{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#ndarray对象的属性\" data-toc-modified-id=\"ndarray对象的属性-1\"><span class=\"toc-item-num\">1 </span>ndarray对象的属性</a></span><ul class=\"toc-item\"><li><span><a href=\"#数组的形状ndarray.shape\" data-toc-modified-id=\"数组的形状ndarray.shape-1.1\"><span class=\"toc-item-num\">1.1 </span>数组的形状ndarray.shape</a></span></li><li><span><a href=\"#数组的维度ndarray.ndim\" data-toc-modified-id=\"数组的维度ndarray.ndim-1.2\"><span class=\"toc-item-num\">1.2 </span>数组的维度ndarray.ndim</a></span></li><li><span><a href=\"#查看数组中的元素个数ndarray.size\" data-toc-modified-id=\"查看数组中的元素个数ndarray.size-1.3\"><span class=\"toc-item-num\">1.3 </span>查看数组中的元素个数ndarray.size</a></span></li><li><span><a href=\"#查看数组中的元素的数据类型ndarray.dtype\" data-toc-modified-id=\"查看数组中的元素的数据类型ndarray.dtype-1.4\"><span class=\"toc-item-num\">1.4 </span>查看数组中的元素的数据类型ndarray.dtype</a></span></li><li><span><a href=\"#查看数组中每个元素所占的内存字节数ndarray.itemsize\" data-toc-modified-id=\"查看数组中每个元素所占的内存字节数ndarray.itemsize-1.5\"><span class=\"toc-item-num\">1.5 </span>查看数组中每个元素所占的内存字节数ndarray.itemsize</a></span></li></ul></li><li><span><a href=\"#ndarray对象的方法一维度变换\" data-toc-modified-id=\"ndarray对象的方法一维度变换-2\"><span class=\"toc-item-num\">2 </span>ndarray对象的方法一维度变换</a></span><ul class=\"toc-item\"><li><span><a href=\"#改变数组的形状ndarray.reshape（不改变原数组）\" data-toc-modified-id=\"改变数组的形状ndarray.reshape（不改变原数组）-2.1\"><span class=\"toc-item-num\">2.1 </span>改变数组的形状ndarray.reshape（不改变原数组）</a></span></li><li><span><a href=\"#改变数组的形状ndarray.resize(改变原数组)\" data-toc-modified-id=\"改变数组的形状ndarray.resize(改变原数组)-2.2\"><span class=\"toc-item-num\">2.2 </span>改变数组的形状ndarray.resize(改变原数组)</a></span></li><li><span><a href=\"#返回一个一维数组\" data-toc-modified-id=\"返回一个一维数组-2.3\"><span class=\"toc-item-num\">2.3 </span>返回一个一维数组</a></span><ul class=\"toc-item\"><li><span><a href=\"#ndarray.flatten\" data-toc-modified-id=\"ndarray.flatten-2.3.1\"><span class=\"toc-item-num\">2.3.1 </span>ndarray.flatten</a></span></li><li><span><a href=\"#np.ravel()\" data-toc-modified-id=\"np.ravel()-2.3.2\"><span class=\"toc-item-num\">2.3.2 </span>np.ravel()</a></span></li><li><span><a href=\"#通过reshape改变形状\" data-toc-modified-id=\"通过reshape改变形状-2.3.3\"><span class=\"toc-item-num\">2.3.3 </span>通过reshape改变形状</a></span></li></ul></li></ul></li><li><span><a href=\"#ndarray对象的方法—数组转列表ndarray.tolist\" data-toc-modified-id=\"ndarray对象的方法—数组转列表ndarray.tolist-3\"><span class=\"toc-item-num\">3 </span>ndarray对象的方法—数组转列表ndarray.tolist</a></span></li><li><span><a href=\"#ndarray的索引与切片\" data-toc-modified-id=\"ndarray的索引与切片-4\"><span class=\"toc-item-num\">4 </span>ndarray的索引与切片</a></span><ul class=\"toc-item\"><li><span><a href=\"#一维ndarray的索引与切片\" data-toc-modified-id=\"一维ndarray的索引与切片-4.1\"><span class=\"toc-item-num\">4.1 </span>一维ndarray的索引与切片</a></span></li><li><span><a href=\"#多维ndarray的索引与切片\" data-toc-modified-id=\"多维ndarray的索引与切片-4.2\"><span class=\"toc-item-num\">4.2 </span>多维ndarray的索引与切片</a></span></li><li><span><a href=\"#布尔索引\" data-toc-modified-id=\"布尔索引-4.3\"><span class=\"toc-item-num\">4.3 </span>布尔索引</a></span></li></ul></li></ul></div>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
" import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#全部行都能输出\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ndarray对象的属性"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#为了确保大家都能生成一样的数组, 我们先设置随机数种子\n",
"np.random.seed(123)\n",
"x1 = np.random.randint(1,10, size=6) # 一维数组\n",
"x2 = np.random.randint(1,10, size=(3, 4)) # 二维数组\n",
"x3 = np.random.randint(1,10, size=(3, 4, 5)) # 三维数组"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 3, 7, 2, 4, 7])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([[2, 1, 2, 1],\n",
" [1, 4, 5, 1],\n",
" [1, 5, 2, 8]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([[[4, 3, 5, 8, 3],\n",
" [5, 9, 1, 8, 4],\n",
" [5, 7, 2, 6, 7],\n",
" [3, 2, 9, 4, 6]],\n",
"\n",
" [[1, 3, 7, 3, 5],\n",
" [5, 7, 4, 1, 7],\n",
" [5, 8, 7, 8, 2],\n",
" [6, 8, 3, 5, 9]],\n",
"\n",
" [[2, 3, 2, 2, 4],\n",
" [6, 1, 9, 2, 7],\n",
" [4, 4, 6, 8, 3],\n",
" [4, 4, 4, 9, 7]]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x1\n",
"x2\n",
"x3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 数组的形状ndarray.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(6,)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(3, 4)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(3, 4, 5)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#查看数据的形状\n",
"x1.shape\n",
"x2.shape\n",
"x3.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 数组的维度ndarray.ndim"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x1.ndim\n",
"x2.ndim\n",
"x3.ndim"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 查看数组中的元素个数ndarray.size"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"60"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x1.size\n",
"x2.size\n",
