pandera.api.pandas.array.SeriesSchema¶

class pandera.api.pandas.array.SeriesSchema(dtype=None, checks=None, parsers=None, index=None, nullable=False, unique=False, report_duplicates='all', coerce=False, name=None, title=None, description=None, default=None, metadata=None, drop_invalid_rows=False)[source]¶

A pandas Series validator.

Initialize series schema base object.

Parameters:

dtype (Union[str, type, DataType, Type, ExtensionDtype, dtype, None]) – datatype of the column. 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]) –
If element_wise is True, then callable signature should be:

Callable[Any, bool] where the Any input is a scalar element in the column. Otherwise, the input is assumed to be a pandas.Series object.
index – specify the datatypes and properties of the index.
nullable (bool) – Whether or not column can contain null values.
unique (bool) – Whether or not column can contain duplicate values.
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.
name (Optional[str]) – series name.
title (Optional[str]) – A human-readable label for the series.
description (Optional[str]) – An arbitrary textual description of the series.
metadata (Optional[dict]) – An optional key-value data.
default (Optional[Any]) – The default value for missing values in the series.
drop_invalid_rows (bool) – if True, drop invalid rows on validation.

Attributes

`BACKEND_REGISTRY`
`dtype`	Get the pandas dtype
`properties`	Get the properties of the schema for serialization purposes.

Methods

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

Initialize series schema base object.

Parameters:

dtype (Union[str, type, DataType, Type, ExtensionDtype, dtype, None]) – datatype of the column. 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]) –
If element_wise is True, then callable signature should be:

Callable[Any, bool] where the Any input is a scalar element in the column. Otherwise, the input is assumed to be a pandas.Series object.
index – specify the datatypes and properties of the index.
nullable (bool) – Whether or not column can contain null values.
unique (bool) – Whether or not column can contain duplicate values.
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.
name (Optional[str]) – series name.
title (Optional[str]) – A human-readable label for the series.
description (Optional[str]) – An arbitrary textual description of the series.
metadata (Optional[dict]) – An optional key-value data.
default (Optional[Any]) – The default value for missing values in the series.
drop_invalid_rows (bool) – if True, drop invalid rows on validation.

example(size=None)[source]¶

Generate an example of a particular size.

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

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

Validate a Series object.

Parameters:

check_obj (Series) – One-dimensional ndarray with axis labels (including time series).
head (Optional[int]) – validate the first n rows. Rows overlapping with tail or sample are de-duplicated.
tail (Optional[int]) – validate the last n rows. Rows overlapping with head or sample are de-duplicated.
sample (Optional[int]) – validate a random sample of n rows. Rows overlapping with head or tail are de-duplicated.
random_state (Optional[int]) – random seed for the sample argument.
lazy (bool) – if True, lazily evaluates dataframe against all validation checks and raises a SchemaErrors. Otherwise, raise SchemaError as soon as one occurs.
inplace (bool) – if True, applies coercion to the object of validation, otherwise creates a copy of the data.

Return type:

Series

Returns:

validated Series.

Raises:

SchemaError – when DataFrame violates built-in or custom checks.

Example:

>>> import pandas as pd
>>> import pandera as pa
>>>
>>> series_schema = pa.SeriesSchema(
...     float, [
...         pa.Check(lambda s: s > 0),
...         pa.Check(lambda s: s < 1000),
...         pa.Check(lambda s: s.mean() > 300),
...     ])
>>> series = pd.Series([1, 100, 800, 900, 999], dtype=float)
>>> print(series_schema.validate(series))
0      1.0
1    100.0
2    800.0
3    900.0
4    999.0
dtype: float64

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

Alias for validate method.

Return type:: ~TDataObject