Advanced Python for science

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

Content¶

• Python initiation: core language, modern workflows, and scientific applications
• Preliminaries
  • About setting up a computer for programming and computing
  • Notes for Windows users
  • Notes for macOS users
  • Notes for Linux and WSL users
  • Python
  • Conda ecosystem
  • Editors and IDEs
  • Versioning tools
  • Clone the repo of this training
  • Check your installation
• Appetizer with JupyterLite
  • First steps pure Python: function calls
  • First steps pure Python: objects, types and variables
  • First usage of Python libraries
• Get and use the training source
• Generalities on Python
  • Main characteristics
  • Python limitations
  • Different ways to use Python
  • Tools to develop Python code
• Basic Pure Python
  • Simple types, variables
  • Strings, bytes and immutability
  • Conditions (if and match)
  • Lists and tuples
  • Indexing and slicing
  • Loops (while and for)
  • Exceptions and traceback
  • Read / write files
  • More data structures
• Structure and reuse code
  • Functions (very basic)
  • Testing
  • Practice: Read corrupted files
  • Modules, packages and imports
  • Classes and objects
  • Practice: intro to classes
  • The standard library
  • Documentation
  • Back to functions (less basics)
• Use, make & share Python software
  • Introduction: PyPI and friends
  • Conda & the conda-forge project
  • Global installation of Python apps
  • Using Third-party libraries
  • Modern Python packaging
• Scientific Python (basics)
  • Python scientific ecosystem
  • Introduction to Numpy and the Python Array API standard
  • NumPy efficiency
  • Creation of Numpy arrays
  • Manipulating NumPy arrays
  • Introduction to Matplotlib and alternatives
  • Practice: image processing
  • Introduction to Pandas and alternatives
  • Practice: weather data
• Recommended file formats
  • HDF5 and NetCDF files
• Software engineering
  • 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