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
Visit the PythonInformer Discussion Forum for numeric Python.
If you found this article useful, you might be interested in the book NumPy Recipes or other books by the same author.