简洁易懂,初学者挑战学习Python编程30天 (四)

目录

  • 第 21 天 - 类和对象
    • 21.1创建一个类
    • 21.2创建对象
    • 21.3类构造函数
    • 21.4对象方法
    • 21.5对象默认方法
    • 21.6修改类默认值的方法
    • 21.7继承
    • 21.8Overriding parent method
  • 第 22 天 - 网页抓取
    • 22.1什么是网页抓取
  • 第 23 天 - 虚拟环境
    • 23.1设置虚拟环境
  • 第 24 天 - 统计
    • 24.1统计数据
    • 24.2什么是数据?
    • 24.3统计模块
    • 24.4NumPy
    • 24.5导入 NumPy
    • 24.6使用创建 numpy 数组
    • 24.7创建 float numpy 数组
    • 24.8创建布尔 numpy 数组
    • 24.9使用numpy创建多维数组
    • 24.10将 numpy 数组转换为列表
    • 24.11从元组创建numpy数组
    • 24.12numpy 数组的形状
    • 24.13numpy数组的数据类型
    • 24.14numpy 数组的大小
    • 24.15使用numpy进行数学运算
    • 24.16添加
    • 24.17减法
    • 24.18乘法
    • 24.19分配
    • 24.20模数;找到余数
    • 24.21楼层划分
    • 24.22指数
    • 24.23检查数据类型
    • 24.24转换类型
    • 24.25多维数组
    • 24.26从 numpy 数组中获取项目
    • 24.27切片 Numpy 数组
    • 24.28如何反转行和整个数组?
    • 24.29反转行列位置
    • 24.30如何表示缺失值?
    • 24.31生成随机数
    • 24.32生成随机数
    • 24.33Numpy 和统计
    • 24.34numpy中的矩阵
    • 24.35numpy numpy.arange()
    • 24.36使用 linspace 创建数字序列
    • 24.37NumPy 统计函数与示例
    • 24.38如何创建重复序列?
    • 24.39如何生成随机数?
    • 24.40线性代数
    • 24.41NumPy 矩阵乘法与 np.matmul()
  • 第 25 天 - Pandas
    • 25.1安装pandas
    • 导入pandas
    • 25.2使用默认索引创建 Pandas 系列
    • 25.3使用自定义索引创建 Pandas 系列
    • 25.4从字典创建 Pandas 系列
    • 25.5创建一个常量 Pandas 系列
    • 25.6使用 Linspace 创建 Pandas 系列
    • 25.7数据帧
      • 从列表列表创建数据帧
    • 25.8使用字典创建 DataFrame
    • 25.9从字典列表创建数据帧
    • 25.10使用 Pandas 读取 CSV 文件
    • 25.11数据探索
    • 25.12修改数据帧
    • 25.13创建数据帧
    • 25.14添加新列
    • 25.15修改列值
    • 25.16格式化 DataFrame 列
    • 25.17检查列值的数据类型
    • 25.18布尔索引

简洁易懂,初学者挑战学习Python编程30天 (四)_第1张图片

你们的三连(点赞,收藏,评论)是我持续输出的动力,感谢。
在兴趣中学习,效益超乎想象,有趣的源码与学习经验,工具安装包,欢迎加我的微信:bobin1124,一起交流学习与分享。

第 21 天 - 类和对象

Python 是一种面向对象的编程语言。Python 中的一切都是一个对象,有它的属性和方法。程序中使用的数字、字符串、列表、字典、元组、集合等是相应内置类的对象。我们创建类来创建一个对象。一个类就像一个对象构造函数,或者是创建对象的“蓝图”。我们实例化一个类来创建一个对象。类定义了对象的属性和行为,而另一方面,对象代表了类。

从这个挑战一开始,我们就在不知不觉中处理类和对象。Python 程序中的每个元素都是一个类的对象。让我们检查一下python中的所有东西是否都是一个类:

asabeneh @ Asabeneh:~ $ python 
Python  3.96(默认,2021628 日 ,152621[11.0 0.0(铛- 1100.033.8)在 达尔文
式 的“帮助”,“版权”,“信用” 或 “许可” 的 更多 信息。
>> >  num  =  10 
>> >  type ( num )
 < class  'int' > 
>> >  string  =  'string' 
>> >'STR' > 
>> > 布尔 =>> > 类型(布尔)
 <'布尔' > 
>> >  LST  = []
 >> > 型(LST)
 <'列表' > 
>> >  TPL  =)
 >> > 类型( tpl )
 < class  'tuple' > 
>> > SET1  = 集()
>> > 类型(set1)
 < class  'set' > 
>> >  dct  = {
     }
 >> > 类型(dct)
 < class  'dict' >

21.1创建一个类

要创建一个类,我们需要关键字类,后跟名称和冒号。类名应该是CamelCase。

#语法
类类名:
  代码在这里

例子:

类 人:
  通过
打印(人)
< __main__.Person 对象在 0x10804e 510>

21.2创建对象

我们可以通过调用类来创建一个对象。

p  =()
打印( p )

21.3类构造函数

在上面的例子中,我们从 Person 类创建了一个对象。然而,没有构造函数的类在实际应用中并没有真正的用处。让我们使用构造函数使我们的类更有用。与Java或JavaScript中的构造函数一样,Python也有内置的init ()构造函数。的初始化构造函数有自参数这对类的当前实例的引用
实施例:

class  Person :
       def  __init__ ( self , name ):
         # self 允许将参数附加到类
          self。姓名 =姓名

p  =  Person ( 'Asabeneh' )
打印( p . name )
打印( p )
#输出
阿萨贝内
< __main__.Person 对象在 0x2abf46907e 80>

让我们向构造函数添加更多参数。

class  Person :
       def  __init__ ( self , firstname , lastname , age , country , city ):
           self。名字 = 名字
          自我。姓氏 = 姓氏
          自我。年龄 = 年龄
          自我。国家 = 国家
          自我。城市 = 城市


p  =  Person ( 'Asabeneh' , 'Yetayeh' , 250 , 'Finland' , 'Helsinki' )
 print ( p . firstname )
 print ( p . lastname )
 print ( p . age )
 print ( p . country )
 print ( p .城市)
#输出
阿萨贝内
耶塔耶
250
芬兰
赫尔辛基

21.4对象方法

对象可以有方法。方法是属于对象的函数。

例子:

class  Person :
       def  __init__ ( self , firstname , lastname , age , country , city ):
           self。名字 = 名字
          自我。姓氏 = 姓氏
          自我。年龄 = 年龄
          自我。国家 = 国家
          自我。city  =  city 
      def  person_info ( self ):
        返回 f' {
       自我。名字}  {
       自我。姓氏}{
        self。年龄}岁。他住在{
       自我。城市}{
       自我。国家} '

p  = 人('Asabeneh''Yetayeh'250'芬兰''赫尔辛基')
打印(p。person_info())
#输出
Asabeneh Yetayeh 已经 250 岁了。他住在芬兰赫尔辛基

21.5对象默认方法

有时,您可能希望为对象方法设置默认值。如果我们在构造函数中给参数赋予默认值,就可以避免在不带参数的情况下调用或实例化我们的类时出错。让我们看看它的外观:

例子:

class  Person :
       def  __init__ ( self , firstname = 'Asabeneh' , lastname = 'Yetayeh' , age = 250 , country = 'Finland' , city = 'Helsinki' ):
           self。名字 = 名字
          自我。姓氏 = 姓氏
          自我。年龄 = 年龄
          自我。国家 = 国家
          自我. 城市 = 城市

      def  person_info ( self ):
        返回 f' {
        self . 名字}  {
       自我。姓氏}{
        self。年龄}岁。他住在{
       自我。城市}{
       自我。国家} .'

P1  = 人()
打印(P1。person_info())
 P2  = 人('约翰''李四'30'Nomanland' , “诺曼城市)
打印(P2。person_info())
#输出
Asabeneh Yetayeh 已经 250 岁了。他住在芬兰赫尔辛基。
约翰·多伊今年 30 岁。他住在诺曼兰的诺曼城。

21.6修改类默认值的方法

在下面的例子中,person 类,所有的构造函数参数都有默认值。除此之外,我们还有技能参数,我们可以使用方法访问它。让我们创建 add_skill 方法来将技能添加到技能列表中。

class  Person :
       def  __init__ ( self , firstname = 'Asabeneh' , lastname = 'Yetayeh' , age = 250 , country = 'Finland' , city = 'Helsinki' ):
           self。名字 = 名字
          自我。姓氏 = 姓氏
          自我。年龄 = 年龄
          自我。国家 = 国家
          自我. 城市 = 城市
          自我。技能 = []

      def  person_info ( self ):
        返回 f' {
        self . 名字}  {
       自我。姓氏}{
        self。年龄}岁。他住在{
       自我。城市}{
       自我。国家} .' 
      def  add_skill(自我,技能):
          自我。技能。追加(技能)

p1  =  Person ()
打印( p1 . person_info ())
 p1 . add_skill ( 'HTML' )
 p1。add_skill ( 'CSS' )
 p1。add_skill('的JavaScript' )
 P2  = 人('约翰''李四'30'Nomanland' , “诺曼城市)
打印(P2。person_info())
印刷(P1. 技能)
打印(p2。技能)
#输出
Asabeneh Yetayeh 已经 250 岁了。他住在芬兰赫尔辛基。
约翰·多伊今年 30 岁。他住在诺曼兰的诺曼城。
[ ' HTML '' CSS '' JavaScript ' ]
[]

21.7继承

使用继承,我们可以重用父类代码。继承允许我们定义一个继承父类的所有方法和属性的类。父类或超类或基类是提供所有方法和属性的类。子类是从另一个类或父类继承的类。让我们通过继承person类来创建一个student类。

班级 学生(人):
通过

S1  = 学生('Eyob''Yetayeh'30'芬兰''赫尔辛基')
 S2  = 学生('了Lidiya''Teklemariam'28'芬兰''埃斯波')
印刷(S1。person_info( ))
 s1。add_skill ( 'JavaScript' )
 s1。add_skill ( '反应' )
 s1。'Python' )
打印( s1 .技能)

打印(S2。person_info())
 S2。add_skill ( '组织' )
 s2。add_skill ( '营销' )
 s2。add_skill ( '数字营销' )
打印( s2 . Skill )
输出
Eyob Yetayeh 30 岁。他住在芬兰赫尔辛基。
[ ' JavaScript '' React '' Python ' ]
Lidiya Teklemariam 28 岁。他住在芬兰的埃斯波。
[ “组织”、“营销”、“数字营销” ]

我们没有在子类中调用init ()构造函数。如果我们没有调用它,那么我们仍然可以从父级访问所有属性。但是如果我们确实调用了构造函数,我们就可以通过调用super来访问父属性。
我们可以向子类添加新方法,也可以通过在子类中创建相同的方法名称来覆盖父类方法。当我们添加init ()函数时,子类将不再继承父类的init ()函数。

21.8Overriding parent method

类 学生(人):
    高清 __init__(自我,名字= 'Asabeneh' ,姓氏= 'Yetayeh' ,年龄= 250,全国= '芬兰,城市= '赫尔辛基',性别= '男'):
        自我。性别 = 性别
        超()。__init__(名字,姓氏,年龄,country , city )
     def  person_info ( self ):
        性别 =  'He'  if  self。性别 == '男' 否则 '她'
        返回 f' {
        self . 名字}  {
       自我。姓氏}{
        self。年龄}岁。{
       性别}生活在{
       自我。城市} , {
       自己. 国家} .'

s1  =  Student ( 'Eyob' , 'Yetayeh' , 30 , 'Finland' , 'Helsinki' , 'male' )
 s2  =  Student ( 'Lidiya' , 'Teklemariam' , 28 , 'Finland' , 'Espoo' , 'female' ' )
打印( s1 . person_info ())
 s1 . add_skill ( 'JavaScript' )
 s1。s1。add_skill('Python的)
印刷(S1,技能)

打印(S2。person_info())
 S2。add_skill ( '组织' )
 s2。add_skill ( '营销' )
 s2。add_skill ( '数字营销' )
打印( s2 . Skill )
Eyob Yetayeh 30 岁。他住在芬兰赫尔辛基。
[ ' JavaScript '' React '' Python ' ]
Lidiya Teklemariam 28 岁。她住在芬兰的埃斯波。
[ “组织”、“营销”、“数字营销” ]

我们可以使用 super() 内置函数或父名 Person 来自动继承其父级的方法和属性。在上面的例子中,我们Overriding parent method的方法。child 方法有一个不同的特点,它可以识别性别是男性还是女性并指定适当的代词(他/她)

第 22 天 - 网页抓取

22.1什么是网页抓取

互联网充满了可用于不同目的的大量数据。为了收集这些数据,我们需要知道如何从网站上抓取数据。

网页抓取是从网站中提取和收集数据并将其存储在本地机器或数据库中的过程。

在本节中,我们将使用 beautifulsoup 和 requests 包来抓取数据。我们使用的包版本是beautifulsoup 4。

要开始抓取网站,您需要请求、beautifoulSoup4和网站。

pip 安装请求
pip 安装 beautifulsoup4

要从网站抓取数据,需要对 HTML 标签和 CSS 选择器有基本的了解。我们使用 HTML 标签、类或/和 ID 定位来自网站的内容。让我们导入 requests 和 BeautifulSoup 模块

进口 请求
从 BS4 进口 BeautifulSoup

让我们为要抓取的网站声明 url 变量。

 来自bs4 的导入请求
import BeautifulSoup url = 'https://archive.ics.uci.edu/ml/datasets.php'   
  

# 让我们使用 requests 的 get 方法从 url 中获取数据

响应 = 请求。get ( url )
 # 让我们检查状态
status  =  response。status_code 
print ( status ) # 200 表示获取成功
200

使用beautifulSoup解析页面内容

 来自bs4 的导入请求
import BeautifulSoup url = 'https://archive.ics.uci.edu/ml/datasets.php'   
  

响应 = 请求。获取(网址)
内容 = 响应。content  # 我们从网站上获取所有内容
soup  =  BeautifulSoup ( content , 'html.parser' ) # beautiful 
Soup将有机会解析print ( soup . title ) # UCI Machine Learning Repository: Data Sets</标题></span>
打印(汤。标题。get_text())#UCI机器学习库:数据集
打印(汤。体)#给网站上的整个页面
打印(响应。STATUS_CODE)

桌子 <span class="token operator">=</span> 汤。find_all <span class="token punctuation">(</span> <span class="token string">'table'</span> <span class="token punctuation">,</span> <span class="token punctuation">{
     </span> <span class="token string">'cellpadding'</span> <span class="token punctuation">:</span> <span class="token string">'3'</span> <span class="token punctuation">}</span><span class="token punctuation">)</span>
 <span class="token comment"># 我们的目标是 cellpadding 属性值为 3 的表格</span>
<span class="token comment"># 我们可以选择使用 id、class 或 HTML 标签,更多信息请查看beautifulsoup doc </span>
table  <span class="token operator">=</span> 表<span class="token punctuation">[</span> <span class="token number">0</span> <span class="token punctuation">]</span> #,结果是一个列表,我们是从它取出数据
为 TD 在 表。找到(<span class="token string">'tr'</span>)。find_all <span class="token punctuation">(</span> <span class="token string">'td'</span> <span class="token punctuation">)</span><span class="token punctuation">:</span>
    打印<span class="token punctuation">(</span> td <span class="token punctuation">.</span> text<span class="token punctuation">)</span>
</code></pre> 
  <p>如果你运行这段代码,你可以看到提取已经完成了一半。</p> 
  <p>你很特别,每天都在进步。您距离通往伟大的道路只剩下八天了。<br> 恭喜! </p> 
  <h1>第 23 天 - 虚拟环境</h1> 
  <h2>23.1设置虚拟环境</h2> 
  <p>从项目开始,最好有一个虚拟环境。虚拟环境可以帮助我们创建一个孤立或分离的环境。这将帮助我们避免跨项目的依赖冲突。如果您在终端上编写 pip freeze ,您将在计算机上看到所有已安装的软件包。如果我们使用 virtualenv,我们将只访问特定于该项目的包。打开终端并安装 virtualenv</p> 
  <blockquote> 
   <p>asabeneh@Asabeneh: ~ $ pip install virtualenv</p> 
  </blockquote> 
  <p>在 30DaysOfPython 文件夹中创建一个 flask_project 文件夹。</p> 
  <p>安装 virtualenv 包后,转到您的项目文件夹并通过编写以下内容创建一个虚拟环境:</p> 
  <p>对于 Mac/Linux:</p> 
  <pre><code class="prism language-python">asabeneh@Asabeneh<span class="token punctuation">:</span> <span class="token operator">~</span> <span class="token operator">/</span>Desktop<span class="token operator">/</span>30DaysOfPython<span class="token operator">/</span>flask_project \$ virtualenv venv
</code></pre> 
  <p>对于 Windows:</p> 
  <pre><code class="prism language-python">C:\Ú SERS \Ú SER \ d ocuments \ <span class="token number">3</span> 0DaysOfPython \˚F lask_project <span class="token operator">></span>蟒<span class="token operator">-</span>m VENV VENV
</code></pre> 
  <p>我更喜欢将新项目称为 venv,但可以随意使用不同的名称。让我们检查 venv 是否是通过使用 ls(或 dir 用于 Windows 命令提示符)命令创建的。</p> 
  <pre><code class="prism language-python">asabeneh@Asabeneh:<span class="token operator">~</span> <span class="token operator">/</span>Desktop<span class="token operator">/</span>30DaysOfPython<span class="token operator">/</span>flask_project$ ls


静脉<span class="token operator">/</span>
</code></pre> 
  <p>让我们通过在我们的项目文件夹中编写以下命令来激活虚拟环境。</p> 
  <p>对于 Mac/Linux:</p> 
  <pre><code class="prism language-python">asabeneh@Asabeneh<span class="token punctuation">:</span> <span class="token operator">~</span> <span class="token operator">/</span>Desktop<span class="token operator">/</span>30DaysOfPython<span class="token operator">/</span>flask_project$ source venv<span class="token operator">/</span><span class="token builtin">bin</span><span class="token operator">/</span>activate
</code></pre> 
  <p>在 Windows 中激活虚拟环境可能非常依赖于 Windows Power shell 和 git bash。</p> 
  <p>对于 Windows 电源外壳:</p> 
  <pre><code class="prism language-python">C:\Ú SERS \Ú SER \ d ocuments \ <span class="token number">3</span> 0DaysOfPython \˚F lask_project <span class="token operator">></span> VENV \ S cripts \一个ctivate
</code></pre> 
  <p>对于 Windows Git bash:</p> 
  <pre><code class="prism language-python">C:\Ú SERS \Ú SER \ d ocuments \ <span class="token number">3</span> 0DaysOfPython \˚F lask_project <span class="token operator">></span> VENV \ S cripts \。启用
</code></pre> 
  <p>编写激活命令后,您的项目目录将以 venv 开头。请参阅下面的示例。</p> 
  <p>(venv) asabeneh@Asabeneh: ~ /Desktop/30DaysOfPython/flask_project$<br> 现在,让我们通过编写 pip freeze 来检查这个项目中的可用包。您将看不到任何包。</p> 
  <p>我们将要做一个Flask小项目,所以让我们将Flask包安装到这个项目中。</p> 
  <pre><code class="prism language-python"><span class="token punctuation">(</span>venv<span class="token punctuation">)</span> asabeneh@Asabeneh<span class="token punctuation">:</span> <span class="token operator">~</span> <span class="token operator">/</span>Desktop<span class="token operator">/</span>30DaysOfPython<span class="token operator">/</span>flask_project$ pip install Flask
</code></pre> 
  <p>现在,让我们编写 pip freeze 来查看项目中已安装包的列表:</p> 
  <pre><code class="prism language-python"><span class="token punctuation">(</span>venv<span class="token punctuation">)</span> asabeneh@Asabeneh<span class="token punctuation">:</span> <span class="token operator">~</span> <span class="token operator">/</span>Desktop<span class="token operator">/</span>30DaysOfPython<span class="token operator">/</span>flask_project$ pip freeze
点击<span class="token operator">==</span><span class="token number">7.0</span>
Flask<span class="token operator">==</span><span class="token number">1.1</span><span class="token number">.1</span>
它的危险<span class="token operator">==</span><span class="token number">1.1</span><span class="token number">.0</span>
Jinja2<span class="token operator">==</span><span class="token number">2.10</span><span class="token number">.3</span>
标记安全<span class="token operator">==</span><span class="token number">1.1</span><span class="token number">.1</span>
Werkzeug<span class="token operator">==</span><span class="token number">0.16</span><span class="token number">.0</span>
</code></pre> 
  <p>完成后,您应该使用deactivate 停用活动项目。</p> 
  <pre><code class="prism language-python"><span class="token punctuation">(</span>venv<span class="token punctuation">)</span> asabeneh@Asabeneh<span class="token punctuation">:</span> <span class="token operator">~</span> <span class="token operator">/</span>Desktop<span class="token operator">/</span>30DaysOfPython$ 停用
</code></pre> 
  <p>安装了使用Flask的必要模块。现在,您的项目目录已准备好用于Flask项目。<br> 恭喜! </p> 
  <h1>第 24 天 - 统计</h1> 
  <h2>24.1统计数据</h2> 
  <p>统计学是研究数据的收集、组织、显示、分析、解释和呈现的学科。统计学是数学的一个分支,建议作为数据科学和机器学习的先决条件。统计学是一个非常广泛的领域,但我们将在本节中只关注最相关的部分。完成此挑战后,您可以进入 Web 开发、数据分析、机器学习和数据科学路径。无论您走哪条路,在您职业生涯的某个阶段,您都会获得可以处理的数据。拥有一些统计知识将帮助您根据数据做出决策,数据如他们所说。</p> 
  <h2>24.2什么是数据?</h2> 
  <p>数据是为某种目的(通常是分析)收集和翻译的任何字符集。它可以是任何字符,包括文本和数字、图片、声音或视频。如果数据没有放在上下文中,它对人或计算机没有任何意义。为了让数据有意义,我们需要使用不同的工具处理数据。</p> 
  <p>数据分析、数据科学或机器学习的工作流程始于数据。可以从某个数据源提供数据,也可以创建数据。有结构化和非结构化数据。</p> 
  <p>可以以小格式或大格式找到数据。我们将获得的大多数数据类型已在文件处理部分中介绍。</p> 
  <h2>24.3统计模块</h2> 
  <p>Python统计模块提供了计算数值数据的数理统计的函数。该模块无意成为第三方库(如 NumPy、SciPy)或面向专业统计学家(如 Minitab、SAS 和 Matlab)的专有全功能统计软件包的竞争对手。它针对图形和科学计算器的级别。</p> 
  <h2>24.4NumPy</h2> 
  <p>在第一部分中,我们将 Python 本身定义为一种出色的通用编程语言,但在其他流行库(numpy、scipy、matplotlib、pandas 等)的帮助下,它成为了一个强大的科学计算环境。</p> 
  <p>NumPy 是 Python 科学计算的核心库。它提供了一个高性能的多维数组对象,以及用于处理数组的工具。</p> 
  <p>到目前为止,我们一直在使用 vscode,但从现在开始我会推荐使用 Jupyter Notebook。要访问 jupyter notebook,让我们安装anaconda。如果您使用的是 anaconda,则大多数常用软件包都已包含在内,如果您安装了 anaconda,则您没有安装软件包。</p> 
  <pre><code class="prism language-python">asabeneh@Asabeneh<span class="token punctuation">:</span> <span class="token operator">~</span> <span class="token operator">/</span>Desktop<span class="token operator">/</span>30DaysOfPython$ pip install numpy
</code></pre> 
  <h2>24.5导入 NumPy</h2> 
  <p>如果您支持 jupyter notebook,则可以使用Jupyter notebook</p> 
  <pre><code class="prism language-python">  <span class="token comment"># 如何导入 numpy </span>
    <span class="token keyword">import</span>  numpy  <span class="token keyword">as</span>  np 
    <span class="token comment"># 如何检查 numpy 包的版本</span>
    <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'numpy:'</span> <span class="token punctuation">,</span> np <span class="token punctuation">.</span> __version__ <span class="token punctuation">)</span>
     <span class="token comment"># 检查可用方法</span>
    <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token builtin">dir</span> <span class="token punctuation">(</span> np <span class="token punctuation">)</span><span class="token punctuation">)</span>
</code></pre> 
  <h2>24.6使用创建 numpy 数组</h2> 
  <p><strong>创建 int numpy 数组</strong></p> 
  <pre><code class="prism language-python">  <span class="token comment"># 创建 python 列表</span>
    python_list  <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span>

