Truth.drop_duplicates

Truth.drop_duplicates#

missionbio.demultiplex.dna.truth.Truth.drop_duplicates

Truth.drop_duplicates(subset: Optional[Union[Hashable, Sequence[Hashable]]] = None, *, keep: Literal['first', 'last', False] = 'first', inplace: bool = False, ignore_index: bool = False) → pandas.core.frame.DataFrame | None#

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.

New in version 1.0.0.

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

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