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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>9.7. NumPy</title><link rel="stylesheet" type="text/css" href="../docbook.css" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><meta name="keywords" content="php,pear,pecl,phar, python, , " /><link rel="home" href="../index.html" title="Netkiller Python 手札" /><link rel="up" href="../ai.html" title="第 9 章 AI 相关" /><link rel="prev" href="../ch09s06.html" title="9.6. GPU" /><link rel="next" href="../tensorflow.html" title="9.8. tensorflow" /></head><body><a xmlns="" href="//www.netkiller.cn/">Home</a> | <a xmlns="" href="//netkiller.github.io/">简体中文</a> | <a xmlns="" href="http://netkiller.sourceforge.net/">繁体中文</a> | <a xmlns="" href="/journal/index.html">杂文</a>
| <a xmlns="" href="https://github.com/netkiller">Github</a> | <a xmlns="" href="https://zhuanlan.zhihu.com/netkiller">知乎专栏</a> | <a xmlns="" href="https://www.facebook.com/bg7nyt">Facebook</a> | <a xmlns="" href="http://cn.linkedin.com/in/netkiller/">Linkedin</a> | <a xmlns="" href="https://www.youtube.com/user/bg7nyt/videos">Youtube</a> | <a xmlns="" href="//www.netkiller.cn/home/donations.html">打赏(Donations)</a> | <a xmlns="" href="//www.netkiller.cn/home/about.html">About</a><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="3" align="center">9.7. NumPy</th></tr><tr><td width="20%" align="left"><a accesskey="p" href="../ch09s06.html">上一页</a> </td><th width="60%" align="center">第 9 章 AI 相关</th><td width="20%" align="right"> <a accesskey="n" href="../tensorflow.html">下一页</a></td></tr></table><hr /></div><table xmlns=""><tr><td><iframe src="//ghbtns.com/github-btn.html?user=netkiller&repo=netkiller.github.io&type=watch&count=true&size=large" height="30" width="170" frameborder="0" scrolling="0" style="width:170px; height: 30px;" allowTransparency="true"></iframe></td><td><iframe src="//ghbtns.com/github-btn.html?user=netkiller&repo=netkiller.github.io&type=fork&count=true&size=large" height="30" width="170" frameborder="0" scrolling="0" style="width:170px; height: 30px;" allowTransparency="true"></iframe></td><td><iframe src="//ghbtns.com/github-btn.html?user=netkiller&type=follow&count=true&size=large" height="30" width="240" frameborder="0" scrolling="0" style="width:240px; height: 30px;" allowTransparency="true"></iframe></td><td></td><td><a href="https://zhuanlan.zhihu.com/netkiller"><img src="/images/logo/zhihu-card-default.svg" height="25" /></a></td><td valign="middle"><a href="https://zhuanlan.zhihu.com/netkiller">知乎专栏</a></td><td></td><td></td><td></td><td></td></tr></table><div class="section"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a id="index"></a>9.7. NumPy</h2></div></div></div>
<p>NumPy is the fundamental package for scientific computing with Python.</p>
<div class="section"><div class="titlepage"><div><div><h3 class="title"><a id="numpy.random"></a>9.7.1. 随机数</h3></div></div></div>
<div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="id1063"></a>9.7.1.1. 随机种子</h4></div></div></div>
<p>np.random.seed(1) 两次产生的随机数相同</p>
<pre class="programlisting">
import numpy as np
np.random.seed(1)
L1 = np.random.randn(3, 3)
L2 = np.random.randn(3, 3)
print(L1)
print(L2)
</pre>
<pre class="screen">
[[-1.32959475 -0.35726593 -1.01824748]
[-0.16875459 -1.40799966 -0.42432159]
[-2.90363742 0.77847352 -0.03868502]]
[[ 0.8140817 0.47350462 -0.44832424]
[-0.21463067 0.18678995 -1.56375306]
[ 0.39748247 -0.74620674 -0.97122838]]
</pre>
</div>
<div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="id1064"></a>9.7.1.2. 随机矩阵数组</h4></div></div></div>
<p>生成随机浮点矩阵数组</p>
<pre class="programlisting">
import numpy as np
number = np.random.rand(10, 5)
print(number)
</pre>
<pre class="screen">
neo@MacBook-Pro-Neo ~/workspace/python % python3.9 /Users/neo/workspace/python/numpy/test.py
[[0.07210811 0.89871612 0.31670349 0.88870892 0.38252093]
[0.08210199 0.37878429 0.09693934 0.53084051 0.81222326]
[0.99527501 0.39815405 0.02937093 0.21271075 0.09775669]
[0.97038382 0.10373132 0.60815363 0.00740848 0.51247618]
[0.77290466 0.7961732 0.21776523 0.27498686 0.84316289]
[0.11457979 0.98606765 0.36357378 0.00754072 0.62702464]
[0.19330684 0.60832298 0.57052479 0.81215836 0.04167786]
[0.71456373 0.9203253 0.27650414 0.6247527 0.28517774]
