This fifth 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 opposition/refusal to this idea)
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.
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_staff_pedagogy 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 library Pandas and the manipulation of dataframes.
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.
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.
The goal of this exercise is to create a Fileloader class containing a load and a display method.
put the path to the csv file in an environment variable
export CSV_PATH="path_to_athletes_events.csv"
python 3-
Should output (approximately) :
from FileLoader import FileLoader import os f = FileLoader() df = f.load(os.environ["CSV_PATH"])
# (271116, 15)
-
Should Output
f.display(df, 3)
# ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal # 0 1 A Dijiang M 24.0 180.0 80.0 China CHN 1992 Summer 1992 Summer Barcelona Basketball Basketball Men's Basketball NaN # 1 2 A Lamusi M 23.0 170.0 60.0 China CHN 2012 Summer 2012 Summer London Judo Judo Men's Extra-Lightweight NaN # 2 3 Gunnar Nielsen Aaby M 24.0 NaN NaN Denmark DEN 1920 Summer 1920 Summer Antwerpen Football Football Men's Football NaN
-
f.display(df, -3)
Should output:
# ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal # 271113 135570 Piotr ya M 27.0 176.0 59.0 Poland POL 2014 Winter 2014 Winter Sochi Ski Jumping Ski Jumping Men's Large Hill, Team NaN # 271114 135571 Tomasz Ireneusz ya M 30.0 185.0 96.0 Poland POL 1998 Winter 1998 Winter Nagano Bobsleigh Bobsleigh Men's Four NaN # 271115 135571 Tomasz Ireneusz ya M 34.0 185.0 96.0 Poland POL 2002 Winter 2002 Winter Salt Lake City Bobsleigh Bobsleigh Men's Four NaN
-
f.display(df, 0) # should display Nothing or the Header (column names of the dataframe)
-
f.display(df, "lol") #shouldnt crash
The goal of this exercise is to create a function that will return a dictionary containing the age of the youngest woman and the youngest man who took part in the Olympics a given year.
Check the results of the following cases:
The name of the function YoungestFellah may be changed to make this code run
from FileLoader import FileLoader
from YoungestFellah import youngestfellah
import os
loader = FileLoader()
data = loader.load(os.environ["CSV_PATH"])
print(youngestfellah(data, 1992))
# output is: "{'f': 12.0, 'm': 11.0}"
print(youngestfellah(data, 2004))
# output is: "{'f': 13.0, 'm': 14.0}"
print(youngestfellah(data, 2010))
# output is: "{'f': 15.0, 'm': 15.0}"
print(youngestfellah(data, 2003))
# output is: "{'f': nan, 'm': nan}"If something does not match, the exercise is failed.
The goal of this exercise is to create a function displaying the proportion of participants who played a given sport, among the participants of a given genders.
Verify the results of the following cases: (please adapt the path to csv file)
from FileLoader import FileLoader
from ProportionBySport import proportionBySport
import os
loader = FileLoader()
data = loader.load(os.environ["CSV_PATH"])
print("")
print(proportionBySport(data, 2004, 'Tennis', 'F'), end = "\n\n")
# output is "0.02307"
print(proportionBySport(data, 2008, 'Hockey', 'F'), end = "\n\n")
# output is "0.03284"
print(proportionBySport(data, 1964, 'Biathlon', 'M'), end = "\n\n")
# output is "0.00659"The rounding of the result does no matter (0.023 or 0.02307969707897584 or 2.3 % are all acceptable
If something does not match, the exercise is failed.
The goal of this exercise is to code a function that will return a dictionary of dictionaries giving the number and type of medals for each year during which the participant won medals.
Verify the results of the following cases: (Please adapt the path to csv file)
import pandas as pd
from HowManyMedals import howManyMedals
import os
data = pd.read_csv(os.environ["CSV_PATH"])
print(howManyMedals(data, 'Gary Abraham'))
# the output is: "{1976: {'G': 0, 'S': 0, 'B': 0}, 1980: {'G': 0, 'S': 0, 'B': 1}}"
print(howManyMedals(data, 'Yekaterina Konstantinovna Abramova'))
# the output is "{2006: {'G': 0, 'S': 0, 'B': 1}, 2010: {'G': 0, 'S': 0, 'B': 0}}"
print(howManyMedals(data, 'Kristin Otto'))
# the output is: "{1988: {'G': 6, 'S': 0, 'B': 0}}"If something does not match, the exercise is failed.
The goal of this exercise is to write a class called SpatioTemporalData that takes a dataset (pandas.DataFrame) as argument in its constructor and implements two methods.
