modin.pandas.DataFrame.duplicated¶
- DataFrame.duplicated(subset=None, keep='first') Series [source]¶
Return boolean Series denoting duplicate rows.
Considering certain columns is optional.
- Parameters:
subset (column label or sequence of labels, optional) – Only consider certain columns for identifying duplicates, by default use all the columns.
keep ({'first', 'last', False}, default 'first') –
Determines which duplicates (if any) to mark.
first
: Mark duplicates asTrue
except for the first occurrence.last
: Mark duplicates asTrue
except for the last occurrence.False : Mark all duplicates as
True
.
- Returns:
Boolean series for each duplicated rows.
- Return type:
See also
Index.duplicated
Equivalent method on index.
Series.duplicated
Equivalent method on Series.
Series.drop_duplicates
Remove duplicate values from Series.
DataFrame.drop_duplicates
Remove duplicate values from DataFrame.
Examples
Consider dataset containing ramen rating.
>>> df = pd.DataFrame({ ... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'], ... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'], ... 'rating': [4, 4, 3.5, 15, 5] ... }) >>> df brand style rating 0 Yum Yum cup 4.0 1 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0
By default, for each set of duplicated values, the first occurrence is set on False and all others on True.
>>> df.duplicated() 0 False 1 True 2 False 3 False 4 False dtype: bool
By using ‘last’, the last occurrence of each set of duplicated values is set on False and all others on True.
>>> df.duplicated(keep='last') 0 True 1 False 2 False 3 False 4 False dtype: bool
By setting
keep
on False, all duplicates are True.>>> df.duplicated(keep=False) 0 True 1 True 2 False 3 False 4 False dtype: bool
To find duplicates on specific column(s), use
subset
.>>> df.duplicated(subset=['brand']) 0 False 1 True 2 False 3 True 4 True dtype: bool