percentify 1.0.2

80% of the checks you run on every dataset. 20% of the code.

Exploratory stats and data-quality diagnostics for pandas and Polars DataFrames. One call each.

Where a function wraps an existing library (pandas, scipy, statsmodels, scikit-learn), it names it, so you always know where to dig deeper.

⭐ The flagship: profiler

pandas .describe() tells you what your data is. profiler() tells you what to do about it: every issue ranked worst-first, each with its fix.

from percentify import profiler

report = profiler(df, target="churn")

report.to_frame()          # every finding, ranked worst-first, with a suggested fix
report.errors              # just the blocking issues
report.health              # a 0 to 100 data-health score
assert not report.errors   # drop it straight into a CI data-quality gate

Point it at any messy DataFrame, pandas or Polars, and see what it flags before you model. Try it on your own data →

📖 Documentation

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

📦 Installation

pip install percentify

Requires Python 3.10+, numpy, and pandas 2.0+.

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

→ 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.