Data quality remains a core concern for practitioners in machine learning, data science, and data engineering, and many specialized packages have emerged to fulfill the need of validating and monitoring data and models. However, as the open source community creates new data processing frameworks - notably, new highly performant entrants such as Polars - existing data quality frameworks need to catch up to support them, and in some cases, the Python community more broadly creates new data validation libraries for these new data frameworks. This paper outlines pandera’s motivation and challenges that took it from being a pandas-only data validation framework Bantilan, 2020 to one that is extensible to other non-pandas-compliant dataframe-like libraries. It also provides an informative case study of the technical and organizational challenges associated with expanding the scope of a library beyond its original boundaries.

Keywords:data validationdata testingdata sciencemachine learningdata engineering


Data validation is the process of falsifying data against a particular set of assumptions Loo & De Jonge, 2018. Framed differently, it is the act of verifying data against a set of properties and constraints that are explicitly established by the data practitioner. In this context, the term “data practitioner” refers to anyone using a programming language to analyze, transform, or otherwise process data. It includes, but is not limited to, data scientists, data engineers, data analysts, machine learning engineers, and machine learning researchers. This paper describes the trajectory of pandera from a pandas-only validation library to a more generic framework that can validate any dataframe-like object.


Pandera started as a small project in 2018 with the goal of providing a lightweight, flexible, and expressive API to validate pandas DataFrames McKinney, 2010. This introductory section provides a brief primer on data validation with pandera, providing insights into how its design facilitates code-first schema authoring and maintenance. The operating assumption is that this, in turn, gives rise to safer and more robust data pipelines.

Why Validate Data?

As stated in the introduction, data validation is the act of falsifying (or verifying) data against a particular set of assumptions, expressed as a schema of validation rules. These rules are explicitly established by the data practitioner without interference from automated processes, like data profiling, and verified at runtime on real-world data.

In machine learning (ML) and statistical analysis use cases, this is critical because invalid data, e.g. incorrect types, invalid values, and otherwise corrupted data, can pass silently along a data pipeline and propagate those errors to various endpoints, which cause adverse ripple-effects to the downstream consumers that rely on high-quality data. These endpoints can be models, analyses, and visualizations, and errors in any of these artifacts call into question the trustworthiness of the conclusions that they entail. Though this is especially important in scientific research and business-critical applications, data validation ought to be a core part of the quality assurance pipeline of data teams.

Data practitioners build statistical domain knowledge about the data they are working with by inspecting the data via exploratory data analysis (EDA), data profiling tools, or a combination of these two approaches. By building a mental model of how their data looks like and envisioning a set of constraints that express what the ideal “clean” dataset looks like, the data practitioner can then encode this understanding as a schema that they can use to validate new incoming data. This schema serves not only as documentation for themselves and future maintainers, but also as a stateless data drift monitoring system for data transformation, model training, and production inference pipelines. The benefit of this statelessness is that the data practitioner can reason about what counts as valid data through their code and their version control system of choice, which captures changes in the assumptions about valid data over time.

However, the process of writing down these schemas is a laborious and often thankless task and not as exciting as getting to the modeling/analysis/visualization part of the development process. As stated in Bantilan, 2020, to lower the barrier to explicitly writing down schemas for maintaining data quality, pandera was created with the following design principles in mind:

  1. Expressing schemas as code should feel familiar to pandas users.
  2. Data validation should be compatible with the different workflows and tools in in the data science and ML stack without a lot of setup or configuration.
  3. Defining custom validation rules should be easy.
  4. The validation interface should make the debugging process easier.
  5. Integration with existing code should be as seamless as possible.

These principles were codified to guide the development of pandera project towards ease of learning and incremental adoption.