"x3.size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 查看数组中的元素的数据类型ndarray.dtype"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('int32')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"dtype('int32')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"dtype('int32')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x1.dtype\n",
"x2.dtype\n",
"x3.dtype"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 查看数组中每个元素所占的内存字节数ndarray.itemsize"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x1.itemsize\n",
"x2.itemsize\n",
"x3.itemsize"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 数组的 dtype 为 int8（一个字节）\n",
"x = np.array([1,2,3,4,5], dtype = np.int8)\n",
"x.itemsize"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.array([1,2,3,4,5], dtype = np.float64)\n",
"x.itemsize"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ndarray对象的方法一维度变换"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[10, 88, 15, 84],\n",
" [71, 13, 55, 28],\n",
" [39, 18, 62, 75]],\n",
"\n",
" [[66, 48, 17, 6],\n",
" [87, 47, 16, 60],\n",
" [41, 26, 46, 50]]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = np.random.randint(1, 100,(2, 3, 4))\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 改变数组的形状ndarray.reshape（不改变原数组）"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 36, 30, 2, 84, 69, 31, 8],\n",
" [94, 61, 66, 77, 68, 45, 52, 8],\n",
" [89, 71, 14, 29, 64, 85, 37, 97]])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.reshape(3,8) #不改变原数组，会返回新的数组"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 3, 4)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "cannot reshape array of size 24 into shape (3,7)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-19-e3effb8f525e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#注意这点，要保证reshape前后数组中的元素是一样的\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 24 into shape (3,7)"
]
}
],
"source": [
"m.reshape(3,7) #注意这点，要保证reshape前后数组中的元素是一样的"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 改变数组的形状ndarray.resize(改变原数组)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[41, 89, 64, 59],\n",
" [78, 9, 79, 7],\n",
" [66, 95, 71, 41]],\n",
"\n",
" [[75, 77, 77, 26],\n",
" [ 8, 14, 45, 2],\n",
" [42, 79, 57, 88]]])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = np.random.randint(1, 100,(2, 3, 4))\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"m.resize(4,6) #改变原数组"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[41, 89, 64, 59, 78, 9],\n",
" [79, 7, 66, 95, 71, 41],\n",
" [75, 77, 77, 26, 8, 14],\n",
" [45, 2, 42, 79, 57, 88]])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4, 6)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.shape"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2, 3, 4)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 返回一个一维数组"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ndarray.flatten"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 6, 82, 36, 78],\n",
" [26, 98, 94, 71],\n",
" [59, 30, 82, 22]],\n",
"\n",
" [[17, 31, 56, 81],\n",
" [31, 13, 91, 84],\n",
" [17, 93, 39, 99]]])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = np.random.randint(1, 100,(2, 3, 4))\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 82, 36, 78, 26, 98, 94, 71, 59, 30, 82, 22, 17, 31, 56, 81, 31,\n",
" 13, 91, 84, 17, 93, 39, 99])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n = m.flatten() #不改变原数组\n",
"n "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### np.ravel()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 6, 82, 36, 78],\n",
" [26, 98, 94, 71],\n",
" [59, 30, 82, 22]],\n",
"\n",
" [[17, 31, 56, 81],\n",
" [31, 13, 91, 84],\n",
" [17, 93, 39, 99]]])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 82, 36, 78, 26, 98, 94, 71, 59, 30, 82, 22, 17, 31, 56, 81, 31,\n",
" 13, 91, 84, 17, 93, 39, 99])"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.ravel()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[36, 59, 39, 2],\n",
" [53, 18, 46, 23],\n",
" [54, 90, 75, 1]],\n",
"\n",
" [[36, 84, 95, 72],\n",
" [40, 88, 84, 79],\n",
" [30, 20, 38, 2]]])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 通过reshape改变形状"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 6, 82, 36, 78, 26, 98, 94, 71, 59, 30, 82, 22, 17, 31, 56, 81,\n",
" 31, 13, 91, 84, 17, 93, 39, 99]])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.reshape(1,-1)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 6],\n",
" [82],\n",
" [36],\n",
" [78],\n",
" [26],\n",
" [98],\n",
" [94],\n",
" [71],\n",
" [59],\n",
" [30],\n",
" [82],\n",
" [22],\n",
" [17],\n",
" [31],\n",
" [56],\n",
" [81],\n",
" [31],\n",
" [13],\n",
" [91],\n",
" [84],\n",
" [17],\n",
" [93],\n",
" [39],\n",
