Percentify is a data science library that turns the 20% of data science operations behind 80% of daily work into single, readable function calls.

⚡ Full Polars DataFrame support: pass a polars DataFrame or Series and get the same kind back, no flag needed.

Built on pandas and numpy, it pairs everyday hard to reach tools with lesser-known ones. Where a function wraps an existing library (pandas, scipy, statsmodels, scikit-learn), it names it, so you always know where to dig deeper.

📖 Documentation

Full guide, every function, and live examples → data-centt.github.io/percentify

📦 Installation

pip install percentify

Requires numpy and pandas.

Quick example

import pandas as pd
from percentify import missing

df = pd.DataFrame({
    "salary": [50000, None, 60000, None],
    "age":    [25, 30, None, 40],
    "city":   ["NY", "LA", "SF", "LA"],
})

missing(df)
#    column  missing_pct
# 0  salary         50.0
# 1     age         25.0
# 2    city          0.0

One import, one line. A clean, sorted DataFrame you can read or feed into the next step.

What's inside

Function	What it answers
profiler	What is wrong with this dataset, and how do I fix it?
change	Growth as numbers, columns, or a whole series
vif	Which features are collinear?
missing	How much of each column is missing?
cv	How variable is each column, relative to its mean?
outliers	What percentage of each column are outliers?
pca_variance	How much variance does each principal component explain?
pca_loadings	What does each principal component consist of?
imbalance	How skewed are the classes in a target column?
difference	How far apart are two values or columns?
split	How does a total divide across weights or groups?
display	Format numbers or a column as clean "%" strings

→ See the documentation for a worked, real-output example of every function.

🛟 Friendly by design

• No cryptic tracebacks; Hand a function a text column where numbers are needed and you get a clear PercentifyWarning, not an Arrow/NumPy stack trace.
• Sensible defaults; Results come back sorted worst-first, and PCA is standardized out of the box.
• DataFrames everywhere; so the output drops straight into your notebook, your next filter, or your model.
• Pandas or polars; pass either a pandas or polars object and you get the same kind back, no flag needed.

🤝 Contributing

Contributions are welcome but they must follow the repo's guiding principle:

Keep each method as direct-to-output as possible. A percentify function should return the single most common answer in one line, and point users to the underlying library (pandas, scipy, statsmodels, scikit-learn) for the full, configurable version when the simplest output isn't what they're after.

It must support polars. Every function accepts both pandas and polars objects (via the @_backend_aware decorator) and returns the same kind, so any new contribution must keep that parity.

If your idea keeps things that simple and direct:

• Open an issue first to discuss it
• Fork the repo
• Create a branch
• Commit your changes
• Open a pull request

Anything that adds knobs and options for their own sake, or duplicates what the parent libraries already do well, is out of scope; those cases should point to the source library instead. I try to keep it compact on purpose, to discuss big new features first.