zWteo7vvvluvfe1rO/79888/r7/4i7/Qv//7v+tHfuRH9Eu/9Eu6/vrrvQgV6Mg6\ncR8/vvmadCrV0IkTnLgBAAAAAP6hyAtM4PoA4/nz5/WXf/mXOn78uF772tfqc5/7nD7ykY/oT//0\nT5VOp3f9/fe//3199KMf1S233KLf+I3f0DPPPKMTJ04ol8vpqquucjtcoCtO3AAAAAAAU1DkBaZw\nfYDxc5/7nG688UZdd911kqSpqSn98z//s/7+7/9eb3/723f9/WOPPaZLL71Ud911lyTp1a9+tb76\n1a/qc5/7HAOM8BUnbgAAAACASbrVCpiY8CkoRJKrORir1aq+8Y1v6A1veMPW72KxmN7whjfo61//\netvPvPDCC01/L0lXX311x78HvEKRFwAAAACASagVAFO4OsB44cIF1et1ZTKZpt9nMhktLy+3/czy\n8nLbvy+Xy9rY2HAtVmAvnLgBAAAAACahyAtM4UmRFyAMstmKTp4saWpqu8gLJ24AAAAAgJ+oFQAT\nuDrAeODAAQ0NDWllZaXp9ysrKxofH2/7mfHx8bZ/n0qlNDw83PYzTzzxhJ588smm31166aU6duyY\n0um0Gg1mmEXB8PCwcrmcq+u44YYNPfFEUSsrMWUyDV122ZCy2czeHwQwEC/6NwB/0L+B8KJ/A+57\n8cWKjhw5sKtWwJe+FNNrX5t0bb307+iIxTbb1qlTp/S9732v6d/e8pa36NChQ5JcHmBMJBL6qZ/6\nKT377LP6hV/4BUlSo9HQc889p1tvvbXtZ17/+tfrX/7lX5p+95WvfEWvf/3rO67n0KFDWxvUqlgs\n8mp1RORyOS0tLbm2/Go1rvn5lKamNgu9jI42dPJkSfn8ihKJmmvrBeB+/wbgH/o3EF70b8B9hUKq\nba2AQqHuav+jf0fH8PCwJiYmdOzYsa5/52oORkl629vepr/7u7/TuXPn9N3vflcnT57U+vq6rr/+\neknSQw89pI997GNbf3/TTTfpe9/7nk6fPq3/+I//0Be/+EX9wz/8g972tre5HSrQVaGQ3Ho9Wto8\naU9NjalQcO+pEAAAAAAAnVArAKZwPQfj5OSkLly4oE9/+tNaXl7Wa17zGv3u7/6u0um0pM2iLj/8\n4Q+3/v6SSy7Rb/3Wb+nP//zP9fnPf16vetWr9Gu/9mu66qqr3A4V6KpbFemJCZ+CAgAAAABEFrUC\nYIpYI+QJChcXF3lFOiLcnqK9uDiqgwfHd+W2WFhY1sTEmmvrddP6ekJLS0kVi5s5JbPZikZGqsav\nq1qNq1DYXFY6vbmsXl9Tb40nk9lQqZTYWmY6vaFicdj2z/3EECS97i/rgmbQ42TZty+jf/u3yp7H\nY31disdjqtWkeFwql2NKp+u+Hj+v+llrv/B7H5kWTyu/23Sv+2hkRKpUhlQuS+Pj7uwfv46Zne/v\ndud9SV3jzWYrqtVirvU/E9oQYDpeoQS8sfM7KZ1uKJdz777OQv+ODusV6b1QRRqwKWxPhtbXEzp3\nLqV7792/lVNyenpV111XdvzLyMl1befCHGvJhVm2fZPWGs9tt1X0jndU9L73bf98xx2Vpn/v9nM/\nMQRJr/trdLShz3ymqKWlIR0/3v9xslSrcX3hCw295z3jXY/HmTNJ3XLLhr74xWHdcsuG7rsv5fvx\n86qftfYLv/eRafG08rtN97qP/sf/WNf3vz/k6v4x+Zi1O++3Ho926/v851f0zW8mXOl/JrQhAACk\nze/J8+dHB7o/ApzADEaEhhdPUPx4MuSWl19OKZ/P7JqROT+/ossuKxu7LidmkrbG8/DDF3T33WN9\n/9xPDEHS6/6SpEceuaBjx5zZR63HvNP6Z2dLTf814fh51c9M20emxdPK7zYt9baPYjE1rduN/ePn\nMdvr+7vdeb/1eLRb35NPrujGG9Ou9D8T2hAQBMxwAtzn15t29O/osDuD0fUiL0BYWE+G8vmMbrgh\no3w+o/PnR1Wtxv0OrS/dckqavC4nltW6jHp9sJ/7iSFIet1fklSruXfMO62/9b+9xBuEtt/Levze\nR6bF0298OznZpnuJoV6P7Vq3G/vH5GPWrh/ttU8kaXXVvf5nQhsCAEDy9r4O6IYBRsCmsFWR9rLa\nmJPrcmJZrcsYGhrs535iCJJe95ckxePuHfNO62/9by/xBqHt97Iev/eRafH0G99OTrbpXmIYGmrs\nWrcb+8fkY9auH+21TyRp/373+p8JbQgAAIkq0jAHA4yATWF7MpTLVTQ9vbr1ZZRKbeamyuWczynp\n5LqsXJg7l3XyZG+5MFvj+dSnRvTAA80/t/57t5/7iSFIet1fqVRDY2N1zcwMdpws2WxFn/jE3sfj\n9OkR3X9/eeu/Jhw/r/pZa7/wex+ZFk8rv9u01Ns+kuT6/jH5mLU777cej3br29houNb/TGhDAABI\nm9+Trd8vQa4VgOAiByNCgyrSvalW47pwIamNjZgqldhWZdJczp2KlmtrwyqVhrW+bq2roVxuve8C\nDYPmwlxbG9byclLFopROS5nMhlZX4z1XkbaqqVpVVMNWEdSq3FqvS/X60Cv7q6HxcXvVUldWkrp4\ncbvybb/7x24VaSvOSqWhZDKm1VUpk/GvinS1GleplFS5vLnvMhlpfLyi0VHnv5fsVgD2ah+ZFk9r\nXP226UIh6Vi/72UfDQ3Fdpw/68pk/Ksi7fQx2+v7u1qNN51LMpn61iChnSrSm+fqIce+50w5LwJB\nQI42wH3ValzPPbdPpdKQarWY4vHNh1pXXnnR1e8X+nd02M3ByAAjQsPtE1xrFUvryVAQq3NZ2/Lw\nwyNNVUBHR92pOGZ96X3jG4mmdc3MlHT4cG/rcqKKtBPLcHI5php0+5zcP3b6t2nHw8l272RMpu0j\nL+Mx6fzhZcymx9Otf5t0HjIxHsB0DEAA7qPIC9zGAOMrGGCMDqpI22d9CbVWAZXc+TJaXBzV176W\ncKR6phNfoE59CYdtVmurQbfPyf1jp3+bdjycbPdOxmTaPvIyHpPOH17GbHo83fq3SechE+MBTMcA\nBOC+l15K6fDh8V2/n5tb1hVXlF1bL/07OqgiDTgsTFWkrXySXlXTLRZ3V0Htd11uVJH2MxaTDbp9\nXu8f046Hk+3eyZiiHI9J5w9T12daPKadh0yLBwAAirzAFAwwAjaFqYq09SXkVTXddHp3FdR+1+VG\nFWk/YzHZoNvn9f4x7Xg42e6djCnK8Zh0/jB1fabFY9p5yLR4AACgyAtMwQAjYFOYZh1YFTlbq4C6\nVdEym61obKy+ta6bb67okUcu6NFHL0hST7NAnagi7cQyWpczyDaZqFqNa2hI+vjH+99PXu8fp46r\nk/HsbPdWTH5e8Jm4j7yKx4k2vTPm226r6OGHL+iv/uqC/vZvi0qn3UnHEuVjtr6ekCR9/OPOVDQf\n9DwUxPMiACAacrm6Tp0q6aGHSpqdLSmXq/sdEiKIHIwIDapI27ezIme93tC+fQ1VKu5WtFxfT7xS\nfTSmZ59N6J579ved4N6JXJhO5dO09uXTTyd0zz3hSNq/swhBPr+hd797XdlsQxMTtZ6rrzq1f+z2\nb5PypFrbXqttnjOsau3ZrL9VY03aR17F42SbtmJ+4olRvfe93vT5sB+zdv17fT2hc+dSuvfe/Uac\nh0w8LwJBQI42wH0UeYHbKPLyCgYYo4Mq0va0bsdtt1V0xx0V3Xtv/wN+vaxz0MIyJlaBDdPgs2Re\nUQQpeFWkWwcjbr/d/WrtvcbldyxexmNim7YrCsesXf9++eWU8vnMrn08P7+iyy7rPWG9acVZwva9\nAXTCAATgPoq8wG0UeQEclkjUNDm5pvn5FT3++Irm5lY0ObkWqMFFaXcuybvuWt8aXJTcyS25