pandera.DataFrameSchema.validate

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

Check if all columns in a dataframe have a column in the Schema.

Parameters
  • dataframe (pd.DataFrame) – the dataframe to be validated.

  • 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 SchemaErrorReport. Otherwise, raise SchemaError as soon as one occurs.

Return type

DataFrame

Returns

validated DataFrame

Raises

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

Example

Calling schema.validate returns the dataframe.

>>> import pandas as pd
>>> import pandera as pa
>>>
>>> df = pd.DataFrame({
...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],
...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]
... })
>>>
>>> schema_withchecks = pa.DataFrameSchema({
...     "probability": pa.Column(
...         pa.Float, pa.Check(lambda s: (s >= 0) & (s <= 1))),
...
...     # check that the "category" column contains a few discrete
...     # values, and the majority of the entries are dogs.
...     "category": pa.Column(
...         pa.String, [
...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),
...             pa.Check(lambda s: (s == "dog").mean() > 0.5),
...         ]),
... })
>>>
>>> schema_withchecks.validate(df)[["probability", "category"]]
   probability category
0         0.10      dog
1         0.40      dog
2         0.52      cat
3         0.23     duck
4         0.80      dog
5         0.76      dog