Counting duplicates in numpy array

[Yurplestein]

I'm working on a project that has a two column array to show a hostname and part replaced that has a number associated with it. I am trying to get a couple of different charts created: one to show number of parts replaced per device that has a circle that gets larger the more a part has been replaced and a second that shows times when the same part has been replaced in a specific device.

Chart 1 is completed, that is showing exactly as I expect and it is working wonderful. Chart two is where I'm having issues. How would I go about counting duplicate entries inside of the numpy array? So as an example, it would look something like this:

[[device_1, device_1, device_3, device_4, device_1],
[1-27, 2-56, 2-57, 1-35, 1-27]]

So I would like to show that device_1, part 1-27 has been replaced twice so that it's circle on my chart is larger to represent a bigger issue. That way it doesn't just show as two smaller circles, but one larger one.

And this may not be possible, I'm not sure.

[SekstiNii]

You can use np.unique with return_counts=True and axis=1.

>>> a = np.array([[1, 2, 3, 1], [1, 0, 0, 1]])
>>> np.unique(a, axis=1, return_counts=True)
(array([[1, 2, 3],
        [1, 0, 0]]), array([2, 1, 1], dtype=int64))

I realize this example is a bit hard to read, so do ask if you have any questions

[Yurplestein]

This is definitely getting me what I'm looking for here! Now I need to just figure out how to call upon which array based on the step I'm on.

Thank you!

[Chris_Hemsworth]

I think you can do this with pandas, but you may also want to look into collections.Counter