Merge 39a95e63d9 into d851c500af

.github/htmldocs/index.html

@@ -58,14 +58,6 @@
         <h2>A lightweight dataframe & math toolkit for Rust</h2>
         <hr style="border: 1px solid #d4d4d4; margin: 20px 0;">
         <p>
-
-            🐙 <a href="https://github.com/Magnus167/rustframe">GitHub</a>
-            <br><br>
-            
-            📖 <a href="https://magnus167.github.io/rustframe/user-guide">User Guide</a>
-            <br><br>
-            
-
             📚 <a href="https://magnus167.github.io/rustframe/docs">Docs</a> |
             📊 <a href="https://magnus167.github.io/rustframe/benchmark-report/">Benchmarks</a>
 
@@ -73,7 +65,8 @@
             🦀 <a href="https://crates.io/crates/rustframe">Crates.io</a> |
             🔖 <a href="https://docs.rs/rustframe/latest/rustframe/">docs.rs</a>
             <br><br>
-            <!-- 🌐 <a href="https://gitea.nulltech.uk/Magnus167/rustframe">Gitea mirror</a> -->
+            🐙 <a href="https://github.com/Magnus167/rustframe">GitHub</a> |
+            🌐 <a href="https://gitea.nulltech.uk/Magnus167/rustframe">Gitea mirror</a>
         </p>
     </main>
 </body>

.github/workflows/docs-and-testcov.yml

@@ -153,6 +153,7 @@ jobs:
 
           echo "<meta http-equiv=\"refresh\" content=\"0; url=../docs/index.html\">" > target/doc/rustframe/index.html
 
+          mkdir output
           cp tarpaulin-report.html target/doc/docs/
           cp tarpaulin-report.json target/doc/docs/
           cp tarpaulin-badge.json target/doc/docs/
@@ -165,30 +166,16 @@ jobs:
           # copy the benchmark report to the output directory
           cp -r benchmark-report target/doc/
 
-          mkdir output
-          cp -r target/doc/* output/
-
-      - name: Build user guide
-        run: |
-          cargo binstall mdbook
-          bash ./docs/build.sh
-
-      - name: Copy user guide to output directory
-        run: |
-          mkdir output/user-guide
-          cp -r docs/book/* output/user-guide/
-
       - name: Add index.html to output directory
         run: |
-          cp .github/htmldocs/index.html output/index.html
+          cp .github/htmldocs/index.html target/doc/index.html
-          cp .github/rustframe_logo.png output/rustframe_logo.png
+          cp .github/rustframe_logo.png target/doc/rustframe_logo.png
 
       - name: Upload Pages artifact
         # if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
         uses: actions/upload-pages-artifact@v3
         with:
-          # path: target/doc/
+          path: target/doc/
-          path: output/
 
       - name: Deploy to GitHub Pages
         # if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'

.github/workflows/run-unit-tests.yml

@@ -78,8 +78,3 @@ jobs:
         uses: codecov/test-results-action@v1
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
-
-      - name: Test build user guide
-        run: |
-          cargo binstall mdbook
-          bash ./docs/build.sh

.gitignore

@@ -16,6 +16,4 @@ data/
 
 tarpaulin-report.*
 
-.github/htmldocs/rustframe_logo.png
+.github/htmldocs/rustframe_logo.png
-
-docs/book/

Cargo.toml

@@ -1,12 +1,11 @@
 [package]
 name = "rustframe"
 authors = ["Palash Tyagi (https://github.com/Magnus167)"]
-version = "0.0.1-a.20250805"
+version = "0.0.1-a.20250716"
 edition = "2021"
 license = "GPL-3.0-or-later"
 readme = "README.md"
-description = "A simple dataframe and math toolkit"
+description = "A simple dataframe library"
-documentation = "https://magnus167.github.io/rustframe/"
 
 [lib]
 name = "rustframe"

README.md

@@ -1,12 +1,11 @@
 # rustframe
 
-🐙 [GitHub](https://github.com/Magnus167/rustframe) | 📚 [Docs](https://magnus167.github.io/rustframe/) | 📖 [User Guide](https://magnus167.github.io/rustframe/user-guide/) | 🦀 [Crates.io](https://crates.io/crates/rustframe) | 🔖 [docs.rs](https://docs.rs/rustframe/latest/rustframe/)
+📚 [Docs](https://magnus167.github.io/rustframe/) | 🐙 [GitHub](https://github.com/Magnus167/rustframe) | 🌐 [Gitea mirror](https://gitea.nulltech.uk/Magnus167/rustframe) | 🦀 [Crates.io](https://crates.io/crates/rustframe) | 🔖 [docs.rs](https://docs.rs/rustframe/latest/rustframe/)
 
 <!-- [![Last commit](https://img.shields.io/endpoint?url=https://magnus167.github.io/rustframe/rustframe/last-commit-date.json)](https://github.com/Magnus167/rustframe) -->
 
 [![codecov](https://codecov.io/gh/Magnus167/rustframe/graph/badge.svg?token=J7ULJEFTVI)](https://codecov.io/gh/Magnus167/rustframe)
 [![Coverage](https://img.shields.io/endpoint?url=https://magnus167.github.io/rustframe/docs/tarpaulin-badge.json)](https://magnus167.github.io/rustframe/docs/tarpaulin-report.html)
-[![gitea-mirror](https://img.shields.io/badge/git_mirror-blue)](https://gitea.nulltech.uk/Magnus167/rustframe)
 
 ---
 
@@ -24,9 +23,11 @@ Rustframe is an educational project, and is not intended for production use. It
 - **Math that reads like math** - element-wise `+`, `−`, `×`, `÷` on entire frames or scalars.
 - **Frames** - Column major data structure for single-type data, with labeled columns and typed row indices.
 - **Compute module** - Implements various statistical computations and machine learning models.
-- **Random number utils** - Built-in pseudo and cryptographically secure generators for simulations.
+
 - **[Coming Soon]** _DataFrame_ - Multi-type data structure for heterogeneous data, with labeled columns and typed row indices.
 
