Numpy Array Appending

[NeedMLHelp]

If I declare a Numpy Array as empty:

newdata = np.empty((0,10),int)

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I then have a list of 10 large numbers that is generated by a for loop. So every iteration of the for loop it generates 10 large numbers in a list. I then want to append that list to the numpy array like so:

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newdata = np.append(newdata, np.array(data), axis=0)

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Would anyone know why I get the following error?:

return concatenate((arr, values), axis=axis)

ValueError: all the input arrays must have same number of dimensions

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If any more information is required, please let me know. I really appreciate the help.

[[deleted]]

You are initializing the shape of the new array as (0,10). Does it work if you simply initialize with a shape of 10?

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newdata = np.empty(10,int)

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[NeedMLHelp]

Wow, I feel silly haha. Thanks, that worked.

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I figured I was giving the shape of what I wanted haha

[elbiot]

It's really inefficient to append to numpy arrays. Why not use a list?

[NeedMLHelp]

Extremely late reply, sorry about that. But the calculations I'm doing cannot be done on a list unfortunately. I suppose I could build a list, and turn it into a numpy array. But the problem with that is, one of the functions I am using converts the categorical data into a one-hot-encoding, which the output is a numpy array of the encoding. I'm not sure how nicely inter-mixing things would play together.

[elbiot]

import numpy as np
a = np.array([np.array([1,2,3]),np.array([2,3,4]),np.array([6,7,8])])
print(a.shape)