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When using the pandas library to interpret a dataset I am trying to sum up all the pairs across 2 columns of the dataframe by using value_counts(subset = ...). Unfortunately it keeps giving me spaces in the results that shouldn't be there and make the output unreadable. Anyone know how to avoid this?

Attempted:

clm = [ 'class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises?', 'odor', 'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color', 'stalk-shape', 'stalk-root', 'stalk-surface-above-ring', 'stalk-surface-below-ring', 'stalk-color-above-ring', 'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number', 'ring-type', 'spore-print-color', 'population', 'habitat' ]

myTable = pd.read_csv(file_path, header=None, names=clm)

print(myTable.value_counts(subset = ['class', 'cap-color']))

Result:

result table

There shouldn't be any blank spaces, each row should have a 'p' or an 'e'

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  • You can add dropna=False Commented Mar 17, 2023 at 1:21
  • 1
    myTable.value_counts(subset = ['class', 'cap-color']).reset_index() Commented Mar 17, 2023 at 1:22
  • dropna=False does not change the result at all. Commented Mar 17, 2023 at 1:23
  • reset_index() worked! thank you! Commented Mar 17, 2023 at 1:29

1 Answer 1

Reset to default
1

In the value_counts() set the parameter normalize=False

myTable.value_counts(subset=['class', 'cap-color'], normalize=False)
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