# Transpose a Numpy array

In this tutorial, we will look at how to transpose a numpy array with the help of some examples. Speicifically, we will look at the usage of the numpy ndarray `transpose()` function and the numpy ndarray `.T` attribute.

The transpose operation in numpy is generally applied on 2d arrays to swipe the rows and columns of an array. For example, a numpy array of shape (2, 3) becomes a numpy array of shape (3, 2) after the operation wherein the first row becomes the first column and the second row becomes the second column. Also, conversely, the first column becomes the first row, the second column becomes the second row, and the third column becomes the third row post the transpose.

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You can use the numpy ndarary `transpose()` function to transpose a numpy array. You can also use the `.T` numpy array attribute to transpose a 2d array. The following is the syntax:

```# arr is a numpy array
arr_t = arr.transpose()```

It returns a view of the array with the axes transposed.

For 1d arrays, the transpose operation has no effect on the array. As a transposed vector it is simply the same vector. Let’s see an example –

```import numpy as np

# create a 1d numpy array
arr = np.array([1, 2, 3, 4])
# transpose the array
arr_t = arr.transpose()
# display the arrays
print("Original Array:\n", arr)
print("After Transpose:\n", arr_t)```

Output:

```Original Array:
[1 2 3 4]
After Transpose:
[1 2 3 4]```

You can see that transposing a 1d array doesn’t change anything. We can further confirm this by looking at the shape of the two arrays:

```# print the shape of the two arrays
print("Original array shape: ", arr.shape)
print("Shape after transpose: ", arr_t.shape)```

Output:

```Original array shape:  (4,)
Shape after transpose:  (4,)```

Both the arrays (original and transposed) have the same shape.

For a 2d array, the transpose operation means to swap out the rows and columns of the array. Here’s an example –

```# create a 2d numpy array
arr = np.array([[1, 2, 3],
[4, 5, 6]])
# transpose the array
arr_t = arr.transpose()
# display the arrays
print("Original Array:\n", arr)
print("After Transpose:\n", arr_t)```

Output:

```Original Array:
[[1 2 3]
[4 5 6]]
After Transpose:
[[1 4]
[2 5]
[3 6]]```

Here, we transpose a 2×3 array. Note that after the transpose, the first row in the original array `[1, 2, 3]` becomes the first column in the transposed array and similarly the second row `[4, 5, 6]` becomes the second column in the transposed array. Let’s look at the shape of the two arrays –

```# print the shape of the two arrays
print("Original array shape: ", arr.shape)
print("Shape after transpose: ", arr_t.shape)```

Output:

```Original array shape:  (2, 3)
Shape after transpose:  (3, 2)```

Alternatively, you can swipe the rows and columns of a numpy array using the `.T` attribute.

```# create a 2d numpy array
arr = np.array([[1, 2, 3],
[4, 5, 6]])
# transpose the array
arr_t = arr.T
# display the arrays
print("Original Array:\n", arr)
print("After Transpose:\n", arr_t)```

Output:

```Original Array:
[[1 2 3]
[4 5 6]]
After Transpose:
[[1 4]
[2 5]
[3 6]]```

We get the same result as we did with the numpy ndarray `transpose()` function.

For more on the numpy ndarray transpose function, refer to its documentation.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5

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