readme and docs correction

Author: Daniel15568

README.md

@@ -10,12 +10,31 @@
 [![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)
 [![Polars](https://img.shields.io/badge/Polars-supported-cd792c?style=flat)](https://data-centt.github.io/percentify/)
 
-**Percentify is a data science library that turns the 20% of data science operations behind 80% of daily work into single, readable function calls.**
+**80% of the checks you run on every dataset. 20% of the code.**
 
-**⚡ Full Polars DataFrame support:** pass a polars DataFrame or Series and get the same kind back, no flag needed.
+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.
 
-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.
+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.
+
+```python
+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 →](https://data-centt.github.io/percentify/documentation/#profiler)
 
 ## 📖 Documentation
 

docs/documentation.md

@@ -47,7 +47,7 @@ polars stays optional: it is only imported when you actually pass a polars objec
 
 ## `profiler`
 
-The flagship: a one-call **data diagnostician**. Instead of dumping statistics for every column, it runs a battery of checks, ranks the problems worst-first, scores the data's health, and tells you how to fix each issue. It composes the rest of the toolkit (`missing`, `imbalance`, and more) under one entry point.
+The flagship, a one-call **data diagnostician**. 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. Instead of dumping statistics for every column, it runs a battery of checks, scores the data's health, and composes the rest of the toolkit (`missing`, `imbalance`, and more) under one entry point.
 
 !!! tip "Similar concept"
     `ydata-profiling` / `pandas.DataFrame.describe`, but diagnostic (it ranks problems and suggests fixes) rather than descriptive.
@@ -94,6 +94,9 @@ The report renders as a compact, color-coded summary in notebooks and terminals,
 
 Pass `target=` to also check for **leakage** (features that predict the target almost perfectly) and class imbalance. Accepts pandas or polars input.
 
+!!! example "Try it on your own data"
+    Point `profiler()` at any messy dataset you have lying around, pandas or Polars, and read the findings top to bottom. It is the fastest way to see what to fix before you model.
+
 ---
 
 ## `change`

docs/index.md

@@ -4,13 +4,14 @@
 
 
 
-*The 20% of data science operations behind 80% of daily work: each a single, readable function call.*
+**80% of the checks you run on every dataset. 20% of the code.**
 
-**⚡ Full Polars DataFrame support:** pass a polars DataFrame or Series and get the same kind back, no flag needed.
+Exploratory stats and data-quality diagnostics for pandas and **Polars** DataFrames. One call each.
 
-Following the *Pareto principle*, Percentify brings the checks you run on every dataset to the forefront, one call at a time. No more digging through six-line recipes and hard-to-remember import paths.
+!!! 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.
 
-Built on pandas and numpy, it pairs the everyday tools you reach for constantly with lesser-known ones worth knowing. Where a function wraps an existing library (pandas, scipy, statsmodels, scikit-learn), it names it, so you always know where to dig deeper.
+Where a function wraps an existing library (pandas, scipy, statsmodels, scikit-learn), it names it, so you always know where to dig deeper.
 
 ---
 

mkdocs.yml

@@ -1,5 +1,5 @@
 site_name: Percentify
-site_description: A niche data science library that brings the 80% of daily data tasks to the forefront.
+site_description: Exploratory stats and data-quality diagnostics for pandas and Polars DataFrames. One call each.
 site_url: https://data-centt.github.io/percentify/
 repo_url: https://github.com/data-centt/percentify
 repo_name: data-centt/percentify