'Summarising results of multiple Pandas Boolean columns

I have a Pandas dataset with multiple Boolean columns (effectively flagging what type of sustainability targets a company has set). I would like to summarise the results of these these columns by counting the number of True and False occurances.

A simplified example of the dataset:

data1 = [['Apple', True, True, True], ['Tesla', True, False, True], ['Nike', True, False, False]]
df1 = pd.DataFrame(data1, columns=['Company', 'Carbon-target', 'Water-target', 'Waste-target'])

  Company  Carbon-target  Water-target  Waste-target
0   Apple           True          True          True
1   Tesla           True         False          True
2    Nike           True         False         False

Desired output:

                True         False
Carbon-target   3            0 
Water-target    1            2
Waste-target    2            1

Currently, the only operator I can think of is the word_count function, but would require several iterations. Is there a simpler way?

Thank you !

Solution 1:^[1]

Select boolean columns

x = df.select_dtypes(bool)

Count True and False values

out = pd.DataFrame({True: x.sum(), False: df.shape[0] - x.sum()})

Output

               True  False
Carbon-target     3      0
Water-target      1      2
Waste-target      2      1

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Solution 1	Vishnudev

'Summarising results of multiple Pandas Boolean columns

Solution 1:[1]

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Related Questions

Solution 1:^[1]