NumPy入门教程<\/h1>

一、NumPy简介<\/h2>
NumPy是Python中用于科学计算的基础库，提供高性能的多维数组对象和工具。它是许多高级数据分析工具的基础，如Pandas、SciPy等。<\/p>

二、安装NumPy<\/h2>

在已安装Python环境的基础上，通过以下命令安装NumPy：<\/p>

pip install numpy
<\/span><\/span><\/code><\/pre>三、基本使用<\/h2>
1. 导入NumPy<\/h3>
import<\/span> numpy as<\/span> np
<\/span><\/span><\/code><\/pre>2. 创建数组<\/h3>
a =<\/span> np.<\/span>arange(5<\/span>)  # 创建一维数组[0,1,2,3,4]<\/span>
<\/span><\/span><\/code><\/pre>3. 查看数组属性<\/h3>
a.<\/span>dtype  # 查看数组数据类型<\/span>
<\/span><\/span>a.<\/span>shape  # 查看数组形状<\/span>
<\/span><\/span><\/code><\/pre>四、多维数组操作<\/h2>
1. 创建多维数组<\/h3>
m =<\/span> np.<\/span>array([np.<\/span>arange(2<\/span>), np.<\/span>arange(2<\/span>)])  # 2x2数组<\/span>
<\/span><\/span><\/code><\/pre>2. 创建二维数组<\/h3>
a =<\/span> np.<\/span>array([[1<\/span>,2<\/span>], [3<\/span>,4<\/span>]])  # 2x2数组<\/span>
<\/span><\/span><\/code><\/pre>3. 访问数组元素<\/h3>
a[0<\/span>,0<\/span>]  # 1<\/span>
<\/span><\/span>a[1<\/span>,0<\/span>]  # 3<\/span>
<\/span><\/span>a[1<\/span>,1<\/span>]  # 4<\/span>
<\/span><\/span><\/code><\/pre>注意<\/strong>：NumPy不允许将复数类型隐式转换为整型，否则会触发TypeError错误。<\/p>
五、数组切片与索引<\/h2>
1. 一维数组切片<\/h3>
a =<\/span> np.<\/span>arange(9<\/span>)  # [0,1,2,3,4,5,6,7,8]<\/span>
<\/span><\/span>a[4<\/span>:6<\/span>]  # array([4, 5])<\/span>
<\/span><\/span><\/code><\/pre>切片参数：<\/p>

起始位置<\/li>
结束位置<\/li>
步长（可选）<\/li>
<\/ul>
六、数组形状操作<\/h2>
1. 重塑数组形状<\/h3>
b =<\/span> np.<\/span>arange(24<\/span>).<\/span>reshape(2<\/span>,3<\/span>,4<\/span>)  # 将一维数组转为3维数组<\/span>
<\/span><\/span>"""
<\/span><\/span><\/span>输出：
<\/span><\/span><\/span>array([[[ 0,  1,  2,  3],
<\/span><\/span><\/span>        [ 4,  5,  6,  7],
<\/span><\/span><\/span>        [ 8,  9, 10, 11]],
<\/span><\/span><\/span>
<\/span><\/span><\/span>       [[12, 13, 14, 15],
<\/span><\/span><\/span>        [16, 17, 18, 19],
<\/span><\/span><\/span>        [20, 21, 22, 23]]])
<\/span><\/span><\/span>"""<\/span>
<\/span><\/span><\/code><\/pre>2. 多维数组转一维<\/h3>
b.<\/span>flatten()  # 将多维数组展平为一维<\/span>
<\/span><\/span><\/code><\/pre>七、数据类型<\/h2>
NumPy支持多种数据类型，包括：<\/p>

整型 (int8, int16, int32, int64)<\/li>
无符号整型 (uint8, uint16, uint32, uint64)<\/li>
浮点型 (float16, float32, float64)<\/li>
复数型 (complex64, complex128)<\/li>
布尔型 (bool)<\/li>
字符串型 (string_)<\/li>
Unicode型 (unicode_)<\/li>
<\/ul>
可以在创建数组时指定数据类型：<\/p>
a =<\/span> np.<\/span>array([1<\/span>,2<\/span>,3<\/span>], dtype=<\/span>np.<\/span>float32)
<\/span><\/span><\/code><\/pre>八、进阶学习资源<\/h2>
对于想深入学习NumPy的用户，可以参考以下课程：<\/p>

玩转NumPy<\/a><\/li>
NumPy进阶课程<\/a><\/li>
<\/ol>
九、总结<\/h2>
NumPy是Python数据分析的基础，掌握NumPy的数组创建、索引、切片和形状操作是进行科学计算的前提。本教程涵盖了NumPy的基本操作，为进一步学习数据分析打下基础。<\/p>

NumPy入门教程<\/h1>

一、NumPy简介<\/h2> NumPy是Python中用于科学计算的基础库，提供高性能的多维数组对象和工具。它是许多高级数据分析工具的基础，如Pandas、SciPy等。<\/p>

三、基本使用<\/h2>

四、多维数组操作<\/h2>

六、数组形状操作<\/h2>

九、总结<\/h2> NumPy是Python数据分析的基础，掌握NumPy的数组创建、索引、切片和形状操作是进行科学计算的前提。本教程涵盖了NumPy的基本操作，为进一步学习数据分析打下基础。<\/p>

一、NumPy简介<\/h2>
NumPy是Python中用于科学计算的基础库，提供高性能的多维数组对象和工具。它是许多高级数据分析工具的基础，如Pandas、SciPy等。<\/p>

九、总结<\/h2>
NumPy是Python数据分析的基础，掌握NumPy的数组创建、索引、切片和形状操作是进行科学计算的前提。本教程涵盖了NumPy的基本操作，为进一步学习数据分析打下基础。<\/p>