Type Casting Whole List and Matrix - Python

The task of type casting a whole list and matrix in Python involves converting all elements of a list or a matrix to a specific data type while preserving the structure. Given a list or matrix of integers, the goal is to transform each element into another data type, such as a string or float, efficiently. For example, with a = [1, 2, 3] and b = [[4, 5], [6, 7]], converting all elements to strings results in ['1', '2', '3'] and [["4", "5"], ["6", "7"]].

Using list comprehension

When type casting a whole list or matrix, list comprehension provides a concise one-liner approach. In the case of a matrix, nested list comprehensions allow us to apply the transformation to each sublist row as well. This method is favored for its readability and efficiency when the transformation is straightforward.

a = [1, 4, 9, 10, 19]
b = [[5, 6, 8], [8, 5, 3], [9, 10, 3]]

c = [str(x) for x in a]
d = [[str(x) for x in sub] for sub in b]

print(c)
print(d)

['1', '4', '9', '10', '19']
[['5', '6', '8'], ['8', '5', '3'], ['9', '10', '3']]

Explanation:[str(x) for x in a] converts each element in a to a string and stores the result in c. Similarly, [[str(x) for x in sub] for sub in b] converts each element in the sublists of b to a string, creating the transformed matrix d.

Using map()

map() allows us to apply a specified function to each item in an iterable. It's particularly useful for type casting because it processes the iterable element by element. For lists, map() returns an iterator, which can be converted to a list. For matrices, map() can be combined with list comprehensions to apply the transformation to each sublist as well.

a = [1, 4, 9, 10, 19]
b = [[5, 6, 8], [8, 5, 3], [9, 10, 3]]

c = list(map(str, a))
d = [list(map(str, sub)) for sub in b]

print(c)
print(d)

['1', '4', '9', '10', '19']
[['5', '6', '8'], ['8', '5', '3'], ['9', '10', '3']]

Explanation: list(map(str, a)) applies str() to each element in a, converting all integers to strings and storing the result in c. Similarly, [list(map(str, sub)) for sub in b] first iterates over each sublist sub in b and then applies map(str, sub) convert each element in the sublist to a string, creating the transformed matrix d.

Using for loop

For loop provides an explicit and flexible way to iterate over each element of a list or matrix and apply type casting. While it’s a more verbose method compared to list comprehension or map(), it offers the advantage of more complex logic, such as additional conditions or transformations within the loop. This method is useful when a more customized approach is needed for transforming data.

a = [1, 4, 9, 10, 19]
b = [[5, 6, 8], [8, 5, 3], [9, 10, 3]]

c = []
d = []

for x in a:
    c.append(str(x))
print(c)
    
for sub in b:
    temp = []
    for x in sub:
        temp.append(str(x))
    d.append(temp)
print(d)

['1', '4', '9', '10', '19']
[['5', '6', '8'], ['8', '5', '3'], ['9', '10', '3']]

Explanation: for x in a iterates through each element in a, converts it to a string using str(x) and appends it to c. Similarly, the nested for loop first iterates over each sublist sub in b, then the inner loop converts each element to a string and stores it in a temporary list temp, which is then appended to d, creating the transformed matrix.