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‎CHANGELOG.md

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FIX wrong example for D01 ex03 "vector"
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- One file per exercise
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- Indentation fixes
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- Makefile update to clean latex compilation files
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‎Makefile

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%.pdf:
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@echo "We are taking care of: $@"
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@$(MAKE) -C $(shell dirname $@)
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@$(MAKE) clean -C $(shell dirname $@)
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cp $@ build/$(shell dirname `dirname $@`).pdf
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dirs: $(TARGETS_DIRS)

‎build/module00.pdf

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‎build/module01.pdf

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‎module04/exercises/m04ex00.tex

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\chapter{Exercise 00}
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\extitle{FileLoader}
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\turnindir{ex00}
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\exnumber{00}
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\exfiles{FileLoader.py}
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\exforbidden{None}
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\makeheaderfilesforbidden
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% ================================== %
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\section*{Objective}
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% ---------------------------------- %
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The goal of this exercise is to create a Fileloader class containing a
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load and a display method.
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% ================================== %
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\section*{Instructions}
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% ---------------------------------- %
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Write a class named \texttt{FileLoader} which implements the following methods:
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\begin{itemize}
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\item \texttt{load(self, path)}: takes as an argument the file path of the dataset to load,
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displays a message specifying the dimensions of the dataset (e.g. 340 x 500)
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and returns the dataset loaded as a pandas.DataFrame.
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\item \texttt{display(self, df, n)}: takes a pandas.DataFrame and an integer as arguments,
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displays the first n rows of the dataset if n is positive, or the last n rows if n is negative.
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\end{itemize}
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\noindent \texttt{FileLoader} object should not raise any exceptions (wrong path, file does not exist, parameters different than a string ...).
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% ================================== %
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\section*{Examples}
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% ---------------------------------- %
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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from FileLoader import FileLoader
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loader = FileLoader()
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data = loader.load("../data/adult_data.csv")
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# Output
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Loading dataset of dimensions 32561 x 15
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loader.display(data, 12)
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# Output
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age workclass fnlwgt ... hours-per-week native-country salary
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0 39 State-gov 77516 ... 40 United-States <=50K
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1 50 Self-emp-not-inc 83311 ... 13 United-States <=50K
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2 38 Private 215646 ... 40 United-States <=50K
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3 53 Private 234721 ... 40 United-States <=50K
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4 28 Private 338409 ... 40 Cuba <=50K
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5 37 Private 284582 ... 40 United-States <=50K
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6 49 Private 160187 ... 16 Jamaica <=50K
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7 52 Self-emp-not-inc 209642 ... 45 United-States >50K
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8 31 Private 45781 ... 50 United-States >50K
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9 42 Private 159449 ... 40 United-States >50K
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10 37 Private 280464 ... 80 United-States >50K
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11 30 State-gov 141297 ... 40 India >50K
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[12 rows x 15 columns]
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\end{minted}
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\hint{NB: Your terminal may display more columns if the window is wider.}

‎module04/exercises/m04ex01.tex

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\chapter{Exercise 01}
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\extitle{YoungestFellah}
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\turnindir{ex01}
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\exnumber{01}
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\exfiles{FileLoader.py, YoungestFellah.py}
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\exforbidden{None}
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\makeheaderfilesforbidden
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% ================================= %
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\section*{Objective}
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% --------------------------------- %
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The goal of this exercise is to create a function that will return a
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dictionary containing the age of the youngest woman and the youngest
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man who took part in the Olympics for a given year.
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% ================================= %
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\section*{Instructions}
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% --------------------------------- %
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This exercise uses the following dataset: \texttt{athlete\_events.csv}.\\
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\\
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Write a function \texttt{youngest\_fellah} that takes two arguments:
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\begin{itemize}
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\item a pandas.DataFrame which contains the dataset
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\item an Olympic year.
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\end{itemize}
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The function returns a dictionary containing the age of the youngest
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woman and man who took part in the Olympics on that year.
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The names of the dictionary's keys are up to you, but they must be explicit and self-explanatory.
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% ================================= %
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\section*{Examples}
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% --------------------------------- %
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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from FileLoader import FileLoader
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loader = FileLoader()
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data = loader.load('../data/athlete_events.csv')
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# Output
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Loading dataset of dimensions 271116 x 15
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from YoungestFellah import youngest_fellah
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youngest_fellah(data, 2004)
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# Output
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{'f': 13.0, 'm': 14.0}
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\end{minted}