c52D\nFpZxIhem0/k0w/T6vGReUQS7TMqTujOWd71rfWtw0aS4/I7Fy3iC2qYljtlOYSrOErbvDQCAf8jv\nC1MwwAjYFJYq0q03NV5Ukt65zkELy5hYBTZsX+qmFUWwy6Qb9p2xeFWtvde4/I7Fy3iC2qYljtlO\nYSrOErbvDQCAf0zL14zoYoARsMm0WST9ar2p8aKS9M51PvjgYIVlTKwCG7Yvdae3x6v9Y9IN+85Y\nvKrW3mtcfsfiZTxBbdNSdI9ZLlfR9HRzcZfp6VXlcv4csyC3IQBAuIXlTTsEHzkYERpu54DwK7eF\n01rzZ91+e0XveMd2DkY3cku2W+eHP1xWoxHrubCME7kw3cinWa3GVShsFz3ws4iHE5wu4jDo/ukn\nB6OfeVK75WA0JS6/Y/E6HtPadC/rCfsx69S/TTtmpsUDBAE52gD3+ZWvmf4dHRR5eQUDjNFBFWl7\ndlaQLpelTKauTGZDpdLw1u/cqCS988Ysl6vr2WcTOn68vy9Bk6pIW8J0o2haUQkpeFWkW/tZNlt/\nJT53q7XbYco+8jIeE9t0L8J+zDr1b5POq0FvQ4BfGIAA3EcVabiNIi+Aw7LZimZmml9nmpkJ1utM\n1g3StdeO601vyujWW9N6/vmEJOmZZxK67rq0rr8+o4MHxzU/n3Isv2Rr/sqvfW17cFHq7XVzJ3Jh\nOp1P09qvBw+O6/Dhccf3n9eCmg7AlDyp7frZv//7kKt9rJfYTNhHXscT1DYtRfeYmXZeDXIbAgCE\nm2n5mhFdDDACPcjl6jp1qqSHHippdrakXK7ud0g96XSDtLTk7o1T63prtf6/BE2sIh3ZeHv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2jxRiEeu7wc/DJtH1FF2hlhH0AFAHjHtGsFRBcDjIBNhUJSR46kdfToAd1555iOHj2gI0fSRt8M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\nt3Xu3DmVy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1yzoYkJd/NbAAAAAADQynrTbucgI2/awQ8MMAI2kYMRAAAAAGASqkjDFAww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\nyOBq2wScbW3aRibraPr6qU48l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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(y_hat, res)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas\n",
"## Основные структуры"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Модуль `pandas` существенно упрощает исследование табличных данных в `python`. Работа в нем во многом напоминает работу с таблицами в SQL с тем отличием, что в `pandas` ~~тебе не хочется рвать волосы на голове~~ это делать гораздо удобнее, и в нем заложены некоторые дополнительные инструменты по работе с данными."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Series"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Основными структурами являются `Series` и `DataFrame`.