Pandera’s Programming Model

With these principles in mind, pandera sought to be minimally invasive, quick to integrate into existing data science and ML code-bases, and easy to learn for data scientists, data engineers, and ML engineers who use Python (refer to the Related Tools section of Bantilan, 2020 for a discussion of similar projects in the Python space). The original object-based syntax makes it clear how defining a DataFrameSchema is similar to defining pandas DataFrames:

import pandera as pa

schema = pa.DataFrameSchema({
    "column1": pa.Column(
        int, pa.Check.gt(0)
    "column2": pa.Column(
        str, pa.Check.isin([*"ABC"])
    "column3": pa.Column(

In the example above, we expect our data to have three columns that have specific names, data types, and data value constraints. By reading the code the data practitioner themselves or their collaborators can immediately see what the minimum requirements are for valid data. For example, the pa.Check.gt(0) constraint indicates that column1 just always be greater than zero.

Pandera emphasizes code-first schema authoring and maintenance. As opposed to yaml-, json- or UI-based schema authoring, code-first schemas lower the barrier for DS/ML practitioners to create and maintain these schemas because they don’t have to learn a DSL or a set of entirely new concepts.

The hypothesis was that this would give rise to safer and more robust data pipelines in different parts of the data ecosystem: from research projects in academia, to nonprofits seeking to create valuable data assets, or to industry practitioners who want to use pandas in a production ETL pipeline. Pandera’s core programming model is simple:

The pandera programming model is an iterative loop between building statistical domain
knowledge, implementing data transforms and schemas, and verifying data.

Figure 1:The pandera programming model is an iterative loop between building statistical domain knowledge, implementing data transforms and schemas, and verifying data.

Pandera embraces the data testing development process, which involves validating real data as well as the functions that produce them. The process of developing data pipelines with data testing in mind involves the iterative definition of both data transformations and schemas, which can be used as “fancy assertions” in your code, or as reusable components in the pipeline’s unit test suite.

As depicted in Figure 1, this process is roughly as follows: by whatever means necessary, typically via EDA or data profiling (the programmatic creation of summary statistics and visualization), the data practitioner arrives at a schema, which states the columns and properties that the data should adhere to. The schema is then used to validate data in-line, or at the interface boundary of critical functions in the data pipeline. The data practitioner can start with a basic schema, which may include column names and their expected types. As they build more statistical domain knowledge about what counts as valid data, the can refine the schemas to better fit the requirements of their analysis using Checks.

import pandas as pd

# inline validation
data = pd.DataFrame({
    "column1": [1, 2, 3],
    "column2": ["A", "B", "C"],
    "column3": [0.2, 0.41, 0.87],

# validating the input-output function boundary
def transform(data):

# pandera automatically validates the input
# when the transform function is called

If validation succeeds, the schema returns the valid data. If it fails, pandera raises a SchemaError or SchemaErrors exception. These exceptions contain metadata about what caused the failure at varying levels of granularity: either at the schema-level, e.g. wrong column types, or at the data-value-level, e.g. numbers being out of range:

invalid_data = pd.DataFrame({
    "column1": [1, -1, 3],
    "column2": ["A", "B", "D"],
    "column3": [0.2, 0.41, 100.0],
# try to validate as all columns and constraints
# before raising an error with lazy=True
    schema.validate(data, lazy=True)
except pa.errors.SchemaErrors as exc:
    print("Failure cases")
            ["column", "failure_case", "index"]

# Output:
Failure cases
    column failure_case  index
0  column1           -1      1
1  column2            D      2
2  column3        100.0      2

The exception raised during validation contains several attributes, including the original failed data in the .data attribute, but more importantly, it contains a normalized DataFrame view of all the failure cases in the data via the .failure_cases attribute. This is reported at the most granular level so that the data practitioner can quickly understand what’s wrong with their data.


After its first set of releases, pandera continued to improve with bug fixes, feature enhancements, and documentation improvements. This section highlights four major events in pandera’s development. In chronological order, these events were: documentation improvements, support for a class-based API, data synthesis strategies, and the pandera type system.

Documentation Improvements

Documentation is one of the most critical pieces to any software project. Even if the underlying code is well-written, performant, and useful, ultimately if the documentation is unclear or otherwise difficult to read and navigate, the software itself will be inaccessible to end users.

