In Python, a matrix can be interpreted as a list of lists. Each element is considered as a row of the matrix. For example, m = [[10, 20], [40, 50], [30, 60]] is a matrix of 3 rows and 2 columns. The first item in the list — m [0] and the item on the first row, the first column — m [0] [0].
Example :
Input: l1 = [[4, 5, 3, 9], [7, 1, 8, 2], [5, 6, 4, 7]] Output: lt = [[4, 7, 5], [5, 1, 6] , [3, 8, 4], [9, 2, 7]]
Method # 1: Using Loops
| # Python transpose program
# list items of two dimensions def transpose (l1, l2): # iterate over the list l1 to the length of the element for i in range ( len (l1 [ 0 ])): # print (s) row = [] for item in l1: # add to a new list with index values and positions # i contains index position and element contains values row.append (item [i]) l2.append (row) return l2
# Driver code l1 = [ [ 4 , 5 , 3 , 9 ], [ 7 , 1 , 8 , 2 ], [ 5 , 6 , 4 , 7 ]] l2 = [] print (transpose (l1, l2)) |
Exit:
[[4, 7, 5], [5, 1, 6], [3, 8, 4], [9, 2, 7]]
Method # 2: Using list views
| # Python transpose program
# list items of two dimensions def transpose (l1, l2): # we have nested loops in understandings # i is assigned using inner loop # then the element value is routed by the string [i] # and added to l2 l2 = [[row [i] for row in l1] for i in range ( len (l1 [ 0 ]))] return l2
# Driver code l1 = [[ 4 , 5 , 3 , 9 ], [ 7 , 1 , 8 , 2 ], [ 5 , 6 , 4 , 7 ]] l2 = [] print (transpose (l1, l2)) |
Output:
[[4, 7, 5], [5, 1, 6], [3, 8, 4], [9, 2, 7]] < / pre> Method # 3: Using NumPy
| # Python transpose program
# list items of two dimensions import numpy l1 = [[ 4 , 5 , 3 , 9 ], [ 7 , 1 , 8 , 2 ], [ 5 , 6 , 4 , 7 ]] print (numpy.transpose (l1)) |
Exit:
[[4 7 5] [5 1 6] [3 8 4] [ 9 2 7]]