+- **Random number utils** - Built-in pseudo and cryptographically secure generators for simulations.
+
 #### Matrix and Frame functionality
 
 - **Matrix operations** - Element-wise arithmetic, boolean logic, transpose, and more.
@@ -153,7 +154,134 @@ let zipped_matrix = a.zip(&b, |x, y| x + y);
 assert_eq!(zipped_matrix.data(), &[6.0, 8.0, 10.0, 12.0]);
 ```
 
-## More examples
+---
+
+## DataFrame Usage Example
+
+```rust
+use chrono::NaiveDate;
+use rustframe::dataframe::DataFrame;
+use rustframe::utils::{BDateFreq, BDatesList};
+use std::any::TypeId;
+use std::collections::HashMap;
+
+// Helper for NaiveDate
+fn d(y: i32, m: u32, d: u32) -> NaiveDate {
+    NaiveDate::from_ymd_opt(y, m, d).unwrap()
+}
+
+// Create a new DataFrame
+let mut df = DataFrame::new();
+
+// Add columns of different types
+df.add_column("col_int1", vec![1, 2, 3, 4, 5]);
+df.add_column("col_float1", vec![1.1, 2.2, 3.3, 4.4, 5.5]);
+df.add_column(
+    "col_string",
+    vec![
+        "apple".to_string(),
+        "banana".to_string(),
+        "cherry".to_string(),
+        "date".to_string(),
+        "elderberry".to_string(),
+    ],
+);
+df.add_column("col_bool", vec![true, false, true, false, true]);
+// df.add_column("col_date", vec![d(2023,1,1), d(2023,1,2), d(2023,1,3), d(2023,1,4), d(2023,1,5)]);
+df.add_column(
+    "col_date",
+    BDatesList::from_n_periods("2023-01-01".to_string(), BDateFreq::Daily, 5)
+        .unwrap()
+        .list()
+        .unwrap(),
+);
+
+println!("DataFrame after initial column additions:\n{}", df);
+
+// Demonstrate frame re-use when adding columns of existing types
+let initial_frames_count = df.num_internal_frames();
+println!(
+    "\nInitial number of internal frames: {}",
+    initial_frames_count
+);
+
+df.add_column("col_int2", vec![6, 7, 8, 9, 10]);
+df.add_column("col_float2", vec![6.6, 7.7, 8.8, 9.9, 10.0]);
+
+let frames_after_reuse = df.num_internal_frames();
+println!(
+    "Number of internal frames after adding more columns of existing types: {}",
+    frames_after_reuse
+);
+assert_eq!(initial_frames_count, frames_after_reuse); // Should be equal, demonstrating re-use
+
+println!(
+    "\nDataFrame after adding more columns of existing types:\n{}",
+    df
+);
+
+// Get number of rows and columns
+println!("Rows: {}", df.rows()); // Output: Rows: 5
+println!("Columns: {}", df.cols()); // Output: Columns: 5
+
+// Get column names
+println!("Column names: {:?}", df.get_column_names());
+// Output: Column names: ["col_int", "col_float", "col_string", "col_bool", "col_date"]
+
+// Get a specific column by name and type
+let int_col = df.get_column::<i32>("col_int1").unwrap();
+// Output: Integer column: [1, 2, 3, 4, 5]
+println!("Integer column (col_int1): {:?}", int_col);
+
+let int_col2 = df.get_column::<i32>("col_int2").unwrap();
+// Output: Integer column: [6, 7, 8, 9, 10]
+println!("Integer column (col_int2): {:?}", int_col2);
+
+let float_col = df.get_column::<f64>("col_float1").unwrap();
+// Output: Float column: [1.1, 2.2, 3.3, 4.4, 5.5]
+println!("Float column (col_float1): {:?}", float_col);
+
+// Attempt to get a column with incorrect type (returns None)
+let wrong_type_col = df.get_column::<bool>("col_int1");
+// Output: Wrong type column: None
+println!("Wrong type column: {:?}", wrong_type_col);
+
+// Get a row by index
+let row_0 = df.get_row(0).unwrap();
+println!("Row 0: {:?}", row_0);
+// Output: Row 0: {"col_int1": "1", "col_float1": "1.1", "col_string": "apple", "col_bool": "true", "col_date": "2023-01-01", "col_int2": "6", "col_float2": "6.6"}
+
+let row_2 = df.get_row(2).unwrap();
+println!("Row 2: {:?}", row_2);
+// Output: Row 2: {"col_int1": "3", "col_float1": "3.3", "col_string": "cherry", "col_bool": "true", "col_date": "2023-01-03", "col_int2": "8", "col_float2": "8.8"}
+
+// Attempt to get an out-of-bounds row (returns None)
+let row_out_of_bounds = df.get_row(10);
+// Output: Row out of bounds: None
+println!("Row out of bounds: {:?}", row_out_of_bounds);
+
+// Drop a column
+df.drop_column("col_bool");
+println!("\nDataFrame after dropping 'col_bool':\n{}", df);
+
+println!("Columns after drop: {}", df.cols());
+println!("Column names after drop: {:?}", df.get_column_names());
+
+// Drop another column, ensuring the underlying Frame is removed if empty
+df.drop_column("col_float1");
+println!("\nDataFrame after dropping 'col_float1':\n{}", df);
+
+println!("Columns after second drop: {}", df.cols());
+println!(
+    "Column names after second drop: {:?}",
+    df.get_column_names()
+);
+
+// Attempt to drop a non-existent column (will panic)
+// df.drop_column("non_existent_col"); // Uncomment to see panic
+```
+
+### More examples
 
 See the [examples](./examples/) directory for some demonstrations of Rustframe's syntax and functionality.
 
@@ -192,21 +320,10 @@ cargo run --example
 
 Each demo runs a couple of mini-scenarios showcasing the APIs.
 
-## Running benchmarks
+### Running benchmarks
 
 To run the benchmarks, use:
 
