October 2012
Beginner to intermediate
463 pages
12h 53m
English
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4. NumPy Basics: Arrays and Vectorized Computation - Python for Data Analysis [Book]
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Chapter 4. NumPy Basics: Arrays and Vectorized Computation
NumPy, short for Numerical Python, is the fundamental package required for high performance scientific computing and data analysis. It is the foundation on which nearly all of the higher-level tools in this book are built. Here are some of the things it provides:
ndarray, a fast and space-efficient multidimensional array providing vectorized arithmetic operations and sophisticated broadcasting capabilitiesStandard mathematical functions for fast operations on entire arrays of data without having to write loops
Tools for reading / writing array data to disk and working with memory-mapped files
Linear algebra, random number generation, and Fourier transform capabilities
Tools for integrating code written in C, C++, and Fortran
The last bullet point is also one of the most important ones from an ecosystem point of view. Because NumPy provides an easy-to-use C API, it is very easy to pass data to external libraries written in a low-level language and also for external libraries to return data to Python as NumPy arrays. This feature has made Python a language of choice for wrapping legacy C/C++/Fortran codebases and giving them a dynamic and easy-to-use interface.
While NumPy by itself does not provide very much high-level data analytical functionality, having an understanding of NumPy arrays and array-oriented computing will help you use tools like pandas much more effectively. If you’re new to Python and just looking to get your ...