Validating with Checks

Checks are one of the fundamental constructs of pandera. They allow you to specify properties about dataframes, columns, indexes, and series objects, which are applied after data type validation/coercion and the core pandera checks are applied to the data to be validated.


You can learn more about how data type validation works Data Type Validation.

Checking column properties

Check objects accept a function as a required argument, which is expected to take a pa.Series input and output a boolean or a Series of boolean values. For the check to pass, all of the elements in the boolean series must evaluate to True, for example:

import pandera as pa
import pandas as pd

check_lt_10 = pa.Check(lambda s: s <= 10)

schema = pa.DataFrameSchema({"column1": pa.Column(int, check_lt_10)})
schema.validate(pd.DataFrame({"column1": range(10)}))
UserWarning: 'PYARROW_IGNORE_TIMEZONE' environment variable was not set. It is required to set this environment variable to '1' in both driver and executor sides if you use pyarrow>=2.0.0. pandas-on-Spark will set it for you but it does not work if there is a Spark context already launched.
0 0
1 1
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Multiple checks can be applied to a column:

schema = pa.DataFrameSchema({
    "column2": pa.Column(str, [
        pa.Check(lambda s: s.str.startswith("value")),
        pa.Check(lambda s: s.str.split("_", expand=True).shape[1] == 2)

Built-in Checks

For common validation tasks, built-in checks are available in pandera.

import pandera as pa

schema = pa.DataFrameSchema({
    "small_values": pa.Column(float, pa.Check.less_than(100)),
    "one_to_three": pa.Column(int, pa.Check.isin([1, 2, 3])),
    "phone_number": pa.Column(str, pa.Check.str_matches(r'^[a-z0-9-]+$')),

See the Check API reference for a complete list of built-in checks.

Vectorized vs. Element-wise Checks

By default, Check objects operate on pd.Series objects. If you want to make atomic checks for each element in the Column, then you can provide the element_wise=True keyword argument:

import pandas as pd
import pandera as pa

schema = pa.DataFrameSchema({
    "a": pa.Column(
            # a vectorized check that returns a bool
            pa.Check(lambda s: s.mean() > 5, element_wise=False),

            # a vectorized check that returns a boolean series
            pa.Check(lambda s: s > 0, element_wise=False),

            # an element-wise check that returns a bool
            pa.Check(lambda x: x > 0, element_wise=True),
df = pd.DataFrame({"a": [4, 4, 5, 6, 6, 7, 8, 9]})
0 4
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element_wise == False by default so that you can take advantage of the speed gains provided by the pd.Series API by writing vectorized checks.

Handling Null Values

By default, pandera drops null values before passing the objects to validate into the check function. For Series objects null elements are dropped (this also applies to columns), and for DataFrame objects, rows with any null value are dropped.

If you want to check the properties of a pandas data structure while preserving null values, specify Check(..., ignore_na=False) when defining a check.

Note that this is different from the nullable argument in Column objects, which simply checks for null values in a column.

Column Check Groups

Column checks support grouping by a different column so that you can make assertions about subsets of the column of interest. This changes the function signature of the Check function so that its input is a dict where keys are the group names and values are subsets of the series being validated.

Specifying groupby as a column name, list of column names, or callable changes the expected signature of the Check function argument to:

Callable[Dict[Any, pd.Series] -> Union[bool, pd.Series]

where the dict keys are the discrete keys in the groupby columns.

In the example below we define a DataFrameSchema with column checks for height_in_feet using a single column, multiple columns, and a more complex groupby function that creates a new column age_less_than_15 on the fly.

import pandas as pd
import pandera as pa

schema = pa.DataFrameSchema({
    "height_in_feet": pa.Column(
        float, [
            # groupby as a single column
                lambda g: g[False].mean() > 6,

            # define multiple groupby columns
                lambda g: g[(True, "F")].sum() == 9.1,
                groupby=["age_less_than_20", "sex"]),

