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Fastest way to count the number of values

I have a numpy array of data that I want to count the frequency of a certain value. I have tried:
import numpy as np
N = 10000
values = np.array([random.choice(np.arange(4)) for i in range(N)])
np.count(values==1,axis=0)

And:
N = 10000
values = np.array([random.choice(np.arange(4)) for i in range(N)])
np.count_nonzero(values==1)

And:
N = 10000
values = np.array([random.choice(np.arange(4)) for i in range(N)])
np.sum(values==1)

And:
N = 10000
values = np.array([random.choice(np.arange(4)) for i in range(N)])
np.unique(values==1)

And:
N = 10000
values = np.array([random.choice(np.arange(4)) for i in range(N)])
np.unique(values, return_counts=True)

The fastest of all of them is the last one, which is very close to the answer.
The value I am looking at is 1.
All of the code only takes a few milliseconds to run on my computer. The problem is that I am using it as part of a larger simulation and as this occurs every second, I would like to speed this up.
Any help would be appreciated.
Thank you.
EDIT1:
To be more clear, it is the comparison against 1 that matters, not the comparison against 4. So I don’t think a single solution will work.

A:

You’re looking at the inverse of
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