pandera.api.polars.components.Column

class pandera.api.polars.components.Column(dtype=None, checks=None, nullable=False, unique=False, coerce=False, required=True, name=None, regex=False, title=None, description=None, default=None, metadata=None, drop_invalid_rows=False, **column_kwargs)

Polars column schema component.

Create column validator object.

Parameters:

• dtype (Union[str, type, DataTypeClass, None]) – datatype of the column. The datatype for type-checking a dataframe. If a string is specified, then assumes one of the valid pandas string values: http://pandas.pydata.org/pandas-docs/stable/basics.html#dtypes

• checks (Union[Check, List[Union[Check, Hypothesis]], None]) – checks to verify validity of the column

• nullable (bool) – Whether or not column can contain null values.

• unique (bool) – whether column values should be unique

• coerce (bool) – If True, when schema.validate is called the column will be coerced into the specified dtype. This has no effect on columns where dtype=None.

• required (bool) – Whether or not column is allowed to be missing

• name (Optional[str]) – column name in dataframe to validate. Names in the format ‘^{regex_pattern}$’ are treated as regular expressions. During validation, this schema will be applied to any columns matching this pattern.

• regex (bool) – whether the name attribute should be treated as a regex pattern to apply to multiple columns in a dataframe. If the name is a regular expression, this attribute will automatically be set to True.

• title (Optional[str]) – A human-readable label for the column.

• description (Optional[str]) – An arbitrary textual description of the column.

• default (Optional[Any]) – The default value for missing values in the column.

• metadata (Optional[dict]) – An optional key value data.

• drop_invalid_rows (bool) – if True, drop invalid rows on validation.

Raises:

SchemaInitError – if impossible to build schema from parameters

Example:

>>> import pandas as pd
>>> import pandera as pa
>>>
>>>
>>> schema = pa.DataFrameSchema({
...     "column": pa.Column(str)
... })
>>>
>>> schema.validate(pd.DataFrame({"column": ["foo", "bar"]}))
  column
0    foo
1    bar

See here for more usage details.

Attributes

BACKEND_REGISTRY
dtype	Get the pandas dtype
properties	Get column properties.
selector

Methods

__init__	Create column validator object.
example	Generate an example of a particular size.
set_name	Set the name of the schema.
set_regex
strategy	Create a hypothesis strategy for generating a Column.
strategy_component	Generate column data object for use by DataFrame strategy.
validate	Validate a Column in a DataFrame object.
__call__	Alias for validate method.