\n",
"`Series` – это проиндексированный одномерный массив значений. Он похож на простой словарь типа `dict`, где имя элемента будет соответствовать индексу, а значение – значению записи."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Задать `Series` можно многими способами, например с помощью массива:"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ser = pd.Series(np.random.rand(5))"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0.095159\n",
"1 0.544860\n",
"2 0.624966\n",
"3 0.719583\n",
"4 0.840589\n",
"dtype: float64"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Колонка слева - это (строчный) индекс - некоторая нумерация записанных значений"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"RangeIndex(start=0, stop=5, step=1)"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser.index"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.09515902, 0.54485963, 0.62496629, 0.7195834 , 0.8405891 ])"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser.values"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.54485962580090408"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Достучаться до одного значения можно так\n",
"ser[1]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 0.095159\n",
"1 0.544860\n",
"dtype: float64"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Можно так - это обычная интервальная индексация в python.\n",
"ser[0:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Но про то, как улучше находить нужные вам значения - чуть позже."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Индексом может быть что угодно, например:"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ser = pd.Series(np.random.rand(5), index=['m', 'i', 'n', 'o', 'r'])"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"m 0.075117\n",
"i 0.830392\n",
"n 0.543025\n",
"o 0.561296\n",
"r 0.664521\n",
"dtype: float64"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.66452098477899268"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser['r']"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"n 0.543025\n",
"o 0.561296\n",
"dtype: float64"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser['n':'o']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Индексация"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Индексация в `pandas` может временами может показаться запутанной"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 0.451602\n",
"3 0.064631\n",
"5 0.303191\n",
"6 0.870209\n",
"9 0.917350\n",
"dtype: float64"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser = pd.Series(np.random.rand(5), index=[1,3,5,6,9])\n",
"ser"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.064630720574033407"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser[3]"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 0.870209\n",
"9 0.917350\n",
"dtype: float64"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser[3:5] #?!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Поэтому придумали разные операторы для индексирования, чтобы можно было явно указать, когда вы хотите использовать значения индекса, а когда позицию."
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3 0.064631\n",
"5 0.303191\n",
"dtype: float64"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser.loc[3:5, ]"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 0.451602\n",
"3 0.064631\n",
"5 0.303191\n",
"6 0.870209\n",
"9 0.917350\n",
"dtype: float64"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser.loc[:, ]"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 0.870209\n",
"9 0.917350\n",
"dtype: float64"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idx = ser > 0.5\n",
"ser.loc[idx, ]"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 False\n",
"3 False\n",
"5 False\n",
"6 True\n",
"9 True\n",
"dtype: bool"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idx"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 0.451602\n",
"3 0.064631\n",
"5 0.303191\n",
"6 0.870209\n",
"9 0.917350\n",
"dtype: float64"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 0.870209\n",
"9 0.917350\n",
"dtype: float64"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ser.iloc[3:5, ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Их же используйте для присваивания!!"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 0.451602\n",
"3 0.064631\n",
"5 0.303191\n",
"6 0.500000\n",
"9 0.500000\n",
"dtype: float64"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idx = ser > 0.5\n",
"ser.loc[idx, ] = 0.5\n",
