pandas.DataFrame.duplicated

DataFrame.duplicated(subset=None, keep='first')[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 of the columns.

keep:{‘first’, ‘last’, False}, default ‘first’

Determines which duplicates (if any) to mark.

• first : Mark duplicates as True except for the first occurrence.

• last : Mark duplicates as True except for the last occurrence.

• False : Mark all duplicates as True.

Returns:

Series

Boolean series for each duplicated rows.

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