How do I use np.newaxis in NumPy?

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1

I’m trying to understand what np.newaxis does in NumPy and when it’s useful.

For example, applying it to a 1D array changes its shape from [0, 1, 2, 3] to either a row or column vector.

Can someone explain how np.newaxis works and when to use it for reshaping or broadcasting?

Looking for practical use cases involving np.newaxis.

2

@Punamhans For me, np.newaxis is super helpful when I want to make two arrays compatible for broadcasting.

Like, say I have a column vector and I want to add it to a row vector:


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a = np.array([1, 2, 3])[:, np.newaxis]  # shape (3, 1)
b = np.array([10, 20, 30])              # shape (3,)

Now I can broadcast a + b.T easily.

Without np.newaxis, I’d have to reshape manually, which feels less readable.

3

I mostly use np.newaxis to reshape a 1D array depending on whether I want a row or column:

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arr = np.array([0, 1, 2, 3])
row = arr[np.newaxis, :]  # shape (1, 4)
col = arr[:, np.newaxis]  # shape (4, 1)

This is useful in plotting with matplotlib or passing data into ML models that expect a specific shape.

It’s basically just a shortcut for reshape() but more intuitive for me.

4

I use np.newaxis a lot when preparing single samples for models that expect 2D or 3D inputs.

For example:

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image = np.array([28, 28])  # say a grayscale image
image = image[np.newaxis, ...]  # now shape is (1, 28, 28)

That “batch dimension” is essential when feeding data into neural nets.

I like newaxis because it makes the intent obvious, you’re inserting a dimension, not just reshaping blindly.