    <span class="token comment"># 检查数据类型</span>
    <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'Type:'</span> <span class="token punctuation">,</span> <span class="token builtin">type</span> <span class="token punctuation">(</span> python_list <span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token comment"># <class 'list'> </span>
    <span class="token comment"># </span>
    <span class="token keyword">print</span> <span class="token punctuation">(</span> python_list <span class="token punctuation">)</span> <span class="token comment"># [1, 2, 3, 4, 5]</span>

    二维列表 <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span> <span class="token number">6</span> <span class="token punctuation">,</span> <span class="token number">7</span> <span class="token punctuation">,</span> <span class="token number">8</span> <span class="token punctuation">]</span><span class="token punctuation">]</span>

    打印(二维列表)   <span class="token comment"># [[0, 1, 2], [3, 4, 5], [6, 7, 8]]</span>

    <span class="token comment"># 从 python 列表创建 Numpy(Numerical Python) 数组</span>

    numpy_array_from_list  <span class="token operator">=</span>  np。array <span class="token punctuation">(</span> python_list <span class="token punctuation">)</span>
     <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token builtin">type</span> <span class="token punctuation">(</span> numpy_array_from_list <span class="token punctuation">)</span><span class="token punctuation">)</span>    <span class="token comment"># <class 'numpy.ndarray'> </span>
    <span class="token keyword">print</span> <span class="token punctuation">(</span> numpy_array_from_list <span class="token punctuation">)</span> <span class="token comment"># array([1, 2, 3, 4, 5])</span>
</code></pre> 
  <h2>24.7创建 float numpy 数组</h2> 
  <p>使用浮点数据类型参数从列表创建浮点 numpy 数组</p> 
  <pre><code class="prism language-python">  <span class="token comment"># Python 列表</span>
    python_list  <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span>

    numy_array_from_list2  <span class="token operator">=</span>  np。阵列(python_list,D型细胞<span class="token operator">=</span>浮动)
    打印(numy_array_from_list2)#阵列(<span class="token punctuation">[</span><span class="token number">1</span>,<span class="token number">2</span>,<span class="token number">3</span>,<span class="token number">4</span>,<span class="token number">5</span><span class="token punctuation">]</span>)
</code></pre> 
  <h2>24.8创建布尔 numpy 数组</h2> 
  <p>从列表创建一个布尔值 numpy 数组</p> 
  <p>numpy_bool_array = np。数组([ 0 , 1 , - 1 , 0 , 0 ], dtype = bool )<br> 打印( numpy_bool_array ) # 数组([假, 真, 真, 假, 假])</p> 
  <h2>24.9使用numpy创建多维数组</h2> 
  <p>一个 numpy 数组可能有一个或多个行和列</p> 
  <pre><code class="prism language-python">  two_Dimension_list  <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span> <span class="token number">6</span> <span class="token punctuation">,</span> <span class="token number">7</span> <span class="token punctuation">,</span> <span class="token number">8</span> <span class="token punctuation">]</span><span class="token punctuation">]</span>
     numpy_two_dimensional_list  <span class="token operator">=</span>  np。数组(二维列表)
    打印(类型(numpy_two_dimensional_list))
    打印(numpy_two_dimensional_list)
</code></pre> 
  <p><类’ numpy.ndarray ’ ><br> [[0 1 2]<br> [3 4 5]<br> [6 7 8]]</p> 
  <h2>24.10将 numpy 数组转换为列表</h2> 
  <pre><code class="prism language-python"><span class="token comment"># 我们总是可以使用 tolist() 将数组转换回 Python 列表。</span>
np_to_list  <span class="token operator">=</span>  numpy_array_from_list。tolist()
打印(类型(np_to_list))
打印(<span class="token string">'一个维阵列:'</span>,np_to_list)
打印(<span class="token string">'二维阵列:'</span>,numpy_two_dimensional_list。tolist())
</code></pre> 
  <pre><code class="prism language-python">   <span class="token operator"><</span>类<span class="token string">'列表'</span> <span class="token operator">></span>
    一维数组:<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">]</span>
    二维数组:<span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span><span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span><span class="token number">6</span><span class="token punctuation">,</span> <span class="token number">7</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">]</span><span class="token punctuation">]</span>
</code></pre> 
  <h2>24.11从元组创建numpy数组</h2> 
  <pre><code class="prism language-python"><span class="token comment"># Numpy array from tuple </span>
<span class="token comment"># 在 Python 中创建元组</span>
python_tuple  <span class="token operator">=</span> <span class="token punctuation">(</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">)</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token builtin">type</span> <span class="token punctuation">(</span> python_tuple <span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token comment"># <class 'tuple'> </span>
<span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'python_tuple: '</span> <span class="token punctuation">,</span> python_tuple <span class="token punctuation">)</span> <span class="token comment"># python_tuple: ( 1, 2, 3, 4, 5)</span>

numpy_array_from_tuple  <span class="token operator">=</span>  np。array <span class="token punctuation">(</span> python_tuple <span class="token punctuation">)</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token builtin">type</span> <span class="token punctuation">(</span> numpy_array_from_tuple <span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token comment"># <class 'numpy.ndarray'> </span>
<span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'numpy_array_from_tuple: '</span> <span class="token punctuation">,</span> numpy_array_from_tuple <span class="token punctuation">)</span> <span class="token comment"># numpy_array_from_tuple: [1 2 3 4 5]</span>
</code></pre> 
  <h2>24.12numpy 数组的形状</h2> 
  <p>shape 方法以元组的形式提供数组的形状。第一个是行,第二个是列。如果数组只是一维,则返回数组的大小。</p> 
  <pre><code class="prism language-python">   数字 <span class="token operator">=</span>  np。阵列(<span class="token punctuation">[</span> <span class="token number">1</span>,<span class="token number">2</span>,<span class="token number">3</span>,<span class="token number">4</span>,<span class="token number">5</span> <span class="token punctuation">]</span>)
    打印(NUMS)
    打印(<span class="token string">'NUMS的形状:'</span>,NUMS。形状)
    打印(numpy_two_dimensional_list)
    打印(<span class="token string">'numpy_two_dimensional_list的形状:'</span>,numpy_two_dimensional_list。形状)
     three_by_four_array  <span class="token operator">=</span>  NP。数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">,</span>
        <span class="token punctuation">[</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">6</span> <span class="token punctuation">,</span> <span class="token number">7</span> <span class="token punctuation">]</span><span class="token punctuation">,</span>
        <span class="token punctuation">[</span> <span class="token number">8</span>,<span class="token number">9</span>,<span class="token number">10</span>,<span class="token number">11</span> <span class="token punctuation">]</span><span class="token punctuation">]</span>)
    打印(three_by_four_array。形状)
</code></pre> 
  <pre><code class="prism language-python">   <span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span> <span class="token number">4</span> <span class="token number">5</span><span class="token punctuation">]</span>
    数字的形状:<span class="token punctuation">(</span><span class="token number">5</span><span class="token punctuation">,</span><span class="token punctuation">)</span>
    <span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0</span> <span class="token number">1</span> <span class="token number">2</span><span class="token punctuation">]</span>
     <span class="token punctuation">[</span><span class="token number">3</span> <span class="token number">4</span> <span class="token number">5</span><span class="token punctuation">]</span>
     <span class="token punctuation">[</span><span class="token number">6</span> <span class="token number">7</span> <span class="token number">8</span><span class="token punctuation">]</span><span class="token punctuation">]</span>
    numpy_two_dimensional_list 的形状:<span class="token punctuation">(</span><span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">)</span>
    <span class="token punctuation">(</span><span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">)</span>
</code></pre> 
  <h2>24.13numpy数组的数据类型</h2> 
  <p>数据类型类型:str、int、float、complex、bool、list、None</p> 
  <pre><code class="prism language-python">int_lists  <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token operator">-</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token operator">-</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token operator">-</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span>
 int_array  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组(int_lists)
 float_array  <span class="token operator">=</span>  np。阵列(int_lists,D型细胞<span class="token operator">=</span>浮动)

打印(INT_ARRAY)
打印(INT_ARRAY。D型细胞)
打印(float_array)
打印(float_array。D型)
</code></pre> 
  <p>[-3 -2 -1 0 1 2 3]<br> int64<br> [-3。-2. -1. 0. 1. 2. 3.]<br> 浮动64</p> 
  <h2>24.14numpy 数组的大小</h2> 
  <p>在 numpy 中要知道 numpy 数组列表中的项目数,我们使用 size</p> 
  <pre><code class="prism language-python">numpy_array_from_list  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
 two_dimensional_list  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">]</span><span class="token punctuation">,</span>
                              <span class="token punctuation">[</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">,</span>
                              <span class="token punctuation">[</span> <span class="token number">6</span> <span class="token punctuation">,</span> <span class="token number">7</span> <span class="token punctuation">,</span> <span class="token number">8</span> <span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>

<span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'The size:'</span> <span class="token punctuation">,</span> numpy_array_from_list <span class="token punctuation">.</span> size <span class="token punctuation">)</span> <span class="token comment"># 5 </span>
<span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'The size:'</span> <span class="token punctuation">,</span> two_dimensional_list <span class="token punctuation">.</span> size <span class="token punctuation">)</span>   <span class="token comment"># 3</span>
</code></pre> 
  <pre><code class="prism language-python">  尺寸:<span class="token number">5</span>
    尺寸:<span class="token number">9</span>
</code></pre> 
  <h2>24.15使用numpy进行数学运算</h2> 
  <p>NumPy 数组并不完全像 python 列表。要在 Python 列表中进行数学运算,我们必须遍历项目,但 numpy 可以允许在不循环的情况下进行任何数学运算。数学运算:</p> 
  <ol> 
   <li>加法 (+)</li> 
   <li>减法 (-)</li> 
   <li>乘法 (*)</li> 
   <li>分配 (/)</li> 
   <li>模块 (%)</li> 
   <li>楼层划分(//)</li> 
   <li>指数(**)</li> 
  </ol> 
  <h2>24.16添加</h2> 
  <pre><code class="prism language-python"><span class="token comment"># 数学运算</span>
<span class="token comment"># 加法</span>
numpy_array_from_list  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> <span class="token string">'原始数组: '</span> <span class="token punctuation">,</span> numpy_array_from_list <span class="token punctuation">)</span>
 ten_plus_original  <span class="token operator">=</span>  numpy_array_from_list   <span class="token operator">+</span>  <span class="token number">10</span>
打印<span class="token punctuation">(</span> ten_plus_original <span class="token punctuation">)</span>
</code></pre> 
  <p>原始数组:[1 2 3 4 5]<br> [11 12 13 14 15]</p> 
  <h2>24.17减法</h2> 
  <pre><code class="prism language-python"><span class="token comment"># 减法</span>
numpy_array_from_list  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> <span class="token string">'原始数组: '</span> <span class="token punctuation">,</span> numpy_array_from_list <span class="token punctuation">)</span>
 ten_minus_original  <span class="token operator">=</span>  numpy_array_from_list   <span class="token operator">-</span>  <span class="token number">10</span>
打印<span class="token punctuation">(</span> ten_minus_original <span class="token punctuation">)</span>
</code></pre> 
  <p>原始数组:[1 2 3 4 5]<br> [-9 -8 -7 -6 -5]</p> 
  <h2>24.18乘法</h2> 
  <pre><code class="prism language-python">numpy_array_from_list  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> <span class="token string">'原始数组: '</span> <span class="token punctuation">,</span> numpy_array_from_list <span class="token punctuation">)</span>
 ten_times_original  <span class="token operator">=</span>  numpy_array_from_list  <span class="token operator">*</span>  <span class="token number">10</span>
打印<span class="token punctuation">(</span> ten_times_original <span class="token punctuation">)</span>
</code></pre> 
  <p>原始数组:[1 2 3 4 5]<br> [10 20 30 40 50]</p> 
  <h2>24.19分配</h2> 
  <pre><code class="prism language-python"><span class="token comment"># 除法</span>
numpy_array_from_list  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> <span class="token string">'原始数组: '</span> <span class="token punctuation">,</span> numpy_array_from_list <span class="token punctuation">)</span>
 ten_times_original  <span class="token operator">=</span>  numpy_array_from_list  <span class="token operator">/</span>  <span class="token number">10</span>
打印<span class="token punctuation">(</span> ten_times_original <span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python"> 原始数组:<span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span> <span class="token number">4</span> <span class="token number">5</span><span class="token punctuation">]</span>
    <span class="token punctuation">[</span><span class="token number">0.1</span> <span class="token number">0.2</span> <span class="token number">0.3</span> <span class="token number">0.4</span> <span class="token number">0.5</span><span class="token punctuation">]</span>
</code></pre> 
  <h2>24.20模数;找到余数</h2> 
  <pre><code class="prism language-python">numpy_array_from_list  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> <span class="token string">'原始数组: '</span> <span class="token punctuation">,</span> numpy_array_from_list <span class="token punctuation">)</span>
 ten_times_original  <span class="token operator">=</span>  numpy_array_from_list  <span class="token operator">%</span>  <span class="token number">3</span>
打印<span class="token punctuation">(</span> ten_times_original <span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python">   原始数组:<span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span> <span class="token number">4</span> <span class="token number">5</span><span class="token punctuation">]</span>
    <span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">0</span> <span class="token number">1</span> <span class="token number">2</span><span class="token punctuation">]</span>
</code></pre> 
  <h2>24.21楼层划分</h2> 
  <pre><code class="prism language-python"><span class="token comment"># 楼层除法:没有余数的除法结果</span>
numpy_array_from_list  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> <span class="token string">'原始数组: '</span> <span class="token punctuation">,</span> numpy_array_from_list <span class="token punctuation">)</span>
 ten_times_original  <span class="token operator">=</span>  numpy_array_from_list  <span class="token operator">//</span>  <span class="token number">10</span>
打印<span class="token punctuation">(</span> ten_times_original <span class="token punctuation">)</span>
</code></pre> 
  <h2>24.22指数</h2> 
  <pre><code class="prism language-python"><span class="token comment"># Exponential 是找到某个数字的另一个幂:</span>
numpy_array_from_list  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> <span class="token string">'原始数组: '</span> <span class="token punctuation">,</span> numpy_array_from_list <span class="token punctuation">)</span>
 ten_times_original  <span class="token operator">=</span>  numpy_array_from_list   <span class="token operator">**</span>  <span class="token number">2</span>
打印<span class="token punctuation">(</span> ten_times_original <span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python">   原始数组:<span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span> <span class="token number">4</span> <span class="token number">5</span><span class="token punctuation">]</span>
    <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token number">4</span> <span class="token number">9</span> <span class="token number">16</span> <span class="token number">25</span><span class="token punctuation">]</span>
</code></pre> 
  <h2>24.23检查数据类型</h2> 
  <pre><code class="prism language-python"><span class="token comment">#Int, 浮点数</span>
numpy_int_arr  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
 numpy_float_arr  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1.1</span> <span class="token punctuation">,</span> <span class="token number">2.0</span> <span class="token punctuation">,</span> <span class="token number">3.2</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
 numpy_bool_arr  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token operator">-</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token operator">-</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> dtype <span class="token operator">=</span> <span class="token string">'bool'</span> <span class="token punctuation">)</span>