"ser"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`DataFrame` — это проиндексированный многомерный массив значений, соответственно каждый столбец `DataFrame`, является структурой `Series`. Индексирование в `DataFrame` ровно тоже, что и в `Series`, с тем отличием, что добавляется второе измерение."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.DataFrame(np.random.randn(10, 3),\n",
" index=range(10),\n",
" columns=['A', 'B', 'C'])"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 1.283762 | \n",
" 0.515803 | \n",
" -0.752565 | \n",
"
\n",
" \n",
" 1 | \n",
" 0.613519 | \n",
" -0.726500 | \n",
" -0.318252 | \n",
"
\n",
" \n",
" 2 | \n",
" 0.262802 | \n",
" 1.196804 | \n",
" 0.661973 | \n",
"
\n",
" \n",
" 3 | \n",
" 1.323470 | \n",
" -0.246536 | \n",
" 0.348757 | \n",
"
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" \n",
" 4 | \n",
" -0.511270 | \n",
" -0.918177 | \n",
" 0.374558 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" A B C\n",
"0 1.283762 0.515803 -0.752565\n",
"1 0.613519 -0.726500 -0.318252\n",
"2 0.262802 1.196804 0.661973\n",
"3 1.323470 -0.246536 0.348757\n",
"4 -0.511270 -0.918177 0.374558"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head() # выводит первые 5 (по-умолчанию) строк таблицы"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')\n",
"Index([u'A', u'B', u'C'], dtype='object')\n"
]
}
],
"source": [
"print df.index\n",
"print df.columns"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
"
\n",
" \n",
" \n",
" \n",
" 1 | \n",
" 0.613519 | \n",
" -0.726500 | \n",
"
\n",
" \n",
" 2 | \n",
" 0.262802 | \n",
" 1.196804 | \n",
"
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" \n",
" 3 | \n",
" 1.323470 | \n",
" -0.246536 | \n",
"
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" \n",
"
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"
"
],
"text/plain": [
" A B\n",
"1 0.613519 -0.726500\n",
"2 0.262802 1.196804\n",
"3 1.323470 -0.246536"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[1:3, ['A', 'B']]"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
"
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" \n",
" \n",
" 1 | \n",
" 0.613519 | \n",
" -0.726500 | \n",
"
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" 2 | \n",
" 0.262802 | \n",
" 1.196804 | \n",
"
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" \n",
"
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"
"
],
"text/plain": [
" A B\n",
"1 0.613519 -0.726500\n",
"2 0.262802 1.196804"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[1:3, 0:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`DataFrame` тоже можно транспонировать!"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" \n",
" \n",
" | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 3 | \n",
" 4 | \n",
" 5 | \n",
" 6 | \n",
" 7 | \n",
" 8 | \n",
" 9 | \n",
"
\n",
" \n",
" \n",
" \n",
" A | \n",
" 1.283762 | \n",
" 0.613519 | \n",
" 0.262802 | \n",
" 1.323470 | \n",
" -0.511270 | \n",
" 0.525151 | \n",
" 0.650805 | \n",
" 0.360842 | \n",
" -0.057644 | \n",
" -0.221555 | \n",
"
\n",
" \n",
" B | \n",
" 0.515803 | \n",
" -0.726500 | \n",
" 1.196804 | \n",
" -0.246536 | \n",
" -0.918177 | \n",
" -0.126969 | \n",
" 0.131791 | \n",
" -0.213805 | \n",
" 1.403712 | \n",
" -0.684884 | \n",
"
\n",
" \n",
" C | \n",
" -0.752565 | \n",
" -0.318252 | \n",
" 0.661973 | \n",
" 0.348757 | \n",
" 0.374558 | \n",
" 1.111150 | \n",
" 0.970014 | \n",
" -0.273131 | \n",
" -1.450807 | \n",
" 1.684813 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" 0 1 2 3 4 5 6 \\\n",
"A 1.283762 0.613519 0.262802 1.323470 -0.511270 0.525151 0.650805 \n",
"B 0.515803 -0.726500 1.196804 -0.246536 -0.918177 -0.126969 0.131791 \n",
"C -0.752565 -0.318252 0.661973 0.348757 0.374558 1.111150 0.970014 \n",
"\n",
" 7 8 9 \n",
"A 0.360842 -0.057644 -0.221555 \n",
"B -0.213805 1.403712 -0.684884 \n",
"C -0.273131 -1.450807 1.684813 "
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.T"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 10.000000 | \n",
" 10.000000 | \n",
" 10.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 0.422988 | \n",
" 0.033124 | \n",
" 0.235651 | \n",
"
\n",
" \n",
" std | \n",
" 0.596214 | \n",
" 0.791463 | \n",
" 0.943167 | \n",
"
\n",
" \n",
" min | \n",
" -0.511270 | \n",
" -0.918177 | \n",
" -1.450807 | \n",
"
\n",
" \n",
" 25% | \n",
" 0.022468 | \n",
" -0.575297 | \n",
" -0.306972 | \n",
"
\n",
" \n",
" 50% | \n",
" 0.442997 | \n",
" -0.170387 | \n",
" 0.361657 | \n",
"
\n",
" \n",
" 75% | \n",
" 0.641483 | \n",
" 0.419800 | \n",
" 0.893004 | \n",
"
\n",
" \n",
" max | \n",
" 1.323470 | \n",
" 1.403712 | \n",
