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Module 02 - Basics 3

Comment:

This third module is the evolution of two original projects created by the Paris-based student organization 42 AI. They are named Bootcamp Python and Bootcamp Machine Learning. active members of 42 AI re-designed both of them for the school curriculum.

Bootcamps has been developped between August 2019 and March/April 2020. Active 42AI members organized severals sessions of 2 weeks to 42Paris students to offer them the possibility to get famliar with Python and basics concepts of machine learning.

The success of those sections brings the pedagogy to accept the idea to integrate the 2 bootcamps to the curriculum (initial discussion (01-05/2019) with 42 Paris pedago team highlighted a categorical opidea)

The transcription had been realized over the direction of Matthieu David several 42AI members contributed to the redaction on the correction scales. For futur corrections on the scale, please contact the 42AI association via [email protected] or the current 42AI pedagogical supervisor.

Introduction:

The Bootcamp Python and Bootcamp Machine Learning were originally created by 42AI active members and were adapted to 'piscine' format for the school 42 curriculum. For any issue or suggestion: 42Paris and 42AI.

As usual, you have to observe the following courtesy rules:

  • Remain polite, courteous, respectful, and constructive throughout the evaluation process. The well-being of the community depends on it.

  • Identify with the evaluated person or group the eventual dysfunctions of the assignment. Take the time to discuss and debate the problems you may have identified.

  • You must consider that there might be some differences in the understanding of and approach to project instructions, and the scope of its functionalities, between you and your peers. Always remain open-minded and grade them as fairly as possible. The pedagogy is valid only and only if peer-evaluation is conducted seriously.

The goal of this module is to get started with the Python language. You will study map, reduce, filter, args and kwargs ...

Disclaimer:

The serie of modules started to be produce at the time of the release of Python 3.7. Students are free to use later version of Python as long as they verified the producted code complies with all the aspects precised in the subjects. As a consequence we recommend to students to perform the modules with the the Python version 3.7 (but this is just an advice). Version can be checked with the command python -V.

Guidelines:

General rules:

  • Only grade the work that is in the student or group's GiT repository.

  • Double-check that the GiT repository does belong to the student. Ensure that the work is the one expected for the corrected exercise and don't forget to verify that the command "git clone" is run in an empty folder.

  • Check carefully that no malicious aliases were used to make you evaluate files that are not from the official repository.

  • To avoid any surprises, carefully check that both the evaluating and the evaluated students have reviewed the possible scripts used to facilitate the grading.

  • If the evaluating student has not completed that particular project yet, it is mandatory for them to read the entire subject prior to starting the defense.

  • Use the flags available on this scale to signal an empty repository, non-functioning program, a Norm error (specified next in general rules), cheating, and so forth. In these cases, the grading is over and the final grade is 0, or -42 in case of cheating. However, except the exception of cheating, you are encouraged to continue to discuss your work even if the later is in progress in order to identify any issues that may have caused the project failure and avoid repeating the same mistake in the future.

  • Use the appropriate flag.

  • Remember that for the duration of the defense, no other unexpected, premature, or uncontrolled termination of the program, else the final grade is 0.

  • You should never have to edit any file except the configuration file if the latter exists. If you want to edit a file, take the time to explain why with the evaluated student and make sure both of you agree on this.

  • The Norm: You will follow the PEP 8 standards.

  • The function eval is never allowed.

  • Your exercises are going to be evaluated by other students, make sure that your variable names and function names are appropriate and civil.

Exercise 0: Map, filter, reduce

In the error management question, function refers to an object function (a lambda function as instance) and iter refers to an iterable object. As mentioned in the subject, we do not expect the identical exception messages to be exactly the same than those of the functions map, filter and reduce. The goal of the exercise is to work on the built-in functions map, filter and reduce.

Errors management:

Guidelines:

each case:
Launch python from the terminal:

python3 -i

Then import the functions in the python prompt

from ft_filter import ft_filter
from ft_map import ft_map
from ft_reduce import ft_reduce
function = lambda x: x + 1
iterable = [1, 2, 3]

You can now copy paste the following bits of code into the python prompt in the terminal.

  • ft_map function: 
    ft_map(function_to_apply = None, iterable = iterable)
    Should print "< generator object at hexa_adress>" 
    list(ft_map(function_to_apply = None, iterable = iterable))
    You should get an Error



  • ft_filter function:

    ft_filter(function_to_apply = None, iterable = iterable)

    Should print < generator object at hexa_adress>

    list(ft_filter(function_to_apply = None, iterable = iterable))

    You should get an Error. (Returning None is also acceptable)



  • ft_reduce function:

    ft_reduce(None, iterable = iterable)

    You should get an Error. (Returning None is also acceptable)

    ft_reduce(function, None)

    You should get an Error. (Returning None is also acceptable)

Basic tests:

Guidelines:

Perform some basic test such as the following:

  • list(ft_map(lambda x: x + 2, [])): you should get [].
  • list(ft_map(lambda x: x + 2, [1])): you should get [3].
  • list(ft_map(lambda x: x ** 2, [1, 2, 3, 4, 5])): you should get [1, 4, 9, 16, 25].
  • list(ft_filter(lambda x: x <= 1, [])): you should get [].
  • ft_reduce((lambda x, y: x + y), [1]): you should get 1.
  • ft_reduce((lambda x, y: x * y), [1, 2, 3, 4]): you should get 24. Feel free to realize few more tests.

