percentify 0.4.0

One import, one line — the percentage and stats functions you actually need.

Percentify

Percentify is a one import, one line code, that covers all stats you need for your data analysis and codebase.

Stop digging through scipy, statsmodels, and sklearn for operations you run every day. Percentify surfaces the most common percentage and statistical calculations into simple, readable function calls.

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📦 Installation

pip install percentify

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✨ Core Percentage Toolkit

percent: Part of a Whole

from percentify import percent

percent(50, 200)          # → 25.0
percent(1, 3)             # → 33.33
percent(5, 0)             # → 0.0  (safe division by zero)

change: Percentage Increase or Decrease

from percentify import change

change(100, 150)  # → 50.0   (50% increase)
change(200, 150)  # → -25.0  (25% decrease)

difference: Difference Between Two Values

from percentify import difference

difference(10, 20)  # → 66.67
difference(50, 50)  # → 0.0

split: Split a Total by Weights

from percentify import split

split(200, [1, 3])       # → [50.0, 150.0]
split(100, [1, 1, 1])    # → [33.33, 33.33, 33.33]

display: Format as a String

from percentify import display

display(25.0)                         # → "25.0%"
display(0.45, multiply=True)          # → "45.0%"
display(change(100, 20))              # → "-80.0%"

Example Use Case

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📊 Beyond Percentages; Data Science & Analytics

The functions below replace multi-step, hard-to-remember imports from scipy, statsmodels, and sklearn with a single line.

vif: Variance Inflation Factor (MultiCollinearity)

Currently buried in statsmodels.stats.outliers_influence. One line instead of six.

from percentify import vif

vif(df)
# → {"age": 1.2, "income": 8.4, "debt": 7.9}

vif(df, flag=5.0)
# → only columns with VIF > 5 (multicollinearity warnings)

missing: Easy Missing Data Profiling

No more typing df.isnull().sum() / len(df) * 100 every time.

from percentify import missing

missing(df)
# → {"salary": 12.4, "age": 3.1, "name": 0.0}

cv: Coefficient of Variation

Not built-in anywhere — one line instead of df.std() / df.mean() * 100.

from percentify import cv

cv(df["salary"])  # → 34.2
cv(df)            # → all numeric columns at once

outliers: Percentage of Outliers (IQR Method)

Stop rewriting the IQR calculation from scratch.

from percentify import outliers

outliers(df["salary"])  # → 4.7
outliers(df)            # → all numeric columns

r_squared: R-Squared

from percentify import r_squared

r_squared(y_true, y_pred)  # → 87.3

variance_explained: PCA Variance Breakdown

from percentify import variance_explained

variance_explained(df)
# → {"PC1": 45.2, "PC2": 23.1, "PC3": 12.8}

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🤝 Contributing

Contributions are welcome!

• If you have an idea (extra helpers, bug fixes or an idea):
• Fork this repo
• Create a branch
• Commit your changes
• Open a pull request

I try to keep it within scope, to discuss big new features first.