Python for beginners

Weekly training session during fall semester (12 × 1h30).
Note

The 2025 session will begin the week of 3 November.

Training materials

https://can.gitbiopages.ens-lyon.fr/python_basis

When, Where?

3 weekly slots (in French or English depending on demand) and 12 places per slot, for a total of 36 places.
Schedule for the three slots:

  • Monday 2 - 3.30 P.M
  • Thursday 10 - 11.30 A.M
  • Thursday 2 - 3.30 P.M

The training course will take place in the IGFL training room.

Registration

If you are interested, please register using the following link:
https://framaforms.org/formation-gratuite-du-can-python-pour-debutantes-free-can-training-course-python-for-beginners

Supervisers can register new people starting their contract in October to reserve a spot : PhD students, post-doctoral fellows, etc.

Important

By registering, you agree to attend the entire course (except for one-time absences in case of professional or personal imperative obviously).

Description

The CAN is organizing a training course “Python for beginners” during the fall semester.

This training session will last around twelve weeks between November and February (except during Christmas Holidays) with 1h30 of tutorial/practical every week.

This training session is free (thanks to volunteering trainers) and available for all (permanent and non-permanent) members of the following labs: CIRI, IGFL, LBMC, RDP, MMSB, LBTI, IVPC, IBCP (SFR BioSciences partners).

Requirements

If you use your own personal laptop, you must install a dedicated Conda environment before starting the training. You can ask Laurent Gilquin for help.

Specific details

Having an @ens-lyon.fr account is not mandatory. Workstations in the training room have a “local” user account that can be used for the duration of the training.

Content

The course focuses on learning the basics of Python and using three data science libraries, NumPy, Pandas and Matplotlib. The training’s program is divided in 5 parts:

  1. the Python core: syntax, variables, operators, control flow, functions (≈4h)
  2. the main Python containers: list, tuple, set, dictionary (≈4h)
  3. how to perform efficient scientific computation using NumPy (≈4h)
  4. how to manipulate and transform tabular data with Pandas (≈4h)
  5. how to visualize data with Matplotlib and Seaborn (≈2h)