 ```bash
 cargo bench --features "bench"
 ```
-
-## Building the user-guide
-
-To build the user guide, use:
-
-```bash
-cargo binstall mdbook
-bash docs/build.sh
-```
-
-This will generate the user guide in the `docs/book` directory.

docs/book.toml

@@ -1,7 +0,0 @@
-[book]
-title = "Rustframe User Guide"
-authors = ["Palash Tyagi (https://github.com/Magnus167)"]
-description = "Guided journey through Rustframe capabilities."
-
-[build]
-build-dir = "book"

docs/build.sh

@@ -1,7 +0,0 @@
-#!/usr/bin/env sh
-# Build and test the Rustframe user guide using mdBook.
-set -e
-
-cd docs
-bash gen.sh "$@"
-cd ..

docs/gen.sh

@@ -1,14 +0,0 @@
-#!/usr/bin/env sh
-
-set -e
-
-cargo clean
-
-cargo build --manifest-path ../Cargo.toml
-
-mdbook test -L ../target/debug/deps "$@"
-
-mdbook build "$@"
-
-cargo build
-# cargo build --release

docs/src/SUMMARY.md

@@ -1,7 +0,0 @@
-# Summary
-
-- [Introduction](./introduction.md)
-- [Data Manipulation](./data-manipulation.md)
-- [Compute Features](./compute.md)
-- [Machine Learning](./machine-learning.md)
-- [Utilities](./utilities.md)

docs/src/compute.md

@@ -1,222 +0,0 @@
-# Compute Features
-
-The `compute` module hosts numerical routines for exploratory data analysis.
-It covers descriptive statistics, correlations, probability distributions and
-some basic inferential tests.
-
-## Basic Statistics
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::stats::{mean, mean_horizontal, mean_vertical, stddev, median, population_variance, percentile};
-use rustframe::matrix::Matrix;
-
-let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-assert_eq!(mean(&m), 2.5);
-assert_eq!(stddev(&m), 1.118033988749895);
-assert_eq!(median(&m), 2.5);
-assert_eq!(population_variance(&m), 1.25);
-assert_eq!(percentile(&m, 50.0), 3.0);
-// column averages returned as 1 x n matrix
-let row_means = mean_horizontal(&m);
-assert_eq!(row_means.data(), &[2.0, 3.0]);
-let col_means = mean_vertical(&m);
-assert_eq!(col_means.data(), & [1.5, 3.5]);
-```
-
-### Axis-specific Operations
-
-Operations can be applied along specific axes (rows or columns):
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::stats::{mean_vertical, mean_horizontal, stddev_vertical, stddev_horizontal};
-use rustframe::matrix::Matrix;
-
-// 3x2 matrix
-let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 3, 2);
-
-// Mean along columns (vertical) - returns 1 x cols matrix
-let col_means = mean_vertical(&m);
-assert_eq!(col_means.shape(), (1, 2));
-assert_eq!(col_means.data(), &[3.0, 4.0]); // [(1+3+5)/3, (2+4+6)/3]
-
-// Mean along rows (horizontal) - returns rows x 1 matrix
-let row_means = mean_horizontal(&m);
-assert_eq!(row_means.shape(), (3, 1));
-assert_eq!(row_means.data(), &[1.5, 3.5, 5.5]); // [(1+2)/2, (3+4)/2, (5+6)/2]
-
-// Standard deviation along columns
-let col_stddev = stddev_vertical(&m);
-assert_eq!(col_stddev.shape(), (1, 2));
-
-// Standard deviation along rows
-let row_stddev = stddev_horizontal(&m);
-assert_eq!(row_stddev.shape(), (3, 1));
-```
-
-## Correlation
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::stats::{pearson, covariance};
-use rustframe::matrix::Matrix;
-
-let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let y = Matrix::from_vec(vec![2.0, 4.0, 6.0, 8.0], 2, 2);
-let corr = pearson(&x, &y);
-let cov = covariance(&x, &y);
-assert!((corr - 1.0).abs() < 1e-8);
-assert!((cov - 2.5).abs() < 1e-8);
-```
-
-## Covariance
-
-### `covariance`
-
-Computes the population covariance between two equally sized matrices by flattening
-their values.
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::stats::covariance;
-use rustframe::matrix::Matrix;
-
-let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let y = Matrix::from_vec(vec![2.0, 4.0, 6.0, 8.0], 2, 2);
-let cov = covariance(&x, &y);
-assert!((cov - 2.5).abs() < 1e-8);
-```
-
-### `covariance_vertical`
-
-Evaluates covariance between columns (i.e. across rows) and returns a matrix of
-column pair covariances.
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::stats::covariance_vertical;
-use rustframe::matrix::Matrix;
-
-let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let cov = covariance_vertical(&m);
-assert_eq!(cov.shape(), (2, 2));
-assert!(cov.data().iter().all(|&v| (v - 1.0).abs() < 1e-8));
-```
-
-### `covariance_horizontal`
-
-Computes covariance between rows (i.e. across columns) returning a matrix that
-describes how each pair of rows varies together.
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::stats::covariance_horizontal;
-use rustframe::matrix::Matrix;
-
-let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let cov = covariance_horizontal(&m);
-assert_eq!(cov.shape(), (2, 2));
-assert!(cov.data().iter().all(|&v| (v - 0.25).abs() < 1e-8));
-```
-
-### `covariance_matrix`
-
-Builds a covariance matrix either between columns (`Axis::Col`) or rows
-(`Axis::Row`). Each entry represents how two series co-vary.
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::stats::covariance_matrix;
-use rustframe::matrix::{Axis, Matrix};
-
-let data = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-
-// Covariance between columns
-let cov_cols = covariance_matrix(&data, Axis::Col);
-assert!((cov_cols.get(0, 0) - 2.0).abs() < 1e-8);
-
-// Covariance between rows
-let cov_rows = covariance_matrix(&data, Axis::Row);
-assert!((cov_rows.get(0, 1) + 0.5).abs() < 1e-8);
-```
-
-## Distributions
-
-Probability distribution helpers are available for common PDFs and CDFs.
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::stats::distributions::normal_pdf;
-use rustframe::matrix::Matrix;
-
-let x = Matrix::from_vec(vec![0.0, 1.0], 1, 2);
-let pdf = normal_pdf(x, 0.0, 1.0);
-assert_eq!(pdf.data().len(), 2);
-```
-
