What is np.c_ and np.r_ in numpy ?

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This is a small and easy explanation on how to use them!

It’s a handy technique for concatenation of numpy arrays using its np.c_ and np.r_

Short definition of what they do:
np.c_[] concatenates arrays along second axis.
Similarly, np.r_[] concatenates arrays along first axis.

Confusing right ?

Let’s create 2 arrays a and b to learn with the example.

import numpy as np
a = np.array([[1, 2, 3],
              [11,22,33]]
            )
b = np.array([[4, 5, 6],
              [44,55,66]]
            )

a’s shape: (2,3) i.e. (no. of rows, no. of columns) i.e. (1st axis, 2nd axis)
So, first axis is along the 2 rows and second axis is along the columns.

Similarly,
b’s shape: (2,3) i.e. (1st axis, 2nd axis)

np.r_ concatenates along the first axis (i.e. along 2 rows)
So, we get below result on running np.r_[a,b] command

np.r_[a,b]
>>> array([[ 1, 2,  3],
           [11, 22, 33],
           [ 4, 5,  6],
           [44, 55, 66]])

i.e. concatination along the rows(first axis), so number of rows will increase here to 4.

While np.c_[a,b] concatenates along the Second axis i.e. concatenation id taking place along columns and give result as below:

>>> array([[ 1, 2, 3, 4, 5, 6],
           [11, 22, 33, 44, 55, 66]
          ])

I hope it was helpful!

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Thanks for reading :)