Python – Matrix Data Type Rectification

0 0 3 minutes read

Sometimes, while working with data, we can have problems in which we need to perform rectification of list data types, i.e convert the Number Strings to Numbers where necessary. This can occur in Matrix form as well. Lets discuss certain ways in which this task can be performed.

Method #1 : Using list comprehension + isdigit() This is one of the way in which this task can be performed. In this, we iterate each element of Matrix and check for digit string using typecasting and then perform a conversion.

Python3

# Python3 code to demonstrate 
# Matrix Data Type Rectification
# using isdigit() + list comprehension
 
# Initializing list
test_list = [['5', 'GFG'], ['1', '3'], ['is', '11'], ['1', 'best']]
 
# printing original list
print("The original list is : " + str(test_list))
 
# Matrix Data Type Rectification
# using isdigit() + list comprehension
res = [[int(ele) if ele.isdigit() else ele for ele in sub] for sub in test_list]
 
# printing result 
print ("The rectified Matrix is : " + str(res))

Output :

The original list is : [['5', 'GFG'], ['1', '3'], ['is', '11'], ['1', 'best']]
The rectified Matrix is : [[5, 'GFG'], [1, 3], ['is', 11], [1, 'best']]

Time complexity: O(m*n), because it performs the same number of iterations as the original code.
Auxiliary space: O(m*n) as well, because it creates a list with m * n keys and a list of m * n elements

Method #2 : Using map() + isdigit() This is yet another way in which this task can be performed. In this, we extend the logic to each element using map() rather than list comprehension.

Python3

# Python3 code to demonstrate 
# Matrix Data Type Rectification
# using map() + isdigit()
 
# Initializing list
test_list = [['5', 'GFG'], ['1', '3'], ['is', '11'], ['1', 'best']]
 
# printing original list
print("The original list is : " + str(test_list))
 
# Matrix Data Type Rectification
# using map() + isdigit()
res = [list(map(lambda ele: int(ele) if ele.isdigit() else ele, sub)) for sub in test_list]
 
# printing result 
print ("The rectified Matrix is : " + str(res))

Output :

The original list is : [['5', 'GFG'], ['1', '3'], ['is', '11'], ['1', 'best']]
The rectified Matrix is : [[5, 'GFG'], [1, 3], ['is', 11], [1, 'best']]

Method #3 : Using re (regular expressions) library

This method makes use of the re library in python which allows for pattern matching in strings. Here, we use re.search() function to check if the element is a digit string or not and then convert it to int if it is.

Python3

import re
 
# Initializing list
test_list = [['5', 'GFG'], ['1', '3'], ['is', '11'], ['1', 'best']]
 
# printing original list
print("The original list is : " + str(test_list))
 
# Matrix Data Type Rectification
# using re library
res = [[int(ele) if re.search("\d", ele) else ele for ele in sub] for sub in test_list]
 
# printing result 
print ("The rectified Matrix is : " + str(res))
#This code is contributed by Edula Vinay Kumar Reddy

Output

The original list is : [['5', 'GFG'], ['1', '3'], ['is', '11'], ['1', 'best']]
The rectified Matrix is : [[5, 'GFG'], [1, 3], ['is', 11], [1, 'best']]

Time complexity: O(n^2) where n is the number of elements in the matrix
Space complexity: O(n^2) where n is the number of elements in the matrix

Method #4 : Using for loops

Python3

# Python3 code to demonstrate
# Matrix Data Type Rectification
 
# Initializing list
test_list = [['5', 'GFG'], ['1', '3'], ['is', '11'], ['1', 'best']]
 
# printing original list
print("The original list is : " + str(test_list))
 
# Matrix Data Type Rectification
res = []
for i in test_list:
    x=[]
    for j in i:
        if j in "0123456789":
            x.append(int(j))
        else:
            x.append(j)
    res.append(x)
 
# printing result
print ("The rectified Matrix is : " + str(res))

Output

The original list is : [['5', 'GFG'], ['1', '3'], ['is', '11'], ['1', 'best']]
The rectified Matrix is : [[5, 'GFG'], [1, 3], ['is', '11'], [1, 'best']]

Time complexity: O(n^2) where n is the number of elements in the matrix
Auxiliary Space: O(n^2) where n is the number of elements in the matrix