Pelage: Defensive analysis for Polars
  • Get started
  • API Reference
  • Examples
  • Coming from dbt
  • Git
  1. Check functions
  2. unique
  • API Reference
  • Check functions
    • has_columns
    • has_dtypes
    • has_no_nulls
    • has_no_infs
    • unique
    • unique_combination_of_columns
    • accepted_values
    • not_accepted_values
    • accepted_range
    • maintains_relationships
    • column_is_within_n_std
    • custom_check
  • Checks with group_by
    • has_shape
    • at_least_one
    • not_constant
    • not_null_proportion
    • has_mandatory_values
    • mutually_exclusive_ranges
    • is_monotonic
  • Exceptions
    • PolarsAssertError

On this page

  • unique
    • Parameters
    • Returns
    • Examples
  1. Check functions
  2. unique

unique

checks.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

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.checks.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
has_no_infs
unique_combination_of_columns