API Reference

Check functions

List of check functions.

has_columns	Check if a DataFrame has the specified
has_dtypes	Check that the columns have the expected types
has_no_nulls	Check if a DataFrame has any null (missing) values.
has_no_infs	Check if a DataFrame has any infinite (inf) values.
unique	Check if there are no duplicated values in each one of the selected columns.
unique_combination_of_columns	Ensure that the selected column have a unique combination per row.
accepted_values	Raises error if columns contains values not specified in items
not_accepted_values	Raises error if columns contains values specified in List of forbbiden items
accepted_range	Check that all the values from specifed columns in the dict items are within the indicated range.
maintains_relationships	Function to help ensuring that set of values in selected column remains the same in both DataFrames. This helps to maintain referential integrity.
column_is_within_n_std	Function asserting values are within a given STD range, thus ensuring the absence of outliers.
custom_check	Use custom Polars expression to check the DataFrame, based on .filter().

Checks with group_by

List of check functions with optional group_by option.

has_shape	Check if a DataFrame has the specified shape.
at_least_one	Ensure that there is at least one not null value in the designated columns.
not_constant	Check if a DataFrame has constant columns.
not_null_proportion	Checks that the proportion of non-null values in a column is within a a specified range [at_least, at_most] where at_most is an optional argument (default: 1.0).
has_mandatory_values	Ensure that all specified values are present in their respective column.
mutually_exclusive_ranges	Ensure that the specified columns contains no overlapping intervals.
is_monotonic	Verify that values in a column are consecutively increasing or decreasing.

Exceptions

Types aliases and custom exceptions

PolarsAssertError

Custom Error providing detailed information about the failed check.