Advanced Python for science

This is a website for a training on Python for science written by the group py-edu-fr.

Content

• About this training
• Preliminaries
  • Setup your computer
  • Clone the repo of this training
• Appetizer with JupyterLite
  • Introduction to JupyterLab
  • First steps with pure Python
  • First usage of Python libraries
• Generalities on Python
  • Main characteristics
  • Python limitations
  • Different ways to use Python
  • Tools to develop Python code
• Basic Pure Python
  • Simple types, variables and objects
  • String, bytes and immutability
  • Conditions (if block)
  • Lists and tuples
  • Loops (while and for)
  • Exceptions and traceback
  • Read / write files
  • More data structures
• Structure and reuse code
  • Functions (very basics)
  • Modules, packages and imports
  • Classes and objects
  • The standard library
  • Test your code
  • Back to functions (less basics)
• Using Python software
  • PyPI and PyPA
  • Conda and the conda-forge project
  • Global installations of Python tools
  • Using Python libraries
  • Basic packaging
• Scientific Python (basics)
  • Python scientific ecosystem
  • Create your own working environment
  • Introduction to Numpy and the Python Array API standard
  • Introduction to Matplotlib and alternatives
  • Introduction to Pandas and alternatives
• Software enginering
  • Versioning
  • Continuous integration
  • Documentation
  • Debugging
  • Gradual typing
• Intermediate pure Python
  • Decorators
  • OOP and magic methods
  • Generators
  • OOP and heritage
  • Define context managers
• Performance optimization
  • Generalities on optimization in sequential
  • Profiling
• Other languages and wrapping
  • Wrapping C and C++
  • Wrapping Rust
  • Wrapping Fortran
• Parallelism
  • Threading
  • Concurrency
  • Multiprocessing
  • MPI and mpi4py
• Tools for Python HPC
  • Cython
  • Pythran
  • Numba
  • JAX
  • Codon
• Application on numerical kernels
  • NBody
  • DTW
• A bit of Web
• Native Python applications
• GPU
• Databases
• Future & science fiction
  • Subinterpreters
  • Free Threading Python
  • Faster CPython and CPython JIT
  • HPy