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What i want is including zero counts categories while generating frequencies for categorical variables

example: 

col1   col2 
a       x
c       y
a       y
f       z

what i want is to generate a frequency table with counts and percentages including zero counts categories

results

  Counts   Cercentage
a  2          50.0%
b  0          0.0%
c  1          25.0%
d  0          0.0%
e  1          25.0%

what i have done is generating the frequency table with counts and percentages but i need to include also the zero counts categories like b and d as illustrated above

here is what i have tried

pd.concat([df.col1.value_counts(dropna=False),
           df.col1.value_counts(normalize=True,
                                dropna=False).mul(100).round(1).astype(str) + '%'],
          axis=1,
          keys=('Counts','Percentage'))

any help please

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  • $\begingroup$ Hello there, could you please try to use Code blocks so your tables look cleaner, and give a clear example of what you try to do ? I can't understand the question... $\endgroup$ – BeamsAdept Sep 23 '20 at 9:50
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I got the answer use of Series.reindex with a list of all categories and if not match replace to 0

  categories = ['a', 'b', 'c', 'd', 'e']
  pd.concat([df.col1.value_counts().reindex(categories[::-1], fill_value=0),        df.col1.value_counts(normalize=True).reindex(categories[::-1], fill_value=0).mul(100).round(1).astype(str) + '%'],axis=1, keys=('Counts','Percentage'))

This worked for me

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  • $\begingroup$ Welcome to DS SE! If you answered your own question, would you please mark it as such? Click the checkmark under the vote buttons on your answer. $\endgroup$ – Benji Albert Sep 23 '20 at 14:30
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I couldn't understand the question, but if I only consider the title "including empty categories with pandas value_counts()" here's the way to do it :

df['COLUMN'].value_counts(dropna=False)
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  • $\begingroup$ sorry maybe that was not well state but i want is to include zero counts $\endgroup$ – Espoir Sep 23 '20 at 10:01

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