Numpy 简明教程
NumPy Tutorial
NumPy（代表 Numerical Python ）是一个使用多维和单维数组元素的开源 Python 库。它是计算 Python 中数字数据的标准。NumPy 在如科学和工程等几乎需要进行数字计算的每个领域中都得到广泛使用；因此，NumPy API 功能在数据科学和科学 Python packages 中得到充分利用，包括 Pandas 、 SciPy 、 Matplotlib 、 scikit-learn 、scikit-image 等。
NumPy, which stands for Numerical Python, is an open-source Python library consisting of multidimensional and single-dimensional array elements. It’s a standard that computes numerical data in Python. NumPy is most widely used in almost every domain where numerical computation is required, like science and engineering; hence, the NumPy API functionalities are highly utilized in data science and scientific Python packages, including Pandas, SciPy, Matplotlib, scikit-learn, scikit-image, and many more.
本 NumPy 教程解释了 NumPy 的基础知识，例如其架构和环境。它还讨论了数组函数、索引类型等，然后扩展至学习 Matplotlib、Pandas、SciPy 和其他重要的 Python 库。所有这些在帮助您理解的基础上提供说明。
This NumPy tutorial explains the basics of NumPy, such as its architecture and environment. It also discusses array functions, types of indexing, etc., and then extends to learn Matplotlib, Pandas, SciPy, and other important Python libraries. All this is explained with the help of examples for better understanding.
Why NumPy - Need of NumPy
NumPy 是 Python 中数字计算的基础软件包。它提供数学函数来计算数据，以及有效地处理多维数组和矩阵的函数。以下是一些原因，说明 NumPy 至关重要：
NumPy is a fundamental package for numerical computation in Python. It provides mathematical functions to compute data as well as functions to operate multi-dimensional arrays and matrices efficiently. Here are some reasons why NumPy is essential:
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NumPy includes a wide range of mathematical functions for basic arithmetic, linear algebra, Fourier analysis, and more.
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NumPy performs numerical operations on large datasets efficiently.
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NumPy supports multi-dimensional arrays, allowing for the representation of complex data structures such as images, sound waves, and tensors in machine learning models.
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It supports the writing of concise and readable code for complex mathematical computations.
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NumPy integrates with other libraries to do scientific computation; these are SciPy (for scientific computing), Pandas (for data manipulation and analysis), and scikit-learn (for machine learning).
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Many scientific and numerical computing libraries and tools are built on top of NumPy.
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Its widespread adoption and stability make it a standard choice for numerical computing tasks.
总体而言，对于 Python 生态系统中的科学计算、数据分析、机器学习等领域，NumPy 起着至关重要的作用。其高效的数组运算和广泛的数学函数使其成为 Python 中处理数值数据的不可或缺的工具。
Overall, NumPy plays a crucial role in the Python ecosystem for scientific computing, data analysis, machine learning, and more. Its efficient array operations and extensive mathematical functions make it an indispensable tool for working with numerical data in Python.
NumPy Applications - Uses of NumPy
NumPy 在 Python 中的 API 主要用于数值计算。它支持广泛的数学函数，能够高效地对数据进行操作。以下是一些 NumPy 被广泛使用的常见应用领域：
The NumPy API in Python is used primarily for numerical computing. It provides support for a wide range of mathematical functions to operate on data efficiently. The following are some common application areas where NumPy is extensively used:
总体而言，NumPy 的多功能性和高效性使其成为科学计算、数据分析及其他领域中各种应用领域中重要的 Python 包。
Overall, NumPy’s versatility and efficiency make it an essential Python package across a wide range of application areas in scientific computing, data analysis, and beyond.
NumPy Example
以下是 Python NumPy 的示例：
The following is an example of Python NumPy:
# Importing NumPy Array
import numpy as np
# Creating an array using np.array() method
arr = np.array([10, 20, 30, 40, 50])
# Printing
print(arr) # Prints [10 20 30 40 50]NumPy Compiler
为了练习 NumPy 示例，我们提供了一个在线编译器。在这里练习你的 NumPy 程序： Online NumPy Compiler
To practice the NumPy example, we provided an online compiler. Practice your NumPy programs here: Online NumPy Compiler
Audience
本 NumPy 教程是为希望了解 NumPy 基础知识和函数的人员准备的。它特别适用于数据科学、工程、农业科学、管理、统计、研究和其他需要数值计算的领域。完成本教程后，你会发现自己处于中级水平，可以从中走向更高的专业水平。
This NumPy tutorial has been prepared for those who want to learn about the basics and functions of NumPy. It is specifically useful in data science, engineering, agriculture science, management, statistics, research, and other related domains where numerical computation is required. After completing this tutorial, you will find yourself at a moderate level of expertise from where you can take yourself to higher levels of expertise.
Prerequisites
您应该对计算机编程术语有基本的理解。对 Python 和任何编程语言的基本了解是加分项。
You should have a basic understanding of computer programming terminologies. A basic understanding of Python and any of the programming languages is a plus.
NumPy Codebase
可以从以下 GitHub 存储库中找到 NumPy 的源代码： https://github.com/numpy/numpy
NumPy’s source code can be found at this github repository: https://github.com/numpy/numpy