The Open-source Framework for Precision Data Testing

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

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pandera is a Union.ai open source project that provides a flexible and expressive API for performing data validation on dataframe-like objects to make data processing pipelines more readable and robust.

Dataframes contain information that pandera explicitly validates at runtime. This is useful in production-critical data pipelines or reproducible research settings. With pandera, you can:

  1. Define a schema once and use it to validate different dataframe types including pandas, polars, dask, modin, and pyspark.pandas.

  2. Check the types and properties of columns in a pd.DataFrame or values in a pd.Series.

  3. Perform more complex statistical validation like hypothesis testing.

  4. Parse data to standardize the preprocessing steps needed to produce valid data.

  5. Seamlessly integrate with existing data analysis/processing pipelines via function decorators.

  6. Define dataframe models with the class-based API with pydantic-style syntax and validate dataframes using the typing syntax.

  7. Synthesize data from schema objects for property-based testing with pandas data structures.

  8. Lazily Validate dataframes so that all validation rules are executed before raising an error.

  9. Integrate with a rich ecosystem of python tools like pydantic, fastapi and mypy.

Install

Install with pip:

pip install pandera

Or conda:

conda install -c conda-forge pandera

Extras

Installing additional functionality:

pip install 'pandera[hypotheses]'  # hypothesis checks
pip install 'pandera[io]'          # yaml/script schema io utilities
pip install 'pandera[strategies]'  # data synthesis strategies
pip install 'pandera[mypy]'        # enable static type-linting of pandas
pip install 'pandera[fastapi]'     # fastapi integration
pip install 'pandera[dask]'        # validate dask dataframes
pip install 'pandera[pyspark]'     # validate pyspark dataframes
pip install 'pandera[modin]'       # validate modin dataframes
pip install 'pandera[modin-ray]'   # validate modin dataframes with ray
pip install 'pandera[modin-dask]'  # validate modin dataframes with dask
pip install 'pandera[geopandas]'   # validate geopandas geodataframes
pip install 'pandera[polars]'      # validate polars dataframes
conda install -c conda-forge pandera-hypotheses  # hypothesis checks
conda install -c conda-forge pandera-io          # yaml/script schema io utilities
conda install -c conda-forge pandera-strategies  # data synthesis strategies
conda install -c conda-forge pandera-mypy        # enable static type-linting of pandas
conda install -c conda-forge pandera-fastapi     # fastapi integration
conda install -c conda-forge pandera-dask        # validate dask dataframes
conda install -c conda-forge pandera-pyspark     # validate pyspark dataframes
conda install -c conda-forge pandera-modin       # validate modin dataframes
conda install -c conda-forge pandera-modin-ray   # validate modin dataframes with ray
conda install -c conda-forge pandera-modin-dask  # validate modin dataframes with dask
conda install -c conda-forge pandera-geopandas   # validate geopandas geodataframes
conda install -c conda-forge pandera-polars      # validate polars dataframes

Quick Start

import pandas as pd
import pandera as pa

# data to validate
df = pd.DataFrame({
    "column1": [1, 4, 0, 10, 9],
    "column2": [-1.3, -1.4, -2.9, -10.1, -20.4],
    "column3": ["value_1", "value_2", "value_3", "value_2", "value_1"],
})

# define schema
schema = pa.DataFrameSchema({
    "column1": pa.Column(int, checks=pa.Check.le(10)),
    "column2": pa.Column(float, checks=pa.Check.lt(-1.2)),
    "column3": pa.Column(str, checks=[
        pa.Check.str_startswith("value_"),
        # define custom checks as functions that take a series as input and
        # outputs a boolean or boolean Series
        pa.Check(lambda s: s.str.split("_", expand=True).shape[1] == 2)
    ]),
})

validated_df = schema(df)
print(validated_df)
   column1  column2  column3
0        1     -1.3  value_1
1        4     -1.4  value_2
2        0     -2.9  value_3
3       10    -10.1  value_2
4        9    -20.4  value_1

You can pass the built-in python types that are supported by pandas, or strings representing the legal pandas datatypes, or pandera’s DataType:

schema = pa.DataFrameSchema({
    # built-in python types
    "int_column": pa.Column(int),
    "float_column": pa.Column(float),
    "str_column": pa.Column(str),

    # pandas dtype string aliases
    "int_column2": pa.Column("int64"),
    "float_column2": pa.Column("float64"),
    # pandas > 1.0.0 support native "string" type
    "str_column2": pa.Column("str"),

    # pandera DataType
    "int_column3": pa.Column(pa.Int),
    "float_column3": pa.Column(pa.Float),
    "str_column3": pa.Column(pa.String),
})