‎module04/exercises/m04ex02.tex

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\chapter{Exercise 02}
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\extitle{ProportionBySport}
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\turnindir{ex02}
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\exnumber{02}
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\exfiles{FileLoader.py, ProportionBySport.py}
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\exforbidden{None}
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\makeheaderfilesforbidden
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% ================================= %
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\section*{Objective}
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% --------------------------------- %
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The goal of this exercise is to create a function displaying
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the proportion of participants who played a given sport, among
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the participants of a given gender.
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% ================================= %
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\section*{Instructions}
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% --------------------------------- %
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This exercise uses the dataset \texttt{athlete\_events.csv}.
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Write a function \texttt{proportion\_by\_sport} that takes four arguments:
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\begin{itemize}
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\item a pandas.DataFrame of the dataset,
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\item an olympic year,
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\item a sport,
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\item a gender.
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\end{itemize}
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The function returns a float corresponding to the proportion (percentage) of participants
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who played the given sport among the participants of the given gender.\\
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\\
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The function answers questions like the following :
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"What was the percentage of female basketball players among all female
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participants in the 2016 Olympics?"
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\hint{
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Here and later on, if needed, drop duplicate sports people to count only unique ones. Beware to call the dropping function
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at the right moment and with the right parameters, in order not to omit any individual.
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}
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% ================================= %
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\section*{Examples}
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% --------------------------------- %
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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from FileLoader import FileLoader
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loader = FileLoader()
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data = loader.load('../data/athlete_events.csv')
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# Output
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Loading dataset of dimensions 271116 x 15
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from ProportionBySport import proportion_by_sport
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proportion_by_sport(data, 2004, 'Tennis', 'F')
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# Output
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0.019302325581395347
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\end{minted}
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\newline
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We assume that we are always using valid arguments as input,
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and thus do not need to handle input errors.

‎module04/exercises/m04ex03.tex

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\chapter{Exercise 03}
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\extitle{HowManyMedals}
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\turnindir{ex03}
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\exnumber{03}
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\exfiles{FileLoader.py, HowManyMedals.py}
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\exforbidden{None}
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\makeheaderfilesforbidden
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% ================================= %
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\section*{Objective}
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% --------------------------------- %
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The goal of this exercise is to implement a function that will return
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a dictionary of dictionaries giving the number and types of medals
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for each year during which the participant won medals.
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% ================================= %
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\section*{Instructions}
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% --------------------------------- %
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This exercise uses the following dataset: \texttt{athlete\_events.csv}.
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Write a function \texttt{how\_many\_medals} that takes two arguments:
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\begin{itemize}
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\item a pandas.DataFrame which contains the dataset,
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\item a participant's name.
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\end{itemize}
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The function returns a dictionary of dictionaries giving the number and type of medals
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for each year during which the participant won medals.\\
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\\
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The keys of the main dictionary are the years of the Olympic games.\\
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\\
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In each year's dictionary, the keys are 'G', 'S', 'B' corresponding to the type of
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medals won (Gold, Silver, Bronze).\
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The innermost values correspond to the number of medals of a given type won for a given year.
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% ================================= %
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\section*{Examples}
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% --------------------------------- %
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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from FileLoader import FileLoader
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loader = FileLoader()
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data = loader.load('../data/athlete_events.csv')
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# Output
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Loading dataset of dimensions 271116 x 15
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from HowManyMedals import how_many_medals
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how_many_medals(data, 'Kjetil Andr Aamodt')
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# Output
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{1992: {'G': 1, 'S': 0, 'B': 1},
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1994: {'G': 0, 'S': 2, 'B': 1},
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1998: {'G': 0, 'S': 0, 'B': 0},
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2002: {'G': 2, 'S': 0, 'B': 0},
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2006: {'G': 1, 'S': 0, 'B': 0}}
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\end{minted}

