pyjanitor
Tools for cleaning pandas DataFrames
Decision gist · record as of 2026-08-14
Yes. pyjanitor solves a real readability and usability gap in pandas workflows. Low install friction, active maintenance, permissive MIT license, no known vulnerabilities, and strong adoption make it a safe, practical choice for teams that value readable data-cleaning code. Install it if you work with pandas and want method chains to replace imperative preprocessing logic.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or later; janitor-rs (a Rust-based dependency) must compile on your platform.
- Low friction install with five runtime dependencies.
- Actively maintained—last commit 2026-08-11, 1500 repository stars.
License · maintenance · safety
MIT (permissive) — MIT license (permissive) places no restrictions on use, modification, or distribution in proprietary or open-source projects.
last release 2026-04-07 (129 days) · last repo commit 2026-08-11 · 1,500 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 383,172 downloads/mo, #7,079 on PyPI
Alternatives
Verify before relying
import pandas as pd
import janitor
df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [None]})
df = df.clean_names().remove_empty()- Performance characteristics when chaining many operations on large DataFrames.
- Compatibility of janitor-rs compilation on all target platforms (Windows, macOS, Linux variants).
- Extent of experimental submodules (finance, biology, chemistry, engineering, pyspark) and their stability.
What it is and what it does
pyjanitor is a pandas extension that adds a collection of data-cleaning methods designed to work as part of method chains. Instead of writing imperative pandas code with intermediate variable assignments, you can express a sequence of cleaning steps as a readable chain of method calls—each with an explicit verb name like clean_names(), remove_empty(), or rename_column(). The package is inspired by the R janitor package and the dplyr paradigm, bringing that fluent, declarative style to Python.
Under the hood, pyjanitor registers its functions as pandas DataFrame methods via pandas-flavor and depends on scipy, natsort, multipledispatch, and janitor-rs for its operations. It handles common preprocessing tasks: standardizing column names, removing null or empty rows and columns, identifying duplicates, encoding categorical data, splitting features and targets, coalescing columns, date conversions, and expanding delimited values into dummy variables. The package is actively maintained, supports Python 3.8 and later, and carries no known security vulnerabilities.
Use it for
- Clean messy column names (spaces, mixed case, special characters) in a single method call before analysis.
- Chain multiple DataFrame transformations (drop nulls, rename columns, add computed fields) in a single readable expression.
- Preprocess raw data exports (Excel, CSV) by removing empty rows and columns and standardizing formats in one pipeline.
- Prepare machine learning datasets by splitting features and targets and encoding categorical variables declaratively.
- Expand delimited or categorical columns into dummy-encoded variables for statistical modeling.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
pyjanitor solves a real readability and usability gap in pandas workflows. Low install friction, active maintenance, permissive MIT license, no known vulnerabilities, and strong adoption make it a safe, practical choice for teams that value readable data-cleaning code. Install it if you work with pandas and want method chains to replace imperative preprocessing logic.
Install
pyjanitor on PyPI
Before you install
Low friction install with five runtime dependencies. Actively maintained—last commit 2026-08-11, 1500 repository stars. Supports Python 3.8 and later.
Requires Python 3.8 or later; janitor-rs (a Rust-based dependency) must compile on your platform.
License in practice
MIT license (permissive) places no restrictions on use, modification, or distribution in proprietary or open-source projects.
Quickstart
import pandas as pd
import janitor
df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [None]})
df = df.clean_names().remove_empty()Verify before relying
- Performance characteristics when chaining many operations on large DataFrames.
- Compatibility of janitor-rs compilation on all target platforms (Windows, macOS, Linux variants).
- Extent of experimental submodules (finance, biology, chemistry, engineering, pyspark) and their stability.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnatsortpandas-flavormultipledispatchscipyjanitor-rs |
| Maintenance | Actively maintained 129 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 383,172 / month, #7,079 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering |
Evidence: pyjanitor-0.32.23-py3-none-any.whl
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