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

Parse and validate unstructured data

Pandera can derive a column from the rest of the dataframe, so a schema both parses and validates your data. Declare the derivation on the column itself with ParsedField / ParsedColumn, and the decisions API to fill typed columns from unstructured text by asking a decision model:

import pandera.decisions as decisions

class Tickets(pa.DataFrameModel):
    body: str
    n_words: int = pa.ParsedField(
        parser=lambda s: s.str.split().str.len(),
        source="body",
    )
    department: Department = pa.ParsedField(
        description="Which team should handle this ticket",
        parser=decisions.Choice(),
    )

    class Config:
        parser_source = "body"

# configure a provider (MockProvider is deterministic and free; swap in a
# real model like TypeSafe Jev or an Ollaya model as a runtime setting)
with decisions.provider(decisions.MockProvider(seed=7)):
    Tickets.validate(df)

See the decision parsing tutorial to get started.

[!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.