Revise introduction for clarity and detail, enhancing the overview of RustFrame's features and capabilities

docs/src/introduction.md

@@ -1,10 +1,38 @@
 # Introduction
 
-Welcome to the **RustFrame User Guide**. This book provides a tour of
+Welcome to the **RustFrame User Guide**. Rustframe is a lightweight dataframe
-RustFrame's capabilities from basic data handling to advanced machine learning
+and math toolkit for Rust written in 100% safe Rust. It focuses on keeping the
-workflows. Each chapter contains runnable snippets so you can follow along.
+API approachable while offering handy features for small analytical or
+educational projects.
 
-1. [Data manipulation](./data-manipulation.md) for loading and transforming data.
+Rustframe bundles:
-2. [Compute features](./compute.md) for statistics and analytics.
+
-3. [Machine learning](./machine-learning.md) for predictive models.
+- column‑labelled frames built on a fast column‑major matrix
-4. [Utilities](./utilities.md) for supporting helpers and upcoming modules.
+- familiar element‑wise math and aggregation routines
+- a growing `compute` module for statistics and machine learning
+- utilities for dates and random numbers
+
+```rust
+# extern crate rustframe;
+use rustframe::{frame::Frame, matrix::{Matrix, SeriesOps}};
+
+let data = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
+let frame = Frame::new(data, vec!["A", "B"], None);
+
+// Perform column wise aggregation
+assert_eq!(frame.sum_vertical(), vec![3.0, 7.0]);
+```
+
+## Resources
+
+- [GitHub repository](https://github.com/Magnus167/rustframe)
+- [Crates.io](https://crates.io/crates/rustframe) & [API docs](https://docs.rs/rustframe)
+- [Code coverage](https://codecov.io/gh/Magnus167/rustframe)
+
+This guide walks through the main building blocks of the library. Each chapter
+contains runnable snippets so you can follow along:
+
+1. [Data manipulation](./data-manipulation.md) for loading and transforming data
+2. [Compute features](./compute.md) for statistics and analytics
+3. [Machine learning](./machine-learning.md) for predictive models
+4. [Utilities](./utilities.md) for supporting helpers and upcoming modules