Numpy - contents
Martin McBride, 2019-09-14
This section covers numpy, a library for performing efficient calculations on large numerical arrays.
- Introduction to numpy - an overview of the numpy library.
- Anatomy of a numpy array - arrays of different shapes and sizes.
- Numpy efficiency - how numpy arrays achieve efficiency.
- Creating numpy arrays
- Fixed value arrays - creating arrays that are filled with a fixed value (eg all zeros).
- Data series - creating arrays that are filled with a data series.
- Arrays from existing data - creating arrays that are filled with data from an existing source.
- Data types - how to create arrays of integers and floats of different precisions.
- Random data - creating arrays that are filled with random data.
Avoiding loops with vectorisation
- Vectorisation - apply expressions to every element in an array.
- Universal functions - apply maths functions to every element in an array.
- Broadcasting - using indexing to efficiently map one array onto a larger array.
- Reducing arrays - reducing all the values in an array to a single representative value.
- Advanced vectorisation - using fancy indexing and related techniques.
- Image processing with numpy - image processing with numpy arrays
If you found this article useful, you might be interested in the book NumPy Recipes or other books by the same author.