unique
unique(data, columns=None)Check if there are no duplicated values in each one of the selected columns.
This is a column oriented check, for a row oriented check see unique_combination_of_columns
Parameters
data: PolarsLazyOrDataFrame-
The input DataFrame to check for unique values.
columns: Optional[PolarsColumnType] = None-
Columns to consider for uniqueness check. By default, all columns are checked.
Returns
| Name | Type | Description |
|---|---|---|
| PolarsLazyOrDataFrame | The original polars DataFrame or LazyFrame when the check passes |
Examples
>>> import polars as pl
>>> import pelage as plg
>>> df = pl.DataFrame({"a": [1, 2]})
>>> df.pipe(plg.unique, "a") # Can also use ["a", ...], pl.col("a)
shape: (2, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 1 │
│ 2 │
└─────┘>>> df = pl.DataFrame({"a": [1, 1, 2]})
>>> df.pipe(plg.unique, "a")
Traceback (most recent call last):
...
pelage.types.PolarsAssertError: Details
shape: (2, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 1 │
│ 1 │
└─────┘
Error with the DataFrame passed to the check function:
--> Somes values are duplicated within the specified columns