-### Additional Distributions
-
-Rustframe provides several other probability distributions:
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::stats::distributions::{normal_cdf, binomial_pmf, binomial_cdf, poisson_pmf};
-use rustframe::matrix::Matrix;
-
-// Normal distribution CDF
-let x = Matrix::from_vec(vec![0.0, 1.0], 1, 2);
-let cdf = normal_cdf(x, 0.0, 1.0);
-assert_eq!(cdf.data().len(), 2);
-
-// Binomial distribution PMF
-// Probability of k successes in n trials with probability p
-let k = Matrix::from_vec(vec![0_u64, 1, 2, 3], 1, 4);
-let pmf = binomial_pmf(3, k.clone(), 0.5);
-assert_eq!(pmf.data().len(), 4);
-
-// Binomial distribution CDF
-let cdf = binomial_cdf(3, k, 0.5);
-assert_eq!(cdf.data().len(), 4);
-
-// Poisson distribution PMF
-// Probability of k events with rate parameter lambda
-let k = Matrix::from_vec(vec![0_u64, 1, 2], 1, 3);
-let pmf = poisson_pmf(2.0, k);
-assert_eq!(pmf.data().len(), 3);
-```
-
-### Inferential Statistics
-
-Rustframe provides several inferential statistical tests:
-
-```rust
-# extern crate rustframe;
-use rustframe::matrix::Matrix;
-use rustframe::compute::stats::inferential::{t_test, chi2_test, anova};
-
-// Two-sample t-test
-let sample1 = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
-let sample2 = Matrix::from_vec(vec![6.0, 7.0, 8.0, 9.0, 10.0], 1, 5);
-let (t_statistic, p_value) = t_test(&sample1, &sample2);
-assert!((t_statistic + 5.0).abs() < 1e-5);
-assert!(p_value > 0.0 && p_value < 1.0);
-
-// Chi-square test of independence
-let observed = Matrix::from_vec(vec![12.0, 5.0, 8.0, 10.0], 2, 2);
-let (chi2_statistic, p_value) = chi2_test(&observed);
-assert!(chi2_statistic > 0.0);
-assert!(p_value > 0.0 && p_value < 1.0);
-
-// One-way ANOVA
-let group1 = Matrix::from_vec(vec![1.0, 2.0, 3.0], 1, 3);
-let group2 = Matrix::from_vec(vec![2.0, 3.0, 4.0], 1, 3);
-let group3 = Matrix::from_vec(vec![3.0, 4.0, 5.0], 1, 3);
-let groups = vec![&group1, &group2, &group3];
-let (f_statistic, p_value) = anova(groups);
-assert!(f_statistic > 0.0);
-assert!(p_value > 0.0 && p_value < 1.0);
-```
-
-With the basics covered, explore predictive models in the
-[machine learning](./machine-learning.md) chapter.

docs/src/data-manipulation.md

@@ -1,157 +0,0 @@
-# Data Manipulation
-
-Rustframe's `Frame` type couples tabular data with
-column labels and a typed row index. Frames expose a familiar API for loading
-data, selecting rows or columns and performing aggregations.
-
-## Creating a Frame
-
-```rust
-# extern crate rustframe;
-use rustframe::frame::{Frame, RowIndex};
-use rustframe::matrix::Matrix;
-
-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);
-assert_eq!(frame["A"], vec![1.0, 2.0]);
-```
-
-## Indexing Rows
-
-Row labels can be integers, dates or a default range. Retrieving a row returns a
-view that lets you inspect values by column name or position.
-
-```rust
-# extern crate rustframe;
-# extern crate chrono;
-use chrono::NaiveDate;
-use rustframe::frame::{Frame, RowIndex};
-use rustframe::matrix::Matrix;
-
-let d = |y, m, d| NaiveDate::from_ymd_opt(y, m, d).unwrap();
-let data = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
-let index = RowIndex::Date(vec![d(2024, 1, 1), d(2024, 1, 2)]);
-let mut frame = Frame::new(data, vec!["A", "B"], Some(index));
-assert_eq!(frame.get_row_date(d(2024, 1, 2))["B"], 4.0);
-
-// mutate by row key
-frame.get_row_date_mut(d(2024, 1, 1)).set_by_index(0, 9.0);
-assert_eq!(frame.get_row_date(d(2024, 1, 1))["A"], 9.0);
-```
-
-## Column operations
-
-Columns can be inserted, renamed, removed or reordered in place.
-
-```rust
-# extern crate rustframe;
-use rustframe::frame::{Frame, RowIndex};
-use rustframe::matrix::Matrix;
-
-let data = Matrix::from_cols(vec![vec![1, 2], vec![3, 4]]);
-let mut frame = Frame::new(data, vec!["X", "Y"], Some(RowIndex::Range(0..2)));
-
-frame.add_column("Z", vec![5, 6]);
-frame.rename("Y", "W");
-let removed = frame.delete_column("X");
-assert_eq!(removed, vec![1, 2]);
-frame.sort_columns();
-assert_eq!(frame.columns(), &["W", "Z"]);
-```
-
-## Aggregations
-
-Any numeric aggregation available on `Matrix` is forwarded to `Frame`.
-
-```rust
-# extern crate rustframe;
-use rustframe::frame::Frame;
-use rustframe::matrix::{Matrix, SeriesOps};
-
-let frame = Frame::new(Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]), vec!["A", "B"], None);
-assert_eq!(frame.sum_vertical(), vec![3.0, 7.0]);
-assert_eq!(frame.sum_horizontal(), vec![4.0, 6.0]);
-```
-
-## Matrix Operations
-
-```rust
-# extern crate rustframe;
-use rustframe::matrix::Matrix;
-
-let data1 = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let data2 = Matrix::from_vec(vec![5.0, 6.0, 7.0, 8.0], 2, 2);
-
-let sum = data1.clone() + data2.clone();
-assert_eq!(sum.data(), vec![6.0, 8.0, 10.0, 12.0]);
-
-let product = data1.clone() * data2.clone();
-assert_eq!(product.data(), vec![5.0, 12.0, 21.0, 32.0]);
-
-let scalar_product = data1.clone() * 2.0;
-assert_eq!(scalar_product.data(), vec![2.0, 4.0, 6.0, 8.0]);
-
-let equals = data1 == data1.clone();
-assert_eq!(equals, true);
-```
-
-### Advanced Matrix Operations
-
-Matrices support a variety of advanced operations:
-
-```rust
-# extern crate rustframe;
-use rustframe::matrix::{Matrix, SeriesOps};
-
-// Matrix multiplication (dot product)
-let a = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let b = Matrix::from_vec(vec![5.0, 6.0, 7.0, 8.0], 2, 2);
-let product = a.matrix_mul(&b);
-assert_eq!(product.data(), vec![23.0, 34.0, 31.0, 46.0]);
-
-// Transpose
-let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let transposed = m.transpose();
-assert_eq!(transposed.data(), vec![1.0, 3.0, 2.0, 4.0]);
-
-// Map function over all elements
-let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let squared = m.map(|x| x * x);
