Скачать или смотреть 1.3 Numpy advanced (data models) - Python for Scientific Computing 2022

Numpy is the standard Python array package. It provides not only the basic data structure, but also plenty of operations on this data. By using built-in functions, many operations can be vectorized and made much faster (just like they are done in any other high-level numerical language). Many other scientific and numerical packages are build using Numpy, so it is very important to learn to use it well.

This is actually the "advanced numpy" lesson, where we go into the data structure behind the arrays. This is important for using numpy efficiently.

https://aaltoscicomp.github.io/python...

00:00 About numpy: fast or slow?
03:20 Why is numpy fast? - the libraries it uses
07:18 Exercise 1: Numpy warm up, generate random numbers
09:01 Going over the exercise
17:32 Numpy tries to avoid copying data
21:02 Numpy data format in memory
23:22 Exercise: create a ravel function
24:51 Going over the exercise
25:52 Array strides
31:14 Copy vs view and using memory buffers

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Python for Scientific Computing is a bridge between basic Python courses and scientific work with Python. This is a basic to intermediate course in Python tools such as NumPy, SciPy, Matplotlib, and Pandas. It also covers some more advanced tools, such as Binder, releasing software, data formats, etc. It is suitable for people who have a basic understanding of Python and want to know some internals and important libraries for science. We don't cover anything in too much depth, but we do introduce you to all of the main tools you will need.

This course was put on as a collaboration between partners in Finland, Norway, and Sweden, coordinated by Aalto Scientific Computing.

Links:

Playlist:    • Python for Scientific Computing 2022  

Course material: https://aaltoscicomp.github.io/python...

Workshop webpage: https://scicomp.aalto.fi/training/sci...

Aalto Scientific Computing: https://scicomp.aalto.fi/

CodeRefinery: https://coderefinery.org