[python] Transpose list of lists

Let's take:

l = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

The result I'm looking for is

r = [[1, 4, 7], [2, 5, 8], [3, 6, 9]]

and not

r = [(1, 4, 7), (2, 5, 8), (3, 6, 9)]

Much appreciated

just for fun, valid rectangles and assuming that m[0] exists

>>> m = [[1,2,3],[4,5,6],[7,8,9]]
>>> [[row[i] for row in m] for i in range(len(m[0]))]
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]

Methods 1 and 2 work in Python 2 or 3, and they work on ragged, rectangular 2D lists. That means the inner lists do not need to have the same lengths as each other (ragged) or as the outer lists (rectangular). The other methods, well, it's complicated.

the setup

import itertools
import six

list_list = [[1,2,3], [4,5,6, 6.1, 6.2, 6.3], [7,8,9]]

method 1 — map(), zip_longest()

>>> list(map(list, six.moves.zip_longest(*list_list, fillvalue='-')))
[[1, 4, 7], [2, 5, 8], [3, 6, 9], ['-', 6.1, '-'], ['-', 6.2, '-'], ['-', 6.3, '-']]

six.moves.zip_longest() becomes

• itertools.izip_longest() in Python 2
• itertools.zip_longest() in Python 3

The default fillvalue is None. Thanks to @jena's answer, where map() is changing the inner tuples to lists. Here it is turning iterators into lists. Thanks to @Oregano's and @badp's comments.

In Python 3, pass the result through list() to get the same 2D list as method 2.

method 2 — list comprehension, zip_longest()

>>> [list(row) for row in six.moves.zip_longest(*list_list, fillvalue='-')]
[[1, 4, 7], [2, 5, 8], [3, 6, 9], ['-', 6.1, '-'], ['-', 6.2, '-'], ['-', 6.3, '-']]

The @inspectorG4dget alternative.

method 3 — map() of map() — broken in Python 3.6

>>> map(list, map(None, *list_list))
[[1, 4, 7], [2, 5, 8], [3, 6, 9], [None, 6.1, None], [None, 6.2, None], [None, 6.3, None]]

This extraordinarily compact @SiggyF second alternative works with ragged 2D lists, unlike his first code which uses numpy to transpose and pass through ragged lists. But None has to be the fill value. (No, the None passed to the inner map() is not the fill value. It means there is no function to process each column. The columns are just passed through to the outer map() which converts them from tuples to lists.)

Somewhere in Python 3, map() stopped putting up with all this abuse: the first parameter cannot be None, and ragged iterators are just truncated to the shortest. The other methods still work because this only applies to the inner map().

method 4 — map() of map() revisited

>>> list(map(list, map(lambda *args: args, *list_list)))
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]   // Python 2.7
[[1, 4, 7], [2, 5, 8], [3, 6, 9], [None, 6.1, None], [None, 6.2, None], [None, 6.3, None]] // 3.6+

Alas the ragged rows do NOT become ragged columns in Python 3, they are just truncated. Boo hoo progress.

Here is a solution for transposing a list of lists that is not necessarily square:

maxCol = len(l[0])
for row in l:
    rowLength = len(row)
    if rowLength > maxCol:
        maxCol = rowLength
lTrans = []
for colIndex in range(maxCol):
    lTrans.append([])
    for row in l:
        if colIndex < len(row):
            lTrans[colIndex].append(row[colIndex])

One more way for square matrix. No numpy, nor itertools, use (effective) in-place elements exchange.

def transpose(m):
    for i in range(1, len(m)):
        for j in range(i):
            m[i][j], m[j][i] = m[j][i], m[i][j]

Maybe not the most elegant solution, but here's a solution using nested while loops:

def transpose(lst):
    newlist = []
    i = 0
    while i < len(lst):
        j = 0
        colvec = []
        while j < len(lst):
            colvec.append(lst[j][i])
            j = j + 1
        newlist.append(colvec)
        i = i + 1
    return newlist

matrix = [[1,2,3],
          [1,2,3],
          [1,2,3],
          [1,2,3],
          [1,2,3],
          [1,2,3],
          [1,2,3]]
    
rows = len(matrix)
cols = len(matrix[0])

transposed = []
while len(transposed) < cols:
    transposed.append([])
    while len(transposed[-1]) < rows:
        transposed[-1].append(0)

for i in range(rows):
    for j in range(cols):
        transposed[j][i] = matrix[i][j]

for i in transposed:
    print(i)

Equivalently to Jena's solution:

>>> l=[[1,2,3],[4,5,6],[7,8,9]]
>>> [list(i) for i in zip(*l)]
... [[1, 4, 7], [2, 5, 8], [3, 6, 9]]

more_itertools.unzip() is easy to read, and it also works with generators.

import more_itertools
l = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
r = more_itertools.unzip(l) # a tuple of generators.
r = list(map(list, r))      # a list of lists

or equivalently

import more_itertools
l = more_itertools.chunked(range(1,10), 3)
r = more_itertools.unzip(l) # a tuple of generators.
r = list(map(list, r))      # a list of lists

One way to do it is with NumPy transpose. For a list, a:

>>> import numpy as np
>>> np.array(a).T.tolist()
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]

Or another one without zip:

>>> map(list,map(None,*a))
[[1, 4, 7], [2, 5, 8], [3, 6, 9]]

Three options to choose from:

1. Map with Zip

solution1 = map(list, zip(*l))

2. List Comprehension

solution2 = [list(i) for i in zip(*l)]

3. For Loop Appending

solution3 = []
for i in zip(*l):
    solution3.append((list(i)))

And to view the results:

print(*solution1)
print(*solution2)
print(*solution3)

# [1, 4, 7], [2, 5, 8], [3, 6, 9]

    #Import functions from library
    from numpy import size, array
    #Transpose a 2D list
    def transpose_list_2d(list_in_mat):
        list_out_mat = []
        array_in_mat = array(list_in_mat)
        array_out_mat = array_in_mat.T
        nb_lines = size(array_out_mat, 0)
        for i_line_out in range(0, nb_lines):
            array_out_line = array_out_mat[i_line_out]
            list_out_line = list(array_out_line)
            list_out_mat.append(list_out_line)
        return list_out_mat

import numpy as np
r = list(map(list, np.transpose(l)))