import pandas as pd
from typing import cast

# The data for this exercise is in ‹zz.frames.csv›. The data
# represents grading of a programming subject (with made-up names
# and numbers, of course). The columns are names, number of points
# from weekly exercises, from assignments and from reviews.
# Implement the following functions:

# Return a ‹DataFrame› which only contains rows of students, which
# achieved the best result among their peers in one of the
# categories (weekly, assignments, reviews). If there are multiple
# such students for a given category, include all of them.

def best( data ):
    pass

# Return a ‹DataFrame› which contains the name and the total score
# (as the only 2 columns). Don't forget that the weekly exercises
# contribute at most 9 points to the total.

def compute_total( data ):
    pass

# Return a dictionary with 4 keys (‹weekly›, ‹assignments›,
# ‹reviews› and ‹total›) where each value is the average number of
# points in the given category. Consider factoring out a helper
# function from ‹compute_total› to get a ‹DataFrame› with 5 columns.

def compute_averages( data ):
    pass

# ----- >% ----- >% -----

def eq( data: pd.DataFrame, student: str, col: str, val: float ) -> bool:
    matches = data[ data[ 'student' ] == student ][ col ]
    return cast( bool, ( matches == val ).all() )

def test_main() -> None:
    import math

    df = pd.read_csv( 'zz.frames.csv' )
    assert len( best( df ) ) == 5

    tot = compute_total( df )
    assert eq( tot, 'Věra Hrbáčková', 'total', 18 )
    assert eq( tot, 'Blanka Pichrtová', 'total', 17.4 )

    avg = compute_averages( df )
    assert math.isclose( avg['weekly'], 61/9 )
    assert math.isclose( avg['assignments'], 245/36 )
    assert math.isclose( avg['reviews'], 87/90 )

if __name__ == '__main__':
    test_main()