Normalise list of tuples in Python. No libraries used.

2021-10-18 Here is a list of tuples that need to be normalised:

(2, 10),
(3, 15),
(4, 16),
(5, 20),
(6, 45)]

The result should be a normalised list of tuples where the data in each column has a value between 0 and 1:

(0.0, 0.0)
(0.25, 0.14285714285714285)
(0.5, 0.17142857142857143)
(0.75, 0.2857142857142857)
(1.0, 1.0)]

The following function shows how to achieve this with list comprehensions in Python:

def normalise(matrix):
    transposed = [[row[col] for row in matrix] for col, _ in enumerate(matrix[0])]
    normalised = [[(v - min(r)) / (max(r) - min(r)) for v in r] for r in transposed]
    return [tuple([row[col] for row in normalised]) for col, _ in enumerate(normalised[0])]

Full code:

def normalise(matrix):
    transposed = [[row[col] for row in matrix] for col, _ in enumerate(matrix[0])]
    normalised = [[(v - min(r)) / (max(r) - min(r)) for v in r] for r in transposed]
    return [tuple([row[col] for row in normalised]) for col, _ in enumerate(normalised[0])]

X = [(2, 10), (3, 15), (4, 16), (5, 20), (6, 45)]
print(X)
print(normalise(X))

Ouput:

[(2, 10), (3, 15), (4, 16), (5, 20), (6, 45)]
[(0.0, 0.0), (0.25, 0.14285714285714285), (0.5, 0.17142857142857143), (0.75, 0.2857142857142857), (1.0, 1.0)]