            # groupby as a callable with signature:
            # (DataFrame) -> DataFrameGroupBy
                lambda g: g[(False, "M")].median() == 6.75,
                groupby=lambda df: (
                    df.assign(age_less_than_15=lambda d: d["age"] < 15)
                    .groupby(["age_less_than_15", "sex"]))),
    "age": pa.Column(int, pa.Check(lambda s: s > 0)),
    "age_less_than_20": pa.Column(bool),
    "sex": pa.Column(str, pa.Check(lambda s: s.isin(["M", "F"])))

df = (
        "height_in_feet": [6.5, 7, 6.1, 5.1, 4],
        "age": [25, 30, 21, 18, 13],
        "sex": ["M", "M", "F", "F", "F"]
    .assign(age_less_than_20=lambda x: x["age"] < 20)

height_in_feet age sex age_less_than_20
0 6.5 25 M False
1 7.0 30 M False
2 6.1 21 F False
3 5.1 18 F True
4 4.0 13 F True

Wide Checks

pandera is primarily designed to operate on long-form data (commonly known as tidy data), where each row is an observation and each column is an attribute associated with an observation.

However, pandera also supports checks on wide-form data to operate across columns in a DataFrame. For example, if you want to make assertions about height across two groups, the tidy dataset and schema might look like this:

import pandas as pd
import pandera as pa

df = pd.DataFrame({
    "height": [5.6, 6.4, 4.0, 7.1],
    "group": ["A", "B", "A", "B"],

schema = pa.DataFrameSchema({
    "height": pa.Column(
        pa.Check(lambda g: g["A"].mean() < g["B"].mean(), groupby="group")
    "group": pa.Column(str)

height group
0 5.6 A
1 6.4 B
2 4.0 A
3 7.1 B

Whereas the equivalent wide-form schema would look like this:

df = pd.DataFrame({
    "height_A": [5.6, 4.0],
    "height_B": [6.4, 7.1],

schema = pa.DataFrameSchema(
        "height_A": pa.Column(float),
        "height_B": pa.Column(float),
    # define checks at the DataFrameSchema-level
        lambda df: df["height_A"].mean() < df["height_B"].mean()

height_A height_B
0 5.6 6.4
1 4.0 7.1

You can see that when checks are supplied to the DataFrameSchema checks key-word argument, the check function should expect a pandas DataFrame and should return a bool, a Series of booleans, or a DataFrame of boolean values.

Raise Warning Instead of Error on Check Failure

In some cases, you might want to raise a warning and continue execution of your program. The Check and Hypothesis classes and their built-in methods support the keyword argument raise_warning, which is False by default. If set to True, the check will warn with a SchemaWarning instead of raising a SchemaError exception.


Use this feature carefully! If the check is for informational purposes and not critical for data integrity then use raise_warning=True. However, if the assumptions expressed in a Check are necessary conditions to considering your data valid, do not set this option to true.

One scenario where you’d want to do this would be in a data pipeline that does some preprocessing, checks for normality in certain columns, and writes the resulting dataset to a table. In this case, you want to see if your normality assumptions are not fulfilled by certain columns, but you still want the resulting table for further analysis.

import warnings

import numpy as np
import pandas as pd
import pandera as pa

from scipy.stats import normaltest


df = pd.DataFrame({
    "var1": np.random.normal(loc=0, scale=1, size=1000),
    "var2": np.random.uniform(low=0, high=10, size=1000),

normal_check = pa.Hypothesis(
    # null hypotheses: sample comes from a normal distribution. The
    # relationship function checks if we cannot reject the null hypothesis,
    # i.e. the p-value is greater or equal to alpha.
    relationship=lambda stat, pvalue, alpha=0.05: pvalue >= alpha,
    error="normality test",

schema = pa.DataFrameSchema(
        "var1": pa.Column(checks=normal_check),
        "var2": pa.Column(checks=normal_check),

# catch and print warnings
with warnings.catch_warnings(record=True) as caught_warnings:
    validated_df = schema(df)
    for warning in caught_warnings:
Column 'var2' failed series or dataframe validator 0: <Check normaltest: normality test>

Registering Custom Checks

pandera now offers an interface to register custom checks functions so that they’re available in the Check namespace. See the extensions document for more information.