pandas.DataFrame.duplicated — pandas 2.2.3 documentation (original) (raw)

DataFrame.duplicated(subset=None, keep='first')[source]#

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters:

subsetcolumn 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.

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