Python NumPy Programming and Project Development

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课程名称:Python NumPy编程与项目开发 课程概述:欢迎参加Uplatz的Python NumPy编程与项目开发课程。NumPy(Numerical Python)是Python中的核心科学计算库,提供高效的多维数组对象以及多种用于处理这些数组的操作。NumPy广泛应用于数组处理,还包括线性代数、傅里叶变换和矩阵运算。 随着Python列表在处理速度上的局限,NumPy旨在提供比传统Python列表快50倍的数组对象(ndarray),并且具备许多便捷的支持函数,极大地简化了对ndarray的操作。与列表不同的是,NumPy数组在内存中是连续存放的,这样可以高效访问和操作,从而实现了计算科学中的局部性引用原则,也就是其速度优势的主要原因。 NumPy不仅是多维数组对象的集合,还包含用于处理这些数组的各种例程。使用NumPy可以进行数学和逻辑运算、傅里叶变换、线性代数运算等。它在数据科学生态系统中处于核心地位,适用于数据提取、转化和加载,以及空间可视化、模型建立和评估等多个环节。 课程内容包括: 1. NumPy基础教程 2. NumPy属性与函数 3. 从现有数据创建数组 4. 从范围生成数组 5. NumPy中的索引与切片 6. 高级切片技术 7. 数组操作函数 8. 数学与统计函数 9. 线性代数与随机模块 10. NumPy项目实战 通过本课程,您将深入学习NumPy的结构和环境,掌握创建和管理多维数组的功能与操作,以及实际应用NumPy开发预测模型和项目的技巧。无论您是初学者还是经验丰富的程序员,NumPy的高层次语法使其易于上手并提高生产力。NumPy是一个开源项目,社区活跃,您可以自由使用和贡献。

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A warm welcome to the Python NumPy Programming and Project Development course by Uplatz.NumPy stands for Numerical Python and it is a core scientific computing library in Python. NumPy provides efficient multi-dimensional array objects and various operations to work with these array objects.NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely. NumPy is written partially in Python, but most of the parts that require fast computation are written in C or C++.Purpose of using NumPyIn Python we have lists that serve the purpose of arrays, but they are slow to process. NumPy aims to provide an array object that is up to 50x faster than traditional Python lists. The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important.NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. This behavior is called locality of reference in computer science. This is the main reason why NumPy is faster than lists. Also it is optimized to work with latest CPU architectures.NumPy is essentially a library consisting of multidimensional array objects and a collection of routines for processing those arrays. Using NumPy, mathematical and logical operations on arrays can be performed.NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:Extract, Transform, Load: Pandas, Intake, PyJanitorExploratory analysis: Jupyter, Seaborn, Matplotlib, AltairModel and evaluate: scikit-learn, statsmodels, PyMC3, spaCyReport in a dashboard: Dash, Panel, VoilaFeatures of NumPyPOWERFUL N-DIMENSIONAL ARRAYSFast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.NUMERICAL COMPUTING TOOLSNumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.INTEROPERABLENumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.PERFORMANTThe core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.EASY TO USENumPy's high level syntax makes it accessible and productive for programmers from any background or experience level.OPEN SOURCEDistributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.Using NumPy, a developer can perform the following operations −Mathematical and logical operations on arrays.Fourier transforms and routines for shape manipulation.Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.Uplatz provides this in-depth training on Python programming using NumPy. This NumPy course explains the concepts & structure of NumPy including its architecture and environment. The course discusses the various array functions, types of indexing, etc. and moves on to using NumPy for creating and managing multi-dimensional arrays with functions and operations. This Python NumPy course also discusses the practical implementation of NumPy to develop prediction models & projects.NumPy Python Programming and Project Development - Course SyllabusINTRODUCTION TO NUMPYNUMPY TUTORIAL BASICSNUMPY ATTRIBUTES AND FUNCTIONSCREATING ARRAYS FROM EXISTING DATACREATING ARRAYS FROM RANGESINDEXING AND SLICING IN NUMPYADVANCED SLICING IN NUMPYAPPEND AND RESIZE FUNCTIONSNDITER AND BROADCASTINGNUMPY BROADCASTINGNDITER FUNCTIONARRAY MANIPULATION FUNCTIONSNUMPY UNIQUE()NUMPY DELETE()NUMPY INSERT FUNCTIONNUMPY RAVEL AND SWAPAXES()SPLIT FUNCTIONHSPLIT FUNCTIONVSPLIT FUNCTIONLEFTSHIFT AND RIGHTSHIFT FUNCTIONSNUMPY TRIGONOMETRIC FUNCTIONSNUMPY ROUND FUNCTIONSNUMPY ARITHMATIC FUNCTIONSNUMPY POWER AND RECIPROCAL FUNCTIONSNUMPY MOD FUNCTIONNUMPY IMAG() AND REAL() FUNCTIONSNUMPY CONCATENATE()NUMPY STATISTICAL FUNCTIONSSTATISTICAL FUNCTIONSNUMPY AVERAGE FUNCTIONNUMPY SEARCH SORT FUNCTIONSSORT FUNCTIONNUMPY SORT FUNCTIONNUMPY ARGSORT()NONZERO AND WHERE FUNCTIONSEXTRACT FUNCTIONNUMPY ARGMAX ARGMIN()BYTESWAP COPIES AND VIEWSNUMPY STRING FUNCTIONSNUMPY CENTER FUNCTIONCAPITALIZE AND CENTER()NUMPY TITLE FUNCTIONSTRING FUNCTIONSNUMPY MATRIX LIBRARYNUMPY JOIN ARRAYSLINEAR ALGEBRARANDOM MODULESECRETS MODULERANDOM MODULE UNIFORM FUNCTIONRANDOM MODULE GENERATE NUMBER EXCEPT KSECRETSMODULE GENERATE TOKENSRANDOM MODULE GENERATE BINARY STRINGNUMPY MODULE REVISENUMPY INDEXINGNUMPY BASIC OPERATIONSNUMPY UNARY OPERATORSBINARY OPERATORS IN NUMPYNUMPY UNIVERSAL FUNCTIONSNUMPY FILTER ARRAYSNUMPY MODULE PROJECTS

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