Exercise 1: args and kwargs

The goal of the exercise is to discover and manipulate *args and **kwargs.

Basic tests

Guidelines:

python -i main.py

  • With None as unique parameter:

    obj = what_are_the_vars(None)
doom_printer(obj)

    Shoud print:

    var_0: None
end

  • With a function as argument and a function as keyword argument:

    obj = what_are_the_vars(lambda x: x, function=what_are_the_vars)
doom_printer(obj)

    Shoud print:

    function: <function what_are_the_vars at 0x...>
var_0: <function <lambda> at 0x...>
end

  • With a kwarg named var_0:

    obj = what_are_the_vars(3, var_0=2)
doom_printer(obj)

    Shoud print:

    ERROR
end

Exercise 2: the logger

The goal of the exercise is to discover the decorator in Python and work with a very current one: "@log".

Basic tests

Guidelines:

Verify log decorator is correctly implemented:

  • It must be define in the same file.
  • The definition of log takes only one parameter which is supposed to be a function.
  • name is obtained via the function received as parameter (function.__name__).
  • username is obtained via the environment variable (os.environ["USER"]).
python logger.py

Verify the machine.log contains: ()Running: [ exec-time = <ms\s> ]

Exercise 3: Json issues

Implementation

Guidelines:

Verify the different methods are implemented:

  • __init__(self, filename=None, sep=',', header=False, skip_top=0, skip_bottom=0)
  • __enter__(self)
  • __exit__(self, type, value, traceback)
  • getdata(self)
  • getheader(self)

Ask the defendee to explain the __func__ functions to you.

Basic tests

Guidelines:

Perform the following tests. They should work and give correct results. Create a file called main.py and copy the following code in that file.

from csvreader import CsvReader
import sys 

if __name__ == "__main__":
    filename = sys.argv[1]
    with CsvReader(filename, skip_top=18, skip_bottom=0) as reader:
        if reader == None:
            print("File is corrupted or missing")
        else:
            print(reader.getheader(), end = "\n")
            print(reader.getdata(), end = "\n\n")

    with CsvReader(filename, header = True, skip_top=17, skip_bottom=0) as reader:
        if reader == None:
            print("File is corrupted or missing")
        else:
            print(reader.getheader(), end = "\n")
            print(reader.getdata(), end = "\n\n")

Put the files bad.csv and good.csv in the current folder then run in terminal:

python main.py good.csv
# None
# [['Ruth', '       "F"', '   28', '       65', '      131']]

#['Name', '     "Sex"', ' "Age"', ' "Height (in)"', ' "Weight (lbs)"']
#[['Ruth', '       "F"', '   28', '       65', '      131']]

The appearance of the output may vary slightly but the content and number of lines must be the same as the output above.

python main.py bad.csv
# File is corrupted or missing
# File is corrupted or missing
python main.py unicorn.csv
# File is corrupted or missing
# File is corrupted or missing

Additional error messages may print no python messages containing Traceback (these indicate a crash of the python interpreter even though the python console keeps running in the terminal)

Exercise 4: MiniPack

The goal of the exercise is to learn how to build a package and a distribution in Python.

Turn-in files

Guidelines:

A python package has to be comprised of the following files according to a similar file structure (one the two setup files is enough):

ex02/
├─ LICENSE.txt      (mandatory for the exercise, recommended otherwise)
├─ pyproject.toml   (recommended)
├─ README.md        (mandatory for the exercise, recommended otherwise)
├─ setup.cfg        (mandatory or setup.py)
├─ setup.py         (mandatory or setup.cfg)
├─ src/
|  └───my_minipack/
│      ├── __init__.py
│      ├── logger.py
│      └── progressbar.py
└── tests/          (not asked here, recommended otherwise)

First, check the different files in the directory. License, README, setup.py and setup.cfg files must be present and a directory containing the sources. In the sources directory you should have 3 python files (names may not be exactly the same):

  • init.py
  • logger.py
  • progressbar.py

Build script

Guidelines:

Begin by running:

find . -name "*.whl"
find . -name "*.tar.gz"

if any files are found delete them (make sure you are in the ex04 folder)

python3 -m venv venv
source venv/bin/activate
bash build.sh

Now the defendee should tell you how to install his package in a few lines.

pip list | grep minipack
# my-minipack        1.0.0

you should now be able to import your package !

python
import my_minipack.progressbar
import my_minipack.logger