" 1.684813 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" A B C\n",
"count 10.000000 10.000000 10.000000\n",
"mean 0.422988 0.033124 0.235651\n",
"std 0.596214 0.791463 0.943167\n",
"min -0.511270 -0.918177 -1.450807\n",
"25% 0.022468 -0.575297 -0.306972\n",
"50% 0.442997 -0.170387 0.361657\n",
"75% 0.641483 0.419800 0.893004\n",
"max 1.323470 1.403712 1.684813"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Краткая описательная статистика\n",
"df.describe() \n",
"\n",
"# Кстати, это тоже DataFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Аггрегация в DataFrame (по-умолчанию) происходит по стоблцам"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"A 0.422988\n",
"B 0.033124\n",
"C 0.235651\n",
"dtype: float64"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mean()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.42298825448228633"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.A.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Перевод данных в нужный тип"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" 1 | \n",
" 0.515803 | \n",
" -0.752565 | \n",
"
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" \n",
" 1 | \n",
" 0 | \n",
" -0.726500 | \n",
" -0.318252 | \n",
"
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" 2 | \n",
" 0 | \n",
" 1.196804 | \n",
" 0.661973 | \n",
"
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" 3 | \n",
" 1 | \n",
" -0.246536 | \n",
" 0.348757 | \n",
"
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" 0 | \n",
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" 0.374558 | \n",
"
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"
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"
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],
"text/plain": [
" A B C\n",
"0 1 0.515803 -0.752565\n",
"1 0 -0.726500 -0.318252\n",
"2 0 1.196804 0.661973\n",
"3 1 -0.246536 0.348757\n",
"4 0 -0.918177 0.374558"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.A = df.A.astype(int)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0])"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.A.unique()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Количество уникальных значений в столбце А = 2\n"
]
}
],
"source": [
"print 'Количество уникальных значений в столбце А = %d' % df.A.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Самые большие значения в стоблце B :\n",
"8 1.403712\n",
"2 1.196804\n",
"Name: B, dtype: float64\n"
]
}
],
"source": [
"print 'Самые большие значения в стоблце B :'\n",
"print df.B.nlargest(2)\n",
"\n",
"# Гораздо быстрее, чем df.B.sort(ascending=False).iloc[:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Важно следить за данными, которые у вас хранятся в `DataFrame`"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"A int64\n",
"B float64\n",
"C float64\n",
"dtype: object"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" A | \n",
" B | \n",
" C | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" lalaley | \n",
" 0.515803 | \n",
" -0.752565 | \n",
"
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" \n",
" 1 | \n",
" 0 | \n",
" -0.726500 | \n",
" -0.318252 | \n",
"
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" 2 | \n",
" 0 | \n",
" 1.196804 | \n",
" 0.661973 | \n",
"
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" 1 | \n",
" -0.246536 | \n",
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" 0 | \n",
" -0.918177 | \n",
" 0.374558 | \n",
"
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"
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"
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],
"text/plain": [
" A B C\n",
"0 lalaley 0.515803 -0.752565\n",
"1 0 -0.726500 -0.318252\n",
"2 0 1.196804 0.661973\n",
"3 1 -0.246536 0.348757\n",
"4 0 -0.918177 0.374558"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[0, 'A'] = 'lalaley'\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 lalaley\n",
"1 0\n",
"2 0\n",
"3 1\n",
"4 0\n",
"5 0\n",
"6 0\n",
"7 0\n",
"8 0\n",
"9 0\n",
"Name: A, dtype: object"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.A"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"A object\n",
"B float64\n",
"C float64\n",
"dtype: object"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Удаление\\добавление строк\\столбцов"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" A B C\n",
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"1 0 -0.726500 -0.318252\n",
"2 0 1.196804 0.661973\n",
"3 1 -0.246536 0.348757\n",
"4 0 -0.918177 0.374558"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" 0 | \n",