打印(numpy_int_arr。D型细胞)
打印(numpy_float_arr。D型细胞)
打印(numpy_bool_arr。D型)
</code></pre> 
  <pre><code class="prism language-python">int64
浮动<span class="token number">64</span>
布尔值
</code></pre> 
  <h2>24.24转换类型</h2> 
  <p>我们可以转换numpy数组的数据类型</p> 
  <p>1.整数到浮动</p> 
  <pre><code class="prism language-python">numpy_int_arr  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> dtype  <span class="token operator">=</span>  <span class="token string">'float'</span> <span class="token punctuation">)</span>
 numpy_int_arr
</code></pre> 
  <pre><code class="prism language-python">array<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <p>2.浮动到整数</p> 
  <pre><code class="prism language-python">numpy_int_arr  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span><span class="token punctuation">.</span> <span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span> <span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">.</span> <span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">.</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> dtype  <span class="token operator">=</span>  <span class="token string">'int'</span> <span class="token punctuation">)</span>
 numpy_int_arr
</code></pre> 
  <pre><code class="prism language-python">   数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <p>3.整数或布尔值</p> 
  <pre><code class="prism language-python">NP。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token operator">-</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token operator">-</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> dtype <span class="token operator">=</span> <span class="token string">'bool'</span> <span class="token punctuation">)</span>
</code></pre> 
  <p>数组([真,真,假,真,真,真])</p> 
  <p>4.整数到 str</p> 
  <pre><code class="prism language-python">numpy_float_list。astype <span class="token punctuation">(</span> <span class="token string">'int'</span> <span class="token punctuation">)</span>。astype(<span class="token string">'str'</span>)
</code></pre> 
  <p>数组([ ’ 1 ’ , ’ 2 ’ , ’ 3 ’ ], dtype= ’ <U21 ’ )</p> 
  <h2>24.25多维数组</h2> 
  <pre><code class="prism language-python"><span class="token comment"># 2 维数组</span>
two_dimension_array  <span class="token operator">=</span>  np <span class="token punctuation">.</span> array <span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">(</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">)</span><span class="token punctuation">,</span><span class="token punctuation">(</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">6</span> <span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span> <span class="token number">7</span> <span class="token punctuation">,</span> <span class="token number">8</span> <span class="token punctuation">,</span> <span class="token number">9</span> <span class="token punctuation">)</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token builtin">type</span> <span class="token punctuation">(</span> two_dimension_array <span class="token punctuation">)</span><span class="token punctuation">)</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> two_dimension_array <span class="token punctuation">)</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'Shape: '</span> <span class="token punctuation">,</span> two_dimension_array <span class="token punctuation">.</span> shape <span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> <span class="token string">'大小:'</span>,two_dimension_array。尺寸)
的打印(<span class="token string">'数据类型:'</span>,two_dimension_array。D型)
</code></pre> 
  <p><类’ numpy.ndarray ’ ><br> [[1 2 3]<br> [4 5 6]<br> [7 8 9]]<br> 形状:(3, 3)<br> 尺寸:9<br> 数据类型:int64</p> 
  <h2>24.26从 numpy 数组中获取项目</h2> 
  <pre><code class="prism language-python"><span class="token comment"># 2 维数组</span>
two_dimension_array  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 阵列(<span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1</span>,<span class="token number">2</span>,<span class="token number">3</span> <span class="token punctuation">]</span>,<span class="token punctuation">[</span> <span class="token number">4</span>,<span class="token number">5</span>,<span class="token number">6</span> <span class="token punctuation">]</span>,<span class="token punctuation">[</span> <span class="token number">7</span>,<span class="token number">8</span>,<span class="token number">9</span> <span class="token punctuation">]</span><span class="token punctuation">]</span>)
 FIRST_ROW  <span class="token operator">=</span>  two_dimension_array <span class="token punctuation">[</span> <span class="token number">0</span> <span class="token punctuation">]</span>
 second_row  <span class="token operator">=</span>  two_dimension_array <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">]</span>
 third_row  <span class="token operator">=</span>  two_dimension_array <span class="token punctuation">[</span> <span class="token number">2</span> <span class="token punctuation">]</span>
打印(“第一行:' <span class="token punctuation">,</span> first_row <span class="token punctuation">)</span>
打印(<span class="token string">'第二行:'</span>,second_row)
打印(<span class="token string">'第三行:'</span>,third_row)
</code></pre> 
  <p>第一行:[1 2 3]<br> 第二行:[4 5 6]<br> 第三行:[7 8 9]</p> 
  <pre><code class="prism language-python">first_column <span class="token operator">=</span>  two_dimension_array <span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">0</span> <span class="token punctuation">]</span>
 second_column  <span class="token operator">=</span>  two_dimension_array <span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">]</span>
 third_column  <span class="token operator">=</span>  two_dimension_array <span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">]</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'First column:'</span> <span class="token punctuation">,</span> first_column <span class="token punctuation">)</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'Second column:'</span> <span class="token punctuation">,</span> second_column <span class="token punctuation">)</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'第三列: '</span> <span class="token punctuation">,</span> third_column <span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> two_dimension_array <span class="token punctuation">)</span>
</code></pre> 
  <p>第一列:[1 4 7]<br> 第二列:[2 5 8]<br> 第三列:[3 6 9]<br> [[1 2 3]<br> [4 5 6]<br> [7 8 9]]</p> 
  <h2>24.27切片 Numpy 数组</h2> 
  <p>在 numpy 中切片类似于在 python list 中切片</p> 
  <pre><code class="prism language-python">two_dimension_array  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token punctuation">[</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">6</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span> <span class="token number">7</span> <span class="token punctuation">,</span> <span class="token number">8</span> <span class="token punctuation">,</span> <span class="token number">9</span> <span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
 first_two_rows_and_columns  <span class="token operator">=</span>  two_dimension_array <span class="token punctuation">[</span> <span class="token number">0</span> <span class="token punctuation">:</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">0</span> <span class="token punctuation">:</span> <span class="token number">2</span> <span class="token punctuation">]</span>
打印<span class="token punctuation">(</span> first_two_rows_and_columns <span class="token punctuation">)</span>
</code></pre> 
  <p>[[1 2]<br> [4 5]]</p> 
  <h2>24.28如何反转行和整个数组?</h2> 
  <pre><code class="prism language-python">二维数组<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">:</span><span class="token punctuation">]</span>
</code></pre> 
  <p>数组([[1, 2, 3],<br> [4, 5, 6],<br> [7, 8, 9]])</p> 
  <h2>24.29反转行列位置</h2> 
  <pre><code class="prism language-python">two_dimension_array  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token punctuation">[</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">6</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span> <span class="token number">7</span> <span class="token punctuation">,</span> <span class="token number">8</span> <span class="token punctuation">,</span> <span class="token number">9</span> <span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
 two_dimension_array <span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">:</span> <span class="token operator">-</span> <span class="token number">1</span> <span class="token punctuation">,</span><span class="token punctuation">:</span><span class="token punctuation">:</span> <span class="token operator">-</span> <span class="token number">1</span> <span class="token punctuation">]</span>
</code></pre> 
  <p>数组([[9, 8, 7],<br> [6, 5, 4],<br> [3, 2, 1]])</p> 
  <h2>24.30如何表示缺失值?</h2> 
  <pre><code class="prism language-python">打印<span class="token punctuation">(</span> two_dimension_array <span class="token punctuation">)</span>
 two_dimension_array <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  <span class="token number">55</span> 
two_dimension_array <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">]</span> <span class="token operator">=</span> <span class="token number">44</span>
打印<span class="token punctuation">(</span> two_dimension_array <span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python"><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span><span class="token punctuation">]</span>
 <span class="token punctuation">[</span><span class="token number">4</span> <span class="token number">5</span> <span class="token number">6</span><span class="token punctuation">]</span>
 <span class="token punctuation">[</span><span class="token number">7</span> <span class="token number">8</span> <span class="token number">9</span><span class="token punctuation">]</span><span class="token punctuation">]</span>
<span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span><span class="token punctuation">]</span>
 <span class="token punctuation">[</span> <span class="token number">4</span> <span class="token number">55</span> <span class="token number">44</span><span class="token punctuation">]</span>
 <span class="token punctuation">[</span> <span class="token number">7</span> <span class="token number">8</span> <span class="token number">9</span><span class="token punctuation">]</span><span class="token punctuation">]</span>
</code></pre> 
  <pre><code class="prism language-python"> <span class="token comment"># Numpy </span>
    Zeroes <span class="token comment"># numpy.zeros(shape, dtype=float, order='C') </span>
    numpy_zeroes  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 零<span class="token punctuation">(</span><span class="token punctuation">(</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">)</span><span class="token punctuation">,</span> dtype <span class="token operator">=</span> <span class="token builtin">int</span> <span class="token punctuation">,</span> order <span class="token operator">=</span> <span class="token string">'C'</span> <span class="token punctuation">)</span>
     numpy_zeroes
</code></pre> 
  <pre><code class="prism language-python">数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python"><span class="token comment"># Numpy</span>
归零 numpy_ones  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 个<span class="token punctuation">(</span><span class="token punctuation">(</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">)</span><span class="token punctuation">,</span> dtype <span class="token operator">=</span> <span class="token builtin">int</span> <span class="token punctuation">,</span> order <span class="token operator">=</span> <span class="token string">'C'</span> <span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> numpy_ones <span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python"><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">1</span> <span class="token number">1</span><span class="token punctuation">]</span>
 <span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">1</span> <span class="token number">1</span><span class="token punctuation">]</span>
 <span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">1</span> <span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">]</span>
</code></pre> 
  <pre><code class="prism language-python">两个 <span class="token operator">=</span>  numpy_ones  <span class="token operator">*</span>  <span class="token number">2</span>
</code></pre> 
  <pre><code class="prism language-python"><span class="token comment"># 重塑</span>
<span class="token comment"># numpy.reshape(), numpy.flatten() </span>
first_shape   <span class="token operator">=</span>  np <span class="token punctuation">.</span> 阵列(<span class="token punctuation">[</span>(<span class="token number">1</span>,<span class="token number">2</span>,<span class="token number">3</span>),(<span class="token number">4</span>,<span class="token number">5</span>,<span class="token number">6</span>)<span class="token punctuation">]</span>)
打印(first_shape)
重构 <span class="token operator">=</span>  first_shape。重塑(<span class="token number">3</span>,<span class="token number">2</span>)
打印(重塑)
    <span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span><span class="token punctuation">]</span>
     <span class="token punctuation">[</span><span class="token number">4</span> <span class="token number">5</span> <span class="token number">6</span><span class="token punctuation">]</span><span class="token punctuation">]</span>
    <span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span><span class="token punctuation">]</span>
     <span class="token punctuation">[</span><span class="token number">3</span> <span class="token number">4</span><span class="token punctuation">]</span>
     <span class="token punctuation">[</span><span class="token number">5</span> <span class="token number">6</span><span class="token punctuation">]</span><span class="token punctuation">]</span>
</code></pre> 
  <pre><code class="prism language-python">扁平 <span class="token operator">=</span> 重塑。flatten <span class="token punctuation">(</span><span class="token punctuation">)</span>
扁平化
</code></pre> 
  <pre><code class="prism language-python">数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
<span class="token comment">## 水平堆栈</span>
np_list_one  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
 np_list_two  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">6</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>

打印(np_list_one  <span class="token operator">+</span>  np_list_two)

打印(<span class="token string">'水平附加:'</span>,NP。hstack((np_list_one,np_list_two)))
<span class="token punctuation">[</span><span class="token number">5</span> <span class="token number">7</span> <span class="token number">9</span><span class="token punctuation">]</span>
水平追加:<span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span> <span class="token number">4</span> <span class="token number">5</span> <span class="token number">6</span><span class="token punctuation">]</span>
</code></pre> 
  <p>##垂直叠<br> 打印(‘垂直附加:’,NP。vstack((np_list_one,np_list_two)))<br> 垂直附加:[[1 2 3]<br> [4 5 6]]</p> 
  <h2>24.31生成随机数</h2> 
  <pre><code class="prism language-python">  <span class="token comment"># 生成一个随机浮点数</span>
    random_float  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 随机的。随机()
     random_float
    <span class="token number">0.018929887384753874</span>
    <span class="token comment"># 生成一个随机浮点数</span>
    random_floats  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 随机的。随机<span class="token punctuation">(</span> <span class="token number">5</span> <span class="token punctuation">)</span>
     random_floats
    数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">0.26392192</span><span class="token punctuation">,</span> <span class="token number">0.35842215</span><span class="token punctuation">,</span> <span class="token number">0.87908478</span><span class="token punctuation">,</span> <span class="token number">0.41902195</span><span class="token punctuation">,</span> <span class="token number">0.78926418</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
    <span class="token comment"># 生成 0 到 10 之间的随机整数</span>

    random_int  <span class="token operator">=</span>  np。随机的。randint <span class="token punctuation">(</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">11</span> <span class="token punctuation">)</span>
     random_int
    <span class="token number">4</span>
    <span class="token comment"># 生成一个 2 到 11 之间的随机整数,并创建一个</span>
    单行 数组random_int <span class="token operator">=</span>  np <span class="token punctuation">.</span> 随机的。randint <span class="token punctuation">(</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">10</span> <span class="token punctuation">,</span> size <span class="token operator">=</span> <span class="token number">4</span> <span class="token punctuation">)</span>
     random_int
    数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">8</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
    <span class="token comment"># 生成 0 到 10 之间的随机整数</span>
    random_int  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 随机的。randint <span class="token punctuation">(</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">10</span> <span class="token punctuation">,</span> size <span class="token operator">=</span> <span class="token punctuation">(</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">)</span><span class="token punctuation">)</span>
     random_int
    数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
           <span class="token punctuation">[</span><span class="token number">7</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
           <span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <h2>24.32生成随机数</h2> 
  <pre><code class="prism language-python"> <span class="token comment"># np.random.normal(mu, sigma, size) </span>
    normal_array  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 随机的。正常<span class="token punctuation">(</span> <span class="token number">79</span> <span class="token punctuation">,</span> <span class="token number">15</span> <span class="token punctuation">,</span> <span class="token number">80</span> <span class="token punctuation">)</span>
     normal_array
</code></pre> 
  <pre><code class="prism language-python"> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">89.49990595</span><span class="token punctuation">,</span> <span class="token number">82.06056961</span><span class="token punctuation">,</span> <span class="token number">107.21445842</span><span class="token punctuation">,</span> <span class="token number">38.69307086</span><span class="token punctuation">,</span>
            <span class="token number">47.85259157</span>、<span class="token number">93.07381061</span>、<span class="token number">76.40724259</span>、<span class="token number">78.55675184</span>、
            <span class="token number">72.17358173</span>、<span class="token number">47.9888899</span>、<span class="token number">65.10370622</span>、<span class="token number">76.29696568</span>、
            <span class="token number">95.58234254</span>、<span class="token number">68.14897213</span>、<span class="token number">38.75862686</span>、<span class="token number">122.5587927</span>、
            <span class="token number">67.0762565</span>、<span class="token number">95.73990864</span>、<span class="token number">81.97454563</span>、<span class="token number">92.54264805</span>、
            <span class="token number">59.37035153</span>、<span class="token number">77.76828101</span>、<span class="token number">52.30752166</span>、<span class="token number">64.43109931</span>、
            <span class="token number">62.63695351</span>、<span class="token number">90.04616138</span>、<span class="token number">75.70009094</span>、<span class="token number">49.87586877</span>、
            <span class="token number">80.22002414</span>、<span class="token number">68.56708848</span>、<span class="token number">76.27791052</span>、<span class="token number">67.24343975</span>、
            <span class="token number">81.86363935</span>、<span class="token number">78.22703433</span>、<span class="token number">102.85737041</span>、<span class="token number">65.15700341</span>、
            <span class="token number">84.87033426</span>、<span class="token number">76.7569997</span>、<span class="token number">64.61321853</span>、<span class="token number">67.37244562</span>、
            <span class="token number">74.4068773</span>、<span class="token number">58.65119655</span>、<span class="token number">71.66488727</span>、<span class="token number">53.42458179</span>、
            <span class="token number">70.26872028</span>、<span class="token number">60.96588544</span>、<span class="token number">83.56129414</span>、<span class="token number">72.14255326</span>、
            <span class="token number">81.00787609</span>、<span class="token number">71.81264853</span>、<span class="token number">72.64168853</span>、<span class="token number">86.56608717</span>、
            <span class="token number">94.94667321</span>、<span class="token number">82.32676973</span>、<span class="token number">70.5165446</span>、<span class="token number">85.43061003</span>、
            <span class="token number">72.45526212</span>、<span class="token number">87.34681775</span>、<span class="token number">87.69911217</span>、<span class="token number">103.02831489</span>、
            <span class="token number">75.28598596</span>、<span class="token number">67.17806893</span>、<span class="token number">92.41274447</span>、<span class="token number">101.06662611</span>、
            <span class="token number">87.70013935</span>、<span class="token number">70.73980645</span>、<span class="token number">46.40368207</span>、<span class="token number">50.17947092</span>、
            <span class="token number">61.75618542</span>、<span class="token number">90.26191397</span>、<span class="token number">78.63968639</span>、<span class="token number">70.84550744</span>、
            <span class="token number">88.91826581</span>、<span class="token number">103.91474733</span>、<span class="token number">66.3064638</span>、<span class="token number">79.49726264</span>、
            <span class="token number">70.81087439</span><span class="token punctuation">,</span> <span class="token number">83.90130623</span><span class="token punctuation">,</span> <span class="token number">87.58555972</span><span class="token punctuation">,</span> <span class="token number">59.95462521</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <h2>24.33Numpy 和统计</h2> 
  <pre><code class="prism language-python">导入 matplotlib。pyplot  <span class="token keyword">as</span>  plt
将 seaborn 作为 sns 
sns导入。设置<span class="token punctuation">(</span><span class="token punctuation">)</span>
 plt。hist <span class="token punctuation">(</span> normal_array <span class="token punctuation">,</span> color <span class="token operator">=</span> <span class="token string">"grey"</span> <span class="token punctuation">,</span> bins <span class="token operator">=</span> <span class="token number">50</span> <span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python"> <span class="token punctuation">(</span>数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">.</span><span class="token punctuation">,</span>
            <span class="token number">4</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">7</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span> <span class="token punctuation">,</span>
            <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
     数组(<span class="token punctuation">[</span> <span class="token number">38.69307086</span><span class="token punctuation">,</span> <span class="token number">40.37038529</span><span class="token punctuation">,</span> <span class="token number">42.04769973</span><span class="token punctuation">,</span> <span class="token number">43.72501417</span><span class="token punctuation">,</span>
             <span class="token number">45.4023286</span>、<span class="token number">47.07964304</span>、<span class="token number">48.75695748</span>、<span class="token number">50.43427191</span>、
             <span class="token number">52.11158635</span>、<span class="token number">53.78890079</span>、<span class="token number">55.46621523</span>、<span class="token number">57.14352966</span>、
             <span class="token number">58.8208441</span>、<span class="token number">60.49815854</span>、<span class="token number">62.17547297</span>、<span class="token number">63.85278741</span>、
             <span class="token number">65.53010185</span>、<span class="token number">67.20741628</span>、<span class="token number">68.88473072</span>、<span class="token number">70.56204516</span>、
             <span class="token number">72.23935959</span>、<span class="token number">73.91667403</span>、<span class="token number">75.59398847</span>、<span class="token number">77.27130291</span>、
             <span class="token number">78.94861734</span>、<span class="token number">80.62593178</span>、<span class="token number">82.30324622</span>、<span class="token number">83.98056065</span>、
             <span class="token number">85.65787509</span>、<span class="token number">87.33518953</span>、<span class="token number">89.01250396</span>、<span class="token number">90.6898184</span>、
             <span class="token number">92.36713284</span>、<span class="token number">94.04444727</span>、<span class="token number">95.72176171</span>、<span class="token number">97.39907615</span>、
             <span class="token number">99.07639058</span>、<span class="token number">100.75370502</span>、<span class="token number">102.43101946</span>、<span class="token number">104.1083339</span>、
            <span class="token number">105.78564833</span>、<span class="token number">107.46296277</span>、<span class="token number">109.14027721</span>、<span class="token number">110.81759164</span>、
            <span class="token number">112.49490608</span>、<span class="token number">114.17222052</span>、<span class="token number">115.84953495</span>、<span class="token number">117.52684939</span>、
            <span class="token number">119.20416383</span><span class="token punctuation">,</span> <span class="token number">120.88147826</span><span class="token punctuation">,</span> <span class="token number">122.5587927</span> <span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
     <span class="token operator"><</span> <span class="token number">50</span> 个 Patch 对象的列表<span class="token operator">></span> <span class="token punctuation">)</span>
</code></pre> 
  <h2>24.34numpy中的矩阵</h2> 
  <pre><code class="prism language-python">Four_by_four_matrix  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 矩阵(NP。者((<span class="token number">4</span>,<span class="token number">4</span>),D型细胞<span class="token operator">=</span>浮动))
Four_by_four_matrix
矩阵<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
            <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
            <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
            <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
NP。asarray(four_by_four_matrix)<span class="token punctuation">[</span> <span class="token number">2</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  <span class="token number">2</span>个
four_by_four_matrix
矩阵<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
            <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
            <span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
            <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <h2>24.35numpy numpy.arange()</h2> 
  <p>有时,您希望创建在定义的间隔内均匀分布的值。例如,您想创建从 1 到 10 的值;你可以使用 numpy.arange() 函数</p> 
  <pre><code class="prism language-python"><span class="token comment"># 使用 range(starting, stop, step) 创建列表</span>
lst  <span class="token operator">=</span>  <span class="token builtin">range</span> <span class="token punctuation">(</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">11</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">)</span>
 lst
范围<span class="token punctuation">(</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">11</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">)</span>
<span class="token keyword">for</span>  l  <span class="token keyword">in</span>  lst <span class="token punctuation">:</span>
    打印<span class="token punctuation">(</span> l <span class="token punctuation">)</span>
    <span class="token number">2</span>
    <span class="token number">4</span>
    <span class="token number">6</span>
    <span class="token number">8</span>
    <span class="token number">10</span>
</code></pre> 
  <pre><code class="prism language-python"><span class="token comment"># 类似于范围 arange numpy.arange(start, stop, step) </span>
whole_numbers  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 范围<span class="token punctuation">(</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">20</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">)</span>
整数
数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">,</span> <span class="token number">7</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">,</span> <span class="token number">9</span><span class="token punctuation">,</span> <span class="token number">10</span><span class="token punctuation">,</span> <span class="token number">11</span><span class="token punctuation">,</span> <span class="token number">12</span><span class="token punctuation">,</span> <span class="token number">13</span><span class="token punctuation">,</span> <span class="token number">14</span><span class="token punctuation">,</span> <span class="token number">15</span><span class="token punctuation">,</span> <span class="token number">16</span><span class="token punctuation">,</span>
           <span class="token number">17</span><span class="token punctuation">,</span> <span class="token number">18</span><span class="token punctuation">,</span> <span class="token number">19</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
natural_numbers  <span class="token operator">=</span>  np。arange <span class="token punctuation">(</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">20</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">)</span>
 natural_numbers
奇数 <span class="token operator">=</span>  np。arange <span class="token punctuation">(</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">20</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">)</span>
奇数
    数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">7</span><span class="token punctuation">,</span> <span class="token number">9</span><span class="token punctuation">,</span> <span class="token number">11</span><span class="token punctuation">,</span> <span class="token number">13</span><span class="token punctuation">,</span> <span class="token number">15</span><span class="token punctuation">,</span> <span class="token number">17</span><span class="token punctuation">,</span> <span class="token number">19</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
even_numbers  <span class="token operator">=</span>  np。arange <span class="token punctuation">(</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">20</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">)</span>
 even_numbers
    数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">,</span> <span class="token number">10</span><span class="token punctuation">,</span> <span class="token number">12</span><span class="token punctuation">,</span> <span class="token number">14</span><span class="token punctuation">,</span> <span class="token number">16</span><span class="token punctuation">,</span> <span class="token number">18</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <h2>24.36使用 linspace 创建数字序列</h2> 
  <pre><code class="prism language-python"><span class="token comment"># numpy.linspace() </span>
<span class="token comment"># numpy.logspace() in Python with Example </span>
<span class="token comment"># 例如,它可以用来创建从 1 到 5 的 10 个均匀间隔的值。</span>
NP。linspace <span class="token punctuation">(</span> <span class="token number">1.0</span> <span class="token punctuation">,</span> <span class="token number">5.0</span> <span class="token punctuation">,</span> num <span class="token operator">=</span> <span class="token number">10</span> <span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python">    数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1.44444444</span><span class="token punctuation">,</span> <span class="token number">1.88888889</span><span class="token punctuation">,</span> <span class="token number">2.33333333</span><span class="token punctuation">,</span> <span class="token number">2.77777778</span><span class="token punctuation">,</span>
           <span class="token number">3.22222222</span><span class="token punctuation">,</span> <span class="token number">3.66666667</span><span class="token punctuation">,</span> <span class="token number">4.11111111</span><span class="token punctuation">,</span> <span class="token number">4.55555556</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
<span class="token comment"># 不包括区间</span>
np 中的最后一个值。linspace <span class="token punctuation">(</span> <span class="token number">1.0</span> <span class="token punctuation">,</span> <span class="token number">5.0</span> <span class="token punctuation">,</span> num <span class="token operator">=</span> <span class="token number">5</span> <span class="token punctuation">,</span>端点<span class="token operator">=</span> <span class="token boolean">False</span> <span class="token punctuation">)</span>
array<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span> <span class="token punctuation">,</span> <span class="token number">1.8</span><span class="token punctuation">,</span> <span class="token number">2.6</span><span class="token punctuation">,</span> <span class="token number">3.4</span><span class="token punctuation">,</span> <span class="token number">4.2</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python"><span class="token comment"># LogSpace </span>
<span class="token comment"># LogSpace 返回对数刻度上的偶数间隔数。Logspace 具有与 np.linspace 相同的参数。</span>

