Method 1: Use conventional ideas
def transpose(M):
# Initialize the transposed matrix result = []
# Get the rows and columns before transpose row, col = shape(M)
# Loop the column first for i in range(col):
# Outer loop container item = []
# Looping rows inside column loops for index in range(row):
(M[index][i])
(item)
return result
Idea: The transposition of a matrix is to turn from a row to a column, and the column to a row
- First define a container that will eventually store the matrix
- First loop i on the column and define a temporary array for storing data. In the loop of each column, loop j on the row again, take the M[j][i]th elements into a temporary array
- After each column loop is completed, save the temporary array into the final array
- When the column loop is completed, the final array is the matrix transposition
Method 2: Unpacking with zip
def transpose(M): # Use zip to unpack iterator directly, and then force it into a list and save it into the final list. return [list(row) for row in zip(*M)]
Ideas:
After unpacking zip, it returns an iterator that combines multiple iterable objects into a sequence of tuples, as:
my_zip = list(zip(['a', 'b', 'c'], [1, 2, 3]))
print(my_zip) # [('a', 1), ('b', 2), ('c', 3)]
In each loop, strongly convert the tuple to a list and save it into the total list
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