nunique(self, dropna: 'bool' = True) -> 'DataFrame'
Don't include NaN in the counts.
Return DataFrame with counts of unique elements in each position.
>>> df = pd.DataFrame({'id': ['spam', 'egg', 'egg', 'spam',This example is valid syntax, but we were not able to check execution
... 'ham', 'ham'],
... 'value1': [1, 5, 5, 2, 5, 5],
... 'value2': list('abbaxy')})
... df id value1 value2 0 spam 1 a 1 egg 5 b 2 egg 5 b 3 spam 2 a 4 ham 5 x 5 ham 5 y
>>> df.groupby('id').nunique() value1 value2 id egg 1 1 ham 1 2 spam 2 1
Check for rows with the same id but conflicting values:
This example is valid syntax, but we were not able to check execution>>> df.groupby('id').filter(lambda g: (g.nunique() > 1).any()) id value1 value2 0 spam 1 a 3 spam 2 a 4 ham 5 x 5 ham 5 ySee :
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