Value_counts() returning no NaN values, but repeat True values

When I apply value_counts to tafe_resignations_up with dropna=False, The values I see are False, True, and True (not NaN). Why is this happening? Is there a way to prevent this?

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My Code:

tafe_dissatisfaction_cols = ['Contributing Factors. Dissatisfaction', 'Contributing Factors. Job Dissatisfaction']
tafe_resignations['dissatisfied'] = tafe_resignations[tafe_dissatisfaction_cols].applymap(update_vals).any(axis=1, skipna=False)
tafe_resignations_up = tafe_resignations.copy()


I would recommend checking existing posts about similar issues -

@the_doctor I am working off of my local machine. How do I upgrade pandas in this case? Do I have to use the terminal?

That would depend on your OS and other factors that I can’t help with since it’s a local installation.

I would recommend simply googling for updating pandas for your OS and you should find tutorials that can help you out.

If it’s difficult or overwhelming for you, I would recommend either completing the project on DQ’s platform or creating a new, separate question asking specifically about updating Pandas version with details related to your local installation - like the OS, what you have tried so far, what package manager you might be using on your system etc.

any returns True if at least 1 value is True. Otherwise return False.

You can read up on documentation any on your local machine using

import pandas pd

or using


To count the number of True, you can use .sum() since True has a value 1 and False has a value of 0 – this is because Boolean is subclass of int. Then, number of False = length of data frame or series - number of True.

1 Like

thanks for solving this for me- i forgot I applied the any method and shouldn’t expect to see NaN anyway!