Python - Row with Minimum difference in extreme values

Given a Matrix, extract the rows with a minimum difference in extreme values.

Examples:

Input : test_list = [[4, 10, 18], [5, 0], [1, 4, 6], [19, 2]] 
Output : [[1, 4, 6], [5, 0]] 
Explanation : 6 - 1 = 5, 5 - 0 = 5, is minimum difference between extreme values.

Input : test_list = [[4, 10, 18], [5, 0], [2, 4, 6], [19, 2]] 
Output : [[2, 4, 6]] 
Explanation : 6 - 2 = 4, is min diff.

Method #1 : Using list comprehension + min()

In this, we compute minimum difference between extreme values, and then use list comprehension to get particular row with that value difference between extreme values.

# Python3 code to demonstrate working of 
# Matrix Minimum difference in extreme values row
# Using min() + list comprehension

# initializing list
test_list = [[4, 10, 18], [5, 3, 10], [1, 4, 6], [19, 2]]

# printing original list
print("The original list is : " + str(test_list))

# getting min value 
min_val = min([max(sub) - min(sub) for sub in test_list])

# using list comprehension to filter 
res = [sub for sub in test_list if max(sub) - min(sub) == min_val]

# printing result 
print("Rows with Minimum difference in extreme values : " + str(res))

Output:

The original list is : [[4, 10, 18], [5, 3, 10], [1, 4, 6], [19, 2]] Rows with Minimum difference in extreme values : [[1, 4, 6]]

Time Complexity: O(n) where n is the number of elements in the list “test_list”.  list comprehension + min() performs n number of operations.
Auxiliary Space: O(n), extra space of size n is required

Method #2 : Using min() + list comprehension + filter() + lambda

In this, we perform task of filtering comparing with minimum value using filter() + lambda.

# Python3 code to demonstrate working of 
# Matrix Minimum difference in extreme values row
# Using min() + list comprehension + filter() + lambda

# initializing list
test_list = [[4, 10, 18], [5, 3, 10], [1, 4, 6], [19, 2]]

# printing original list
print("The original list is : " + str(test_list))

# getting min value 
min_val = min([max(sub) - min(sub) for sub in test_list])

# using filter() + lambda to filter 
res = list(filter(lambda sub : max(sub) - min(sub) == min_val, test_list))

# printing result 
print("Rows with Minimum difference in extreme values : " + str(res))

Output:

The original list is : [[4, 10, 18], [5, 3, 10], [1, 4, 6], [19, 2]] Rows with Minimum difference in extreme values : [[1, 4, 6]]

Method #3: Using loops

Step-by-step approach:

• Initialize a variable 'min_diff' to a very large value.
• Initialize an empty list 'rows'.
• Loop through each row 'sub' in 'test_list'.
  • Calculate the difference between the maximum and minimum value in 'sub'.
  • If the difference is less than 'min_diff', update 'min_diff' to this value.
  • Clear the 'rows' list.

# initializing list
test_list = [[4, 10, 18], [5, 3, 10], [1, 4, 6], [19, 2]]

# printing original list
print("The original list is : " + str(test_list))

# using loops to find the rows with minimum difference in extreme values
min_diff = float('inf')
rows = []
for sub in test_list:
    diff = max(sub) - min(sub)
    if diff < min_diff:
        min_diff = diff
        rows.clear()
    if diff == min_diff:
        rows.append(sub)

# printing result 
print("Rows with Minimum difference in extreme values : " + str(rows))

The original list is : [[4, 10, 18], [5, 3, 10], [1, 4, 6], [19, 2]]
Rows with Minimum difference in extreme values : [[1, 4, 6]]

Time complexity: O(nm), where n is the number of rows and m is the length of the longest row.
Auxiliary space: O(k), where k is the number of rows with the minimum difference in extreme values.