# 句法:

<span class="token comment"># numpy.logspace(开始,停止,数量,端点)</span>

NP。日志空间<span class="token punctuation">(</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">4.0</span> <span class="token punctuation">,</span> num <span class="token operator">=</span> <span class="token number">4</span> <span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python">数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">100</span><span class="token punctuation">.</span> <span class="token punctuation">,</span> <span class="token number">464.15888336</span><span class="token punctuation">,</span> <span class="token number">2154.43469003</span><span class="token punctuation">,</span> <span class="token number">10000</span><span class="token punctuation">.</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
<span class="token comment"># 检查数组的大小</span>
x  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 阵列(<span class="token punctuation">[</span> <span class="token number">1</span>,<span class="token number">2</span>,<span class="token number">3</span> <span class="token punctuation">]</span>,D型细胞<span class="token operator">=</span> NP。complex128)
X
    数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token operator">+</span><span class="token number">0.j</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">.</span><span class="token operator">+</span><span class="token number">0.j</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">.</span><span class="token operator">+</span><span class="token number">0.j</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
×。项目大小
<span class="token number">16</span>
<span class="token comment"># 在 Python 中索引和切片 NumPy 数组</span>
np_list  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">(</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token punctuation">(</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">6</span> <span class="token punctuation">)</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
 np_list
    数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
           <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
打印(<span class="token string">'第一行:'</span>,np_list <span class="token punctuation">[</span> <span class="token number">0</span> <span class="token punctuation">]</span>)
打印(<span class="token string">'第二行:'</span>,np_list <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">]</span>)
    第一行:<span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span><span class="token punctuation">]</span>
    第二行:<span class="token punctuation">[</span><span class="token number">4</span> <span class="token number">5</span> <span class="token number">6</span><span class="token punctuation">]</span>
打印(<span class="token string">'第一列:'</span>,np_list<span class="token punctuation">[</span> :,<span class="token number">0</span> <span class="token punctuation">]</span>)
打印(<span class="token string">'第二列:'</span>,np_list<span class="token punctuation">[</span> :,<span class="token number">1</span> <span class="token punctuation">]</span>)
打印(<span class="token string">'第三列:'</span>,np_list<span class="token punctuation">[</span> :,<span class="token number">2</span> <span class="token punctuation">]</span>)
    第一列:<span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">4</span><span class="token punctuation">]</span>
    第二列:<span class="token punctuation">[</span><span class="token number">2</span> <span class="token number">5</span><span class="token punctuation">]</span>
    第三列:<span class="token punctuation">[</span><span class="token number">3</span> <span class="token number">6</span><span class="token punctuation">]</span>
</code></pre> 
  <h2>24.37NumPy 统计函数与示例</h2> 
  <p>NumPy 具有非常有用的统计函数,用于从数组中的给定元素中查找最小值、最大值、平均值、中位数、百分位数、标准偏差和方差等。函数解释如下 - 统计函数 Numpy 配备了如下所列的稳健统计函数</p> 
  <ul> 
   <li>Numpy 函数</li> 
  </ul> 
  <ul> 
   <li>最小 np.min()</li> 
   <li>最大 np.max()</li> 
   <li>平均 np.mean()</li> 
   <li>中位数 np.median()</li> 
   <li>差异</li> 
   <li>百分位</li> 
   <li>标准差 np.std()</li> 
  </ul> 
  <pre><code class="prism language-python">np_normal_dis  <span class="token operator">=</span>  np。随机的。正常(<span class="token number">5</span>,<span class="token number">0.5</span>,<span class="token number">100</span>)
 np_normal_dis 
<span class="token comment">##最小值,最大值,平均值,中位数,SD</span>
打印(<span class="token string">'分钟:'</span>,two_dimension_array。分钟())
的打印(<span class="token string">'最大:'</span>,two_dimension_array。最大())
的打印(“平均: ' two_dimension_array。意思是())
#打印('中位数”,two_dimension_array<span class="token punctuation">.</span>median())
打印(<span class="token string">'SD:'</span><span class="token punctuation">,</span> two_dimension_array。标准<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python"><span class="token builtin">min</span><span class="token punctuation">:</span>  <span class="token number">1</span>
<span class="token builtin">max</span><span class="token punctuation">:</span>  <span class="token number">55</span>
mean<span class="token punctuation">:</span>  <span class="token number">14.777777777777779</span>
sd<span class="token punctuation">:</span>  <span class="token number">18.913709183069525</span>
最小值:   <span class="token number">1</span>
最大值:   <span class="token number">55</span>
平均值:   <span class="token number">14.777777777777779</span> 标准
差:   <span class="token number">18.913709183069525</span>
</code></pre> 
  <pre><code class="prism language-python">打印(two_dimension_array)
打印(<span class="token string">'列具有最小:'</span>,NP。阿明(two_dimension_array,轴<span class="token operator">=</span> <span class="token number">0</span>))
的打印(<span class="token string">'列具有最大:'</span>,NP。AMAX(two_dimension_array,轴<span class="token operator">=</span> <span class="token number">0</span>))
的打印(“<span class="token operator">==</span><span class="token operator">=</span>行<span class="token operator">==</span>”)
打印(<span class="token string">'行用最小的:'</span>,NP。阿明(two_dimension_array,轴<span class="token operator">=</span> <span class="token number">1</span>))
的打印(<span class="token string">'行用最大:'</span>,NP。AMAX(two_dimension_array,轴<span class="token operator">=</span> <span class="token number">1</span>))


<span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1</span>  <span class="token number">2</span>  <span class="token number">3</span><span class="token punctuation">]</span>
 <span class="token punctuation">[</span> <span class="token number">4</span> <span class="token number">55</span> <span class="token number">44</span><span class="token punctuation">]</span>
 <span class="token punctuation">[</span> <span class="token number">7</span>  <span class="token number">8</span>  <span class="token number">9</span><span class="token punctuation">]</span><span class="token punctuation">]</span>
Column <span class="token keyword">with</span> minimum<span class="token punctuation">:</span>  <span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span><span class="token punctuation">]</span>
Column <span class="token keyword">with</span> maximum<span class="token punctuation">:</span>  <span class="token punctuation">[</span> <span class="token number">7</span> <span class="token number">55</span> <span class="token number">44</span><span class="token punctuation">]</span>
<span class="token operator">==</span><span class="token operator">=</span> Row <span class="token operator">==</span>
Row <span class="token keyword">with</span> minimum<span class="token punctuation">:</span>  <span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">4</span> <span class="token number">7</span><span class="token punctuation">]</span>
Row <span class="token keyword">with</span> maximum<span class="token punctuation">:</span>  <span class="token punctuation">[</span> <span class="token number">3</span> <span class="token number">55</span>  <span class="token number">9</span><span class="token punctuation">]</span>
</code></pre> 
  <h2>24.38如何创建重复序列?</h2> 
  <pre><code class="prism language-python">a  <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span>

#整个重复的<span class="token string">'A'</span>两次
打印(<span class="token string">'平铺:'</span>,NP。瓷砖(一,<span class="token number">2</span>))

#重复的<span class="token string">'A'</span>两次各元件
打印(<span class="token string">'重复:'</span>,NP。重复(一,<span class="token number">2</span>))
Tile<span class="token punctuation">:</span>    <span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span> <span class="token number">1</span> <span class="token number">2</span> <span class="token number">3</span><span class="token punctuation">]</span>
Repeat<span class="token punctuation">:</span>  <span class="token punctuation">[</span><span class="token number">1</span> <span class="token number">1</span> <span class="token number">2</span> <span class="token number">2</span> <span class="token number">3</span> <span class="token number">3</span><span class="token punctuation">]</span>
</code></pre> 
  <h2>24.39如何生成随机数?</h2> 
  <pre><code class="prism language-python"><span class="token comment"># [0,1) </span>
one_random_num  <span class="token operator">=</span>  np之间的一个随机数。随机的。随机<span class="token punctuation">(</span><span class="token punctuation">)</span>
 one_random_in  <span class="token operator">=</span>  np。随机
打印(one_random_num)
<span class="token number">0.6149403282678213</span>
<span class="token number">0.4763968133790438</span>
<span class="token number">0.4763968133790438</span>
<span class="token comment"># 形状为 2,3 的 [0,1) 之间的随机数</span>
r  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 随机的。随机<span class="token punctuation">(</span> size <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> r <span class="token punctuation">)</span>
<span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0.13031737</span> <span class="token number">0.4429537</span>  <span class="token number">0.1129527</span> <span class="token punctuation">]</span>
 <span class="token punctuation">[</span><span class="token number">0.76811539</span> <span class="token number">0.88256594</span> <span class="token number">0.6754075</span> <span class="token punctuation">]</span><span class="token punctuation">]</span>
打印<span class="token punctuation">(</span> np <span class="token punctuation">.</span> random <span class="token punctuation">.</span> choice <span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token string">'a'</span> <span class="token punctuation">,</span> <span class="token string">'e'</span> <span class="token punctuation">,</span> <span class="token string">'i'</span> <span class="token punctuation">,</span> <span class="token string">'o'</span> <span class="token punctuation">,</span> <span class="token string">'u'</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> size <span class="token operator">=</span> <span class="token number">10</span> <span class="token punctuation">)</span><span class="token punctuation">)</span>
<span class="token punctuation">[</span><span class="token string">'u'</span> <span class="token string">'o'</span> <span class="token string">'o'</span> <span class="token string">'i'</span> <span class="token string">'e'</span> <span class="token string">'e'</span> <span class="token string">'u'</span> <span class="token string">'o'</span> <span class="token string">'u'</span> <span class="token string">'a'</span><span class="token punctuation">]</span>
<span class="token punctuation">[</span> <span class="token string">'i'</span>  <span class="token string">'u'</span>  <span class="token string">'e'</span>  <span class="token string">'o'</span>  <span class="token string">'a'</span>  <span class="token string">'i'</span>  <span class="token string">'e'</span>  <span class="token string">'u'</span>  <span class="token string">'o'</span>  <span class="token string">'i'</span> <span class="token punctuation">]</span>
<span class="token punctuation">[</span><span class="token string">'iueoaieuoi'</span><span class="token punctuation">]</span>
<span class="token comment">## 形状 2, 2 的 [0, 1] 之间的随机数</span>
rand  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 随机的。兰特<span class="token punctuation">(</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">)</span>
兰特
array<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0.97992598</span><span class="token punctuation">,</span> <span class="token number">0.79642484</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">0.65263629</span><span class="token punctuation">,</span> <span class="token number">0.55763145</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
rand2  <span class="token operator">=</span>  np。随机的。randn <span class="token punctuation">(</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">)</span>
 rand2
array<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1.65593322</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">0.52326621</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span> <span class="token number">0.39071179</span><span class="token punctuation">,</span> <span class="token operator">-</span><span class="token number">2.03649407</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
<span class="token comment"># 形状为 2,5 的 [0, 10) 之间的随机整数</span>
rand_int  <span class="token operator">=</span>  np <span class="token punctuation">.</span> 随机的。randint <span class="token punctuation">(</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">10</span> <span class="token punctuation">,</span> size <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
 rand_int
array<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">7</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">,</span> <span class="token number">7</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <pre><code class="prism language-python">从 scipy 导入 统计
np_normal_dis  <span class="token operator">=</span>  np。随机的。正常(<span class="token number">5</span>,<span class="token number">0.5</span>,<span class="token number">1000</span>)#平均值,标准偏差,样本数
np_normal_dis 
<span class="token comment">##最小值,最大值,平均值,中位数,SD</span>
打印(<span class="token string">'分钟:'</span>,NP。分钟(np_normal_dis))
打印(<span class="token string">'最大:'</span>,np <span class="token punctuation">.</span> <span class="token builtin">max</span> <span class="token punctuation">(</span> np_normal_dis <span class="token punctuation">)</span><span class="token punctuation">)</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'mean: '</span> <span class="token punctuation">,</span> np <span class="token punctuation">.</span>均值(np_normal_dis))
打印(<span class="token string">'中位数:'</span>,NP。中值(np_normal_dis))
打印(<span class="token string">'模式:'</span>,统计信息。模式(np_normal_dis))
打印(<span class="token string">'SD:'</span>,NP。STD(np_normal_dis))
    分钟:<span class="token number">3.557811005458804</span>
    最大:<span class="token number">6.876317743643499</span>
    平均值:<span class="token number">5.035832048106663</span>
    中位数:<span class="token number">5.020161980441937</span>
    模式:模式结果(模式<span class="token operator">=</span>数组(<span class="token punctuation">[</span><span class="token number">3.55781101</span><span class="token punctuation">]</span>),计数<span class="token operator">=</span>数组(<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span>))
    标准差:<span class="token number">0.489682424165213</span>
PLT。hist <span class="token punctuation">(</span> np_normal_dis <span class="token punctuation">,</span> color <span class="token operator">=</span> <span class="token string">"grey"</span> <span class="token punctuation">,</span> bins <span class="token operator">=</span> <span class="token number">21</span> <span class="token punctuation">)</span>
 plt。显示<span class="token punctuation">(</span><span class="token punctuation">)</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/c9fbd233a88643dbb62d85f2f7d57c73.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/c9fbd233a88643dbb62d85f2f7d57c73.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第2张图片" width="388" height="264" style="border:1px solid black;"></a></p> 
  <pre><code class="prism language-python"><span class="token comment"># numpy.dot(): Python 中使用 Numpy 的</span>
Dot Product 
<span class="token comment"># Dot Product # Numpy 是强大的矩阵计算库。例如,您可以使用 np.dot 计算点积</span>

# 句法

<span class="token comment"># numpy.dot(x, y, out=None)</span>
</code></pre> 
  <h2>24.40线性代数</h2> 
  <p>点积</p> 
  <pre><code class="prism language-python"><span class="token comment">## 线性代数</span>
<span class="token comment">### 点积:两个数组的乘积</span>
f  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
 g  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
 <span class="token comment">### 1*4+2*5 + 3*6 </span>
np <span class="token punctuation">.</span> 点<span class="token punctuation">(</span> f <span class="token punctuation">,</span> g <span class="token punctuation">)</span>   <span class="token comment"># 23</span>
</code></pre> 
  <h2>24.41NumPy 矩阵乘法与 np.matmul()</h2> 
  <pre><code class="prism language-python"><span class="token comment">### Matmul:两个数组的矩阵乘积</span>
h  <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token punctuation">[</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">]</span><span class="token punctuation">]</span>
 i  <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">[</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">6</span> <span class="token punctuation">]</span><span class="token punctuation">,</span><span class="token punctuation">[</span> <span class="token number">7</span> <span class="token punctuation">,</span> <span class="token number">8</span> <span class="token punctuation">]</span><span class="token punctuation">]</span>
 <span class="token comment">### 1*5+2*7 = 19</span>
纳米。matmul <span class="token punctuation">(</span> h <span class="token punctuation">,</span> i <span class="token punctuation">)</span>
    数组<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">19</span><span class="token punctuation">,</span> <span class="token number">22</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
           <span class="token punctuation">[</span><span class="token number">43</span><span class="token punctuation">,</span> <span class="token number">50</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
<span class="token comment">##行列式2*2矩阵</span>
<span class="token comment">###5*8-7*6np.linalg.det(i)</span>
NP。linalg。检测<span class="token punctuation">(</span> i <span class="token punctuation">)</span>
<span class="token operator">-</span><span class="token number">1.999999999999999</span>
Z  <span class="token operator">=</span>  np。零<span class="token punctuation">(</span><span class="token punctuation">(</span> <span class="token number">8</span> <span class="token punctuation">,</span> <span class="token number">8</span> <span class="token punctuation">)</span><span class="token punctuation">)</span>
 Z <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">:</span><span class="token punctuation">:</span> <span class="token number">2</span> <span class="token punctuation">,</span><span class="token punctuation">:</span><span class="token punctuation">:</span> <span class="token number">2</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  <span class="token number">1</span> 
Z <span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token punctuation">:</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">:</span><span class="token punctuation">:</span> <span class="token number">2</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  <span class="token number">1</span>
Z
</code></pre> 
  <pre><code class="prism language-python">array<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
       <span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">.</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">.</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
new_list  <span class="token operator">=</span> <span class="token punctuation">[</span> x  <span class="token operator">+</span>  <span class="token number">2</span>  <span class="token keyword">for</span>  x  <span class="token keyword">in</span>  <span class="token builtin">range</span> <span class="token punctuation">(</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">11</span> <span class="token punctuation">)</span><span class="token punctuation">]</span>
</code></pre> 
  <p>新列表</p> 
  <pre><code class="prism language-python"><span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">,</span> <span class="token number">7</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">,</span> <span class="token number">9</span><span class="token punctuation">,</span> <span class="token number">10</span><span class="token punctuation">,</span> <span class="token number">11</span><span class="token punctuation">,</span> <span class="token number">12</span><span class="token punctuation">]</span>
<span class="token punctuation">[</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">6</span> <span class="token punctuation">,</span> <span class="token number">7</span> <span class="token punctuation">,</span> <span class="token number">8</span> <span class="token punctuation">,</span> <span class="token number">9</span> <span class="token punctuation">,</span> <span class="token number">10</span> <span class="token punctuation">,</span> <span class="token number">11</span> <span class="token punctuation">,</span> <span class="token number">12</span> <span class="token punctuation">]</span>
<span class="token punctuation">[</span><span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">,</span> <span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">,</span> <span class="token number">7</span><span class="token punctuation">,</span> <span class="token number">8</span><span class="token punctuation">,</span> <span class="token number">9</span><span class="token punctuation">,</span> <span class="token number">10</span><span class="token punctuation">,</span> <span class="token number">11</span><span class="token punctuation">,</span> <span class="token number">12</span><span class="token punctuation">]</span>
np_arr  <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span>范围<span class="token punctuation">(</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">11</span> <span class="token punctuation">)</span><span class="token punctuation">)</span>
 np_arr  <span class="token operator">+</span>  <span class="token number">2</span>
</code></pre> 
  <p>数组([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])</p> 
  <p>对于具有线性关系的量,我们使用线性方程。让我们看看下面的例子:</p> 
  <pre><code class="prism language-python">温度 <span class="token operator">=</span>  np。数组<span class="token punctuation">(</span><span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
 pressure  <span class="token operator">=</span>  temp  <span class="token operator">*</span>  <span class="token number">2</span>  <span class="token operator">+</span>  <span class="token number">5</span> 
pressure
</code></pre> 
  <p>数组([ 7, 9, 11, 13, 15])</p> 
  <pre><code class="prism language-python">PLT。绘图(温度,压力)
 plt。xlabel(<span class="token string">'摄氏温度'</span>)
 plt。ylabel(<span class="token string">'atm 中的压力'</span>)
 plt。标题(<span class="token string">'温度与压力'</span>)
 plt。xticks(NP。人气指数(<span class="token number">0</span>,<span class="token number">6</span>,步骤<span class="token operator">=</span> <span class="token number">0.5</span>))
 PLT。显示<span class="token punctuation">(</span><span class="token punctuation">)</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/18e82ed8cbd74f4d9af8cd63bbac5b62.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/18e82ed8cbd74f4d9af8cd63bbac5b62.jpg" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第3张图片" width="386" height="278" style="border:1px solid black;"></a></p> 
  <p>使用 numpy 绘制高斯正态分布。如下所示,numpy 可以生成随机数。要创建随机样本,我们需要均值(mu)、sigma(标准差)、数据点数。</p> 
  <pre><code class="prism language-python">mu  <span class="token operator">=</span>  <span class="token number">28</span>
西格玛 <span class="token operator">=</span>  <span class="token number">15</span> 个
样本 <span class="token operator">=</span>  <span class="token number">100000</span>

