numpy array not broadcastable

Question

This is an example of my error. Say i created a numpy array

X = np.zeros((1000, 50))

Where 1000 is the features (rows) and 50 is the examples (columns)

Since i am adding examples one by one i will have to replace columns in the array 1 by 1 to get the final feature array. I tried this:

X[:,i] = example

where example is of size (1000, 1), and i is iterated for every example. This does not work because X[:,i] is of shape (1000,), a rank 1 array. How do i code it so that each example replaces a row of the X array without throwing the broadcast error. Thank you.

X[:,i] = example.reshape(1000) ?

sam46
– sam46

2019-01-29 22:11:50 +00:00
Commented Jan 29, 2019 at 22:11 — sam46
– sam46, Commented Jan 29, 2019 at 22:11

TPereira · Accepted Answer · 2019-01-29 22:22:54Z

1

Reshape your vector before assigning it.

X[:,i] = example.reshape(-1,)

This will suppress the second dimension and turn example into shape (1000,)

Or, avoiding assigning one by one in the loop you can put all of your arrays in a list and then call np.array on your list and transpose it to have them as columns. This will probably work better if you can construct your list of arrays in a list comprehension.

Example:

arrs = [np.random.randint(10, size=5) for _ in range(5)]
X = np.array(arrs).T

edited Jan 29, 2019 at 22:22

answered Jan 29, 2019 at 22:12

TPereira

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1 Comment

Ratchet Over a year ago

First solution worked, I will try to use a method like the second in the future, Thanks!

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