Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How do you reduce the dimension of a numpy array** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How do you reduce the dimension of a numpy array?

**How do you reduce the dimension of a numpy array?**I stumbled upon

`A.reshape(1,27,1)`

and first without conserving the size and I got a`ValueError: total size of new array must be unchanged`

**you reduce the dimension of a numpy array**I stumbled upon

`A.reshape(1,27,1)`

and first without conserving the size and I got a`ValueError: total size of new array must be unchanged`

## Method 1

You could use numpy.squeeze()

x = np.array([[[0], [1], [2]]]) x.shape (1, 3, 1) np.squeeze(x).shape (3,) np.squeeze(x, axis=(2,)).shape (1, 3)

## Method 2

I stumbled upon `A.reshape(1,27,1)`

and first without conserving the size and I got a

ValueError: total size of new array must be unchanged

error, but then accidentally, I ended up trying omitting the third dimension in the reshape,

In [21]: A[:,:,:1].reshape(2,27) Out[21]: array([[ 21., 20., 15., 23., 22., 16., 25., 24., 12., 4., 7., 1., 6., 3., 19., 13., 11., 9., 8., 27., 14., 18., 10., 17., 26., 2., 5.], [ 20., 21., 12., 1., 17., 4., 22., 23., 16., 6., 15., 26., 13., 19., 24., 2., 10., 25., 3., 7., 8., 11., 27., 14., 9., 18., 5.]])

and magically the third dimension disappeared.

And this is exactly what I wanted.

**Conclusion**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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