x  <span class="token operator">=</span>  np。随机的。正常(mu <span class="token punctuation">,</span> sigma <span class="token punctuation">,</span>样本)
 ax  <span class="token operator">=</span>  sns。分布图<span class="token punctuation">(</span> x <span class="token punctuation">)</span><span class="token punctuation">;</span>
斧头。设置<span class="token punctuation">(</span> xlabel <span class="token operator">=</span> <span class="token string">"x"</span> <span class="token punctuation">,</span> ylabel <span class="token operator">=</span> <span class="token string">'y'</span> <span class="token punctuation">)</span>
 plt。显示<span class="token punctuation">(</span><span class="token punctuation">)</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/4beaadeb30ef4f14b25c73b5e0cbcdd3.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/4beaadeb30ef4f14b25c73b5e0cbcdd3.jpg" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第4张图片" width="400" height="261" style="border:1px solid black;"></a></p> 
  <p>总而言之,与 python 列表的主要区别是:</p> 
  <ol> 
   <li>数组支持向量化操作,而列表不支持。</li> 
   <li>一旦创建了数组,就不能更改其大小。您将不得不创建一个新阵列或覆盖现有阵列。</li> 
   <li>每个数组都有一个且只有一个 dtype。其中的所有项目都应该是那个 dtype。</li> 
   <li>等效的 numpy 数组比 Python 列表占用的空间少得多。</li> 
   <li>numpy 数组支持布尔索引。</li> 
  </ol> 
  <h1>第 25 天 - Pandas</h1> 
  <p>Pandas 是一种开源、高性能、易于使用的 Python 编程语言的数据结构和数据分析工具。Pandas 添加了数据结构和工具,旨在处理类似表格的数据,即Series和Data Frames。Pandas 提供了用于数据操作的工具:</p> 
  <ol> 
   <li>重塑</li> 
   <li>合并</li> 
   <li>排序</li> 
   <li>切片</li> 
   <li>聚合</li> 
   <li>插补。如果您使用的是 anaconda,则无需安装 pandas。</li> 
  </ol> 
  <h2>25.1安装pandas</h2> 
  <p>对于 Mac:</p> 
  <blockquote> 
   <p>pip 安装 conda<br> conda 安装 pandas</p> 
  </blockquote> 
  <p>对于 Windows:</p> 
  <blockquote> 
   <p>pip 安装 conda<br> pip 安装 熊猫</p> 
  </blockquote> 
  <p>Pandas 数据结构基于Series和DataFrames。</p> 
  <p>一个系列是一列和一个数据帧是一个多维表的集合组成系列。为了创建一个pandas系列,我们应该使用numpy来创建一个一维数组或一个python列表。让我们看一个系列的例子:</p> 
  <p>命名pandas系列</p> 
  <p><a href="http://img.e-com-net.com/image/info8/19e0a0c8fd64436bb4a872272a9c5091.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/19e0a0c8fd64436bb4a872272a9c5091.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第5张图片" width="231" height="194" style="border:1px solid black;"></a></p> 
  <p>国家系列<br> <a href="http://img.e-com-net.com/image/info8/8db922e9c7fb4b5298e62c103fa19cef.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/8db922e9c7fb4b5298e62c103fa19cef.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第6张图片" width="216" height="187" style="border:1px solid black;"></a></p> 
  <p>城市系列<br> <a href="http://img.e-com-net.com/image/info8/235d1178d6cb4fcfb62a0abc43f0621a.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/235d1178d6cb4fcfb62a0abc43f0621a.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第7张图片" width="218" height="193" style="border:1px solid black;"></a></p> 
  <p>如您所见,pandas 系列只是一列数据。如果我们想要多列,我们使用数据框。下面的示例显示了 Pandas DataFrames。</p> 
  <p>让我们看看一个 Pandas 数据框的例子:</p> 
  <p><a href="http://img.e-com-net.com/image/info8/f0e4c86ffb9c464fbab8280d8f3784d6.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/f0e4c86ffb9c464fbab8280d8f3784d6.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第8张图片" width="575" height="187" style="border:1px solid black;"></a></p> 
  <p>数据框是行和列的集合。看下表;它比上面的例子有更多的列:</p> 
  <p><a href="http://img.e-com-net.com/image/info8/19c548f8dfd64630a886bac657c6ac34.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/19c548f8dfd64630a886bac657c6ac34.jpg" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第9张图片" width="650" height="141" style="border:1px solid black;"></a></p> 
  <p>接下来,我们将看到如何导入pandas以及如何使用pandas创建Series和DataFrames</p> 
  <h2>导入pandas</h2> 
  <pre><code class="prism language-python"><span class="token keyword">import</span>  pandas  <span class="token keyword">as</span>  pd  <span class="token comment"># 将pandas 导入为pd </span>
<span class="token keyword">import</span>  numpy   <span class="token keyword">as</span>  np  <span class="token comment"># 将numpy 导入为np</span>
</code></pre> 
  <h2>25.2使用默认索引创建 Pandas 系列</h2> 
  <pre><code class="prism language-python">nums  <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span>
 s  <span class="token operator">=</span>  pd。系列<span class="token punctuation">(</span> nums <span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> s <span class="token punctuation">)</span>
    <span class="token number">0</span> <span class="token number">1</span>
    <span class="token number">1</span> <span class="token number">2</span>
    <span class="token number">2</span> <span class="token number">3</span>
    <span class="token number">3</span> <span class="token number">4</span>
    <span class="token number">4</span> <span class="token number">5</span>
    数据类型:int64
</code></pre> 
  <h2>25.3使用自定义索引创建 Pandas 系列</h2> 
  <pre><code class="prism language-python">nums  <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span>
 s  <span class="token operator">=</span>  pd。系列<span class="token punctuation">(</span> nums <span class="token punctuation">,</span> index <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">,</span> <span class="token number">4</span> <span class="token punctuation">,</span> <span class="token number">5</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> s <span class="token punctuation">)</span>
    <span class="token number">1</span> <span class="token number">1</span>
    <span class="token number">2</span> <span class="token number">2</span>
    <span class="token number">3</span> <span class="token number">3</span>
    <span class="token number">4</span> <span class="token number">4</span>
    <span class="token number">5</span> <span class="token number">5</span>
    数据类型:int64
水果 <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token string">'Orange'</span> <span class="token punctuation">,</span> <span class="token string">'Banana'</span> <span class="token punctuation">,</span> <span class="token string">'Mango'</span> <span class="token punctuation">]</span>
水果 <span class="token operator">=</span>  pd <span class="token punctuation">.</span> 系列(水果,指数<span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">1</span>,<span class="token number">2</span>,<span class="token number">3</span> <span class="token punctuation">]</span>)
打印(水果)
    <span class="token number">1</span> 橙色
    <span class="token number">2</span> 香蕉
    <span class="token number">3</span> 芒果
    数据类型:对象
</code></pre> 
  <h2>25.4从字典创建 Pandas 系列</h2> 
  <pre><code class="prism language-python">dct  <span class="token operator">=</span> <span class="token punctuation">{
     </span> <span class="token string">'name'</span> <span class="token punctuation">:</span> <span class="token string">'Asabeneh'</span> <span class="token punctuation">,</span> <span class="token string">'country'</span> <span class="token punctuation">:</span> <span class="token string">'芬兰'</span> <span class="token punctuation">,</span> <span class="token string">'city'</span> <span class="token punctuation">:</span> <span class="token string">'赫尔辛基'</span> <span class="token punctuation">}</span>
s  <span class="token operator">=</span>  pd。系列<span class="token punctuation">(</span> dct <span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> s <span class="token punctuation">)</span>
    姓名 Asabeneh
    国家芬兰
    赫尔辛基市
    数据类型:对象
</code></pre> 
  <h2>25.5创建一个常量 Pandas 系列</h2> 
  <pre><code class="prism language-python">s  <span class="token operator">=</span>  pd。系列<span class="token punctuation">(</span> <span class="token number">10</span> <span class="token punctuation">,</span> index  <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">,</span> <span class="token number">3</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> s <span class="token punctuation">)</span>
    <span class="token number">1</span> <span class="token number">10</span>
    <span class="token number">2</span> <span class="token number">10</span>
    <span class="token number">3</span> <span class="token number">10</span>
    数据类型:int64
</code></pre> 
  <h2>25.6使用 Linspace 创建 Pandas 系列</h2> 
  <pre><code class="prism language-python">s  <span class="token operator">=</span>  pd。系列<span class="token punctuation">(</span> np <span class="token punctuation">.</span> linspace <span class="token punctuation">(</span> <span class="token number">5</span> <span class="token punctuation">,</span> <span class="token number">20</span> <span class="token punctuation">,</span> <span class="token number">10</span> <span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token comment"># linspace(starting, end, items)</span>
打印<span class="token punctuation">(</span> s <span class="token punctuation">)</span>
    <span class="token number">0</span> <span class="token number">5.000000</span>
    <span class="token number">1</span> <span class="token number">6.666667</span>
    <span class="token number">2</span> <span class="token number">8.333333</span>
    <span class="token number">3</span> <span class="token number">10.000000</span>
    <span class="token number">4</span> <span class="token number">11.666667</span>
    <span class="token number">5</span> <span class="token number">13.333333</span>
    <span class="token number">6</span> <span class="token number">15.000000</span>
    <span class="token number">7</span> <span class="token number">16.666667</span>
    <span class="token number">8</span> <span class="token number">18.333333</span>
    <span class="token number">9</span> <span class="token number">20.000000</span>
    数据类型:float64
</code></pre> 
  <h2>25.7数据帧</h2> 
  <p>Pandas 数据框可以用不同的方式创建。</p> 
  <h3>从列表列表创建数据帧</h3> 
  <pre><code class="prism language-python">数据 <span class="token operator">=</span> <span class="token punctuation">[</span>
    <span class="token punctuation">[</span> “阿萨本尼”、“芬兰”、“赫尔辛克” <span class="token punctuation">]</span>、
    <span class="token punctuation">[</span> <span class="token string">'大卫'</span>,<span class="token string">'英国'</span>,<span class="token string">'伦敦'</span> <span class="token punctuation">]</span>,
    <span class="token punctuation">[</span> “约翰”、“瑞典”、“斯德哥尔摩” <span class="token punctuation">]</span>
<span class="token punctuation">]</span>
df  <span class="token operator">=</span>  pd。DataFrame <span class="token punctuation">(</span> data <span class="token punctuation">,</span> columns <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token string">'Names'</span> <span class="token punctuation">,</span> <span class="token string">'Country'</span> <span class="token punctuation">,</span> <span class="token string">'City'</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> df <span class="token punctuation">)</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/08ffd0f5c3b5428f9fa92173e70618cb.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/08ffd0f5c3b5428f9fa92173e70618cb.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第10张图片" width="318" height="183" style="border:1px solid black;"></a></p> 
  <h2>25.8使用字典创建 DataFrame</h2> 
  <pre><code class="prism language-python">data  <span class="token operator">=</span> <span class="token punctuation">{
     </span> <span class="token string">'Name'</span> <span class="token punctuation">:</span> <span class="token punctuation">[</span> <span class="token string">'Asabeneh'</span> <span class="token punctuation">,</span> <span class="token string">'David'</span> <span class="token punctuation">,</span> <span class="token string">'John'</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token string">'国家'</span> <span class="token punctuation">:</span><span class="token punctuation">[</span>
     <span class="token string">'芬兰'</span> <span class="token punctuation">,</span> <span class="token string">'英国'</span> <span class="token punctuation">,</span> <span class="token string">'瑞典'</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token string">'城市'</span> <span class="token punctuation">:</span> <span class="token punctuation">[</span> <span class="token string">'赫尔斯基'</span> <span class="token punctuation">,</span> <span class="token string">'伦敦'</span> <span class="token punctuation">,</span> <span class="token string">'斯德哥尔摩'</span> <span class="token punctuation">]</span><span class="token punctuation">}</span>
 df  <span class="token operator">=</span>  pd <span class="token punctuation">.</span> DataFrame(数据)
打印(df)
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/1642e89f3dfb4902813ec533108c725c.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/1642e89f3dfb4902813ec533108c725c.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第11张图片" width="310" height="179" style="border:1px solid black;"></a></p> 
  <h2>25.9从字典列表创建数据帧</h2> 
  <pre><code class="prism language-python">数据 <span class="token operator">=</span> <span class="token punctuation">[</span>
    <span class="token punctuation">{
     </span> <span class="token string">'姓名'</span> <span class="token punctuation">:</span> <span class="token string">'Asabeneh'</span> <span class="token punctuation">,</span> <span class="token string">'国家'</span> <span class="token punctuation">:</span> <span class="token string">'芬兰'</span> <span class="token punctuation">,</span> <span class="token string">'城市'</span> <span class="token punctuation">:</span> <span class="token string">'赫尔辛基'</span> <span class="token punctuation">}</span><span class="token punctuation">,</span>
    <span class="token punctuation">{
     </span> <span class="token string">'姓名'</span>:<span class="token string">'大卫'</span>,<span class="token string">'国家'</span>:<span class="token string">'英国'</span>,<span class="token string">'城市'</span>:<span class="token string">'伦敦'</span> <span class="token punctuation">}</span>,
    <span class="token punctuation">{
     </span> <span class="token string">'姓名'</span>:<span class="token string">'约翰'</span>,<span class="token string">'国家'</span>:<span class="token string">'瑞典'</span>,<span class="token string">'城市'</span>:<span class="token string">'斯德哥尔摩'</span> <span class="token punctuation">}</span><span class="token punctuation">]</span>
 df  <span class="token operator">=</span>  pd <span class="token punctuation">.</span> DataFrame(数据)
打印(df)
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/0ca80048917d463ca7389d8ca2710b2f.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/0ca80048917d463ca7389d8ca2710b2f.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第12张图片" width="321" height="189" style="border:1px solid black;"></a></p> 
  <h2>25.10使用 Pandas 读取 CSV 文件</h2> 
  <p>要下载 CSV 文件,本例中需要什么,控制台/命令行就足够了:</p> 
  <pre><code class="prism language-python">curl <span class="token operator">-</span>O https<span class="token punctuation">:</span><span class="token operator">//</span>raw<span class="token punctuation">.</span>githubusercontent<span class="token punctuation">.</span>com<span class="token operator">/</span>Asabeneh<span class="token operator">/</span><span class="token number">30</span><span class="token operator">-</span>Days<span class="token operator">-</span>Of<span class="token operator">-</span>Python<span class="token operator">/</span>master<span class="token operator">/</span>data<span class="token operator">/</span>weight<span class="token operator">-</span>height<span class="token punctuation">.</span>csv
</code></pre> 
  <p>将下载的文件放在您的工作目录中。</p> 
  <pre><code class="prism language-python">将 Pandas 导入为 pd

df  <span class="token operator">=</span>  pd。read_csv <span class="token punctuation">(</span> <span class="token string">'weight-height.csv'</span> <span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> df <span class="token punctuation">)</span>
</code></pre> 
  <h2>25.11数据探索</h2> 
  <p>让我们使用 head() 仅读取前 5 行</p> 
  <pre><code class="prism language-python"><span class="token keyword">print</span> <span class="token punctuation">(</span> df <span class="token punctuation">.</span> head <span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token comment"># 给五行我们可以通过将参数传递给 head() 方法来增加行数</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/98d513e77f6c4afe9dc991b46db2e120.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/98d513e77f6c4afe9dc991b46db2e120.jpg" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第13张图片" width="320" height="265" style="border:1px solid black;"></a><br> 让我们还使用 tail() 方法探索数据帧的最后记录。</p> 
  <pre><code class="prism language-python">打印(DF。尾()) #尾巴给最后的五排,我们可以通过传递参数尾法提高行
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/4c700cd50f20472492421d100efc1a39.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/4c700cd50f20472492421d100efc1a39.jpg" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第14张图片" width="362" height="266" style="border:1px solid black;"></a></p> 
  <p>如您所见,csv 文件有三行:性别、身高和体重。如果 DataFrame 有很长的行,就很难知道所有的列。因此,我们应该使用一种方法来知道列。我们不知道行数。让我们使用形状肉类。</p> 
  <pre><code class="prism language-python"><span class="token keyword">print</span> <span class="token punctuation">(</span> df <span class="token punctuation">.</span> shape <span class="token punctuation">)</span> <span class="token comment"># 如你所见 10000 行和三列</span>
<span class="token punctuation">(</span><span class="token number">10000</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">)</span>
</code></pre> 
  <p>让我们使用列获取所有列。</p> 
  <pre><code class="prism language-python">打印(df。列)
Index<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token string">'Gender'</span><span class="token punctuation">,</span> <span class="token string">'Height'</span><span class="token punctuation">,</span> <span class="token string">'Weight'</span><span class="token punctuation">]</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span><span class="token string">'object'</span><span class="token punctuation">)</span>
</code></pre> 
  <p>现在,让我们使用列键获取特定列</p> 
  <pre><code class="prism language-python">heights  <span class="token operator">=</span>  df <span class="token punctuation">[</span> <span class="token string">'Height'</span> <span class="token punctuation">]</span> <span class="token comment"># 这是一个系列</span>
打印(高度)
    <span class="token number">0</span> <span class="token number">73.847017</span>
    <span class="token number">1</span> <span class="token number">68.781904</span>
    <span class="token number">2</span> <span class="token number">74.110105</span>
    <span class="token number">3</span> <span class="token number">71.730978</span>
    <span class="token number">4</span> <span class="token number">69.881796</span>
              <span class="token punctuation">.</span><span class="token punctuation">.</span><span class="token punctuation">.</span>    
    <span class="token number">9995</span> <span class="token number">66.172652</span>
    <span class="token number">9996</span> <span class="token number">67.067155</span>
    <span class="token number">9997</span> <span class="token number">63.867992</span>
    <span class="token number">9998</span> <span class="token number">69.034243</span>
    <span class="token number">9999</span> <span class="token number">61.944246</span>
    名称:高度,长度:<span class="token number">10000</span>,数据类型:float64
weights  <span class="token operator">=</span>  df <span class="token punctuation">[</span> <span class="token string">'Weight'</span> <span class="token punctuation">]</span> <span class="token comment"># 这是一个系列</span>
打印(重量)
    <span class="token number">0</span> <span class="token number">241.893563</span>
    <span class="token number">1</span> <span class="token number">162.310473</span>
    <span class="token number">2</span> <span class="token number">212.740856</span>
    <span class="token number">3</span> <span class="token number">220.042470</span>
    <span class="token number">4</span> <span class="token number">206.349801</span>
               <span class="token punctuation">.</span><span class="token punctuation">.</span><span class="token punctuation">.</span>    
    <span class="token number">9995</span> <span class="token number">136.777454</span>
    <span class="token number">9996</span> <span class="token number">170.867906</span>
    <span class="token number">9997</span> <span class="token number">128.475319</span>
    <span class="token number">9998</span> <span class="token number">163.852461</span>
    <span class="token number">9999</span> <span class="token number">113.649103</span>
    名称:重量,长度:<span class="token number">10000</span>,dtype:float64
打印(<span class="token builtin">len</span>(高度)<span class="token operator">==</span>  <span class="token builtin">len</span>(权重))
<span class="token boolean">True</span>
</code></pre> 
  <p>describe() 方法提供数据集的描述性统计值。</p> 
  <pre><code class="prism language-python"><span class="token keyword">print</span> <span class="token punctuation">(</span> heights <span class="token punctuation">.</span> describe <span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token comment"># 给出高度数据的统计信息</span>
    数 <span class="token number">10000.000000</span>
    平均 <span class="token number">66.367560</span>
    标准 <span class="token number">3.847528</span>
    分钟 <span class="token number">54.263133</span>
    <span class="token number">25</span><span class="token operator">%</span> <span class="token number">63.505620</span>
    <span class="token number">50</span><span class="token operator">%</span> <span class="token number">66.318070</span>
    <span class="token number">75</span><span class="token operator">%</span> <span class="token number">69.174262</span>
    最大 <span class="token number">78.998742</span>
    名称:高度,数据类型:float64
打印(权重。描述())
    数 <span class="token number">10000.000000</span>
    平均 <span class="token number">161.440357</span>
    标准 <span class="token number">32.108439</span>
    最低 <span class="token number">64.700127</span>
    <span class="token number">25</span><span class="token operator">%</span> <span class="token number">135.818051</span>
    <span class="token number">50</span><span class="token operator">%</span> <span class="token number">161.212928</span>
    <span class="token number">75</span><span class="token operator">%</span> <span class="token number">187.169525</span>
    最大 <span class="token number">269.989699</span>
    名称:重量,数据类型:float64
</code></pre> 
  <pre><code class="prism language-python"><span class="token keyword">print</span> <span class="token punctuation">(</span> df <span class="token punctuation">.</span> describe <span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>   <span class="token comment"># describe 还可以给出来自数据帧的统计信息</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/1273783618524e1a802d91824d22d723.jpg" target="_blank"><img src="http://img.e-com-net.com/image/info8/1273783618524e1a802d91824d22d723.jpg" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第15张图片" width="363" height="379" style="border:1px solid black;"></a></p> 
  <p>与 describe() 类似,info() 方法也提供有关数据集的信息。</p> 
  <h2>25.12修改数据帧</h2> 
  <p>修改DataFrame: * 我们可以创建一个新的DataFrame * 我们可以创建一个新列并将其添加到DataFrame, * 我们可以从DataFrame 中删除现有列, * 我们可以修改DataFrame 中的现有列, * 我们可以更改 DataFrame 中列值的数据类型</p> 
  <h2>25.13创建数据帧</h2> 
  <p>与往常一样,首先我们导入必要的包。现在,让我们导入 pandas 和 numpy,这两个最好的朋友。</p> 
  <pre><code class="prism language-python">将Pandas 导入为 pd
将 numpy 导入为 np
数据 <span class="token operator">=</span> <span class="token punctuation">[</span>
    <span class="token punctuation">{
     </span> “姓名”:“阿萨本尼”,“国家”:“芬兰”,“城市”:“赫尔辛基” <span class="token punctuation">}</span>,
    <span class="token punctuation">{
     </span> “姓名”:“大卫”,“国家”:“英国”,“城市”:“伦敦” <span class="token punctuation">}</span>,
    <span class="token punctuation">{
     </span> “姓名”:“约翰”,“国家”:“瑞典”,“城市”:“斯德哥尔摩” <span class="token punctuation">}</span><span class="token punctuation">]</span>
 df  <span class="token operator">=</span>  pd <span class="token punctuation">.</span> DataFrame(数据)
打印(df)
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/05b3a63f2c704a669413facb9e76fa01.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/05b3a63f2c704a669413facb9e76fa01.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第16张图片" width="315" height="179" style="border:1px solid black;"></a></p> 
  <p>向 DataFrame 添加列就像向字典添加键。</p> 
  <p>首先让我们使用前面的示例来创建一个 DataFrame。创建 DataFrame 后,我们将开始修改列和列值。</p> 
  <h2>25.14添加新列</h2> 
  <p>让我们在 DataFrame 中添加一个权重列</p> 
  <pre><code class="prism language-python">权重 <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">74</span> <span class="token punctuation">,</span> <span class="token number">78</span> <span class="token punctuation">,</span> <span class="token number">69</span> <span class="token punctuation">]</span>
 df <span class="token punctuation">[</span> <span class="token string">'Weight'</span> <span class="token punctuation">]</span> <span class="token operator">=</span> 权重
df
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/6c8a658531184986a0c314192f72d2ea.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/6c8a658531184986a0c314192f72d2ea.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第17张图片" width="384" height="183" style="border:1px solid black;"></a></p> 
  <p>让我们在 DataFrame 中添加一个高度列</p> 
  <pre><code class="prism language-python">高度 <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">173</span> <span class="token punctuation">,</span> <span class="token number">175</span> <span class="token punctuation">,</span> <span class="token number">169</span> <span class="token punctuation">]</span>
 df <span class="token punctuation">[</span> <span class="token string">'高度'</span> <span class="token punctuation">]</span> <span class="token operator">=</span> 高度
打印<span class="token punctuation">(</span> df <span class="token punctuation">)</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/2ea3d6ffbdf64359b0fd09a213be9da9.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/2ea3d6ffbdf64359b0fd09a213be9da9.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第18张图片" width="427" height="177" style="border:1px solid black;"></a></p> 
  <p>正如您在上面的 DataFrame 中看到的,我们确实添加了新的列,重量和高度。让我们通过使用他们的体重和身高计算他们的 BMI 来添加一个额外的列,称为 BMI(身体质量指数)。BMI 是质量除以身高的平方(以米为单位)- 体重/身高 * 身高。</p> 
  <p>如您所见,高度以厘米为单位,因此我们应该将其更改为米。让我们修改高度行。</p> 
  <h2>25.15修改列值</h2> 
  <pre><code class="prism language-python">df <span class="token punctuation">[</span> <span class="token string">'高度'</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  df <span class="token punctuation">[</span> <span class="token string">'高度'</span> <span class="token punctuation">]</span> <span class="token operator">*</span>  <span class="token number">0.01</span> 
df
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/f68103bbf35e4fcca6f64b2c76f723f4.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/f68103bbf35e4fcca6f64b2c76f723f4.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第19张图片" width="432" height="179" style="border:1px solid black;"></a></p> 
  <pre><code class="prism language-python"><span class="token comment"># 使用函数使我们的代码更简洁,但是你可以不用一个来计算 bmi </span>
<span class="token keyword">def</span>  <span class="token function">calculate_bmi</span> <span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
     weights  <span class="token operator">=</span>  df <span class="token punctuation">[</span> <span class="token string">'Weight'</span> <span class="token punctuation">]</span>
     heights  <span class="token operator">=</span>  df <span class="token punctuation">[</span> <span class="token string">'Height'</span> <span class="token punctuation">]</span>
     bmi  <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
     <span class="token keyword">for</span>  w <span class="token punctuation">,</span> h  <span class="token keyword">in</span>  <span class="token builtin">zip</span> <span class="token punctuation">(</span> weights <span class="token punctuation">,</span>高度<span class="token punctuation">)</span><span class="token punctuation">:</span>
         b  <span class="token operator">=</span>  w <span class="token operator">/</span> <span class="token punctuation">(</span> h <span class="token operator">*</span> h <span class="token punctuation">)</span>
         bmi。追加<span class="token punctuation">(</span> b <span class="token punctuation">)</span>
    返回 体重指数
    
