Training January 2024

Logistics

• Foresee the number of tables needed
• Give the link to the webpage in the mail

Standard types and basic statements

• Comments are used several times without being presented
• Wording of ex. 5 is confusing ("fields"?)
• l variable name can be mistaken by 1
• Lots of slicing examples at the end of Shallow copy (too many?) Is all useful there ?
• Ex 6 talks about iterating on the list but we didn't see list iterations.
• Exception example is not corresponding to the code example

Functions (basics)

• Separate introductory code snippet: first function without parameter, second function with parameter
• Docstrings are not explained but used in the first example
• Is duck typing worth presenting ? 🦆

Files

• f-string in not needed in first example
• Always Windows vs. Linux paths issues. Show equivalent code for Windows.

TP0

• There is list comprehension and zip in the correction but it has not been seen before
• Statistics might be wrong on the output of Exercise 14
• The full correction is not really possible to explain (regexp, logging, ...)
• The last part about default values is not really possible (shown as a dict which is not seen for now)

Data structures

• Ex 17 has default arguments but these were not seen yet
• Some things are just repetitions from the presentation of list/tuple` before
• Spend less time on set ?
• No need to have a isinstance when presenting looping over dict
• Print of d1 is missing in the dict comprehension example (also the example is super confusing)

Functions (advanced)

• Examples are super-confusing. Even as a trainer, we don't really know what is the message of each example (expect showing what to not do).
• Example of the print docstring should come way before (to explain how to read the docstring)
• Example for default arguments is confusing (myfunc1 calling myfunc...)

TP1

• Corrections include bad habits (mutation of dic_station defined outside the function)

Numpy, manipulating arrays

• Use a fixed matrix as example to avoid confusing states
• Remove dtypes ? Pandas does it much better.

Matplotlib

• First example is not really representative