-assert_eq!(squared.data(), vec![1.0, 4.0, 9.0, 16.0]);
-
-// Zip two matrices with a function
-let a = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let b = Matrix::from_vec(vec![5.0, 6.0, 7.0, 8.0], 2, 2);
-let zipped = a.zip(&b, |x, y| x + y);
-assert_eq!(zipped.data(), vec![6.0, 8.0, 10.0, 12.0]);
-```
-
-### Matrix Reductions
-
-Matrices support various reduction operations:
-
-```rust
-# extern crate rustframe;
-use rustframe::matrix::{Matrix, SeriesOps};
-
-let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 3, 2);
-
-// Sum along columns (vertical)
-let col_sums = m.sum_vertical();
-assert_eq!(col_sums, vec![9.0, 12.0]); // [1+3+5, 2+4+6]
-
-// Sum along rows (horizontal)
-let row_sums = m.sum_horizontal();
-assert_eq!(row_sums, vec![3.0, 7.0, 11.0]); // [1+2, 3+4, 5+6]
-
-// Cumulative sum along columns
-let col_cumsum = m.cumsum_vertical();
-assert_eq!(col_cumsum.data(), vec![1.0, 4.0, 9.0, 2.0, 6.0, 12.0]);
-
-// Cumulative sum along rows
-let row_cumsum = m.cumsum_horizontal();
-assert_eq!(row_cumsum.data(), vec![1.0, 3.0, 5.0, 3.0, 7.0, 11.0]);
-```
-
-With the basics covered, continue to the [compute features](./compute.md)
-chapter for statistics and analytics.

docs/src/introduction.md

@@ -1,40 +0,0 @@
-# Introduction
-
-🐙 [GitHub](https://github.com/Magnus167/rustframe) | 📚 [Docs](https://magnus167.github.io/rustframe/) | 📖 [User Guide](https://magnus167.github.io/rustframe/user-guide/) | 🦀 [Crates.io](https://crates.io/crates/rustframe) | 🔖 [docs.rs](https://docs.rs/rustframe/latest/rustframe/)
-
-Welcome to the **Rustframe User Guide**. Rustframe is a lightweight dataframe
-and math toolkit for Rust written in 100% safe Rust. It focuses on keeping the
-API approachable while offering handy features for small analytical or
-educational projects.
-
-Rustframe bundles:
-
-- column‑labelled frames built on a fast column‑major matrix
-- 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

docs/src/machine-learning.md

@@ -1,282 +0,0 @@
-# Machine Learning
-
-The `compute::models` module bundles several learning algorithms that operate on
-`Matrix` structures. These examples highlight the basic training and prediction
-APIs. For more end‑to‑end walkthroughs see the examples directory in the
-repository.
-
-Currently implemented models include:
-
-- Linear and logistic regression
-- K‑means clustering
-- Principal component analysis (PCA)
-- Gaussian Naive Bayes
-- Dense neural networks
-
-## Linear Regression
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::linreg::LinReg;
-use rustframe::matrix::Matrix;
-
-let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
-let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
-let mut model = LinReg::new(1);
-model.fit(&x, &y, 0.01, 100);
-let preds = model.predict(&x);
-assert_eq!(preds.rows(), 4);
-```
-
-## K-means Walkthrough
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::k_means::KMeans;
-use rustframe::matrix::Matrix;
-
-let data = Matrix::from_vec(vec![1.0, 1.0, 5.0, 5.0], 2, 2);
-let (model, _labels) = KMeans::fit(&data, 2, 10, 1e-4);
-let new_point = Matrix::from_vec(vec![0.0, 0.0], 1, 2);
-let cluster = model.predict(&new_point)[0];
-```
-
-## Logistic Regression
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::logreg::LogReg;
-use rustframe::matrix::Matrix;
-
-let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
-let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
-let mut model = LogReg::new(1);
-model.fit(&x, &y, 0.1, 200);
-let preds = model.predict_proba(&x);
-assert_eq!(preds.rows(), 4);
-```
-
-## Principal Component Analysis
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::pca::PCA;
-use rustframe::matrix::Matrix;
-
-let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-let pca = PCA::fit(&data, 1, 0);
-let transformed = pca.transform(&data);
-assert_eq!(transformed.cols(), 1);
-```
-
-## Gaussian Naive Bayes
-
-Gaussian Naive Bayes classifier for continuous features:
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::gaussian_nb::GaussianNB;
-use rustframe::matrix::Matrix;
-
-// Training data with 2 features
-let x = Matrix::from_rows_vec(vec![
-    1.0, 2.0,
-    2.0, 3.0,
-    3.0, 4.0,
-    4.0, 5.0
-], 4, 2);
-
-// Class labels (0 or 1)
-let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
-
-// Train the model
-let mut model = GaussianNB::new(1e-9, true);
-model.fit(&x, &y);
-
-// Make predictions
-let predictions = model.predict(&x);
-assert_eq!(predictions.rows(), 4);
-```
-
-## Dense Neural Networks
-
-Simple fully connected neural network:
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::dense_nn::{DenseNN, DenseNNConfig, ActivationKind, InitializerKind, LossKind};
-use rustframe::matrix::Matrix;
-
-// Training data with 2 features
-let x = Matrix::from_rows_vec(vec![
-    0.0, 0.0,
-    0.0, 1.0,
-    1.0, 0.0,
-    1.0, 1.0
-], 4, 2);
-
-// XOR target outputs
-let y = Matrix::from_vec(vec![0.0, 1.0, 1.0, 0.0], 4, 1);
-
-// Create a neural network with 2 hidden layers
-let config = DenseNNConfig {
-    input_size: 2,
-    hidden_layers: vec![4, 4],
-    output_size: 1,
-    activations: vec![ActivationKind::Sigmoid, ActivationKind::Sigmoid, ActivationKind::Sigmoid],
-    initializer: InitializerKind::Uniform(0.5),
-    loss: LossKind::MSE,
-    learning_rate: 0.1,
-    epochs: 1000,
-};
-let mut model = DenseNN::new(config);
-
-// Train the model
-model.train(&x, &y);
-
-// Make predictions
-let predictions = model.predict(&x);
-assert_eq!(predictions.rows(), 4);
-```
-
-## Real-world Examples
-
-### Housing Price Prediction
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::linreg::LinReg;
-use rustframe::matrix::Matrix;
-
-// Features: square feet and bedrooms