bmi  <span class="token operator">=</span>  calculate_bmi <span class="token punctuation">(</span><span class="token punctuation">)</span>
df <span class="token punctuation">[</span> <span class="token string">'BMI'</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  bmi 
df
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/2ce68f9f385242a9872fc644154294ef.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/2ce68f9f385242a9872fc644154294ef.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第20张图片" width="527" height="183" style="border:1px solid black;"></a></p> 
  <h2>25.16格式化 DataFrame 列</h2> 
  <p>DataFrame 的 BMI 列值是浮点数,小数点后有许多有效数字。让我们将其更改为一位有效数字。</p> 
  <pre><code class="prism language-python">df <span class="token punctuation">[</span> <span class="token string">'BMI'</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  <span class="token builtin">round</span> <span class="token punctuation">(</span> df <span class="token punctuation">[</span> <span class="token string">'BMI'</span> <span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">)</span>
打印<span class="token punctuation">(</span> df <span class="token punctuation">)</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/f6cb4ab0ebcc43dbbcb8ba57d3d0435f.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/f6cb4ab0ebcc43dbbcb8ba57d3d0435f.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第21张图片" width="518" height="186" style="border:1px solid black;"></a></p> 
  <p>DataFrame 中的信息似乎还没有完成,让我们添加出生年份和当前年份列。</p> 
  <pre><code class="prism language-python">birth_year  <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token string">'1769'</span> <span class="token punctuation">,</span> <span class="token string">'1985'</span> <span class="token punctuation">,</span> <span class="token string">'1990'</span> <span class="token punctuation">]</span>
 current_year  <span class="token operator">=</span>  pd。系列<span class="token punctuation">(</span> <span class="token number">2020</span> <span class="token punctuation">,</span> index <span class="token operator">=</span> <span class="token punctuation">[</span> <span class="token number">0</span> <span class="token punctuation">,</span> <span class="token number">1</span> <span class="token punctuation">,</span> <span class="token number">2</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
 df <span class="token punctuation">[</span> <span class="token string">'Birth Year'</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  birth_year 
df <span class="token punctuation">[</span> <span class="token string">'Current Year'</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  current_year 
df
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/0210e1481554450a86dd2835b29211b6.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/0210e1481554450a86dd2835b29211b6.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第22张图片" width="676" height="183" style="border:1px solid black;"></a></p> 
  <h2>25.17检查列值的数据类型</h2> 
  <pre><code class="prism language-python">打印(DF,重量,D型)
    数据类型(<span class="token string">' int64 '</span>)
df <span class="token punctuation">[</span> <span class="token string">'出生年份'</span> <span class="token punctuation">]</span>。dtype  <span class="token comment"># 它给出字符串对象,我们应该将其更改为数字</span>
df <span class="token punctuation">[</span> <span class="token string">'出生年份'</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  df <span class="token punctuation">[</span> <span class="token string">'出生年份'</span> <span class="token punctuation">]</span>。astype <span class="token punctuation">(</span> <span class="token string">'int'</span> <span class="token punctuation">)</span>
 <span class="token keyword">print</span> <span class="token punctuation">(</span> df <span class="token punctuation">[</span> <span class="token string">'Birth Year'</span> <span class="token punctuation">]</span><span class="token punctuation">.</span> dtype <span class="token punctuation">)</span> <span class="token comment"># 现在检查数据类型</span>
    数据类型(<span class="token string">' int32 '</span>)
</code></pre> 
  <p>现在与当年相同:</p> 
  <pre><code class="prism language-python">df <span class="token punctuation">[</span> <span class="token string">'当年'</span> <span class="token punctuation">]</span> <span class="token operator">=</span>  df <span class="token punctuation">[</span> <span class="token string">'当年'</span> <span class="token punctuation">]</span>。astype <span class="token punctuation">(</span> <span class="token string">'int'</span> <span class="token punctuation">)</span>
 df <span class="token punctuation">[</span> <span class="token string">'本年'</span> <span class="token punctuation">]</span>。数据类型
    数据类型(<span class="token string">' int32 '</span>)
</code></pre> 
  <p>现在,出生年份和当前年份的列值为整数。我们可以计算年龄。</p> 
  <pre><code class="prism language-python">年龄 <span class="token operator">=</span>  df <span class="token punctuation">[</span> <span class="token string">'本年'</span> <span class="token punctuation">]</span> <span class="token operator">-</span>  df <span class="token punctuation">[</span> <span class="token string">'出生年份'</span> <span class="token punctuation">]</span>
年龄
<span class="token number">0</span>    <span class="token number">251</span>
<span class="token number">1</span>     <span class="token number">35</span>
<span class="token number">2</span>     <span class="token number">30</span>
dtype<span class="token punctuation">:</span> int32
df <span class="token punctuation">[</span> <span class="token string">'年龄'</span> <span class="token punctuation">]</span> <span class="token operator">=</span> 年龄
打印(df)
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/061b31948bdf4d0e8864011ad4568c39.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/061b31948bdf4d0e8864011ad4568c39.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第23张图片" width="738" height="176" style="border:1px solid black;"></a></p> 
  <p>第一排的人迄今活了251岁。一个人不可能活这么久。要么是打字错误,要么是数据被煮熟了。因此,让我们用列的平均值填充该数据,而不包括异常值。</p> 
  <p>平均值 = (35 + 30)/ 2</p> 
  <pre><code class="prism language-python">mean  <span class="token operator">=</span> <span class="token punctuation">(</span> <span class="token number">35</span>  <span class="token operator">+</span>  <span class="token number">30</span> <span class="token punctuation">)</span> <span class="token operator">/</span>  <span class="token number">2</span> 
<span class="token keyword">print</span> <span class="token punctuation">(</span> <span class="token string">'Mean: '</span> <span class="token punctuation">,</span> mean <span class="token punctuation">)</span>	 <span class="token comment">#在输出中添加一些描述很好,所以我们知道什么是什么</span>
   平均值:<span class="token number">32.5</span>
</code></pre> 
  <h2>25.18布尔索引</h2> 
  <pre><code class="prism language-python">打印<span class="token punctuation">(</span> df <span class="token punctuation">[</span> df <span class="token punctuation">[</span> <span class="token string">'年龄'</span> <span class="token punctuation">]</span> <span class="token operator">></span>  <span class="token number">120</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/7e42bd636831415bb23142c377e2841d.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/7e42bd636831415bb23142c377e2841d.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第24张图片" width="723" height="102" style="border:1px solid black;"></a></p> 
  <pre><code class="prism language-python">打印<span class="token punctuation">(</span> df <span class="token punctuation">[</span> df <span class="token punctuation">[</span> <span class="token string">'年龄'</span> <span class="token punctuation">]</span> <span class="token operator"><</span>  <span class="token number">120</span> <span class="token punctuation">]</span><span class="token punctuation">)</span>
</code></pre> 
  <p><a href="http://img.e-com-net.com/image/info8/9306ffb6ee504946a64db9c620acc483.png" target="_blank"><img src="http://img.e-com-net.com/image/info8/9306ffb6ee504946a64db9c620acc483.png" alt="简洁易懂,初学者挑战学习Python编程30天 (四)_第25张图片" width="698" height="147" style="border:1px solid black;"></a></p> 
  <p>初学者挑战学习Python编程30天还有最后一节续集就要结束了,感兴趣了解下面的学习内容,记得关注我。</p> 
 </div> 
</div>
                            </div>
                        </div>
                    </div>
                    <!--PC和WAP自适应版-->
                    <div id="SOHUCS" sid="1445164669760221184"></div>
                    <script type="text/javascript" src="/views/front/js/chanyan.js"></script>
                    <!-- 文章页-底部 动态广告位 -->
                    <div class="youdao-fixed-ad" id="detail_ad_bottom"></div>
                </div>
                <div class="col-md-3">
                    <div class="row" id="ad">
                        <!-- 文章页-右侧1 动态广告位 -->
                        <div id="right-1" class="col-lg-12 col-md-12 col-sm-4 col-xs-4 ad">
                            <div class="youdao-fixed-ad" id="detail_ad_1"> </div>
                        </div>
                        <!-- 文章页-右侧2 动态广告位 -->
                        <div id="right-2" class="col-lg-12 col-md-12 col-sm-4 col-xs-4 ad">
                            <div class="youdao-fixed-ad" id="detail_ad_2"></div>
                        </div>
                        <!-- 文章页-右侧3 动态广告位 -->
                        <div id="right-3" class="col-lg-12 col-md-12 col-sm-4 col-xs-4 ad">
                            <div class="youdao-fixed-ad" id="detail_ad_3"></div>
                        </div>
                    </div>
                </div>
            </div>
        </div>
    </div>
    <div class="container">
        <h4 class="pt20 mb15 mt0 border-top">你可能感兴趣的:(Python编程30天,python,经验分享,初学者挑战)</h4>
        <div id="paradigm-article-related">
            <div class="recommend-post mb30">
                <ul class="widget-links">
                    <li><a href="/article/1939446520067715072.htm"
                           title="列表反转:reverse() 方法的深度剖析" target="_blank">列表反转:reverse() 方法的深度剖析</a>
                        <span class="text-muted">测试者家园</span>
<a class="tag" taget="_blank" href="/search/%E6%B5%8B%E8%AF%95%E5%BC%80%E5%8F%91%E5%92%8C%E6%B5%8B%E8%AF%95/1.htm">测试开发和测试</a><a class="tag" taget="_blank" href="/search/Python/1.htm">Python</a><a class="tag" taget="_blank" href="/search/%E9%9B%B6%E5%9F%BA%E7%A1%80%E5%AD%A6Python/1.htm">零基础学Python</a><a class="tag" taget="_blank" href="/search/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD/1.htm">人工智能</a><a class="tag" taget="_blank" href="/search/Python/1.htm">Python</a><a class="tag" taget="_blank" href="/search/%E9%9B%B6%E5%9F%BA%E7%A1%80%E5%AD%A6Python/1.htm">零基础学Python</a><a class="tag" taget="_blank" href="/search/%E9%9B%B6%E5%9F%BA%E7%A1%80/1.htm">零基础</a><a class="tag" taget="_blank" href="/search/%E8%81%8C%E5%9C%BA%E5%92%8C%E5%8F%91%E5%B1%95/1.htm">职场和发展</a><a class="tag" taget="_blank" href="/search/%E8%BD%AF%E4%BB%B6%E5%BC%80%E5%8F%91%E5%92%8C%E6%B5%8B%E8%AF%95/1.htm">软件开发和测试</a><a class="tag" taget="_blank" href="/search/%E6%99%BA%E8%83%BD%E5%8C%96%E6%B5%8B%E8%AF%95/1.htm">智能化测试</a>
                        <div>数据结构的基本操作始终是打牢编程基础的关键。而在对列表(list)这一核心数据结构的操作中,反转(reversing)是一项既常用又容易被低估的重要操作。Python提供了原地反转的reverse()方法,与返回新序列的切片[::-1]或内置函数reversed()形成了鲜明对比。本文将全面剖析list.reverse()方法,从其语义、实现机制、适用场景,到其在测试、开发与自动化中的实际运用,力</div>
                    </li>
                    <li><a href="/article/1939446393630420992.htm"
                           title="Python dlib(HOG+SVM)人脸识别总结" target="_blank">Python dlib(HOG+SVM)人脸识别总结</a>
                        <span class="text-muted">程序媛一枚~</span>
<a class="tag" taget="_blank" href="/search/%E4%BA%BA%E8%84%B8%E8%AF%86%E5%88%AB/1.htm">人脸识别</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E6%94%AF%E6%8C%81%E5%90%91%E9%87%8F%E6%9C%BA/1.htm">支持向量机</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a><a class="tag" taget="_blank" href="/search/%E8%AF%BB%E4%B9%A6%E7%AC%94%E8%AE%B0/1.htm">读书笔记</a><a class="tag" taget="_blank" href="/search/%E4%BA%BA%E8%84%B8%E6%A3%80%E6%B5%8B%E8%AF%86%E5%88%AB/1.htm">人脸检测识别</a>
                        <div>Pythondlib(HOG+SVM)人脸识别总结面部标志检测dlib68点(HOG+SVM),194点人脸识别模型,包括口(外嘴唇,内嘴唇),鼻,眉毛(左右眉),眼睛(左右眼),下鄂5点面部标志检测器(左眼2点,右眼2点,鼻子1点)面部对齐更高效眨眼检测ear眨眼瞬间达到0疲劳驾驶检测—连续帧ear面部对齐眼睛连线反正切获取旋转角度,期望图像眼睛横长度计算比率左眼计算右眼相对坐标眼睛横中心点作为</div>
                    </li>
                    <li><a href="/article/1939444251884580864.htm"
                           title="Python开发从新手到专家:第十四章 面向对象( OOP) 程序设计" target="_blank">Python开发从新手到专家:第十四章 面向对象( OOP) 程序设计</a>
                        <span class="text-muted">caifox菜狐狸</span>
<a class="tag" taget="_blank" href="/search/Python%E5%BC%80%E5%8F%91%E4%BB%8E%E6%96%B0%E6%89%8B%E5%88%B0%E4%B8%93%E5%AE%B6/1.htm">Python开发从新手到专家</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/OOP/1.htm">OOP</a><a class="tag" taget="_blank" href="/search/%E9%9D%A2%E5%90%91%E5%AF%B9%E8%B1%A1/1.htm">面向对象</a><a class="tag" taget="_blank" href="/search/%E7%B1%BB/1.htm">类</a><a class="tag" taget="_blank" href="/search/%E7%BB%A7%E6%89%BF/1.htm">继承</a><a class="tag" taget="_blank" href="/search/%E5%A4%9A%E6%80%81/1.htm">多态</a><a class="tag" taget="_blank" href="/search/%E9%9D%99%E6%80%81%E6%96%B9%E6%B3%95/1.htm">静态方法</a>
                        <div>在Python开发的旅程中,我们已经探索了诸多基础概念与实用技巧,从简单的变量赋值到复杂的函数嵌套,每一步都为构建更强大的程序奠定了坚实的基础。如今,我们即将踏入一个全新的领域——面向对象程序设计(OOP)。这一章将带你领略OOP的独特魅力,它不仅是一种编程范式,更是一种全新的思考问题和解决问题的方式。面向对象程序设计的核心在于“对象”和“类”。通过将数据和操作数据的方法封装在一起,我们可以构建出</div>
                    </li>
                    <li><a href="/article/1939441479227338752.htm"
                           title="用 Python 打造立体数据世界:3D 堆叠条形图绘制全解析" target="_blank">用 Python 打造立体数据世界:3D 堆叠条形图绘制全解析</a>
                        <span class="text-muted">Code_Verse</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E7%A7%91%E7%A0%94/1.htm">科研</a><a class="tag" taget="_blank" href="/search/%E7%BB%98%E5%9B%BE/1.htm">绘图</a>
                        <div>在数据可视化的工具箱里,3D图表总能带来眼前一亮的效果——它突破了二维平面的限制,用立体空间展示多维度数据关系,让复杂的数据层级一目了然。今天我们要解锁的「3D堆叠条形图」,就是一种能同时呈现类别、子类别、数值大小的强大可视化工具,特别适合展示具有分层结构的数据。无论是商业报表中的多维度业绩分析,还是科研数据中的多指标对比,它都能让你的数据呈现瞬间高级起来~为什么选择3D堆叠条形图?先聊聊这种图表</div>
                    </li>
                    <li><a href="/article/1939439462194933760.htm"
                           title="微服务架构实战:案例分析与解决方案探讨" target="_blank">微服务架构实战:案例分析与解决方案探讨</a>
                        <span class="text-muted">野老杂谈</span>
<a class="tag" taget="_blank" href="/search/%E5%BE%AE%E6%9C%8D%E5%8A%A1/1.htm">微服务</a>
                        <div>摘要微服务架构以其模块化和灵活性在软件开发领域迅速崛起。然而,这种架构模式并非没有挑战。本文通过深入分析几个实际的微服务项目案例,探讨了在设计、开发和部署过程中遇到的问题,以及相应的解决方案。同时,文章还展示了微服务架构如何提升系统的可维护性和可扩展性,为读者提供了宝贵的第一手经验和最佳实践。引言微服务架构通过将应用程序拆分为一系列小型、松散耦合的服务,每个服务实现特定功能,并通过轻量级通信机制相</div>
                    </li>
                    <li><a href="/article/1939438831090593792.htm"
                           title="python爬取京东图片" target="_blank">python爬取京东图片</a>
                        <span class="text-muted">通信小小白</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E7%88%AC%E8%99%AB/1.htm">爬虫</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E7%88%AC%E8%99%AB/1.htm">爬虫</a><a class="tag" taget="_blank" href="/search/%E5%9B%BE%E7%89%87/1.htm">图片</a>
                        <div>网上的淘宝爬取图片的代码一般都已经不能实际运行了,在查看淘宝网源代码是找不到图片源地址,估计采取了反爬技术。又去京东看了下,发现很容易爬取。根据下面网址构建urlhttps://list.jd.com/list.html?cat=670%2C671%2C1105&go=0https://list.jd.com/list.html?cat=670,671,1105&page=2&sort=sort_</div>
                    </li>
                    <li><a href="/article/1939438831992369152.htm"
                           title="数据图的类型以及如何在 Python 中创建和自定义" target="_blank">数据图的类型以及如何在 Python 中创建和自定义</a>
                        <span class="text-muted">唐 城</span>
<a class="tag" taget="_blank" href="/search/%E5%94%90%E5%9F%8E/1.htm">唐城</a><a class="tag" taget="_blank" href="/search/%E5%A5%87%E5%A6%99%E4%B9%8B%E6%97%85-GIS/1.htm">奇妙之旅-GIS</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E4%BF%A1%E6%81%AF%E5%8F%AF%E8%A7%86%E5%8C%96/1.htm">信息可视化</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90/1.htm">数据分析</a>
                        <div>有人说:一个人从1岁活到80岁很平凡,但如果从80岁倒着活,那么一半以上的人都可能不凡。生活没有捷径,我们踩过的坑都成为了生活的经验,这些经验越早知道࿰</div>
                    </li>
                    <li><a href="/article/1939438075218292736.htm"
                           title="探索PyRDP:远程桌面协议的瑞士军刀" target="_blank">探索PyRDP:远程桌面协议的瑞士军刀</a>
                        <span class="text-muted">彭宏彬</span>

                        <div>探索PyRDP:远程桌面协议的瑞士军刀pyrdpRDPmonster-in-the-middle(mitm)andlibraryforPythonwiththeabilitytowatchconnectionsliveorafterthefact项目地址:https://gitcode.com/gh_mirrors/py/pyrdp在网络安全领域,攻防两端的对决不断推动着工具的创新。今天,让我们聚</div>
                    </li>
                    <li><a href="/article/1939437695847690240.htm"
                           title="第一章 城市道路工程" target="_blank">第一章 城市道路工程</a>
                        <span class="text-muted">泽克</span>
<a class="tag" taget="_blank" href="/search/%E4%B8%80%E5%BB%BA%E5%B8%82%E6%94%BF%E5%AE%9E%E5%8A%A1/1.htm">一建市政实务</a><a class="tag" taget="_blank" href="/search/%E7%AC%94%E8%AE%B0/1.htm">笔记</a>
                        <div>1.城市道路工程1.1道路结构特征1.城镇道路分类道路网地位、交通功能、对沿线服务功能划分快速路水泥30沥青20砌块混凝土10,石材20完全交通功能服务,必须有中央分隔带主干路水泥30沥青20砌块混凝土10,石材20交通功能为主,连接主要干路,城市道路网的主要骨架,应有中央分隔带次干路水泥20沥青15砌块混凝土10,石材20兼有服务功能,组成干路网,区域交通集散支路水泥20沥青10砌块混凝土10,</div>
                    </li>
                    <li><a href="/article/1939437443778408448.htm"
                           title="第一章 城镇道路工程" target="_blank">第一章 城镇道路工程</a>
                        <span class="text-muted"></span>

