pandas-flavor
The easy way to write your own Pandas flavor
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, imposes minimal install friction, and solves a real problem: extending Pandas cleanly without monkeypatching or external wrapper functions. Use it if you are building a Pandas-based library or tool that needs to add custom methods or accessors. The MIT license carries no restrictions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction installation with a pure Python wheel.
- Active maintenance (last commit 2026-06-08, 265 days since release) and broad Python support (3.9–3.12, CPython and PyPy).
License · maintenance · safety
MIT (permissive) — MIT license (permissive) imposes no restrictions on use, modification, or redistribution in proprietary or open-source projects.
last release 2025-11-22 (265 days) · last repo commit 2026-06-08 · 309 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,089,726 downloads/mo, #4,380 on PyPI
Alternatives
Verify before relying
pip install pandas-flavor
import pandas as pd
import pandas_flavor as pf
@pf.register_dataframe_method
def my_method(df, col, value):
return df[df[col] == value]
df = pd.DataFrame({'x': [10, 20], 'y': [0, 2]})
df.my_method('x', 10)- Whether xarray is a required runtime dependency or an optional feature; the fact sheet lists it but does not clarify its role.
- Scope and stability of the tracing feature introduced in 0.5.0 and how it integrates with method registration.
- Whether backwards compatibility with Pandas <0.23 is still actively maintained or was a historical feature.
What it is and what it does
Pandas Flavor provides decorators to register custom methods and accessors on Pandas objects—DataFrames, Series, and GroupBy—without forking or modifying Pandas itself. Instead of writing utility functions that operate on Pandas objects from the outside, you use decorators like `@register_dataframe_method` or `@register_dataframe_accessor` to attach your functions directly to the object, so they appear as native methods or namespaced accessor attributes.
The package originated to backport Pandas' extension API to older versions, but has evolved into a tool for building domain-specific Pandas extensions. It is used by projects like pyjanitor and pdvega to add specialized data-cleaning and visualization methods. The library handles the subtle work of creating accessor classes that mimic method behavior, preserving docstrings and call semantics, so your custom code integrates cleanly into Pandas workflows.
Use it for
- Build a data-cleaning library that adds methods like `.clean_column_names()` or `.remove_duplicates()` directly to DataFrames.
- Create domain-specific accessors (e.g., `.bio.translate()` for bioinformatics) that namespace related methods without polluting the DataFrame API.
- Extend GroupBy objects with custom aggregation or transformation methods for specialized analytics workflows.
- Develop internal company data tools that add proprietary methods to Pandas objects for consistent, discoverable APIs.
- Register Series methods for specialized data types or transformations (e.g., `.text.clean()` for text processing).
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, imposes minimal install friction, and solves a real problem: extending Pandas cleanly without monkeypatching or external wrapper functions. Use it if you are building a Pandas-based library or tool that needs to add custom methods or accessors. The MIT license carries no restrictions.
Install
pandas-flavor on PyPI
Before you install
Low friction installation with a pure Python wheel. Active maintenance (last commit 2026-06-08, 265 days since release) and broad Python support (3.9–3.12, CPython and PyPy). Depends only on pandas and xarray, both widely available.
Requires Python 3.10 or later.
License in practice
MIT license (permissive) imposes no restrictions on use, modification, or redistribution in proprietary or open-source projects.
Quickstart
pip install pandas-flavor
import pandas as pd
import pandas_flavor as pf
@pf.register_dataframe_method
def my_method(df, col, value):
return df[df[col] == value]
df = pd.DataFrame({'x': [10, 20], 'y': [0, 2]})
df.my_method('x', 10)Verify before relying
- Whether xarray is a required runtime dependency or an optional feature; the fact sheet lists it but does not clarify its role.
- Scope and stability of the tracing feature introduced in 0.5.0 and how it integrates with method registration.
- Whether backwards compatibility with Pandas <0.23 is still actively maintained or was a historical feature.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagespandasxarray |
| Maintenance | Actively maintained 265 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,089,726 / month, #4,380 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPy |
Evidence: pandas_flavor-0.8.1-py3-none-any.whl
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