-let features = Matrix::from_rows_vec(vec![
-    2100.0, 3.0,
-    1600.0, 2.0,
-    2400.0, 4.0,
-    1400.0, 2.0,
-], 4, 2);
-
-// Sale prices
-let target = Matrix::from_vec(vec![400_000.0, 330_000.0, 369_000.0, 232_000.0], 4, 1);
-
-let mut model = LinReg::new(2);
-model.fit(&features, &target, 1e-8, 10_000);
-
-// Predict price of a new home
-let new_home = Matrix::from_vec(vec![2000.0, 3.0], 1, 2);
-let predicted_price = model.predict(&new_home);
-println!("Predicted price: ${}", predicted_price.data()[0]);
-```
-
-### Spam Detection
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::logreg::LogReg;
-use rustframe::matrix::Matrix;
-
-// 20 e-mails × 5 features = 100 numbers (row-major, spam first)
-let x = Matrix::from_rows_vec(
-    vec![
-        // ─────────── spam examples ───────────
-        2.0, 1.0, 1.0, 1.0, 1.0, // "You win a FREE offer - click for money-back bonus!"
-        1.0, 0.0, 1.0, 1.0, 0.0, // "FREE offer! Click now!"
-        0.0, 2.0, 0.0, 1.0, 1.0, // "Win win win - money inside, click…"
-        1.0, 1.0, 0.0, 0.0, 1.0, // "Limited offer to win easy money…"
-        1.0, 0.0, 1.0, 0.0, 1.0, // ...
-        0.0, 1.0, 1.0, 1.0, 0.0, // ...
-        2.0, 0.0, 0.0, 1.0, 1.0, // ...
-        0.0, 1.0, 1.0, 0.0, 1.0, // ...
-        1.0, 1.0, 1.0, 1.0, 0.0, // ...
-        1.0, 0.0, 0.0, 1.0, 1.0, // ...
-        // ─────────── ham examples ───────────
-        0.0, 0.0, 0.0, 0.0, 0.0, // "See you at the meeting tomorrow."
-        0.0, 0.0, 0.0, 1.0, 0.0, // "Here's the Zoom click-link."
-        0.0, 0.0, 0.0, 0.0, 1.0, // "Expense report: money attached."
-        0.0, 0.0, 0.0, 1.0, 1.0, // ...
-        0.0, 1.0, 0.0, 0.0, 0.0, // "Did we win the bid?"
-        0.0, 0.0, 0.0, 0.0, 0.0, // ...
-        0.0, 0.0, 0.0, 1.0, 0.0, // ...
-        1.0, 0.0, 0.0, 0.0, 0.0, // "Special offer for staff lunch."
-        0.0, 0.0, 0.0, 0.0, 0.0, // ...
-        0.0, 0.0, 0.0, 1.0, 0.0,
-    ],
-    20,
-    5,
-);
-
-// Labels: 1 = spam, 0 = ham
-let y = Matrix::from_vec(
-    vec![
-        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, // 10 spam
-        0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, // 10 ham
-    ],
-    20,
-    1,
-);
-
-// Train
-let mut model = LogReg::new(5);
-model.fit(&x, &y, 0.01, 5000);
-
-// Predict
-// e.g. "free money offer"
-let email_data = vec![1.0, 0.0, 1.0, 0.0, 1.0];
-let email = Matrix::from_vec(email_data, 1, 5);
-let prob_spam = model.predict_proba(&email);
-println!("Probability of spam: {:.4}", prob_spam.data()[0]);
-```
-
-### Iris Flower Classification
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::gaussian_nb::GaussianNB;
-use rustframe::matrix::Matrix;
-
-// Features: sepal length and petal length
-let x = Matrix::from_rows_vec(vec![
-    5.1, 1.4, // setosa
-    4.9, 1.4, // setosa
-    6.2, 4.5, // versicolor
-    5.9, 5.1, // virginica
-], 4, 2);
-
-let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 2.0], 4, 1);
-let names = vec!["setosa", "versicolor", "virginica"];
-
-let mut model = GaussianNB::new(1e-9, true);
-model.fit(&x, &y);
-
-let sample = Matrix::from_vec(vec![5.0, 1.5], 1, 2);
-let predicted_class = model.predict(&sample);
-let class_name = names[predicted_class.data()[0] as usize];
-println!("Predicted class: {} ({:?})", class_name, predicted_class.data()[0]);
-```
-
-### Customer Segmentation
-
-```rust
-# extern crate rustframe;
-use rustframe::compute::models::k_means::KMeans;
-use rustframe::matrix::Matrix;
-
-// Each row: [age, annual_income]
-let customers = Matrix::from_rows_vec(
-    vec![
-        25.0, 40_000.0, 34.0, 52_000.0, 58.0, 95_000.0, 45.0, 70_000.0,
-    ],
-    4,
-    2,
-);
-
-let (model, labels) = KMeans::fit(&customers, 2, 20, 1e-4);
-
-let new_customer = Matrix::from_vec(vec![30.0, 50_000.0], 1, 2);
-let cluster = model.predict(&new_customer)[0];
-println!("New customer belongs to cluster: {}", cluster);
-println!("Cluster labels: {:?}", labels);
-```
-
-For helper functions and upcoming modules, visit the
-[utilities](./utilities.md) section.

docs/src/utilities.md

@@ -1,63 +0,0 @@
-# Utilities
-
-Utilities provide handy helpers around the core library. Existing tools
-include:
-
-- Date utilities for generating calendar sequences and business‑day sets
-- Random number generators for simulations and testing
-
-## Date Helpers
-
-```rust
-# extern crate rustframe;
-use rustframe::utils::dateutils::{BDatesList, BDateFreq, DatesList, DateFreq};
-
-// Calendar sequence
-let list = DatesList::new("2024-01-01".into(), "2024-01-03".into(), DateFreq::Daily);
-assert_eq!(list.count().unwrap(), 3);
-
-// Business days starting from 2024‑01‑02
-let bdates = BDatesList::from_n_periods("2024-01-02".into(), BDateFreq::Daily, 3).unwrap();
-assert_eq!(bdates.list().unwrap().len(), 3);
-```
-
-## Random Numbers
-
-The `random` module offers deterministic and cryptographically secure RNGs.
-
-```rust
-# extern crate rustframe;
-use rustframe::random::{Prng, Rng};
-
-let mut rng = Prng::new(42);
-let v1 = rng.next_u64();
-let v2 = rng.next_u64();
-assert_ne!(v1, v2);
-```
-
-## Stats Functions
-
-```rust
-# extern crate rustframe;
-use rustframe::matrix::Matrix;
-use rustframe::compute::stats::descriptive::{mean, median, stddev};
-
-let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
-
-let mean_value = mean(&data);
-assert_eq!(mean_value, 3.0);
-
-let median_value = median(&data);
-assert_eq!(median_value, 3.0);
-
-let std_value = stddev(&data);
-assert_eq!(std_value, 2.0_f64.sqrt());
-```
-
-Upcoming utilities will cover:
-
-- Data import/export helpers
-- Visualization adapters
-- Streaming data interfaces
-
-Contributions to these sections are welcome!

src/compute/mod.rs

@@ -1,16 +1,3 @@