The first set of major contributions came with the help of Nigel Markey, who helped considerably in documentation efforts, making pandera easy to learn and adopt through examples, tutorials, and a comprehensive API reference. This helped pandera to become part of pyOpenSci pyOpenSci, n.d., which helped further improve its quality and usability through further review and refinement.

Class-based API

The second major improvement in pandera was contributed by Jean-Francois Zinque, who implemented the class-based syntax that’s more akin to Python dataclasses and the pydantic library pydantic, n.d.. This modernized pandera to use syntax that was familiar to developers who use classes as types to express the form and properties of the data structures they want to use.

class Model(pa.DataFrameModel):
   column1: int = pa.Field(gt=0, lt=100)
   column2: str = pa.Field(isin=[*"ABC"])
   column3: float = pa.Field(
       in_range={"min_value": 0.0, "max_value": 1.0}

This also enabled pandera to take advantage of type hints as a convenient way of expressing the input-output types of a function and enforcing data quality at runtime.

from pandera.typing import DataFrame

class Input(pa.DataFrameModel):
    x: float
    y: float

class Output(Input):
    z: float

    def check_z(cls, df):
        """Column z must be the sum of x and y."""
        return df["z"] == (df["x"] + df["y"])

# This decorator does runtime checks on the
# input and output dataframe.
def fn(data: DataFrame[Input]) -> DataFrame[Output]:
    return data.assign(z=lambda df: df.x + df.y)

Data Synthesis Strategies

The third major improvement was adding support for data synthesis strategies using the hypothesis library MacIver et al., 2019. This expanded pandera’s scope from a data validation library to a “data testing” toolkit by allowing the data practitioner to easily create mock data for testing not only real data, but the functions that produce/clean/transform the data. Note that the hypothesis library for doing property-based testing is not to be confused with statistical Hypothesis checks, which were already supported by pandera.

import pytest
from hypothesis import given

# This will generate data for testing the correct
# implementation of fn
def test_fn(input_data)

class WrongInput(pa.DataFrameModel):
    a: int
    b: str

# This will fail on the output check
def test_fn_wrong_input(input_data)
    with pytest.raises(pa.SchemaError):

Hypothesis handles generating valid data under the pandera schema’s constraints, which relieves the developer from manually hand-crafting dataframes and allows unit tests to catch edge cases that would not otherwise be caught by the hand-crafted test cases. This can be seamlessly integrated with pytest, since one can think of pandera schemas as essentially “fancy assertion” statements.

Pandera Type System

Finally, the fourth major improvement was contributed by Jean-Francois Zinque, who implemented pandera’s type system, which provides a consistent interface for defining semantic and logical types not only for pandas, but also potentially for other dataframe libraries like pyspark and modin.

This allows pandera users to, for example, implement an IPAddress type, which requires both specifying the data type and checking the actual values of the data to verify:

import re
from typing import Optional, Iterable, Union
from pandera import dtypes
from pandera.engines import pandas_engine

class IPAddress(pandas_engine.NpString):

    REGEX = re.compile(

    def check(
        pandera_dtype: dtypes.DataType,
        data_container: Optional[pd.Series] = None,
    ) -> Union[bool, Iterable[bool]]:
        # ensure that the data container's data
        # type is correct
        correct_type = super().check(pandera_dtype)
        if not correct_type:
            return correct_type
         if data_container is None:
             raise ValueError

        # ensure IP address pattern
        return data_container.map(
            lambda x: self.REGEX.match(x) is not None

# using it in a DataFrame model
class IPAddressModel(pa.DataFrameModel):
    ip_address: IPAddress

Expanding Scope

After gaining traction over the years, the author, the contributors, and the growing community of pandera users also began to expand pandera’s scope to support pandas-compliant data frameworks such as GeoPandas Jordahl et al., 2020, Dask Rocklin, 2015, Modin Petersohn et al., 2020, and Pyspark Pandas pysparkPandas, n.d. (formerly Koalas). As requests for other dataframe-like libraries increased in frequency, it became clear that pandera in its existing state was not well-suited for extension beyond Pandas objects.