" 1.403712 | \n",
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" 0 | \n",
" -0.684884 | \n",
" 1.684813 | \n",
"
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" \n",
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"
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],
"text/plain": [
" A B C\n",
"1 0 -0.726500 -0.318252\n",
"2 0 1.196804 0.661973\n",
"3 1 -0.246536 0.348757\n",
"4 0 -0.918177 0.374558\n",
"5 0 -0.126969 1.111150\n",
"6 0 0.131791 0.970014\n",
"7 0 -0.213805 -0.273131\n",
"8 0 1.403712 -1.450807\n",
"9 0 -0.684884 1.684813"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.drop(0, axis=0)\n",
"# Пока df не изменился !"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
" C\n",
"0 -0.752565\n",
"1 -0.318252\n",
"2 0.661973\n",
"3 0.348757\n",
"4 0.374558\n",
"5 1.111150\n",
"6 0.970014\n",
"7 -0.273131\n",
"8 -1.450807\n",
"9 1.684813"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.drop(['A', 'B'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" 0 | \n",
" lalaley | \n",
" 0.515803 | \n",
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],
"text/plain": [
" A B C D\n",
"0 lalaley 0.515803 -0.752565 0\n",
"1 0 -0.726500 -0.318252 0\n",
"2 0 1.196804 0.661973 0\n",
"3 1 -0.246536 0.348757 0\n",
"4 0 -0.918177 0.374558 0"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[:, 'D'] = 0\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
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],
"text/plain": [
" A B C D\n",
"6 0 0.131791 0.970014 0.0\n",
"7 0 -0.213805 -0.273131 0.0\n",
"8 0 1.403712 -1.450807 0.0\n",
"9 0 -0.684884 1.684813 0.0\n",
"10 0 0.000000 0.000000 0.0"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[10, :] = 0\n",
"df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Продолжим обучение на \"реальных данных\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"В 1968 году была [опубликована](http://www.ncbi.nlm.nih.gov/pubmed/5676802) статья под интригующем названием Correlation of Performance Test Scores with Tissue Concentration of Lysergic Acid Diethylamide in Human Subjects.\n",
"\n",
"К статье приложен небольшой набор [данных](https://www.dropbox.com/s/ui14yeeckbc6z7c/drugs-and-math.csv?dl=0), состоящий из 7 наблюдений"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('drugs-and-math.csv', \n",
" index_col=0, \n",
" sep=',')"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
",Drugs,Score\r\n",
"0,1.17,78.93\r\n",
"1,2.97,58.2\r\n",
"2,3.26,67.47\r\n",
"3,4.69,37.47\r\n",
"4,5.83,45.65\r\n",
"5,6,32.92\r\n",
"6,6.41,29.97\r\n"
]
}
],
"source": [
"!head drugs-and-math.csv"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Drugs | \n",
" Score | \n",
"
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" \n",
" \n",
" 0 | \n",
" 1.17 | \n",
" 78.93 | \n",
"
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" 1 | \n",
" 2.97 | \n",
" 58.20 | \n",
"
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" 2 | \n",
" 3.26 | \n",
" 67.47 | \n",
"
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" 3 | \n",
" 4.69 | \n",
" 37.47 | \n",
"
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" 4 | \n",
" 5.83 | \n",
" 45.65 | \n",
"
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"
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"
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],
"text/plain": [
" Drugs Score\n",
"0 1.17 78.93\n",
"1 2.97 58.20\n",
"2 3.26 67.47\n",
"3 4.69 37.47\n",
"4 5.83 45.65"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(7, 2)\n",
"Index([u'Drugs', u'Score'], dtype='object')\n",
"Int64Index([0, 1, 2, 3, 4, 5, 6], dtype='int64')\n"
]
}
],
"source": [
"print df.shape\n",
"print df.columns\n",
"print df.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Таблица уже отсортирована по колонке Drugs - отсортируем по колонке Score"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = df.sort_values('Score', \n",
" ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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" \n",
" \n",
" | \n",
" Drugs | \n",
" Score | \n",
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" \n",
" \n",
" 0 | \n",
" 1.17 | \n",
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" 4.69 | \n",
" 37.47 | \n",
"
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"text/plain": [
" Drugs Score\n",
"0 1.17 78.93\n",
"2 3.26 67.47\n",
"1 2.97 58.20\n",
"4 5.83 45.65\n",
"3 4.69 37.47"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" count | \n",
" mean | \n",
" std | \n",
" min | \n",
" 25% | \n",
" 50% | \n",
" 75% | \n",