-//! Algorithms and statistical utilities built on top of the core matrices.
-//!
-//! This module groups together machine‑learning models and statistical helper
-//! functions. For quick access to basic statistics see [`stats`](crate::compute::stats), while
-//! [`models`](crate::compute::models) contains small learning algorithms.
-//!
-//! ```
-//! use rustframe::compute::stats;
-//! use rustframe::matrix::Matrix;
-//!
-//! let m = Matrix::from_vec(vec![1.0, 2.0, 3.0], 3, 1);
-//! assert_eq!(stats::mean(&m), 2.0);
-//! ```
 pub mod models;
 
 pub mod stats;

src/compute/models/activations.rs

@@ -1,15 +1,3 @@
-//! Common activation functions used in neural networks.
-//!
-//! Functions operate element-wise on [`Matrix`] values.
-//!
-//! ```
-//! use rustframe::compute::models::activations::sigmoid;
-//! use rustframe::matrix::Matrix;
-//!
-//! let x = Matrix::from_vec(vec![0.0], 1, 1);
-//! let y = sigmoid(&x);
-//! assert!((y.get(0,0) - 0.5).abs() < 1e-6);
-//! ```
 use crate::matrix::{Matrix, SeriesOps};
 
 pub fn sigmoid(x: &Matrix<f64>) -> Matrix<f64> {

src/compute/models/dense_nn.rs

@@ -1,30 +1,3 @@
-//! A minimal dense neural network implementation for educational purposes.
-//!
-//! Layers operate on [`Matrix`] values and support ReLU and Sigmoid
-//! activations. This is not meant to be a performant deep‑learning framework
-//! but rather a small example of how the surrounding matrix utilities can be
-//! composed.
-//!
-//! ```
-//! use rustframe::compute::models::dense_nn::{ActivationKind, DenseNN, DenseNNConfig, InitializerKind, LossKind};
-//! use rustframe::matrix::Matrix;
-//!
-//! // Tiny network with one input and one output neuron.
-//! let config = DenseNNConfig {
-//!     input_size: 1,
-//!     hidden_layers: vec![],
-//!     output_size: 1,
-//!     activations: vec![ActivationKind::Relu],
-//!     initializer: InitializerKind::Uniform(0.5),
-//!     loss: LossKind::MSE,
-//!     learning_rate: 0.1,
-//!     epochs: 1,
-//! };
-//! let mut nn = DenseNN::new(config);
-//! let x = Matrix::from_vec(vec![1.0, 2.0], 2, 1);
-//! let y = Matrix::from_vec(vec![2.0, 3.0], 2, 1);
-//! nn.train(&x, &y);
-//! ```
 use crate::compute::models::activations::{drelu, relu, sigmoid};
 use crate::matrix::{Matrix, SeriesOps};
 use crate::random::prelude::*;

src/compute/models/gaussian_nb.rs

@@ -1,16 +1,3 @@
-//! Gaussian Naive Bayes classifier for dense matrices.
-//!
-//! ```
-//! use rustframe::compute::models::gaussian_nb::GaussianNB;
-//! use rustframe::matrix::Matrix;
-//!
-//! let x = Matrix::from_vec(vec![1.0, 2.0, 1.0, 2.0], 2, 2); // two samples
-//! let y = Matrix::from_vec(vec![0.0, 1.0], 2, 1);
-//! let mut model = GaussianNB::new(1e-9, false);
-//! model.fit(&x, &y);
-//! let preds = model.predict(&x);
-//! assert_eq!(preds.rows(), 2);
-//! ```
 use crate::matrix::Matrix;
 use std::collections::HashMap;
 

src/compute/models/k_means.rs

@@ -1,14 +1,3 @@
-//! Simple k-means clustering working on [`Matrix`] data.
-//!
-//! ```
-//! use rustframe::compute::models::k_means::KMeans;
-//! use rustframe::matrix::Matrix;
-//!
-//! let data = Matrix::from_vec(vec![1.0, 1.0, 5.0, 5.0], 2, 2);
-//! let (model, labels) = KMeans::fit(&data, 2, 10, 1e-4);
-//! assert_eq!(model.centroids.rows(), 2);
-//! assert_eq!(labels.len(), 2);
-//! ```
 use crate::compute::stats::mean_vertical;
 use crate::matrix::Matrix;
 use crate::random::prelude::*;

src/compute/models/linreg.rs

@@ -1,16 +1,3 @@
-//! Ordinary least squares linear regression.
-//!
-//! ```
-//! use rustframe::compute::models::linreg::LinReg;
-//! use rustframe::matrix::Matrix;
-//!
-//! let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
-//! let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
-//! let mut model = LinReg::new(1);
-//! model.fit(&x, &y, 0.01, 100);
-//! let preds = model.predict(&x);
-//! assert_eq!(preds.rows(), 4);
-//! ```
 use crate::matrix::{Matrix, SeriesOps};
 
 pub struct LinReg {

src/compute/models/logreg.rs

@@ -1,16 +1,3 @@
-//! Binary logistic regression classifier.
-//!
-//! ```
-//! use rustframe::compute::models::logreg::LogReg;
-//! use rustframe::matrix::Matrix;
-//!
-//! let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
-//! let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
-//! let mut model = LogReg::new(1);
-//! model.fit(&x, &y, 0.1, 100);
-//! let preds = model.predict(&x);
-//! assert_eq!(preds[(0,0)], 0.0);
-//! ```
 use crate::compute::models::activations::sigmoid;
 use crate::matrix::{Matrix, SeriesOps};
 

src/compute/models/mod.rs

@@ -1,19 +1,3 @@
-//! Lightweight machine‑learning models built on matrices.
-//!
-//! Models are intentionally minimal and operate on the [`Matrix`](crate::matrix::Matrix) type for
-//! inputs and parameters.
-//!
-//! ```
-//! use rustframe::compute::models::linreg::LinReg;
-//! use rustframe::matrix::Matrix;
-//!
-//! let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
-//! let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
-//! let mut model = LinReg::new(1);
-//! model.fit(&x, &y, 0.01, 1000);
-//! let preds = model.predict(&x);
-//! assert_eq!(preds.rows(), 4);
-//! ```
 pub mod activations;
 pub mod dense_nn;
 pub mod gaussian_nb;

src/compute/models/pca.rs

@@ -1,14 +1,3 @@
-//! Principal Component Analysis using covariance matrices.
-//!
-//! ```
-//! use rustframe::compute::models::pca::PCA;
-//! use rustframe::matrix::Matrix;
-//!
-//! let data = Matrix::from_rows_vec(vec![1.0, 1.0, 2.0, 2.0], 2, 2);
-//! let pca = PCA::fit(&data, 1, 0);
-//! let projected = pca.transform(&data);
-//! assert_eq!(projected.cols(), 1);
-//! ```
 use crate::compute::stats::correlation::covariance_matrix;
 use crate::compute::stats::descriptive::mean_vertical;
