polars.DataFrame.unique#

DataFrame.unique( subset: ColumnNameOrSelector | Collection[ColumnNameOrSelector] | None = None, *, keep: UniqueKeepStrategy = 'any', maintain_order: bool = False, ) → DataFrame[source]#

Drop duplicate rows from this dataframe.

Parameters:

subset

Column name(s) or selector(s), to consider when identifying duplicate rows. If set to None (default), use all columns.

keep{‘first’, ‘last’, ‘any’, ‘none’}

Which of the duplicate rows to keep.

‘any’: Does not give any guarantee of which row is kept.
This allows more optimizations.
‘none’: Don’t keep duplicate rows.
‘first’: Keep first unique row.
‘last’: Keep last unique row.

maintain_order

Keep the same order as the original DataFrame. This is more expensive to compute. Settings this to True blocks the possibility to run on the streaming engine.

Returns:

DataFrame: DataFrame with unique rows.

Warning

This method will fail if there is a column of type List in the DataFrame or subset.

Examples

>>> df = pl.DataFrame(
...     {
...         "foo": [1, 2, 3, 1],
...         "bar": ["a", "a", "a", "a"],
...         "ham": ["b", "b", "b", "b"],
...     }
... )
>>> df.unique(maintain_order=True)
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ a   ┆ b   │
│ 2   ┆ a   ┆ b   │
│ 3   ┆ a   ┆ b   │
└─────┴─────┴─────┘
>>> df.unique(subset=["bar", "ham"], maintain_order=True)
shape: (1, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str │
╞═════╪═════╪═════╡
│ 1   ┆ a   ┆ b   │
└─────┴─────┴─────┘
>>> df.unique(keep="last", maintain_order=True)
shape: (3, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ ham │
│ --- ┆ --- ┆ --- │
│ i64 ┆ str ┆ str │
╞═════╪═════╪═════╡
│ 2   ┆ a   ┆ b   │
│ 3   ┆ a   ┆ b   │
│ 1   ┆ a   ┆ b   │
└─────┴─────┴─────┘