Python program to find the redundancy rates for each row of a matrix

When it is required to find the redundancy rates for every row of a matrix, a simple iteration and the append method can be used. The redundancy rate represents how many duplicate values exist in each row, calculated as 1 - (unique_elements / total_elements).

Understanding Redundancy Rate

The redundancy rate formula is ?

redundancy_rate = 1 - (number_of_unique_elements / total_elements)

A redundancy rate of 0 means all elements are unique, while 1 means all elements are identical.

Example

Below is a demonstration of finding redundancy rates for each row ?

my_matrix = [[91, 52, 12, 29, 33], [54, 54, 54, 54, 54], [11, 22, 33, 59, 95]]

print("The matrix is :")
print(my_matrix)

redundancy_rates = []
for row in my_matrix:
    redundancy_rates.append(1 - len(set(row)) / len(row))

print("The redundancy rates are :")
print(redundancy_rates)

The output of the above code is ?

The matrix is :
[[91, 52, 12, 29, 33], [54, 54, 54, 54, 54], [11, 22, 33, 59, 95]]
The redundancy rates are :
[0.0, 0.8, 0.0]

How It Works

The algorithm works as follows ?

• Row 1: [91, 52, 12, 29, 33] has 5 unique elements out of 5 total ? redundancy = 1 - 5/5 = 0.0
• Row 2: [54, 54, 54, 54, 54] has 1 unique element out of 5 total ? redundancy = 1 - 1/5 = 0.8
• Row 3: [11, 22, 33, 59, 95] has 5 unique elements out of 5 total ? redundancy = 1 - 5/5 = 0.0

Alternative Approach Using List Comprehension

You can achieve the same result more concisely ?

my_matrix = [[91, 52, 12, 29, 33], [54, 54, 54, 54, 54], [11, 22, 33, 59, 95]]

redundancy_rates = [1 - len(set(row)) / len(row) for row in my_matrix]

print("The redundancy rates are :")
print(redundancy_rates)

The redundancy rates are :
[0.0, 0.8, 0.0]

Conclusion

The redundancy rate formula 1 - len(set(row)) / len(row) efficiently calculates duplicate ratios in matrix rows. Use list comprehension for concise code or explicit loops for better readability.