‎module04/exercises/m04ex04.tex

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\chapter{Exercise 04}
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\extitle{SpatioTemporalData}
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\turnindir{ex04}
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\exnumber{04}
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\exfiles{FileLoader.py, SpatioTemporalData.py}
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\exforbidden{None}
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\makeheaderfilesforbidden
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% ================================= %
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\section*{Objective}
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% --------------------------------- %
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The goal of this exercise is to implement a class called \texttt{SpatioTemporalData}
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that takes a dataset (pandas.DataFrame) as argument in its constructor
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and implements two methods.
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% ================================= %
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\section*{Instructions}
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% --------------------------------- %
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This exercise uses the dataset \texttt{athlete\_events.csv}.\\
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\\
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Write a class called \texttt{SpatioTemporalData} that takes a dataset
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(pandas.DataFrame) as argument in its constructor and implements the
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following methods:
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\begin{itemize}
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\item \texttt{when(location)}: takes a location as an argument and returns
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a list containing the years where games were held in the given location,
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\item \texttt{where(date)}: takes a date as an argument and returns the location
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where the Olympics took place in the given year.
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\end{itemize}
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% ================================= %
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\section*{Examples}
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% --------------------------------- %
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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from FileLoader import FileLoader
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loader = FileLoader()
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data = loader.load('../data/athlete_events.csv')
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# Output
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Loading dataset of dimensions 271116 x 15
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from SpatioTemporalData import SpatioTemporalData
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sp = SpatioTemporalData(data)
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sp.where(1896)
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# Output
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['Athina']
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sp.where(2016)
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# Output
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['Rio de Janeiro']
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sp.when('Athina')
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# Output
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[2004, 1906, 1896]
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sp.when('Paris')
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# Output
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[1900, 1924]
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\end{minted}

‎module04/exercises/m04ex05.tex

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\chapter{Exercise 05}
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\extitle{HowManyMedalsByCountry}
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\turnindir{ex05}
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\exnumber{05}
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\exfiles{FileLoader.py, HowManyMedalsByCountry.py}
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\exforbidden{None}
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\makeheaderfilesforbidden
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% ================================= %
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\section*{Objective}
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% --------------------------------- %
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The goal of this exercise is to write a function that returns a
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dictionary of dictionaries giving the number and types of medals for
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each competition where a given country delegation earned medals.
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% ================================= %
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\section*{Instructions}
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% --------------------------------- %
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This exercise uses the following dataset: \texttt{athlete\_events.csv}\\
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\\
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Write a function \texttt{how\_many\_medals\_by\_country} that takes two arguments:
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\begin{itemize}
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\item a pandas.DataFrame which contains the dataset
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\item a country name.
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\end{itemize}
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The function returns a dictionary of dictionaries giving the number and
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types of medals for each competition where the country delegation earned medals.\\
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\\
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The keys of the main dictionary are the Olympic games' years. In each
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year's dictionary, the keys are 'G', 'S', 'B' corresponding to the
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types of medals won.
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\\
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Duplicated medals per team games should be handled and not counted twice.\\
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\\
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Hint: You may find this list to be of some use.
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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team_sports = ['Basketball', 'Football', 'Tug-Of-War', 'Badminton', 'Sailing',
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'Handball', 'Water Polo', 'Hockey', 'Rowing', 'Bobsleigh', 'Softball',
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'Volleyball', 'Synchronized Swimming', 'Baseball', 'Rugby Sevens',
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'Rugby', 'Lacrosse', 'Polo']
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\end{minted}
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% ================================= %
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\section*{Examples}
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% --------------------------------- %
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\begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
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from FileLoader import FileLoader
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loader = FileLoader()
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data = loader.load('../data/athlete_events.csv')
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# Output
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Loading dataset of dimensions 271116 x 15
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from HowManyMedalsByCountry import how_many_medals_by_country
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how_many_medals_by_country(data, 'Martian Federation')
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# Output
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{2192: {'G': 17, 'S': 14, 'B': 23}, 2196: {'G': 8, 'S': 21, 'B': 19}, 2200: {'G': 26, 'S': 19, 'B': 7}}
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\end{minted}
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\newline
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You probably guessed by now that we gave up providing real examples...\\
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\\
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If you want real examples, you can easily look online.\\
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\\
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Do beware that some medals might be awarded or removed years after the games
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are over, for example if a previous medallist was found to have cheated.\\
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\\
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The \texttt{athlete\_events.csv} dataset might not always take these posterior
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changes into account.

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