pandas.DataFrame.drop_duplicates#
- DataFrame.drop_duplicates(subset=None, *, keep='first', inplace=False, ignore_index=False)[source]#
Return DataFrame with duplicate rows removed.
Considering certain columns is optional. Indexes, including time indexes are ignored.
- 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 keep.
‘first’ : Drop duplicates except for the first occurrence.
‘last’ : Drop duplicates except for the last occurrence.
False
: Drop all duplicates.
- inplacebool, default
False
Whether to modify the DataFrame rather than creating a new one.
- ignore_indexbool, default
False
If
True
, the resulting axis will be labeled 0, 1, …, n - 1.
- Returns
- DataFrame or None
DataFrame with duplicates removed or None if
inplace=True
.
See also
DataFrame.value_counts
Count unique combinations of columns.
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, it removes duplicate rows based on all columns.
>>> df.drop_duplicates() brand style rating 0 Yum Yum cup 4.0 2 Indomie cup 3.5 3 Indomie pack 15.0 4 Indomie pack 5.0
To remove duplicates on specific column(s), use
subset
.>>> df.drop_duplicates(subset=['brand']) brand style rating 0 Yum Yum cup 4.0 2 Indomie cup 3.5
To remove duplicates and keep last occurrences, use
keep
.>>> df.drop_duplicates(subset=['brand', 'style'], keep='last') brand style rating 1 Yum Yum cup 4.0 2 Indomie cup 3.5 4 Indomie pack 5.0