Hướng dẫn dùng pandas duplicates python

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Only consider certain columns for identifying duplicates, by default use all of the columns.

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.

Whether to drop duplicates in place or to return a copy.

If True, the resulting axis will be labeled 0, 1, …, n - 1.

New in version 1.0.0.

DataFrame with duplicates removed or None if inplace=True.

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