Document de travail sur le plan de la formation py-edu-fr-init

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Document de travail sur le plan de la formation py-edu-fr-init#

About#

Note

This is an historical document. We now can work and modify the source of the py-edu-fr-init training.

Note

Ce potentiel cours (dépôt ici https://foss.heptapod.net/py-edu-fr/py-edu-fr-init) est à destination de personnes avec pas ou peu d’expérience en Python mais (i) quelques bases en programmation et (ii) un niveau Master ou plus ou formation permanente. Ce n’est donc pas un cours pour (i) des vrais débutants en programmation (les notions de variables et de boucles doivent être connues) ni pour (ii) des lycéens ou étudiants en 1er cycle universitaire. Une maîtrise correcte de l’anglais à l’écrit est nécessaire.

Premier jet très inspiré de https://python-uga.gricad-pages.univ-grenoble-alpes.fr/py-training-uga mais aussi d’autres ressources listées ici https://py-edu-fr.pages.heptapod.net/resources.html.

Content#

0. Preliminaries (to be done before the training)#

0.1 Setup your development environment#

Install:

  • Python from something like apt (Linux) or python.org (Windows)

  • Pipx

  • PDM

  • UV

  • Miniforge

  • Pixi

With instructions for Windows, Linux and macOS.

0.2 Clone this repository locally on your computer#

1. Appetizer with JupyterLite#

Tiny introduction without installing anything…

1.0 Quick discovery of JupyterLab#

1.1 First steps with Python#

  • First objects of simple types, first variables, first function calls (with print())

  • A point on function calls (with print() and round())

1.2 First usage of Python libraries (a simple Matplotlib plot)#

2. Generalities on Python#

2.1 Characteristics of the Python language#

Take away: dynamic languages strong thanks to tooling and testing.

2.2 Different ways to use Python#

  • Where/how Python code can be executed

  • Tools to install Python and install Python libraries and programs (basics)

2.3 Tools to develop Python code (short introduction)#

  • Linters, formatters and type checkers

  • Documentation (get and write docstrings) and comments

  • Testing (principle and very basic realization with assert)

  • Editors / IDE:

    • JupyterLab

    • VSCode

    • Spyder

3. Basic pure Python#

3.1 Simple types, variables and objects#

  • int, bool, float, complex, NoneType

  • True, False, None

  • Keyword is, and, or and not

  • Notions of objects and attributes (with complex)

  • Built-in functions isinstance(), dir()

3.2 String, bytes and immutability#

  • First intro immutability

  • First sequence: indexing, slicing, lookup (keyword in)

  • Notions of objects and methods

  • utf-8 and bytes

3.3 Conditions and if/elif/else#

  • Notion of block

  • Block if, keywords elif and else

  • Keywords pass and built-in constant ... is Ellipsis

  • Again isinstance()… and notion of duck typing

3.4 Simple data structures (list and tuple, basics)#

  • Immutability and mutability

  • Keyword in

3.5. Loops (while and for)#

  • Blocks while and for

  • Keywords in, break and continue

3.6. Exceptions and traceback#

  • As a user: don’t panic, read

  • Block try with keywords except and finally

  • Keyword raise

3.7. Read / write files#

Built-in function open() and context manager with

3.8. More data structures (set and dict, basics)#

4. Structure your code and reuse existant code#

4.0 Introduction#

Notes on documentation and testing…

4.1 Functions (very basics)#

  • Keyword return

4.2 Modules, packages and imports (basics)#

  • Keywords import, as and from

4.3 Classes and objects (very basics)#

  • Only show how to define simple objects with class

4.4 The standard library#

  • Small examples from (or just mention) sys, os/shutils, copy, pathlib, argparse, math, re, dataclasses, itertools, turtle, functools, unittest, collections

  • https://docs.python.org/3/tutorial/stdlib.html

  • Mention good alternatives outside of the standard library (Pytest, Click, …)

4.5 Test your code#

  • assert

  • Pytest

4.6 Back to functions (less basics)#

  • Usage of tuples for functions

  • Function calls: namespaces and objects “passed by references”

  • Different types of arguments

  • Global vs Local variables

  • Keywords global, nonlocal, lambda

4.7 Generators with functions and yield (only for advanced training)#

  • Keyword yield

Note

At this point, we have introduce all Python keywords, except async and await!

5. Use Python software#

5.1 Global installations of Python tools#

  • Packaging tools: specific to the OS or others, like Pipx, PDM, UV, Pixi, …

  • Windows specificity and native applications

5.2 Using and installing Python libraries#

  • Virtual environment (Python envs and others, in particular conda)

  • Python projects, project managers and lock files (PDM, UV, Pixi…)

5.3 Basic packaging (with testing, linting, formatting and doc) of a simple tool#

Goal: create a simple package with PDM for a simple application with dev tools like PDM, Pytest and Sphinx.

We base the presentation on a simple tool (with a CLI[1] and TUI[2]) to visualize info from Wikipedia using packages like sys, os, pathlib, argparse and runtime dependencies like Requests, Pycowsay and Textual…

Mention PyPA, PEP 517 (frontend/backend and isolated builds), PEP 518 (pyproject.toml versus setup.py)

6. Scientific Python (basics)#

6.1. Python scientific ecosystem#

6.2. Create your own working environment#

6.3. Introduction to Numpy and the Python Array API standard#

https://data-apis.org/array-api/2023.12/index.html

6.4. Introduction to Matplotlib and alternatives#

6.5. Introduction to Pandas and alternatives#

7. Intermediate pure Python (not needed for all students)#

7.1 Decorators#

7.2 Object Oriented Programming and magic (aka “dunder”) methods#

7.3 Generators#

7.4 Object Oriented Programming and heritage#

  • classmethod(), staticmethod(), property() and __slots__

  • Abstract Base Classes (abc)

7.5 Gradual typing and type checkers#

7.6 Simple parallelism (threading and multiprocessing)#

  • Explanation about the GIL

  • Note about Python free-threading

  • Note on subinterpreters

  • Simple examples threading and multiprocessing

7.7 Asynchronous code and concurrency#

7.8 Define context managers#

8. More#

8.1 Introduction about performance#

  • Importance of profiling

  • Alternative Python implementations (PyPy and GraalPy)

8.2 List of useful projects#

8.3 List of web resources to find and get help#

8.4 Tools based on AI to develop with Python#

8.5 List of subjects not approached during this py-edu-fr training#

In particular metaclasses, dynamical type creation, descriptors, coroutines, weakref, inspect.

CR visio py-edu-fr 2025-03-04   Document de travail sur le plan de la formation py-edu-fr-science
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