Readme cleaned

Author: Daniel15568

README.md

@@ -6,9 +6,11 @@
 [![License](https://img.shields.io/pypi/l/percentify.svg?style=flat&color=orange)](LICENSE)
 [![Build Status](https://github.com/data-centt/percentify/actions/workflows/python-app.yml/badge.svg)](https://github.com/data-centt/percentify/actions/workflows/python-app.yml)
 
-**Percentify** brings the everyday data-science checks to your doorstep — one import, one line.
+**Percentify — a niche data science library for practitioners and learners alike, drawing its main dependencies from pandas and numpy, and including everyday statistics.**
 
-Stop digging through scipy, statsmodels, and sklearn for the operations you run on every dataset. Percentify surfaces the common answer — the one 80% of practitioners actually want — as a single readable function call. Need to go deeper? The underlying libraries are still right there.
+Following the **Pareto principle**, Percentify brings the 20% of operations that make up 80% of daily data work to the forefront — each as a single, readable function call. No more digging through six-line recipes and hard-to-remember import paths for the checks you run on every dataset.
+
+Percentify **does not aim to compete** with pandas, scipy, statsmodels, or scikit-learn — it stands on their shoulders and works *alongside* them. The goal is to make the core concepts easy to learn, quick to use, and simple to remember. Every function names the underlying library it draws from, so the moment you need the full, configurable version, you know exactly where to go.
 
 Every function takes a pandas `DataFrame` (or `Series`) and hands back a clean `DataFrame` you can read, sort, or feed straight into the next step.
 
@@ -27,23 +29,25 @@ Requires `numpy` and `pandas`.
 
 ### `change` — Percentage Change
 Two numbers, two columns, or a whole series at once.
+> _Underlying library:_ `pandas.DataFrame.pct_change`
 ```python
 from percentify import change
 
-change(100, 150)                   # → 50.0   (a 50% increase)
+change(100, 150)                       # → 50.0   (a 50% increase)
 
 change(df["forecast"], df["actual"])   # element-wise % change between two columns
 
-change(df["revenue"])              # period-over-period % change down the column
+change(df["revenue"])                  # period-over-period % change down the column
 # 0     NaN
 # 1    50.0
 # 2   -40.0
 
-change(df)                         # every numeric column at once
+change(df)                             # every numeric column at once
 ```
 
 ### `vif` — Variance Inflation Factor (Multicollinearity)
-Buried in `statsmodels.stats.outliers_influence` behind a six-line loop. One call here.
+The classic multicollinearity check, without the six-line loop.
+> _Underlying library:_ `statsmodels.stats.outliers_influence.variance_inflation_factor`
 ```python
 from percentify import vif
 
@@ -58,6 +62,7 @@ vif(df, flag=5.0)   # only rows above the threshold (your problem columns)
 
 ### `missing` — Missing Data Profiling
 No more `df.isnull().sum() / len(df) * 100`.
+> _Underlying library:_ `pandas.DataFrame.isna`
 ```python
 from percentify import missing
 
@@ -69,7 +74,8 @@ missing(df)
 ```
 
 ### `cv` — Coefficient of Variation
-Not built in anywhere — instead of `df.std() / df.mean() * 100`.
+Relative variability (std ÷ mean), as a percentage.
+> _Underlying library:_ `scipy.stats.variation`
 ```python
 from percentify import cv
 
@@ -79,6 +85,7 @@ cv(df)             # → DataFrame of every numeric column, most variable first
 
 ### `outliers` — Percentage of Outliers (IQR Method)
 Stop rewriting the IQR bounds from scratch.
+> _Underlying library:_ `scipy.stats.iqr`
 ```python
 from percentify import outliers
 
@@ -92,11 +99,13 @@ from percentify import r_squared
 
 r_squared(y_true, y_pred)   # → 87.3
 ```
+> _Underlying library:_ `sklearn.metrics.r2_score`
 
 ### `pca_variance` — PCA Variance Breakdown
 Columns are standardized by default, so a feature measured in large units (e.g.
 dollars) can't dominate the result just because of its scale. Pass
 `standardize=False` for covariance-based PCA on the raw values.
+> _Underlying library:_ `sklearn.decomposition.PCA` (`.explained_variance_ratio_`)
 ```python
 from percentify import pca_variance
 
@@ -131,11 +140,15 @@ warnings.filterwarnings("ignore", category=PercentifyWarning)
 
 ## 🤝 Contributing
 
-Contributions are welcome!
-- If you have an idea (extra helpers, bug fixes or an idea):
-- Fork this repo
+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.
+
+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
 
-I try to keep it within scope, so please open an issue to discuss big new features first.
+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.