Converting array of ints to binary in Python3: "TypeError: only integer scalar arrays can be converted?"

Question

this is not a duplicate question as far as I can tell. I will list related-but-different questions below.

Here's the code I'm trying to run, to convert an array of ints to binary as a single vectorized operation:

import numpy as np

a = np.array([1,2,3,4],dtype=int)
b = bin(a)

----> 4 b = bin(a)

TypeError: only integer scalar arrays can be converted to a scalar index

...Um... I am literally operating on an "integer scalar array". (And changing dtype=int to something like dtype=np.int64 has no effect.)

I realize could do do this with a loop (Python 2 question), or as a list comprehension (I understand that bin produces strings), so...this post is more a question of: Why can't I do it as a single vectorized operation, and why that error message? It seems so inappropriate in this case. Any thoughts?

Related but non-dup questions: here, here, here, and here, and here.

The docs for bin read "Convert an integer number..." suggesting that arrays are just not allowed, and it's a Python function not a numpy function. (Ok, so then list comprehension or loop it is -- these just seem to be slow and I'm hoping for something fast). But then why the error message about integer scalar arrays?

Thanks.

bin attempts to call __index__ on the object if it is not an int as described in the docs you linked. An integer scalar array is something like np.array(1). np.array(1).__index__() returns 1 but np.array([1]).__index__() gives the same TypeError. — iz_
– iz_, Commented May 1, 2021 at 17:30
To do the actual conversion, check this question: stackoverflow.com/q/57836960 — iz_
– iz_, Commented May 1, 2021 at 17:34
np.vectorize and map and all those are still essentially loops and aren't going to show a magic "speedup". Also np.vectorize can slow your code down, especially if there's not an underlying numpy operation happening, so I'd stay away from that. — Chrispresso
– Chrispresso, Commented May 1, 2021 at 17:44
@Mark Yes, I even wrote in the OP, "I understand that bin produces strings". It's ok that you don't grasp the utility. ;-) Not that it's any of your business, but I'm looking at binary representations of Champerowne's Number. — sh37211
– sh37211, Commented May 1, 2021 at 18:05
Sorry @sh37211 for not catching that line the strings. I assumed since your code tried to pass a numpy array to bin() that you were just getting started and might not understand the basics. ;-) Just trying to help. Good luck. — Mark
– Mark, Commented May 1, 2021 at 18:13

Nk03 · Accepted Answer · 2021-05-01 17:23:53Z

1

You need to map the function to every element of the numpy array -

import numpy as np
a = np.array([1,2,3,4],dtype=int)
b = np.array(list(map(bin,a)))

Additionally, you can check-out this link - Most efficient way to map function over numpy array

answered May 1, 2021 at 17:23

Nk03

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sh37211 Over a year ago

Hey, thanks! And that link you shared helped me do a comparison of my own. Not sure if comments will show images but: perfplot comparison (also included whether to wrap the map in np.array or not). So it looks like I was wrong and list-comprehension is plenty fast! The first test I ran before creating this post had some latency that I hadn't accounted for.

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