pandera.api.pandas.components.Column¶

class pandera.api.pandas.components.Column(dtype=None, checks=None, parsers=None, nullable=False, unique=False, report_duplicates='all', coerce=False, required=True, name=None, regex=False, title=None, description=None, default=None, metadata=None, drop_invalid_rows=False)[source]¶

Validate types and properties of pandas DataFrame columns.

Create column validator object.

Parameters:

dtype (Union[str, type, DataType, Type, ExtensionDtype, dtype]) – 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
parsers (Union[Parser, List[Parser], None]) – parsers to verify validity of the column
nullable (bool) – Whether or not column can contain null values.
unique (bool) – whether column values should be unique
report_duplicates (Union[Literal[‘exclude_first’], Literal[‘exclude_last’], Literal[‘all’]]) – how to report unique errors - exclude_first: report all duplicates except first occurence - exclude_last: report all duplicates except last occurence - all: (default) report all duplicates
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 (Union[str, Tuple[str, …], None]) – column name in dataframe to validate.
regex (bool) – whether the name attribute should be treated as a regex pattern to apply to multiple columns in a dataframe.
title (Optional[str, None]) – A human-readable label for the column.
description (Optional[str, None]) – An arbitrary textual description of the column.
default (Optional[Any, None]) – The default value for missing values in the column.
metadata (Optional[dict, None]) – 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.

Methods

__init__(dtype=None, checks=None, parsers=None, nullable=False, unique=False, report_duplicates='all', coerce=False, required=True, name=None, regex=False, title=None, description=None, default=None, metadata=None, drop_invalid_rows=False)[source]¶

Create column validator object.

Parameters:

dtype (Union[str, type, DataType, Type, ExtensionDtype, dtype]) – 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
parsers (Union[Parser, List[Parser], None]) – parsers to verify validity of the column
nullable (bool) – Whether or not column can contain null values.
unique (bool) – whether column values should be unique
report_duplicates (Union[Literal[‘exclude_first’], Literal[‘exclude_last’], Literal[‘all’]]) – how to report unique errors - exclude_first: report all duplicates except first occurence - exclude_last: report all duplicates except last occurence - all: (default) report all duplicates
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 (Union[str, Tuple[str, …], None]) – column name in dataframe to validate.
regex (bool) – whether the name attribute should be treated as a regex pattern to apply to multiple columns in a dataframe.
title (Optional[str, None]) – A human-readable label for the column.
description (Optional[str, None]) – An arbitrary textual description of the column.
default (Optional[Any, None]) – The default value for missing values in the column.
metadata (Optional[dict, None]) – 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.

example(size=None)[source]¶

Generate an example of a particular size.

Parameters:: size – number of elements in the generated Index.
Return type:: DataFrame
Returns:: pandas DataFrame object.

get_regex_columns(check_obj)[source]¶

Get matching column names based on regex column name pattern.

Parameters:: columns – columns to regex pattern match
Return type:: Iterable
Returns:: matchin columns

set_name(name)[source]¶

Used to set or modify the name of a column object.

Parameters:: name (str) – the name of the column object

strategy(*, size=None)[source]¶

Create a hypothesis strategy for generating a Column.

Parameters:: size – number of elements to generate
Returns:: a dataframe strategy for a single column.

strategy_component()[source]¶: Generate column data object for use by DataFrame strategy.

__call__(check_obj, head=None, tail=None, sample=None, random_state=None, lazy=False, inplace=False)[source]¶

Alias for validate method.

Return type:: ~TDataObject