Simple use of .apply()

Let's get some handful experience with .apply()!

You are given the full scores dataset containing students' performance as well as their background information.

Your task is to define the prevalence() function and apply it to the groups_to_consider columns of the scores DataFrame. This function should retrieve the most prevalent group/category for a given column (e.g. if the most prevalent category in the lunch column is standard, then prevalence() should return standard).

The reduce() function from the functools module is already imported.

Tip: pd.Series is an Iterable object. Therefore, you can use standard operations on it.

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Oefeninstructies

Create a tuple list with unique items from passed object series and their counts.
Extract a tuple with the highest counts using reduce().
Return the item with the highest counts.
Apply the prevalence function on the scores DataFrame using columns specified in groups_to_consider.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

def prevalence(series):
    vals = list(series)
    # Create a tuple list with unique items and their counts
    itms = [(____, ____) for x in set(____)]
    # Extract a tuple with the highest counts using reduce()
    res = reduce(lambda x, y: ____, ____)
    # Return the item with the highest counts
    return ____[____]

# Apply the prevalence function on the scores DataFrame
result = scores[groups_to_consider].____
print(result)

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