The Open-source Framework for Dataset Validation

📊 🔎 ✅

Data validation for scientists, engineers, and analysts seeking correctness.

Pandera is a Union.ai open source project that provides a flexible and expressive API for performing data validation on dataframe-like objects. The goal of Pandera is to make data processing pipelines more readable and robust with statistically typed dataframes.

Install

Pandera supports multiple dataframe libraries, including pandas, polars, pyspark, and more. To validate pandas DataFrames, install Pandera with the pandas extra:

With pip:

pip install 'pandera[pandas]'

With uv:

uv pip install 'pandera[pandas]'

With conda:

conda install -c conda-forge pandera-pandas

Get started

First, create a dataframe:

import pandas as pd
import pandera.pandas as pa

# data to validate
df = pd.DataFrame({
    "column1": [1, 2, 3],
    "column2": [1.1, 1.2, 1.3],
    "column3": ["a", "b", "c"],
})

Validate the data using the object-based API:

# define a schema
schema = pa.DataFrameSchema({
    "column1": pa.Column(int, pa.Check.ge(0)),
    "column2": pa.Column(float, pa.Check.lt(10)),
    "column3": pa.Column(
        str,
        [
            pa.Check.isin([*"abc"]),
            pa.Check(lambda series: series.str.len() == 1),
        ]
    ),
})

print(schema.validate(df))
#    column1  column2 column3
# 0        1      1.1       a
# 1        2      1.2       b
# 2        3      1.3       c

Or validate the data using the class-based API:

# define a schema
class Schema(pa.DataFrameModel):
    column1: int = pa.Field(ge=0)
    column2: float = pa.Field(lt=10)
    column3: str = pa.Field(isin=[*"abc"])

    @pa.check("column3")
    def custom_check(cls, series: pd.Series) -> pd.Series:
        return series.str.len() == 1

print(Schema.validate(df))
#    column1  column2 column3
# 0        1      1.1       a
# 1        2      1.2       b
# 2        3      1.3       c

[!WARNING] Pandera v0.24.0 introduces the pandera.pandas module, which is now the (highly) recommended way of defining DataFrameSchemas and DataFrameModels for pandas data structures like DataFrames. Defining a dataframe schema from the top-level pandera module will produce a FutureWarning:
import pandera as pa

schema = pa.DataFrameSchema({"col": pa.Column(str)})
Update your import to:
import pandera.pandas as pa
And all of the rest of your pandera code should work. Using the top-level pandera module to access DataFrameSchema and the other pandera classes or functions will be deprecated in version 0.29.0

Next steps

See the official documentation to learn more.

Release files for pandera 0.33.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pandera 0.33.1
File	Size	Uploaded
pandera-0.33.1.tar.gz	976.0 kB	Sep 1, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for pandera 0.33.1
File	Interpreter	ABI	Platform	Reset
pandera-0.33.1-py3-none-any.whl 502.0 kB Sep 1, 2026	Python 3	none	any	Details

Total release size:1.5 MB

Release files / pandera-0.33.1.tar.gz

Download URL	pandera-0.33.1.tar.gz
Size	976.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`909e2e269a3f9c17d94c11268f92a5f48e889af633ea1414053993874d613626`
BLAKE2b-256 checksum How to use checksums	`477a702612a2b5e3335722395d6de71206549f64a43588527f411ff60a7c3456`
Upload date	Sep 1, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Release files / pandera-0.33.1-py3-none-any.whl

Download URL	pandera-0.33.1-py3-none-any.whl
Size	502.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`8283bc12f7c85f0b429107d71b55150205f7e5ca59d68117aff971273a3bd2d8`
BLAKE2b-256 checksum How to use checksums	`d284107a480f7aa34a6878fb4fb85cda66f37e8aaee769c5917e3eaa2d55222e`
Upload date	Sep 1, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`