                        <div>1.1道路结构特征1.城镇道路分类根据道路在道路网的地位、交通功能、对沿线的服务功能划分*快速路60~100>=43.5-3.75必须有分隔带双、四幅路20年完全交通功能服务*主干路40~60>=43.25-3.5应设三、四幅路20年交通功能为主、城市道路网主要骨架*次干路30~502-43.25-3.5可设单、双幅路15年区域性的交通干道,*支路20~4023.25-3.5不设单幅路10-15年</div>
                    </li>
                    <li><a href="/article/1939436436046540800.htm"
                           title="python采集淘宝评论,API接口丨json数据示例参考" target="_blank">python采集淘宝评论,API接口丨json数据示例参考</a>
                        <span class="text-muted">ID_18007905473</span>
<a class="tag" taget="_blank" href="/search/API/1.htm">API</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E5%A4%A7%E6%95%B0%E6%8D%AE/1.htm">大数据</a><a class="tag" taget="_blank" href="/search/json/1.htm">json</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a>
                        <div>在Python中采集淘宝商品评论数据,通常需要通过淘宝开放平台提供的API接口来实现。然而,淘宝开放平台并没有直接提供公开的评论API接口,因此需要通过其他方式间接获取评论数据。以下是一个使用Python通过网页爬虫技术获取淘宝商品评论数据的示例。请注意,这个示例仅用于学习和研究目的,请确保遵守淘宝的使用条款和相关法律法规。示例代码importrequestsfrombs4importBeauti</div>
                    </li>
                    <li><a href="/article/1939436308183183360.htm"
                           title="Python采集京东商品详情数据API接口概述及JSON数据格式参考" target="_blank">Python采集京东商品详情数据API接口概述及JSON数据格式参考</a>
                        <span class="text-muted">ID_18007905473</span>
<a class="tag" taget="_blank" href="/search/API/1.htm">API</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E5%89%8D%E7%AB%AF/1.htm">前端</a><a class="tag" taget="_blank" href="/search/%E6%9C%8D%E5%8A%A1%E5%99%A8/1.htm">服务器</a><a class="tag" taget="_blank" href="/search/json/1.htm">json</a>
                        <div>前言一、京东商品详情API接口概述京东开放平台提供了多种API接口,允许开发者通过编程方式获取商品详情数据。以下是常见的接口类型及功能:商品基础信息接口接口名称:jd.union.open.goods.query功能:获取商品标题、价格、图片、库存等基础信息。适用场景:商品列表展示、价格监控等。商品详情接口接口名称:jd.union.open.goods.detail.query功能:获取商品详细</div>
                    </li>
                    <li><a href="/article/1939436180357574656.htm"
                           title="Python采集京东商品详情API接口概述" target="_blank">Python采集京东商品详情API接口概述</a>
                        <span class="text-muted">ID_18007905473</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/PHP/1.htm">PHP</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E5%BA%93/1.htm">数据库</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a>
                        <div>前言京东开放平台提供了多种API接口用于获取商品详情信息,以下是主要的API接口概述及Python采集示例。一、主要商品详情API接口1.商品基础信息接口接口名称:jd.union.open.goods.query功能:获取商品标题、价格、图片、库存等基础信息2.商品详情接口接口名称:jd.union.open.goods.detail.query功能:获取商品详细描述、规格参数、售后政策等丰富信</div>
                    </li>
                    <li><a href="/article/1939436181192241152.htm"
                           title="Python采集淘宝商品评论API接口概述,json格式数据参考" target="_blank">Python采集淘宝商品评论API接口概述,json格式数据参考</a>
                        <span class="text-muted">ID_18007905473</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/API/1.htm">API</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/json/1.htm">json</a><a class="tag" taget="_blank" href="/search/%E5%89%8D%E7%AB%AF/1.htm">前端</a>
                        <div>一、淘宝商品评论API接口概述淘宝开放平台提供了taobao.item.reviews.get接口,用于获取指定商品的评论数据。该接口支持分页查询、多条件筛选(如时间范围、评分等级)和自定义返回字段,适用于电商数据分析、竞品研究和用户行为洞察等场景。核心功能:分页获取评论:支持通过page_no和page_size参数控制返回数据的分页。多维度筛选:可按时间范围(start_date、end_da</div>
                    </li>
                    <li><a href="/article/1939434668801716224.htm"
                           title="基于Python的京东商品信息采集实战:用Playwright+Pandas打造高效数据抓取工具" target="_blank">基于Python的京东商品信息采集实战:用Playwright+Pandas打造高效数据抓取工具</a>
                        <span class="text-muted">Python爬虫项目</span>
<a class="tag" taget="_blank" href="/search/2025%E5%B9%B4%E7%88%AC%E8%99%AB%E5%AE%9E%E6%88%98%E9%A1%B9%E7%9B%AE/1.htm">2025年爬虫实战项目</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/pandas/1.htm">pandas</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a><a class="tag" taget="_blank" href="/search/%E7%88%AC%E8%99%AB/1.htm">爬虫</a><a class="tag" taget="_blank" href="/search/%E6%B8%B8%E6%88%8F/1.htm">游戏</a><a class="tag" taget="_blank" href="/search/%E7%AC%94%E8%AE%B0/1.htm">笔记</a>
                        <div>一、项目背景与目标在当今电商生态中,价格、销量、评论等商品信息对用户和商家来说至关重要。无论是做数据分析、电商监控,还是构建商品推荐系统,第一步都是:获取真实的商品数据。本项目以京东商城搜索结果页为目标,通过构建一个高效、可复用的商品信息采集爬虫系统,实现对商品名称、价格、店铺、评论数、链接等核心信息的提取。二、技术路线概述我们采用如下技术架构:模块技术选型浏览器自动化Playwright(现代、</div>
                    </li>
                    <li><a href="/article/1939434667979632640.htm"
                           title="Python爬虫:爬取物流公司运输数据与包裹跟踪信息" target="_blank">Python爬虫:爬取物流公司运输数据与包裹跟踪信息</a>
                        <span class="text-muted">Python爬虫项目</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E7%88%AC%E8%99%AB/1.htm">爬虫</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E6%8C%96%E6%8E%98/1.htm">数据挖掘</a><a class="tag" taget="_blank" href="/search/%E6%97%85%E6%B8%B8/1.htm">旅游</a>
                        <div>一、前言随着电商行业的蓬勃发展,物流服务已成为不可或缺的一部分。消费者对物流运输状态的关注越来越高,实时查询包裹的运输进度成为日常生活的一部分。物流公司爬虫正是为了自动化获取物流公司的运输数据和包裹的跟踪信息,帮助消费者、商家以及物流公司本身进行数据分析、优化物流链条和提高客户体验。本文将详细介绍如何使用Python爬虫从多个物流公司网站或API接口中抓取运输数据、包裹跟踪信息以及相关的统计分析数</div>
                    </li>
                    <li><a href="/article/1939433533990825984.htm"
                           title="Python采集京东商品API接口概述及JSON格式数据参考" target="_blank">Python采集京东商品API接口概述及JSON格式数据参考</a>
                        <span class="text-muted">ID_18007905473</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/API/1.htm">API</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E6%8D%AE%E5%BA%93/1.htm">数据库</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a>
                        <div>前言一、接口概述京东商品详情API接口是京东开放平台为开发者提供的服务,用于获取京东平台上商品的详细信息。通过调用该接口,开发者可以获取商品的名称、价格、库存、图片、规格参数、用户评价等结构化数据,适用于电商应用、价格监控、数据分析等场景。二、接口特点数据全面性接口返回的数据涵盖多个维度,包括:商品基本信息:名称、品牌、型号、分类等。价格信息:当前售价、原价、促销价、折扣信息等。库存信息:库存数量</div>
                    </li>
                    <li><a href="/article/1939432021260562432.htm"
                           title="Leetcode【串联所有单词的子串】" target="_blank">Leetcode【串联所有单词的子串】</a>
                        <span class="text-muted"></span>

                        <div>30.串联所有单词的子串给定一个字符串s和一个字符串数组words。words中所有字符串长度相同。s中的串联子串是指一个包含words中所有字符串以任意顺序排列连接起来的子串。例如,如果words=["ab","cd","ef"],那么"abcdef","abefcd","cdabef","cdefab","efabcd",和"efcdab"都是串联子串。"acdbef"不是串联子串,因为他不是</div>
                    </li>
                    <li><a href="/article/1939431516618682368.htm"
                           title="【Python】科研代码学习:十三 Accelerate" target="_blank">【Python】科研代码学习:十三 Accelerate</a>
                        <span class="text-muted">溢流眼泪</span>
<a class="tag" taget="_blank" href="/search/%E3%80%90%E7%A7%91%E7%A0%94%E4%BB%A3%E7%A0%81%E3%80%91/1.htm">【科研代码】</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E5%AD%A6%E4%B9%A0/1.htm">学习</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a>
                        <div>【Python】科研代码学习:十三AccelerateAccelerate统一的加速接口修改训练代码(torch.nn)更简单的使用Accelerate【HF官网-Doc-Accelerate:API】HFAccelerate是一个库,能够让PyTorch代码添加几行代码之后,就能在分布式配置中运行(比如多Gpus卡)前言:建议Python3.8+pipinstallaccelerate统一的加速</div>
                    </li>
                    <li><a href="/article/1939431517319131136.htm"
                           title="【python】2.set集合" target="_blank">【python】2.set集合</a>
                        <span class="text-muted">一个玉米栗</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a>
                        <div>Set集合创建一个空集合使用set(),若创建的集合内元素有值可以使用creatset={'tom','arry','张三','李四'}集合内重复的元素会被自动去掉集合是无序的,可变类型的数据集合添加元素set.add('addname')-addname为要添加的元素set.remove():删除集合的元素set.update('添加元素包含字典,列表,集合'):向集合中更新元素set.clea</div>
                    </li>
                    <li><a href="/article/1939431389535465472.htm"
                           title="pip install accelerate后accelerate命令无法执行的问题" target="_blank">pip install accelerate后accelerate命令无法执行的问题</a>
                        <span class="text-muted">轩轩的学习之路</span>
<a class="tag" taget="_blank" href="/search/pip/1.htm">pip</a><a class="tag" taget="_blank" href="/search/linux/1.htm">linux</a><a class="tag" taget="_blank" href="/search/windows/1.htm">windows</a>
                        <div>这是因为默认使用的是.local/bin/accelerate而不是conda环境里的accelerate查看accelerate路径与python是否一致whichpythonwhichaccelerate打印输出果然accelerate路径有问题(python)/home/ubuntu/.conda/envs/Emb/bin/python(accelerate)/home/ubuntu/.lo</div>
                    </li>
                    <li><a href="/article/1939430630425161728.htm"
                           title="Python小知识" target="_blank">Python小知识</a>
                        <span class="text-muted">感情谁不曾无奈</span>
<a class="tag" taget="_blank" href="/search/%23/1.htm">#</a><a class="tag" taget="_blank" href="/search/Python%E7%AC%94%E8%AE%B0/1.htm">Python笔记</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a>
                        <div>文章目录一、技巧二、错误解决办法三、Pycharm3.1添加安装包python知识点梳理AI股票可以读取指数一、技巧1.1镜像元安装指令:pipinstall-ihttps://pypi.doubanio.com/simple/--trusted-hostpypi.doubanio.comxxxx1.2唤醒虚拟环境.\venv\Scripts\activate1.3解决包不兼容问题pipinsta</div>
                    </li>
                    <li><a href="/article/1939429496172113920.htm"
                           title="统一认证、限流、Mock 一网打尽!用 APISIX/Kong 让低代码平台更清爽" target="_blank">统一认证、限流、Mock 一网打尽!用 APISIX/Kong 让低代码平台更清爽</a>
                        <span class="text-muted">网罗开发</span>
<a class="tag" taget="_blank" href="/search/%E5%AE%9E%E6%88%98%E6%BA%90%E7%A0%81/1.htm">实战源码</a><a class="tag" taget="_blank" href="/search/%E5%89%8D%E7%AB%AF/1.htm">前端</a><a class="tag" taget="_blank" href="/search/kong/1.htm">kong</a><a class="tag" taget="_blank" href="/search/%E4%BD%8E%E4%BB%A3%E7%A0%81/1.htm">低代码</a>
                        <div>网罗开发(小红书、快手、视频号同名)  大家好,我是展菲,目前在上市企业从事人工智能项目研发管理工作,平时热衷于分享各种编程领域的软硬技能知识以及前沿技术,包括iOS、前端、HarmonyOS、Java、Python等方向。在移动端开发、鸿蒙开发、物联网、嵌入式、云原生、开源等领域有深厚造诣。图书作者:《ESP32-C3物联网工程开发实战》图书作者:《SwiftUI入门,进阶与实战》超级个体:CO</div>
                    </li>
                    <li><a href="/article/1939427607506382848.htm"
                           title="LeetCode题解:30.串联所有单词的子串【Python题解超详细,KMP搜索、滑动窗口法】,知识拓展:Python中的排列组合" target="_blank">LeetCode题解:30.串联所有单词的子串【Python题解超详细,KMP搜索、滑动窗口法】,知识拓展:Python中的排列组合</a>
                        <span class="text-muted"></span>

                        <div>题目描述给定一个字符串s和一个字符串数组words。words中所有字符串长度相同。s中的串联子串是指一个包含words中所有字符串以任意顺序排列连接起来的子串。例如,如果words=["ab","cd","ef"],那么"abcdef","abefcd","cdabef","cdefab","efabcd"和"efcdab"都是串联子串。"acdbef"不是串联子串,因为他不是任何words排列</div>
                    </li>
                    <li><a href="/article/1939427226445475840.htm"
                           title="【高频考点精讲】手写下拉选择组件:从点击展开到搜索过滤,实现select的增强版" target="_blank">【高频考点精讲】手写下拉选择组件:从点击展开到搜索过滤,实现select的增强版</a>
                        <span class="text-muted">全栈老李技术面试</span>
<a class="tag" taget="_blank" href="/search/%E5%89%8D%E7%AB%AF%E9%AB%98%E9%A2%91%E8%80%83%E7%82%B9%E7%B2%BE%E8%AE%B2/1.htm">前端高频考点精讲</a><a class="tag" taget="_blank" href="/search/%E5%89%8D%E7%AB%AF/1.htm">前端</a><a class="tag" taget="_blank" href="/search/javascript/1.htm">javascript</a><a class="tag" taget="_blank" href="/search/html/1.htm">html</a><a class="tag" taget="_blank" href="/search/css/1.htm">css</a><a class="tag" taget="_blank" href="/search/%E9%9D%A2%E8%AF%95%E9%A2%98/1.htm">面试题</a><a class="tag" taget="_blank" href="/search/react/1.htm">react</a><a class="tag" taget="_blank" href="/search/vue/1.htm">vue</a>
                        <div>手写下拉选择组件:从点击展开到搜索过滤,实现select的增强版‍作者:全栈老李更新时间:2025年5月‍适合人群:前端初学者、进阶开发者版权:本文由全栈老李原创,转载请注明出处。今天咱们聊聊如何手写一个功能完善的下拉选择组件。相信不少前端er都用过ElementUI或者AntDesign的Select组件,但你知道它们底层是怎么实现的吗?老李今天就带大家从零开始,实现一个支持点击展开、搜索过滤的</div>
                    </li>
                    <li><a href="/article/1939426974250364928.htm"
                           title="python udsoncan 详解" target="_blank">python udsoncan 详解</a>
                        <span class="text-muted">车载testing</span>
<a class="tag" taget="_blank" href="/search/%E6%99%BA%E8%83%BD%E6%B1%BD%E8%BD%A6%E6%B5%8B%E8%AF%95/1.htm">智能汽车测试</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a>
                        <div>pythonudsoncan详解udsoncan是一个Python库,用于实现汽车统一诊断服务(UnifiedDiagnosticServices,UDS)协议。UDS是一种用于汽车诊断的标准化通信协议,它定义了一系列的服务和流程,用于ECU(电子控制单元)的诊断和通信。udsoncan库支持通过CAN(ControllerAreaNetwork)和DoIP(DiagnosticoverIP)等不</div>
                    </li>
                    <li><a href="/article/1939425735164882944.htm"
                           title="HarmonyOS(OHOS)引擎编译常见问题" target="_blank">HarmonyOS(OHOS)引擎编译常见问题</a>
                        <span class="text-muted"></span>
<a class="tag" taget="_blank" href="/search/harmonyos/1.htm">harmonyos</a>
                        <div>ohos引擎产物编译相关问题flutter_engine环境编译配置参考FlutterOpenHarmony化引擎编译环境推荐配置版本python3.8-3.11,3.12版本会出现报错java17DevEco-Studio/command-line-tools,5.0.3.300+包含了ohpm,hvigorw,node,OpenHarmonySDKXcode14.3如何生成flutter.ha</div>
                    </li>
                    <li><a href="/article/1939424954311307264.htm"
                           title="C++ | Leetcode C++题解之第30题串联所有单词的子串" target="_blank">C++ | Leetcode C++题解之第30题串联所有单词的子串</a>
                        <span class="text-muted">Ddddddd_158</span>
<a class="tag" taget="_blank" href="/search/%E7%BB%8F%E9%AA%8C%E5%88%86%E4%BA%AB/1.htm">经验分享</a><a class="tag" taget="_blank" href="/search/c%2B%2B/1.htm">c++</a><a class="tag" taget="_blank" href="/search/Leetcode/1.htm">Leetcode</a><a class="tag" taget="_blank" href="/search/%E9%A2%98%E8%A7%A3/1.htm">题解</a>
                        <div>题目:题解:classSolution{public:vectorfindSubstring(string&s,vector&words){vectorres;intm=words.size(),n=words[0].size(),ls=s.size();for(inti=0;idiffer;for(intj=0;j<m;++j){++differ[s.substr(i+j*n,n)];}for(</div>
                    </li>
                    <li><a href="/article/1939423441484574720.htm"
                           title="python-can + can-isotp + udsoncan 实现基础的UDS诊断功能;附代码" target="_blank">python-can + can-isotp + udsoncan 实现基础的UDS诊断功能;附代码</a>
                        <span class="text-muted">dujunqiu</span>
<a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E5%BC%80%E5%8F%91%E8%AF%AD%E8%A8%80/1.htm">开发语言</a>
                        <div>1:功能说明在网上搜了一下python-can+udsoncan的使用说明,发现都是很笼统的介绍,没有详细的使用说明;下面根据我自己的使用经验,来给大家介绍一下;2:源代码介绍这里主要修改的配置是“bus1=can.interface.Bus(interface=‘canalystii’,channel=0,bitrate=500000)”这一行代码,需要根据实际使用的CAN盒进行配置;详细的代码</div>
                    </li>
                    <li><a href="/article/1939418024868114432.htm"
                           title="Python打卡训练营-Day41-简单CNN" target="_blank">Python打卡训练营-Day41-简单CNN</a>
                        <span class="text-muted">traMpo1ine</span>
<a class="tag" taget="_blank" href="/search/cnn/1.htm">cnn</a><a class="tag" taget="_blank" href="/search/python/1.htm">python</a><a class="tag" taget="_blank" href="/search/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/1.htm">深度学习</a>
                        <div>@浙大疏锦行知识回顾数据增强卷积神经网络定义的写法batch归一化:调整一个批次的分布,常用与图像数据特征图:只有卷积操作输出的才叫特征图调度器:直接修改基础学习率卷积操作常见流程如下:1.输入→卷积层→Batch归一化层(可选)→池化层→激活函数→下一层Flatten->Dense(withDropout,可选)->Dense(Output)这里相关的概念比较多,如果之前没有学习过复试班强化班中</div>
                    </li>
                                <li><a href="/article/125.htm"
                                       title="apache 安装linux windows" target="_blank">apache 安装linux windows</a>
                                    <span class="text-muted">墙头上一根草</span>
<a class="tag" taget="_blank" href="/search/apache/1.htm">apache</a><a class="tag" taget="_blank" href="/search/inux/1.htm">inux</a><a class="tag" taget="_blank" href="/search/windows/1.htm">windows</a>
                                    <div>linux安装Apache 有两种方式一种是手动安装通过二进制的文件进行安装,另外一种就是通过yum 安装,此中安装方式,需要物理机联网。以下分别介绍两种的安装方式 
  
  
通过二进制文件安装Apache需要的软件有apr,apr-util,pcre 
 1,安装 apr        下载地址:htt</div>
                                </li>
                                <li><a href="/article/252.htm"
                                       title="fill_parent、wrap_content和match_parent的区别" target="_blank">fill_parent、wrap_content和match_parent的区别</a>
                                    <span class="text-muted">Cb123456</span>
<a class="tag" taget="_blank" href="/search/match_parent/1.htm">match_parent</a><a class="tag" taget="_blank" href="/search/fill_parent/1.htm">fill_parent</a>
                                    <div>fill_parent、wrap_content和match_parent的区别: 
  
1)fill_parent 
  设置一个构件的布局为fill_parent将强制性地使构件扩展,以填充布局单元内尽可能多的空间。这跟Windows控件的dockstyle属性大体一致。设置一个顶部布局或控件为fill_parent将强制性让它布满整个屏幕。 
2) wrap_conte</div>
                                </li>
                                <li><a href="/article/379.htm"
                                       title="网页自适应设计" target="_blank">网页自适应设计</a>
                                    <span class="text-muted">天子之骄</span>
<a class="tag" taget="_blank" href="/search/html/1.htm">html</a><a class="tag" taget="_blank" href="/search/css/1.htm">css</a><a class="tag" taget="_blank" href="/search/%E5%93%8D%E5%BA%94%E5%BC%8F%E8%AE%BE%E8%AE%A1/1.htm">响应式设计</a><a class="tag" taget="_blank" href="/search/%E9%A1%B5%E9%9D%A2%E8%87%AA%E9%80%82%E5%BA%94/1.htm">页面自适应</a>
                                    <div>网页自适应设计 
       网页对浏览器窗口的自适应支持变得越来越重要了。自适应响应设计更是异常火爆。再加上移动端的崛起,更是如日中天。以前为了适应不同屏幕分布率和浏览器窗口的扩大和缩小,需要设计几套css样式,用js脚本判断窗口大小,选择加载。结构臃肿,加载负担较大。现笔者经过一定时间的学习,有所心得,故分享于此,加强交流,共同进步。同时希望对大家有所</div>
                                </li>
                                <li><a href="/article/506.htm"
                                       title="[sql server] 分组取最大最小常用sql" target="_blank">[sql server] 分组取最大最小常用sql</a>
                                    <span class="text-muted">一炮送你回车库</span>
<a class="tag" taget="_blank" href="/search/SQL+Server/1.htm">SQL Server</a>
                                    <div>--分组取最大最小常用sql--测试环境if OBJECT_ID('tb') is not null drop table tb;gocreate table tb( col1 int, col2 int, Fcount int)insert into tbselect 11,20,1 union allselect 11,22,1 union allselect 1</div>
                                </li>
                                <li><a href="/article/633.htm"
                                       title="ImageIO写图片输出到硬盘" target="_blank">ImageIO写图片输出到硬盘</a>
                                    <span class="text-muted">3213213333332132</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/image/1.htm">image</a>
                                    <div>package awt; 
 
import java.awt.Color; 
import java.awt.Font; 
import java.awt.Graphics; 
import java.awt.image.BufferedImage; 
import java.io.File; 
import java.io.IOException; 
 
import javax.imagei</div>
                                </li>
                                <li><a href="/article/760.htm"
                                       title="自己的String动态数组" target="_blank">自己的String动态数组</a>
                                    <span class="text-muted">宝剑锋梅花香</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/%E5%8A%A8%E6%80%81%E6%95%B0%E7%BB%84/1.htm">动态数组</a><a class="tag" taget="_blank" href="/search/%E6%95%B0%E7%BB%84/1.htm">数组</a>
                                    <div>数组还是好说,学过一两门编程语言的就知道,需要注意的是数组声明时需要把大小给它定下来,比如声明一个字符串类型的数组:String str[]=new String[10];    但是问题就来了,每次都是大小确定的数组,我需要数组大小不固定随时变化怎么办呢?  动态数组就这样应运而生,龙哥给我们讲的是自己用代码写动态数组,并非用的ArrayList 看看字符</div>
                                </li>
                                <li><a href="/article/887.htm"
                                       title="pinyin4j工具类" target="_blank">pinyin4j工具类</a>
                                    <span class="text-muted">darkranger</span>
<a class="tag" taget="_blank" href="/search/.net/1.htm">.net</a>
                                    <div>pinyin4j工具类Java工具类 2010-04-24 00:47:00 阅读69 评论0 字号:大中小 
引入pinyin4j-2.5.0.jar包: 
pinyin4j是一个功能强悍的汉语拼音工具包,主要是从汉语获取各种格式和需求的拼音,功能强悍,下面看看如何使用pinyin4j。 
 