For more details on data types, see DataType

Dataframe Model

pandera also provides an alternative API for expressing schemas inspired by dataclasses and pydantic. The equivalent DataFrameModel for the above DataFrameSchema would be:

from pandera.typing import Series

class Schema(pa.DataFrameModel):

    column1: int = pa.Field(le=10)
    column2: float = pa.Field(lt=-1.2)
    column3: str = pa.Field(str_startswith="value_")

    @pa.check("column3")
    def column_3_check(cls, series: Series[str]) -> Series[bool]:
        """Check that column3 values have two elements after being split with '_'"""
        return series.str.split("_", expand=True).shape[1] == 2

Schema.validate(df)
column1 column2 column3
0 1 -1.3 value_1
1 4 -1.4 value_2
2 0 -2.9 value_3
3 10 -10.1 value_2
4 9 -20.4 value_1

Informative Errors

If the dataframe does not pass validation checks, pandera provides useful error messages. An error argument can also be supplied to Check for custom error messages.

In the case that a validation Check is violated:

simple_schema = pa.DataFrameSchema({
    "column1": pa.Column(
        int, pa.Check(lambda x: 0 <= x <= 10, element_wise=True,
                    error="range checker [0, 10]"))
})

# validation rule violated
fail_check_df = pd.DataFrame({
    "column1": [-20, 5, 10, 30],
})

try:
    simple_schema(fail_check_df)
except pa.errors.SchemaError as exc:
    print(exc)
Column 'column1' failed element-wise validator number 0: <Check <lambda>: range checker [0, 10]> failure cases: -20, 30

And in the case of a mis-specified column name:

# column name mis-specified
wrong_column_df = pd.DataFrame({
    "foo": ["bar"] * 10,
    "baz": [1] * 10
})


try:
    simple_schema(wrong_column_df)
except pa.errors.SchemaError as exc:
    print(exc)
column 'column1' not in dataframe. Columns in dataframe: ['foo', 'baz']

Error Reports

If the dataframe is validated lazily with lazy=True, errors will be aggregated into an error report. The error report groups DATA and SCHEMA errors to to give an overview of error sources within a dataframe. Take the following schema and dataframe:

schema = pa.DataFrameSchema({"id": pa.Column(int, pa.Check.lt(10))}, name="MySchema", strict=True)
df = pd.DataFrame({"id": [1, None, 30], "extra_column": [1, 2, 3]})

try:
    schema.validate(df, lazy=True)
except pa.errors.SchemaErrors as exc:
    print(exc)
{
    "SCHEMA": {
        "COLUMN_NOT_IN_SCHEMA": [
            {
                "schema": "MySchema",
                "column": "MySchema",
                "check": "column_in_schema",
                "error": "column 'extra_column' not in DataFrameSchema {'id': <Schema Column(name=id, type=DataType(int64))>}"
            }
        ],
        "SERIES_CONTAINS_NULLS": [
            {
                "schema": "MySchema",
                "column": "id",
                "check": "not_nullable",
                "error": "non-nullable series 'id' contains null values:1   NaNName: id, dtype: float64"
            }
        ],
        "WRONG_DATATYPE": [
            {
                "schema": "MySchema",
                "column": "id",
                "check": "dtype('int64')",
                "error": "expected series 'id' to have type int64, got float64"
            }
        ]
    },
    "DATA": {
        "DATAFRAME_CHECK": [
            {
                "schema": "MySchema",
                "column": "id",
                "check": "less_than(10)",
                "error": "Column 'id' failed element-wise validator number 0: less_than(10) failure cases: 30.0"
            }
        ]
    }
}

Validating the above dataframe will result in data level errors, namely the id column having a value which fails a check, as well as schema level errors, such as the extra column and the None value.

This error report can be useful for debugging, with each item in the various lists corresponding to a SchemaError

Contributing

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.

A detailed overview on how to contribute can be found in the contributing guide on GitHub.

Issues

Submit issues, feature requests or bugfixes on github.

Need Help?

There are many ways of getting help with your questions. You can ask a question on Github Discussions page or reach out to the maintainers and pandera community on Discord

How to Cite

If you use pandera in the context of academic or industry research, please consider citing the paper and/or software package.

Paper

@InProceedings{ niels_bantilan-proc-scipy-2020,
  author    = { {N}iels {B}antilan },
  title     = { pandera: {S}tatistical {D}ata {V}alidation of {P}andas {D}ataframes },
  booktitle = { {P}roceedings of the 19th {P}ython in {S}cience {C}onference },
  pages     = { 116 - 124 },
  year      = { 2020 },
  editor    = { {M}eghann {A}garwal and {C}hris {C}alloway and {D}illon {N}iederhut and {D}avid {S}hupe },
  doi       = { 10.25080/Majora-342d178e-010 }
}

Software Package

DOI

License and Credits

pandera is licensed under the MIT license. and is written and maintained by Niels Bantilan (mailto:niels@pandera.ci)

Indices and tables