Matrix Transpose in Python

Matrix Transpose in Python

Python 2:

>>> theArray = [['a','b','c'],['d','e','f'],['g','h','i']]
>>> zip(*theArray)
[('a', 'd', 'g'), ('b', 'e', 'h'), ('c', 'f', 'i')]

Python 3:

>>> [*zip(*theArray)]
[('a', 'd', 'g'), ('b', 'e', 'h'), ('c', 'f', 'i')]

Transpose Of Matrix in Python

You need to install numpy in order to import it
Numpy transpose returns similar result when

applied on 1D matrix

import numpy  
mymatrix=[[1,2,3],[4,5,6]] 
print(mymatrix) 
print("\n") 
print(numpy.transpose(mymatrix)) 

Basic matrix transpose in python

Your problem is two fold:

1- B was a label on matrix A, that is every modification to A, also modified B

2- B was local to the transpose function, and could not be accessed outside

A = [[1, 1, 1, 1], 
     [2, 2, 2, 2], 
     [3, 3, 3, 3], 
     [4, 4, 4, 4]] 

def TS (A):
    B = [row[:] for row in A]   # make a copy of A, not assigning a new label on it.
    for i in (range(len(A))):
        for j in (range(len(A))):
            B[i][j] = A[j][i]
    return B

B = TS(A)

for i in range(len(A)): 
    for j in range(len(A)): 
        print(B[i][j], " ", end='') 
    print() 

output:

1  2  3  4  
1  2  3  4  
1  2  3  4  
1  2  3  4 

Transpose only the matrix values with numpy if multiple of 3

You need to convert the mask M % 3 == 0 to indices. Something like this should work:

M = M.T
i, j = np.where(M % 3 == 0)
M[i, j] = (3 * j + i)**2

python matrix transpose and zip

question answers:

>>> import numpy as np
>>> first_answer = np.transpose(a)
>>> second_answer = [list(i) for i in zip(*a)]

thanks to afg for helping out

Transposing matrix twice using classes without numpy

You can create a new Matrix object in transpose and return that after printing its content.
For convenience I moved your printing code to a method for converting the matrix to a string (Note that a method called __repr__ will be implicit called by print).
Code:

from copy import deepcopy

class Matrix:
     def __init__(self, rows):
         self.rows = rows[:]

     def transpose(self):
         copy = deepcopy(self.rows)
         transposed = [[copy[j][i] for j in range(len(copy))] for i in range(len(copy[0]))]
         transposed_matrix = Matrix(transposed)
         print(transposed_matrix)
         return transposed_matrix

     def __repr__(self):
         matrix = ''
         for element in self.rows:
             for i in element:
                 matrix += '%2d' % ((i))
                 matrix += ' '
             matrix = matrix[:-1]
             matrix += '\n'
         return matrix

Additional improvements:

• in transpose you can change the matrix transposition to transposed = list(map(list, zip(*self.rows)))
• use format string instead of '%2d' % ((i)) -> f'{i:2}'
• join is very helpful for formatting the matrix:
  return '\n'.join(' '.join(f'{i:2}' for i in row) for row in self.rows)

Now the full code looks like this

class Matrix:
     def __init__(self, rows):
         self.rows = rows[:]

     def transpose(self):
         transposed = list(map(list, zip(*self.rows)))
         transposed_matrix = Matrix(transposed)
         print(transposed_matrix)
         return transposed_matrix

     def __repr__(self):
         return '\n'.join(' '.join(f'{i:2}' for i in row) for row in self.rows)

python, matrix transpose with odd row reversed

Using no numpy functions to reverse odd columns you could do something like:

import numpy as np

# just your input data
mat = np.arange(1.0, 13.0).reshape(3, 4).T

mat_no_numpy = np.zeros_like(mat)
rows, cols = mat.shape
for i in range(rows):
    for j in range(cols):
        if j % 2 == 0:
            mat_no_numpy[i, j] = mat[i, j]
        else:
            # flip row-coordinate for odd columns
            mat_no_numpy[i, j] = mat[rows - i - 1, j]

print(mat_no_numpy)
# [[ 1.  8.  9.]
#  [ 2.  7. 10.]
#  [ 3.  6. 11.]
#  [ 4.  5. 12.]]

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Alternatively, if numpy functions are allowed, you can use a combination of slicing and np.flip to flip every odd column:

import numpy as np

# just your input data
mat = np.arange(1.0, 13.0).reshape(3, 4).T

mat[:, 1::2] = np.flip(mat[:, 1::2])
print(mat)
# [[ 1.  8.  9.]
#  [ 2.  7. 10.]
#  [ 3.  6. 11.]
#  [ 4.  5. 12.]]

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