[0.85126634 0.06420073 0.92123025 0.84654969 0.11828913]
[0.38481704 0.95317434 0.62498057 0.5297113 0.22969415]]
</pre>
<pre class="programlisting">
pd.DataFrame(np.random.rand(10,2))
</pre>
</div>
<div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="id1065"></a>9.7.1.3. 生成随机整数矩阵数组</h4></div></div></div>
<p>np.random.randint(从 0 ,到 255,(5,6,3))</p>
<pre class="programlisting">
import numpy as np
np.random.seed(1)
x = np.random.randint(0,255,(5,6,3))
print(x)
</pre>
<pre class="screen">
[[[ 37 235 140]
[ 72 137 203]
[133 79 192]
[144 129 204]
[ 71 237 252]
[134 25 178]]
[[ 20 254 101]
[146 212 139]
[252 234 156]
[157 142 50]
[ 68 215 215]
[233 241 247]]
[[222 96 86]
[141 233 137]
[ 7 63 61]
[ 22 57 1]
[128 60 209]
[ 8 216 141]]
[[115 175 234]
[121 200 30]
[ 71 131 198]
[149 49 57]
[ 3 196 24]
[241 43 76]]
[[ 26 52 80]
[109 115 41]
[210 15 64]
[196 25 111]
[226 215 135]
[ 26 153 104]]]
</pre>
</div>
</div>
<div class="section"><div class="titlepage"><div><div><h3 class="title"><a id="numpy.linspace"></a>9.7.2. 生成数列</h3></div></div></div>
<p>从0 到 100，间隔为10的数值序列</p>
<pre class="programlisting">
n = np.linspace(start = 0, stop = 100, num = 11)
x = np.linspace(-3, 3, 50)
</pre>
</div>
<div class="section"><div class="titlepage"><div><div><h3 class="title"><a id="numpy.shape"></a>9.7.3. 查看矩阵或者数组的维度</h3></div></div></div>
<pre class="programlisting">
import numpy as np
np.random.seed(1)
matrix = np.random.randint(0,255,(5,6,3))
print(matrix.shape)
</pre>
<pre class="screen">
(5, 6, 3)
</pre>
<div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="id1066"></a>9.7.3.1. 数组维度</h4></div></div></div>
<pre class="programlisting">
from numpy import array
a = array([[1,1],[1,2],[1,3],[1,4]])
print(a.shape)
</pre>
<pre class="screen">
(4, 2)
</pre>
</div>
</div>
<div class="section"><div class="titlepage"><div><div><h3 class="title"><a id="numpy.dtype"></a>9.7.4. 数据类型</h3></div></div></div>
<div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="id1067"></a>9.7.4.1. 显示数据类型</h4></div></div></div>
<pre class="programlisting">
import numpy
numpy.random.seed(1)
matrix = numpy.random.randint(0,255,(5,6,3))
print(matrix.dtype)
</pre>
</div>
<div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="id1068"></a>9.7.4.2. 转换数据类型</h4></div></div></div>
<pre class="programlisting">
import numpy
numpy.random.seed(1)
matrix = numpy.random.randint(0,255,(5,6,3))
print(matrix.dtype)
matrix1 = matrix.astype(np.int8)
print(matrix1.dtype)
</pre>
</div>
</div>
<div class="section"><div class="titlepage"><div><div><h3 class="title"><a id="numpy.max"></a>9.7.5. max</h3></div></div></div>
<pre class="programlisting">
# 创建一个包含学生成绩的二维数组
data = np.array([
['Alice', 85, 92, 78],
['Bob', 89, 90, 95],
['Charlie', 91, 85, 88],
['David', 78, 80, 82]
])
# 提取成绩列（忽略姓名列）
grades = data[:, 1:].astype(int)
print(grades)
# 计算每位学生的最高成绩
max_grades = np.max(grades, axis=1)
print(max_grades)
# 打印结果
for i, student in enumerate(data[:, 0]):
print(f"{student} 的最好成绩是: {max_grades[i]}")
</pre>
</div>
<div class="section"><div class="titlepage"><div><div><h3 class="title"><a id="numpy.amax"></a>9.7.6. max</h3></div></div></div>
<pre class="programlisting">
import numpy as np
help(np.amax)
a = np.arange(9).reshape((3, 3))
max_all = np.amax(a)
max_dimension1 = np.amax(a, axis=0)
max_dimension2 = np.amax(a, axis=1)
print('a:\n', a)
print('max_all:', max_all)
print('max_dimension1:', max_dimension1)
print('max_dimension2:', max_dimension2)
</pre>
<pre class="screen">
D:\workspace\netkiller\.venv\Scripts\python.exe D:\workspace\netkiller\test\test1.py
Help on _ArrayFunctionDispatcher in module numpy:
amax(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)
Return the maximum of an array or maximum along an axis.
`amax` is an alias of `~numpy.max`.
See Also
--------
max : alias of this function
ndarray.max : equivalent method
a:
[[0 1 2]
[3 4 5]
[6 7 8]]
max_all: 8
max_dimension1: [6 7 8]
max_dimension2: [2 5 8]
</pre>
</div>
<div class="section"><div class="titlepage"><div><div><h3 class="title"><a id="numpy.imshow"></a>9.7.7. 显示矩阵</h3></div></div></div>
<pre class="programlisting">
import numpy
from matplotlib import pyplot as plt
numpy.random.seed(1)
matrix = numpy.random.randint(0,255,(5,6,3))
print(matrix.shape)
plt.imshow(matrix)
</pre>
</div>
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