" [99]])"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.reshape(-1,1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这种改变的就是原数组的形状而已"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ndarray对象的方法—数组转列表ndarray.tolist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"有时候我们需要把把数组转化为列表来处理数据，因为列表有很多方法"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"之前我们有学过ndarray.astype()用来修改数组元素的数据类型"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"也知道怎么把列表 元组变成数组，反过来我再学一个怎么把ndarray变成列表"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6, 7, 8])"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([1, 2, 3, 4, 5, 6, 7, 8]) #列表变数组\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4, 5, 6, 7, 8]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = a.tolist() #不会改变原数组，重新返回一个新生成的列表\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6, 7, 8])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ndarray的索引与切片"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**和字符串 列表 元祖基本上是一样的** \n",
"[开始位置, 终止位置， 步长和方向]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 一维ndarray的索引与切片"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.arange(5,16)\n",
"a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**索引单个元素**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[1]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[-1]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[-2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**多个元素的连续索引**"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([5, 6, 7])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0:3]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 8, 9, 10, 11, 12])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[3:8]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 11, 12, 13, 14, 15])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[5:]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 11, 12, 13, 14])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[5:-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**间隔索引**"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([5, 7, 9])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0:6:2]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5, 9, 13])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0::4]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([15, 13, 11, 9, 7, 5])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[::-2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 多维ndarray的索引与切片"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6],\n",
" [7, 8, 9]])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([[1, 2, 3], [4, 5, 6,], [7, 8, 9]])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6]])"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0:2]"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0][1]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([7, 8, 9])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[2][:]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6],\n",
" [7, 8, 9]])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 4])"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0:2,0] #[行,列]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6],\n",
" [7, 8, 9]])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[:,:]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 5, 8])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[:,1]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, True, True])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[1]==a[1,:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 布尔索引 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"当结果对象是布尔运算（例如比较运算符）的结果时，将使用此类型的高级索引。"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0, 1, 2],\n",
" [ 3, 4, 5],\n",
" [ 6, 7, 8],\n",
" [ 9, 10, 11]])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.arange(12).reshape(4,3)\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[False, False, False],\n",
" [False, False, False],\n",
" [ True, True, True],\n",
" [ True, True, True]])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x>5"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6, 7, 8, 9, 10, 11])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x[x>5]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([nan, 1., 2., nan, 3., 4., 5.])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([np.nan,1,2,np.nan,3,4,5])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False, False, True, False, False, False])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.isnan(a)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([nan, nan])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[np.isnan(a)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {
"height": "523.537px",
"left": "0px",
"top": "110.284px",
"width": "269.813px"
},
"toc_section_display": true,
"toc_window_display": true
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"toc-autonumbering": true
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"nbformat": 4,
"nbformat_minor": 2
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