本人以前用AscII编码提取工具,效果不理想,现在用pinyin4j简单实现了一个。功能还不是很完美,</div>
                                </li>
                                <li><a href="/article/1014.htm"
                                       title="StarUML学习笔记----基本概念" target="_blank">StarUML学习笔记----基本概念</a>
                                    <span class="text-muted">aijuans</span>
<a class="tag" taget="_blank" href="/search/UML%E5%BB%BA%E6%A8%A1/1.htm">UML建模</a>
                                    <div>介绍StarUML的基本概念,这些都是有效运用StarUML?所需要的。包括对模型、视图、图、项目、单元、方法、框架、模型块及其差异以及UML轮廓。 
        模型、视与图(Model, View and Diagram) 
       &</div>
                                </li>
                                <li><a href="/article/1141.htm"
                                       title="Activiti最终总结" target="_blank">Activiti最终总结</a>
                                    <span class="text-muted">avords</span>
<a class="tag" taget="_blank" href="/search/Activiti+id+%E5%B7%A5%E4%BD%9C%E6%B5%81/1.htm">Activiti id 工作流</a>
                                    <div>1、流程定义ID:ProcessDefinitionId,当定义一个流程就会产生。 
2、流程实例ID:ProcessInstanceId,当开始一个具体的流程时就会产生,也就是不同的流程实例ID可能有相同的流程定义ID。 
3、TaskId,每一个userTask都会有一个Id这个是存在于流程实例上的。 
4、TaskDefinitionKey和(ActivityImpl activityId </div>
                                </li>
                                <li><a href="/article/1268.htm"
                                       title="从省市区多重级联想到的,react和jquery的差别" target="_blank">从省市区多重级联想到的,react和jquery的差别</a>
                                    <span class="text-muted">bee1314</span>
<a class="tag" taget="_blank" href="/search/jquery/1.htm">jquery</a><a class="tag" taget="_blank" href="/search/UI/1.htm">UI</a><a class="tag" taget="_blank" href="/search/react/1.htm">react</a>
                                    <div>在我们的前端项目里经常会用到级联的select,比如省市区这样。通常这种级联大多是动态的。比如先加载了省,点击省加载市,点击市加载区。然后数据通常ajax返回。如果没有数据则说明到了叶子节点。       针对这种场景,如果我们使用jquery来实现,要考虑很多的问题,数据部分,以及大量的dom操作。比如这个页面上显示了某个区,这时候我切换省,要把市重新初始化数据,然后区域的部分要从页面</div>
                                </li>
                                <li><a href="/article/1395.htm"
                                       title="Eclipse快捷键大全" target="_blank">Eclipse快捷键大全</a>
                                    <span class="text-muted">bijian1013</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/eclipse/1.htm">eclipse</a><a class="tag" taget="_blank" href="/search/%E5%BF%AB%E6%8D%B7%E9%94%AE/1.htm">快捷键</a>
                                    <div>Ctrl+1 快速修复(最经典的快捷键,就不用多说了)Ctrl+D: 删除当前行 Ctrl+Alt+↓ 复制当前行到下一行(复制增加)Ctrl+Alt+↑ 复制当前行到上一行(复制增加)Alt+↓ 当前行和下面一行交互位置(特别实用,可以省去先剪切,再粘贴了)Alt+↑ 当前行和上面一行交互位置(同上)Alt+← 前一个编辑的页面Alt+→ 下一个编辑的页面(当然是针对上面那条来说了)Alt+En</div>
                                </li>
                                <li><a href="/article/1522.htm"
                                       title="js 笔记 函数" target="_blank">js 笔记 函数</a>
                                    <span class="text-muted">征客丶</span>
<a class="tag" taget="_blank" href="/search/JavaScript/1.htm">JavaScript</a>
                                    <div>一、函数的使用 
1.1、定义函数变量 
var vName = funcation(params){ 
} 
 
1.2、函数的调用 
函数变量的调用:      vName(params); 
函数定义时自发调用:(function(params){})(params); 
 
1.3、函数中变量赋值 
var a = 'a'; 
var ff</div>
                                </li>
                                <li><a href="/article/1649.htm"
                                       title="【Scala四】分析Spark源代码总结的Scala语法二" target="_blank">【Scala四】分析Spark源代码总结的Scala语法二</a>
                                    <span class="text-muted">bit1129</span>
<a class="tag" taget="_blank" href="/search/scala/1.htm">scala</a>
                                    <div>1. Some操作 
  
在下面的代码中,使用了Some操作:if (self.partitioner == Some(partitioner)),那么Some(partitioner)表示什么含义?首先partitioner是方法combineByKey传入的变量, 
Some的文档说明: 
  
/** Class `Some[A]` represents existin</div>
                                </li>
                                <li><a href="/article/1776.htm"
                                       title="java 匿名内部类" target="_blank">java 匿名内部类</a>
                                    <span class="text-muted">BlueSkator</span>
<a class="tag" taget="_blank" href="/search/java%E5%8C%BF%E5%90%8D%E5%86%85%E9%83%A8%E7%B1%BB/1.htm">java匿名内部类</a>
                                    <div>组合优先于继承 
Java的匿名类,就是提供了一个快捷方便的手段,令继承关系可以方便地变成组合关系 
继承只有一个时候才能用,当你要求子类的实例可以替代父类实例的位置时才可以用继承。 
  
在Java中内部类主要分为成员内部类、局部内部类、匿名内部类、静态内部类。 
内部类不是很好理解,但说白了其实也就是一个类中还包含着另外一个类如同一个人是由大脑、肢体、器官等身体结果组成,而内部类相</div>
                                </li>
                                <li><a href="/article/1903.htm"
                                       title="盗版win装在MAC有害发热,苹果的东西不值得买,win应该不用" target="_blank">盗版win装在MAC有害发热,苹果的东西不值得买,win应该不用</a>
                                    <span class="text-muted">ljy325</span>
<a class="tag" taget="_blank" href="/search/%E6%B8%B8%E6%88%8F/1.htm">游戏</a><a class="tag" taget="_blank" href="/search/apple/1.htm">apple</a><a class="tag" taget="_blank" href="/search/windows/1.htm">windows</a><a class="tag" taget="_blank" href="/search/XP/1.htm">XP</a><a class="tag" taget="_blank" href="/search/OS/1.htm">OS</a>
                                    <div>Mac mini 型号: MC270CH-A RMB:5,688 
  
Apple 对windows的产品支持不好,有以下问题: 
  
1.装完了xp,发现机身很热虽然没有运行任何程序!貌似显卡跑游戏发热一样,按照那样的发热量,那部机子损耗很大,使用寿命受到严重的影响! 
  
2.反观安装了Mac os的展示机,发热量很小,运行了1天温度也没有那么高 
&nbs</div>
                                </li>
                                <li><a href="/article/2030.htm"
                                       title="读《研磨设计模式》-代码笔记-生成器模式-Builder" target="_blank">读《研磨设计模式》-代码笔记-生成器模式-Builder</a>
                                    <span class="text-muted">bylijinnan</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/%E8%AE%BE%E8%AE%A1%E6%A8%A1%E5%BC%8F/1.htm">设计模式</a>
                                    <div>声明: 本文只为方便我个人查阅和理解,详细的分析以及源代码请移步 原作者的博客http://chjavach.iteye.com/ 
 
 



/**
 * 生成器模式的意图在于将一个复杂的构建与其表示相分离,使得同样的构建过程可以创建不同的表示(GoF)
 * 个人理解:
 * 构建一个复杂的对象,对于创建者(Builder)来说,一是要有数据来源(rawData),二是要返回构</div>
                                </li>
                                <li><a href="/article/2157.htm"
                                       title="JIRA与SVN插件安装" target="_blank">JIRA与SVN插件安装</a>
                                    <span class="text-muted">chenyu19891124</span>
<a class="tag" taget="_blank" href="/search/SVN/1.htm">SVN</a><a class="tag" taget="_blank" href="/search/jira/1.htm">jira</a>
                                    <div>JIRA安装好后提交代码并要显示在JIRA上,这得需要用SVN的插件才能看见开发人员提交的代码。 
1.下载svn与jira插件安装包,解压后在安装包(atlassian-jira-subversion-plugin-0.10.1) 
2.解压出来的包里下的lib文件夹下的jar拷贝到(C:\Program Files\Atlassian\JIRA 4.3.4\atlassian-jira\WEB</div>
                                </li>
                                <li><a href="/article/2284.htm"
                                       title="常用数学思想方法" target="_blank">常用数学思想方法</a>
                                    <span class="text-muted">comsci</span>
<a class="tag" taget="_blank" href="/search/%E5%B7%A5%E4%BD%9C/1.htm">工作</a>
                                    <div>  对于搞工程和技术的朋友来讲,在工作中常常遇到一些实际问题,而采用常规的思维方式无法很好的解决这些问题,那么这个时候我们就需要用数学语言和数学工具,而使用数学工具的前提却是用数学思想的方法来描述问题。。下面转帖几种常用的数学思想方法,仅供学习和参考 
 
 
 
  函数思想 
  把某一数学问题用函数表示出来,并且利用函数探究这个问题的一般规律。这是最基本、最常用的数学方法</div>
                                </li>
                                <li><a href="/article/2411.htm"
                                       title="pl/sql集合类型" target="_blank">pl/sql集合类型</a>
                                    <span class="text-muted">daizj</span>
<a class="tag" taget="_blank" href="/search/oracle/1.htm">oracle</a><a class="tag" taget="_blank" href="/search/%E9%9B%86%E5%90%88/1.htm">集合</a><a class="tag" taget="_blank" href="/search/type/1.htm">type</a><a class="tag" taget="_blank" href="/search/pl%2Fsql/1.htm">pl/sql</a>
                                    <div>--集合类型 
/* 
  单行单列的数据,使用标量变量 
  单行多列数据,使用记录 
  单列多行数据,使用集合(。。。) 
  *集合:类似于数组也就是。pl/sql集合类型包括索引表(pl/sql table)、嵌套表(Nested Table)、变长数组(VARRAY)等 
*/ 
/* 
    --集合方法 
&n</div>
                                </li>
                                <li><a href="/article/2538.htm"
                                       title="[Ofbiz]ofbiz初用" target="_blank">[Ofbiz]ofbiz初用</a>
                                    <span class="text-muted">dinguangx</span>
<a class="tag" taget="_blank" href="/search/%E7%94%B5%E5%95%86/1.htm">电商</a><a class="tag" taget="_blank" href="/search/ofbiz/1.htm">ofbiz</a>
                                    <div>从github下载最新的ofbiz(截止2015-7-13),从源码进行ofbiz的试用 
1. 加载测试库 
ofbiz内置derby,通过下面的命令初始化测试库 
./ant load-demo (与load-seed有一些区别) 
  
2. 启动内置tomcat 
./ant start 
或 
./startofbiz.sh 
或 
java -jar ofbiz.jar 
&</div>
                                </li>
                                <li><a href="/article/2665.htm"
                                       title="结构体中最后一个元素是长度为0的数组" target="_blank">结构体中最后一个元素是长度为0的数组</a>
                                    <span class="text-muted">dcj3sjt126com</span>
<a class="tag" taget="_blank" href="/search/c/1.htm">c</a><a class="tag" taget="_blank" href="/search/gcc/1.htm">gcc</a>
                                    <div>在Linux源代码中,有很多的结构体最后都定义了一个元素个数为0个的数组,如/usr/include/linux/if_pppox.h中有这样一个结构体: struct pppoe_tag {     __u16 tag_type;     __u16 tag_len;   &n</div>
                                </li>
                                <li><a href="/article/2792.htm"
                                       title="Linux cp 实现强行覆盖" target="_blank">Linux cp 实现强行覆盖</a>
                                    <span class="text-muted">dcj3sjt126com</span>
<a class="tag" taget="_blank" href="/search/linux/1.htm">linux</a>
                                    <div>发现在Fedora 10 /ubutun 里面用cp -fr src dest,即使加了-f也是不能强行覆盖的,这时怎么回事的呢?一两个文件还好说,就输几个yes吧,但是要是n多文件怎么办,那还不输死人呢?下面提供三种解决办法。 方法一 
 
 我们输入alias命令,看看系统给cp起了一个什么别名。 
  
  [root@localhost ~]# aliasalias cp=’cp -i’a</div>
                                </li>
                                <li><a href="/article/2919.htm"
                                       title="Memcached(一)、HelloWorld" target="_blank">Memcached(一)、HelloWorld</a>
                                    <span class="text-muted">frank1234</span>
<a class="tag" taget="_blank" href="/search/memcached/1.htm">memcached</a>
                                    <div>一、简介 
高性能的架构离不开缓存,分布式缓存中的佼佼者当属memcached,它通过客户端将不同的key hash到不同的memcached服务器中,而获取的时候也到相同的服务器中获取,由于不需要做集群同步,也就省去了集群间同步的开销和延迟,所以它相对于ehcache等缓存来说能更好的支持分布式应用,具有更强的横向伸缩能力。 
二、客户端 
选择一个memcached客户端,我这里用的是memc</div>
                                </li>
                                <li><a href="/article/3046.htm"
                                       title="Search in Rotated Sorted Array II" target="_blank">Search in Rotated Sorted Array II</a>
                                    <span class="text-muted">hcx2013</span>
<a class="tag" taget="_blank" href="/search/search/1.htm">search</a>
                                    <div>Follow up for "Search in Rotated Sorted Array":What if duplicates are allowed? 
Would this affect the run-time complexity? How and why? 
Write a function to determine if a given ta</div>
                                </li>
                                <li><a href="/article/3173.htm"
                                       title="Spring4新特性——更好的Java泛型操作API" target="_blank">Spring4新特性——更好的Java泛型操作API</a>
                                    <span class="text-muted">jinnianshilongnian</span>
<a class="tag" taget="_blank" href="/search/spring4/1.htm">spring4</a><a class="tag" taget="_blank" href="/search/generic+type/1.htm">generic type</a>
                                    <div>Spring4新特性——泛型限定式依赖注入 
Spring4新特性——核心容器的其他改进 
Spring4新特性——Web开发的增强 
Spring4新特性——集成Bean Validation 1.1(JSR-349)到SpringMVC  
Spring4新特性——Groovy Bean定义DSL 
Spring4新特性——更好的Java泛型操作API  
Spring4新</div>
                                </li>
                                <li><a href="/article/3300.htm"
                                       title="CentOS安装JDK" target="_blank">CentOS安装JDK</a>
                                    <span class="text-muted">liuxingguome</span>
<a class="tag" taget="_blank" href="/search/centos/1.htm">centos</a>
                                    <div>1、行卸载原来的: 
[root@localhost opt]# rpm -qa | grep java 
tzdata-java-2014g-1.el6.noarch 
java-1.7.0-openjdk-1.7.0.65-2.5.1.2.el6_5.x86_64 
java-1.6.0-openjdk-1.6.0.0-11.1.13.4.el6.x86_64 
[root@localhost</div>
                                </li>
                                <li><a href="/article/3427.htm"
                                       title="二分搜索专题2-在有序二维数组中搜索一个元素" target="_blank">二分搜索专题2-在有序二维数组中搜索一个元素</a>
                                    <span class="text-muted">OpenMind</span>
<a class="tag" taget="_blank" href="/search/%E4%BA%8C%E7%BB%B4%E6%95%B0%E7%BB%84/1.htm">二维数组</a><a class="tag" taget="_blank" href="/search/%E7%AE%97%E6%B3%95/1.htm">算法</a><a class="tag" taget="_blank" href="/search/%E4%BA%8C%E5%88%86%E6%90%9C%E7%B4%A2/1.htm">二分搜索</a>
                                    <div>1,设二维数组p的每行每列都按照下标递增的顺序递增。 
用数学语言描述如下:p满足 
(1),对任意的x1,x2,y,如果x1<x2,则p(x1,y)<p(x2,y); 
(2),对任意的x,y1,y2, 如果y1<y2,则p(x,y1)<p(x,y2); 
2,问题: 
给定满足1的数组p和一个整数k,求是否存在x0,y0使得p(x0,y0)=k? 
3,算法分析: 
(</div>
                                </li>
                                <li><a href="/article/3554.htm"
                                       title="java 随机数 Math与Random" target="_blank">java 随机数 Math与Random</a>
                                    <span class="text-muted">SaraWon</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/Math/1.htm">Math</a><a class="tag" taget="_blank" href="/search/Random/1.htm">Random</a>
                                    <div>今天需要在程序中产生随机数,知道有两种方法可以使用,但是使用Math和Random的区别还不是特别清楚,看到一篇文章是关于的,觉得写的还挺不错的,原文地址是 
http://www.oschina.net/question/157182_45274?sort=default&p=1#answers 
 
产生1到10之间的随机数的两种实现方式: 
 

//Math
Math.roun</div>
                                </li>
                                <li><a href="/article/3681.htm"
                                       title="oracle创建表空间" target="_blank">oracle创建表空间</a>
                                    <span class="text-muted">tugn</span>
<a class="tag" taget="_blank" href="/search/oracle/1.htm">oracle</a>
                                    <div>create temporary tablespace TXSJ_TEMP   
tempfile 'E:\Oracle\oradata\TXSJ_TEMP.dbf'   
size 32m   
autoextend on   
next 32m maxsize 2048m   
extent m</div>
                                </li>
                                <li><a href="/article/3808.htm"
                                       title="使用Java8实现自己的个性化搜索引擎" target="_blank">使用Java8实现自己的个性化搜索引擎</a>
                                    <span class="text-muted">yangshangchuan</span>
<a class="tag" taget="_blank" href="/search/java/1.htm">java</a><a class="tag" taget="_blank" href="/search/superword/1.htm">superword</a><a class="tag" taget="_blank" href="/search/%E6%90%9C%E7%B4%A2%E5%BC%95%E6%93%8E/1.htm">搜索引擎</a><a class="tag" taget="_blank" href="/search/java8/1.htm">java8</a><a class="tag" taget="_blank" href="/search/%E5%85%A8%E6%96%87%E6%A3%80%E7%B4%A2/1.htm">全文检索</a>
                                    <div>需要对249本软件著作实现句子级别全文检索,这些著作均为PDF文件,不使用现有的框架如lucene,自己实现的方法如下: 
1、从PDF文件中提取文本,这里的重点是如何最大可能地还原文本。提取之后的文本,一个句子一行保存为文本文件。 
2、将所有文本文件合并为一个单一的文本文件,这样,每一个句子就有一个唯一行号。 
3、对每一行文本进行分词,建立倒排表,倒排表的格式为:词=包含该词的总行数N=行号</div>
                                </li>
                </ul>
            </div>
        </div>
    </div>

<div>
    <div class="container">
        <div class="indexes">
            <strong>按字母分类:</strong>
            <a href="/tags/A/1.htm" target="_blank">A</a><a href="/tags/B/1.htm" target="_blank">B</a><a href="/tags/C/1.htm" target="_blank">C</a><a
                href="/tags/D/1.htm" target="_blank">D</a><a href="/tags/E/1.htm" target="_blank">E</a><a href="/tags/F/1.htm" target="_blank">F</a><a
                href="/tags/G/1.htm" target="_blank">G</a><a href="/tags/H/1.htm" target="_blank">H</a><a href="/tags/I/1.htm" target="_blank">I</a><a
                href="/tags/J/1.htm" target="_blank">J</a><a href="/tags/K/1.htm" target="_blank">K</a><a href="/tags/L/1.htm" target="_blank">L</a><a
                href="/tags/M/1.htm" target="_blank">M</a><a href="/tags/N/1.htm" target="_blank">N</a><a href="/tags/O/1.htm" target="_blank">O</a><a
                href="/tags/P/1.htm" target="_blank">P</a><a href="/tags/Q/1.htm" target="_blank">Q</a><a href="/tags/R/1.htm" target="_blank">R</a><a
                href="/tags/S/1.htm" target="_blank">S</a><a href="/tags/T/1.htm" target="_blank">T</a><a href="/tags/U/1.htm" target="_blank">U</a><a
                href="/tags/V/1.htm" target="_blank">V</a><a href="/tags/W/1.htm" target="_blank">W</a><a href="/tags/X/1.htm" target="_blank">X</a><a
                href="/tags/Y/1.htm" target="_blank">Y</a><a href="/tags/Z/1.htm" target="_blank">Z</a><a href="/tags/0/1.htm" target="_blank">其他</a>
        </div>
    </div>
</div>
<footer id="footer" class="mb30 mt30">
    <div class="container">
        <div class="footBglm">
            <a target="_blank" href="/">首页</a> -
            <a target="_blank" href="/custom/about.htm">关于我们</a> -
            <a target="_blank" href="/search/Java/1.htm">站内搜索</a> -
            <a target="_blank" href="/sitemap.txt">Sitemap</a> -
            <a target="_blank" href="/custom/delete.htm">侵权投诉</a>
        </div>
        <div class="copyright">版权所有 IT知识库 CopyRight © 2000-2050 E-COM-NET.COM , All Rights Reserved.
<!--            <a href="https://beian.miit.gov.cn/" rel="nofollow" target="_blank">京ICP备09083238号</a><br>-->
        </div>
    </div>
</footer>
<!-- 代码高亮 -->
<script type="text/javascript" src="/static/syntaxhighlighter/scripts/shCore.js"></script>
<script type="text/javascript" src="/static/syntaxhighlighter/scripts/shLegacy.js"></script>
<script type="text/javascript" src="/static/syntaxhighlighter/scripts/shAutoloader.js"></script>
<link type="text/css" rel="stylesheet" href="/static/syntaxhighlighter/styles/shCoreDefault.css"/>
<script type="text/javascript" src="/static/syntaxhighlighter/src/my_start_1.js"></script>





</body>

</html>