# Python | Numpy numpy.transpose()

Last Updated : 07 Mar, 2022

With the help of Numpy numpy.transpose(), We can perform the simple function of transpose within one line by using numpy.transpose() method of Numpy. It can transpose the 2-D arrays on the other hand it has no effect on 1-D arrays. This method transpose the 2-D numpy array.

Parameters:
axes : [None, tuple of ints, or n ints] If anyone wants to pass the parameter then you can but it’s not all required. But if you want than remember only pass (0, 1) or (1, 0). Like we have array of shape (2, 3) to change it (3, 2) you should pass (1, 0) where 1 as 3 and 0 as 2.
Returns: ndarray

Example #1 :
In this example we can see that it’s really easy to transpose an array with just one line.

## Python3

 `# importing python module named numpy` `import` `numpy as np`   `# making a 3x3 array` `gfg ``=` `np.array([[``1``, ``2``, ``3``],` `                ``[``4``, ``5``, ``6``],` `                ``[``7``, ``8``, ``9``]])`   `# before transpose` `print``(gfg, end ``=``'\n\n'``)`   `# after transpose` `print``(gfg.transpose())`

Output:

```[[1 2 3]
[4 5 6]
[7 8 9]]

[[1 4 7]
[2 5 8]
[3 6 9]]```

Example #2 :
In this example we demonstrate the use of tuples in numpy.transpose().

## Python3

 `# importing python module named numpy` `import` `numpy as np`   `# making a 3x3 array` `gfg ``=` `np.array([[``1``, ``2``],` `                ``[``4``, ``5``],` `                ``[``7``, ``8``]])`   `# before transpose` `print``(gfg, end ``=``'\n\n'``)`   `# after transpose` `print``(gfg.transpose(``1``, ``0``))`

Output:

```[[1 2]
[4 5]
[7 8]]

[[1 4 7]
[2 5 8]]```

Method 2: Using  Numpy ndarray.T object.

## Python3

 `# importing python module named numpy` `import` `numpy as np` `  `  `# making a 3x3 array` `gfg ``=` `np.array([[``1``, ``2``, ``3``],` `                ``[``4``, ``5``, ``6``],` `                ``[``7``, ``8``, ``9``]])` `  `  `# before transpose` `print``(gfg, end ``=``'\n\n'``)` `  `  `# after transpose` `print``(gfg.T)`

#### Output

```[[1 2 3]
[4 5 6]
[7 8 9]]

[[1 4 7]
[2 5 8]
[3 6 9]]```