pandas pivot table on predefined values-CodePudding

I am trying to count values for multiple columns including values that might not occur in the column. Given example data frame:

foo = pd.DataFrame({'A':[False,False,True], 'B':[None, False, True], 'C': [None, None, None]})

my final result would look like:

.value_counts() would not work here for me since it will miss some values plus it works primary on pd.Series. So far I tried pivot table but without success:

# returns KeyError: False
foo.pivot_table(index=[False, True, None], columns=foo.columns, aggfunc='count')

How can pandas count all occurrences for specified values in multiple columns ?

CodePudding user response：

You can melt and crosstab:

bar = foo.melt(var_name='pivot_values').replace({None: 'None'})
# for some reason, even using "dropna=False" in crosstab still removes None

out = (pd.crosstab(bar['pivot_values'], bar['variable'], dropna=False)
         .reset_index().rename_axis(columns=None)
      )

Output:

  pivot_values  A  B  C
0        False  2  1  0
1         True  1  1  0
2         None  0  1  3