percentify 1.0.0

One import, one line — the everyday data science checks (VIF, PCA, outliers, missingness) you actually need.

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.

[!TIP] ⚡ Polars is first-class, not an afterthought. Pass a Polars DataFrame or Series and get the same kind straight back, with no flag and no manual conversion. Every function works on both backends, which is what sets Percentify apart from the pandas-only tools.

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

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?
correlate	Which features move together, and is it significant?
skew_report	How skewed is each column, and what transform helps?
bootstrap_ci	What is the confidence interval for a statistic?
permutation_test	Are two groups really different? (a p-value)
effect_size	How big is the difference, not just whether it is significant?
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.