Check the result of the following cases:
import pandas as pd
from SpationTemporalData import SpatioTemporalData
import os
df = pd.read_csv(os.environ["CSV_PATH"])
sp = SpatioTemporalData(df)
print(sp.where(2000))
# output is: ['Sydney']
print(sp.where(1980))
# output is: ['Lake Placid', 'Moskva'] If a single of these locations is returned it's ok.
print(sp.when('London'))
# output is: [2012, 1948, 1908]You should try other locations and years. If something does not match, the exercise is failed.
The goal of this exercise is to write a function that returns a dictionary of dictionaries giving the number and type of medal for each competition where the country delegation earned medals.
Print the result of howManyMedalsByCountry functions calls with various countries and check that the format respects the following:
import pandas as pd
from HowManyMedalsByCountry import howManyMedalsByCountry
import os
df = pd.read_csv(os.environ["CSV_PATH"])
print(howManyMedalsByCountry(df, "United States") == {1896: {'G': 11, 'S': 7, 'B': 2}, 1900: {'G': 18, 'S': 14, 'B': 13}, 1904: {'G': 65, 'S': 68, 'B': 66}, 1906: {'G': 12, 'S': 6, 'B': 6}, 1908: {'G': 34, 'S': 16, 'B': 15}, 1912: {'G': 46, 'S': 25, 'B': 36}, 1920: {'G': 87, 'S': 41, 'B': 35}, 1924: {'G': 65, 'S': 41, 'B': 36}, 1928: {'G': 39, 'S': 22, 'B': 18}, 1932: {'G': 60, 'S': 57, 'B': 43}, 1936: {'G': 30, 'S': 29, 'B': 28}, 1948: {'G': 57, 'S': 34, 'B': 30}, 1952: {'G': 55, 'S': 38, 'B': 25}, 1956: {'G': 39, 'S': 57, 'B': 21}, 1960: {'G': 83, 'S': 27, 'B': 19}, 1964: {'G': 75, 'S': 36, 'B': 28}, 1968: {'G': 86, 'S': 36, 'B': 35}, 1972: {'G': 70, 'S': 58, 'B': 33}, 1976: {'G': 62, 'S': 46, 'B': 30}, 1980: {'G': 24, 'S': 4, 'B': 2}, 1984: {'G': 143, 'S': 75, 'B': 33}, 1988: {'G': 66, 'S': 48, 'B': 36}, 1992: {'G': 79, 'S': 46, 'B': 52}, 1994: {'G': 6, 'S': 8, 'B': 5}, 1996: {'G': 98, 'S': 41, 'B': 28}, 1998: {'G': 25, 'S': 2, 'B': 3}, 2000: {'G': 69, 'S': 34, 'B': 48}, 2002: {'G': 9, 'S': 52, 'B': 9}, 2004: {'G': 65, 'S': 66, 'B': 38}, 2006: {'G': 9, 'S': 7, 'B': 32}, 2008: {'G': 64, 'S': 61, 'B': 47}, 2010: {'G': 8, 'S': 61, 'B': 20}, 2012: {'G': 82, 'S': 44, 'B': 38}, 2014: {'G': 8, 'S': 28, 'B': 16}, 2016: {'G': 95, 'S': 52, 'B': 45}})Should output True
If the previous test fails the defendee should use the following list for team sports, If he does not filter for team sports, he failed the exercise.
team_sports = ['Basketball', 'Football', 'Tug-Of-War', 'Badminton', 'Sailing', 'Handball', 'Water Polo', 'Hockey', 'Rowing', 'Bobsleigh', 'Softball', 'Volleyball', 'Synchronized Swimming', 'Baseball', 'Rugby Sevens', 'Rugby', 'Lacrosse', 'Polo']The goal the exercise is to introduce plotting methods among the different libraries Pandas, Matplotlib, Seaborn or Scipy
Performs the following basic tests with the given dataset with at least 3 numerical features:
- run the method histogram with one, two and three valid features
- run the method density with one, two and three valid features
- run the method pair_plot with one, two and three valid features
- run the method box_plot with one, two and three valid features
The goal the exercise is to introduce plotting methods among the different libraries Pandas, Matplotlib, Seaborn or Scipy.
- giving 'Medal' and 'Age' for the method compare_box_plots, you should observe 3 boxes: Bronze. Silver and Gold
- giving 'Medal' and 'Height' for the method compare_histograms, you should observe 3 histograms
- giving 'Medal' and 'Weight' for the method density, you should observe 3 curves of density plot