Design Weaknesses

The fundamental design flaw in pandera’s internals was that the schema specification and validation engine were interleaved through out the code base. This presented the following challenges for supporting non-pandas dataframe libraries:

  • Schemas were strongly coupled to pandas: The schema class had a lot of assumptions about pandas, which manifested as method calls and operations that assumed that pandera was operating on a pandas DataFrame.
  • Checks were strongly coupled to pandas: Pandera has core checks that are exposed in the schema/schema component object, which were all implemented with pandas-specific code.
  • Error reporting assumed in-memory data: Error reporting of metadata and value checks assumed in-memory, small-to-medium-sized datasets. For any larger scale data that requires a distributed dataframe, the error-reporting mechanism doesn’t work well because the worst case scenario of all data values being invalid would produce an failure case report that was potentially even larger than the original data.
  • Leaky abstractions: The pandera schema API leaked certain pandas-specific abstractions, e.g. Index and MultiIndex, which don’t apply to other frameworks, e.g. Spark and Polars.

These weaknesses were uncovered after-the-fact, when the author and contributors analyzed the existing codebase to determine how to best support other dataframe objects.

Design Strengths

With these limitations in mind, it’s also important to note some of the design choices that significantly eased the subsequent internals rewrite. In particular:

  • Generic schema interface: Within the domain of tabular, dataframe-like datastructures, pandera’s schema API is generic enough to support both columnar and row-wise statistical data objects, which can be defined as objects that expose methods for statistical analysis.
  • Flexible Check abstraction: pandera’s Check object — the core validator abstraction — was sufficiently flexible. Check functions assume that it returns a boolean scalar, Series or DataFrame. This allows data pandera to report value errors at varying levels of granularity: e.g. for distributed dataframes, reporting all failure cases incurs unacceptable overhead for distributed dataframes, which would require full table scans.
  • Flexible type system: The type system was also sufficiently flexible to support types for different dataframe libraries, allowing for simple types, generic types, parameterized types, and logical types.

Rewriting Pandera Internals

For practical purposes, the first set of DataFrame libraries supported by pandera were pandas-compliant frameworks such as GeoPandas, Modin, Dask, and Koalas (now pyspark.pandas). Even though these libraries do deviate somewhat from the pandas API, they were close enough such that the parts of the pandas API that pandera leveraged were just a subset of the full API. Therefore, supporting these additional libraries required only a few code changes Bantilan, 2023. This approach was the path to least resistance for making data validation more scalable, and validating the notion that the community would actually find it useful.

In contrast, in order to support additional non-pandas-compliant libraries like pyspark, polars, and vaex, pandera needed to overhaul the schema objects by decoupling the schema specification from the validation engine. At a high-level, the approach was to introduce the following abstractions:

  • A pandera.api subpackage, which contains the schema specification that defines the properties of an underlying data structure.
  • A pandera.backends subpackage, which leverages the schema specification and implements the actual validation logic.
  • A backend registry, which maps a particular API specification to a backend based on the DataFrame type being validated.
  • A common type-aware Check namespace and registry, which registers type-specific implementations of built-in checks and allows contributors to easily add new built-in checks.

This new architecture allows contributors to implement a schema validator for any data structure they want. In pseudo-code, supporting a fictional dataframe library called sloth would look something like this:

import sloth as sl
from pandera.api.base.schema import BaseSchema
from pandera.backends.base import BaseSchemaBackend

class DataFrameSchema(BaseSchema):
    def __init__(self, **kwargs):
        # add properties that this dataframe
        # would contain

class DataFrameSchemaBackend(BaseSchemaBackend):
    def validate(
        check_obj: sl.DataFrame,
        schema: DataFrameSchema,
        # implement custom validation logic

# register the backend

Similarly, the built-in checks can easily be extended to support sloth data structures:

import sloth as sl

from pandera.api.extensions import register_builtin_check

def equal_to(
    data: sl.Series, value: Any
) -> sl.Series:
    return data.equals(value)