" max | \n",
"
\n",
" \n",
" \n",
" \n",
" Drugs | \n",
" 7.0 | \n",
" 4.332857 | \n",
" 1.935413 | \n",
" 1.17 | \n",
" 3.115 | \n",
" 4.69 | \n",
" 5.915 | \n",
" 6.41 | \n",
"
\n",
" \n",
" Score | \n",
" 7.0 | \n",
" 50.087143 | \n",
" 18.610854 | \n",
" 29.97 | \n",
" 35.195 | \n",
" 45.65 | \n",
" 62.835 | \n",
" 78.93 | \n",
"
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"text/plain": [
" count mean std min 25% 50% 75% max\n",
"Drugs 7.0 4.332857 1.935413 1.17 3.115 4.69 5.915 6.41\n",
"Score 7.0 50.087143 18.610854 29.97 35.195 45.65 62.835 78.93"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe().T # Иногда так лучше"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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P/2cKo8eV5BgNDQ0lGRcAAICLl4IQ4CzV1dUlm4FXrLkkZ4ZekqqJ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\nAABABfs/GnhQyxeIiLEAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind='box')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.Drugs.hist()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/5Ec/+lFNhgQAGEmTmiq5/uK5mX6KpznOmNKa6y+em0lNlRpNBgAAjWvYYfIn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\nFYvFPPnkk+ns7MzUqVOzfv36tLW1Zfr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""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# df.plot(x='Drugs', y='Score')\n",
"df.plot(x='Drugs', y='Score', kind='scatter', s=300)"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
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"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Drugs | \n",
" Score | \n",
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\n",
" \n",
" \n",
" \n",
" Drugs | \n",
" 1.000000 | \n",
" -0.936928 | \n",
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\n",
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" Score | \n",
" -0.936928 | \n",
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\n",
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\n",
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"
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"text/plain": [
" Drugs Score\n",
"Drugs 1.000000 -0.936928\n",
"Score -0.936928 1.000000"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.corr()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Мы явно видим тенденцию.."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Качество вина"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Загрузите [датасет](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv) с информацией о характеристиках вина и его качестве."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Что из себя представляет объект в этом наборе данных? Сколько их?\n",
"* Какие признаки описывают объекты? Сколько их?\n",
"* Какой признак является целевым?\n",
"* Каковы их области значений?\n",
"* Есть ли пропуски?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Какие признаки больше всего влияют на целевую переменную?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Создайте новый столбец `quality_cat`, которая будет иметь значение `\"good\"` если `quality > 5` и `\"bad\"` - иначе.
"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Нарисуйте гистрограммы признака alcohol в группах с `quality_cat == \"good\"` и `quality_cat == \"bad\"`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Можете ли вы придумать правило для классификации вина на хорошее и плохое по рисунку выше? Пусть это будет нашей первой моделью)\n",
"\n",
"Напишите функцию `brute_clf_train()` которая бы перебирала пороговое значение по признаку `alcohol` и находило бы \"оптимальное\" (кстати, что значит оптимальное?)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Напишите функцию `brute_clf_predict()` которая бы по значению признака `alcohol` и найденному выше порогу говорила какое качество у вина.\n",
"\n",
"А заодно выводила бы количество \"ошибок\" на текущем наборе данных"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Проверим, как обобщается наша модель на другие данные.\n",
"\n",
"* Загрузите другой [датасет](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv)\n",
"* Выполните те же панипуляции с признаками\n",
"* Используйте нашу простейшую модель для предсказания качества на новых данных"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Your code here"
]
}
],
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"anaconda-cloud": {},
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"display_name": "Python [default]",
"language": "python",
"name": "python2"
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