English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 58m | 364 MB
This course offers scientists and engineers (ranging from students of those disciplines to experienced professionals) a dedicated, empowering introduction to Python for scientific and engineering applications. Theoretical astrophysicist and Python enthusiast Michele Vallisneri explains how Python can help you become a better engineer or physicist by making your work more efficient, accurate, and agile. Michele walks you through installing Python for macOS, Windows, and Linux, as well as setting up Jupyter notebooks. He explains how you can make Python fast using NumPy arrays, the SciPy library, Numba, and Cython. Michele then tackles ways to ensure your code is correct with tools for symbolic computation, differential equations, interpolation, and more. He finishes up with ideas to make your computational life easier with Python, including JSON, pandas, HDF5, automation with Python scripts, and scientific workflows with Snakemake.
Table of Contents
Introduction
1 Become a better engineer or scientist with Python
2 What you should know
1. Installation
3 macOS installation
4 Windows and Linux installation
5 Working with Jupyter notebooks
6 Using the exercise files
2. Make It Fast
7 Making Python code fast
8 Introduction to NumPy arrays
9 Matrix operations with NumPy
10 Linear algebra and sparse matrices with NumPy and SciPy
11 Code generation with Numba and Cython
12 Wrapping legacy code with Cython, CFFI, and F2PY
13 Challenge Diffusion equation
14 Solution Diffusion equation
3. Make It Right
15 Making Python code right
16 Symbolic computation with SymPy
17 Units, constants, timescales, and more with Astropy
18 Differential equations with SciPy
19 Interpolation and optimization with SciPy
20 Debugging with ipdb
21 Challenge Planetary conjunctions
22 Solution Planetary conjunctions
4. Make It Easy
23 Making Python code easy
24 Web resources with requests and JSON
25 Tables with pandas
26 Scientific datasets with HDF5
27 Automation with Python scripts
28 Scientific workflows with Snakemake
29 Challenge Perfect numbers
30 Solution Perfect numbers
Conclusion
31 Next steps
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