Organizational and Development Challenges

Although the road to an internals rewrite was fairly straightforward from a technical perspective, there were additional meta-challenges that added to the complexity of implementing the rewrite in practice:

  • Multi-tasking the rewrite with PR reviews: As with any open source project, there were community-contributed PRs for bug fixes and feature enhancements, many of which created merge conflicts since they assumed the pre-rewrite state of the code base. The author had to block such contributions until the rewrite was complete and fast-forward these PRs to fit the structure of the new code base.
  • Centralized knowledge: Because the author was the primary maintainer of the project and was the only maintainer who understood the codebase as a whole well enough to make the changes, incorporating non-conflicting pull requests took time away from the rewrite, further delaying the timeline that would unblock other would-be contributors who wanted to implement support for other libraries, e.g. polars.
  • Informal governance: Because pandera has an informal contributor and governance structure, the author effectively made unilateral decisions with respect to the abstractions necessary to decouple the schema specification from the validation backend. This turned out to be appropriate, with a successful case of a community-contributed pyspark.sql integration being almost complete as of the writing of this paper. This integration is planned for release in the next minor version 0.16.0. However, the pandera project would benefit from a more formal governance structure involving a broader set of stakeholders when it comes to wide-sweeping internal or user-facing changes.


With all of these challenges in mind, the internals rewrite was completed in pull request 913 Bantilan, 2023 on January 24th, 2023 and the follow-up pull request 1109 Bantilan, 2023 on March 13th, 2023. A few factors facilitated the rewrite itself and also reduced the risk of regressions:

  • Unit tests: A comprehensive unit test suite caught many issues, but not all of them. This was partly due to lack of complete test coverage, but new tests also had to be written for abstractions introduced during the re-write process.
  • Localized pandas coupling: Pandas-specific code was mostly localized in easy-to-identify locations in the codebase.
  • Lessons learned from pandas-compliant integrations: Earlier integrations with pandas-compliant libraries revealed operations/assumptions that are likely to break in out-of-core DataFrame libraries, which typically involved indexes and sorting assumptions.

In retrospect, there are additionally things the author would have done differently to make pandera more flexible and extensible:

  • Thoughtful design work: With some careful design work, it would have been obvious to decouple schema specification from validation backend much sooner.
  • Library-independent error reporting: Make error reporting more flexible by decoupling error reporting data structures from the specific DataFrame library, e.g. by using native python data structures like lists and dictionaries instead of pandas DataFrames to report failure cases.
  • Decoupling metadata from data: Distinguish between DataFrame metadata schema errors (e.g. missing columns) and data value errors (e.g. out-of-range values).
  • Investing in governance and community: Invest more in governance and formalize contributor and community RFC processes sooner to help with design and feature enhancement efforts.

Updated Design Principles

Given all of the developments and updates that pandera has seen in recent years, pandera’s design principles also need to be updated with one amendment and one additions:

  1. Amendment: Expressing schemas as code should feel familiar to Python users, regardless of the dataframe library they’re using.
  2. Data validation should be compatible with the different workflows and tools in in the data science and ML stack without a lot of setup or configuration.
  3. Defining custom validation rules should be easy.
  4. The validation interface should make the debugging process easier.
  5. Integration with existing code should be as seamless as possible.
  6. Addition: Extending the interface to other statistical data structures should be easy using a core set of building blocks and abstractions.


Pandera has evolved from a pandas-specific data validation library to a comprehensive toolkit that provides a standard schema interface for easily extending and supporting validation backends for arbitrary statistical data containers. This paper provides an overview of data validation and testing, focusing on pandera’s core programming model and its extended functionality to support property-based testing. This paper also provides a useful case study of the technical and organizational challenges associated with expanding the scope of a library beyond its original boundaries.

The author’s hope is that, by highlighting the technical and organizational dimensions of this evolution, that other open source authors and maintainers can learn and avoid some of the pitfalls encountered during the internals rewrite that now enables pandera to support a whole suite of statistical data containers moving forward.

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