NumPy

1. What is NumPy

   The ndarray, vectorization benefits, and why NumPy arrays are faster than Python lists.

2. Absolute Basics for Beginners

   Creating arrays, shape, size, ndim, reshape, basic operations, and array attributes.

3. Quickstart

   Array creation, printing, basic operations, universal functions, indexing, slicing, iterating, shape manipulation, and stacking.

4. Array Creation

   np.array, zeros, ones, arange, linspace, random generators, and creating arrays from files.

5. Indexing on ndarrays

   Basic slicing, integer and boolean indexing, advanced indexing, and fancy indexing assignment.

6. Data Types

   dtype, type casting, integer overflow, floating point precision, and array scalars.

7. Broadcasting

   Broadcasting rules, shape compatibility, and vectorized operations across differing shapes.

8. Copies and Views

   View versus copy semantics, base attribute, and when slicing shares memory.

9. Universal Functions

   ufuncs, element-wise operations, reductions, accumulate, where and out parameters.

10. Structured Arrays

    Structured dtypes, record arrays, field access, and heterogeneous data.

11. Strings and Bytes Arrays

    Unicode and byte string dtypes, StringDType, and the np.strings functions.

12. I/O with NumPy

    Loading and saving text and binary data, genfromtxt, save, load, and memory mapping.

13. Interoperability

    Array protocols, buffer protocol, DLPack, __array_function__, and working with other array libraries.

14. Writing Performant Code

    Multi-core threading, avoiding Python loops, and parallel performance considerations.

15. NumPy for MATLAB Users

    Mapping MATLAB idioms to NumPy, array versus matrix semantics, and indexing differences.

16. How-Tos

    Task-oriented recipes such as reading CSV data, masking, and writing a NumPy-based tool.

Each section links to the official page. Titles and summaries by Extra Credit; the docs belong to their authors.

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