 use crate::matrix::{Axis, Matrix, SeriesOps};

src/compute/stats/correlation.rs

@@ -1,16 +1,3 @@
-//! Covariance and correlation helpers.
-//!
-//! This module provides routines for measuring the relationship between
-//! columns or rows of matrices.
-//!
-//! ```
-//! use rustframe::compute::stats::correlation;
-//! use rustframe::matrix::Matrix;
-//!
-//! let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-//! let cov = correlation::covariance(&x, &x);
-//! assert!((cov - 1.25).abs() < 1e-8);
-//! ```
 use crate::compute::stats::{mean, mean_horizontal, mean_vertical, stddev};
 use crate::matrix::{Axis, Matrix, SeriesOps};
 

src/compute/stats/descriptive.rs

@@ -1,15 +1,3 @@
-//! Descriptive statistics for matrices.
-//!
-//! Provides means, variances, medians and other aggregations computed either
-//! across the whole matrix or along a specific axis.
-//!
-//! ```
-//! use rustframe::compute::stats::descriptive;
-//! use rustframe::matrix::Matrix;
-//!
-//! let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-//! assert_eq!(descriptive::mean(&m), 2.5);
-//! ```
 use crate::matrix::{Axis, Matrix, SeriesOps};
 
 pub fn mean(x: &Matrix<f64>) -> f64 {

src/compute/stats/distributions.rs

@@ -1,16 +1,3 @@
-//! Probability distribution functions applied element-wise to matrices.
-//!
-//! Includes approximations for the normal, uniform and gamma distributions as
-//! well as the error function.
-//!
-//! ```
-//! use rustframe::compute::stats::distributions;
-//! use rustframe::matrix::Matrix;
-//!
-//! let x = Matrix::from_vec(vec![0.0], 1, 1);
-//! let pdf = distributions::normal_pdf(x.clone(), 0.0, 1.0);
-//! assert!((pdf.get(0,0) - 0.3989).abs() < 1e-3);
-//! ```
 use crate::matrix::{Matrix, SeriesOps};
 
 use std::f64::consts::PI;

src/compute/stats/inferential.rs

@@ -1,14 +1,3 @@
-//! Basic inferential statistics such as t‑tests and chi‑square tests.
-//!
-//! ```
-//! use rustframe::compute::stats::inferential;
-//! use rustframe::matrix::Matrix;
-//!
-//! let a = Matrix::from_vec(vec![1.0, 2.0], 2, 1);
-//! let b = Matrix::from_vec(vec![1.1, 1.9], 2, 1);
-//! let (t, _p) = inferential::t_test(&a, &b);
-//! assert!(t.abs() < 1.0);
-//! ```
 use crate::matrix::{Matrix, SeriesOps};
 
 use crate::compute::stats::{gamma_cdf, mean, sample_variance};

src/compute/stats/mod.rs

@@ -1,16 +1,3 @@
-//! Statistical routines for matrices.
-//!
-//! Functions are grouped into submodules for descriptive statistics,
-//! correlations, probability distributions and basic inferential tests.
-//!
-//! ```
-//! use rustframe::compute::stats;
-//! use rustframe::matrix::Matrix;
-//!
-//! let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
-//! let cov = stats::covariance(&m, &m);
-//! assert!((cov - 1.25).abs() < 1e-8);
-//! ```
 pub mod correlation;
 pub mod descriptive;
 pub mod distributions;

src/dataframe/df.rs

@@ -0,0 +1,659 @@
+use crate::frame::{Frame, RowIndex};
+use std::any::{Any, TypeId};
+use std::collections::HashMap;
+use std::fmt; // Import TypeId
+
+const DEFAULT_DISPLAY_ROWS: usize = 5;
+const DEFAULT_DISPLAY_COLS: usize = 10;
+
+// Trait to enable type-agnostic operations on Frame objects within DataFrame
+pub trait SubFrame: Send + Sync + fmt::Debug + Any {
+    fn rows(&self) -> usize;
+    fn get_value_as_string(&self, physical_row_idx: usize, col_name: &str) -> String;
+    fn clone_box(&self) -> Box<dyn SubFrame>;
+    fn delete_column_from_frame(&mut self, col_name: &str);
+    fn get_frame_cols(&self) -> usize; // Add a method to get the number of columns in the underlying frame
+
+    // Methods for downcasting to concrete types
+    fn as_any(&self) -> &dyn Any;
+    fn as_any_mut(&mut self) -> &mut dyn Any;
+}
+
+// Implement SubFrame for any Frame<T> that meets the requirements
+impl<T> SubFrame for Frame<T>
+where
+    T: Clone + PartialEq + fmt::Display + fmt::Debug + 'static + Send + Sync + Any,
+{
+    fn rows(&self) -> usize {
+        self.rows()
+    }
+
+    fn get_value_as_string(&self, physical_row_idx: usize, col_name: &str) -> String {
+        self.get_row(physical_row_idx).get(col_name).to_string()
+    }
+
+    fn clone_box(&self) -> Box<dyn SubFrame> {
+        Box::new(self.clone())
+    }
+
+    fn delete_column_from_frame(&mut self, col_name: &str) {
+        self.delete_column(col_name);
+    }
+
+    fn get_frame_cols(&self) -> usize {
+        self.cols()
+    }
+
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn as_any_mut(&mut self) -> &mut dyn Any {
+        self
+    }
+}
+
+pub struct DataFrame {
+    frames_by_type: HashMap<TypeId, Box<dyn SubFrame>>, // Maps TypeId to the Frame holding columns of that type
+    column_to_type: HashMap<String, TypeId>,            // Maps column name to its TypeId
+    column_names: Vec<String>,
+    index: RowIndex,
+}
+
+impl DataFrame {
+    pub fn new() -> Self {
+        DataFrame {
+            frames_by_type: HashMap::new(),
+            column_to_type: HashMap::new(),
+            column_names: Vec::new(),
+            index: RowIndex::Range(0..0), // Initialize with an empty range index
+        }
+    }
+
+    /// Returns the number of rows in the DataFrame.
+    pub fn rows(&self) -> usize {
+        self.index.len()
+    }
+
+    /// Returns the number of columns in the DataFrame.
+    pub fn cols(&self) -> usize {
+        self.column_names.len()
+    }
+
+    /// Returns a reference to the vector of column names.
+    pub fn get_column_names(&self) -> &Vec<String> {
+        &self.column_names
+    }
+
+    /// Returns the number of internal Frame objects (one per unique data type).
+    pub fn num_internal_frames(&self) -> usize {
+        self.frames_by_type.len()
+    }
+
+    /// Returns a reference to a column of a specific type, if it exists.
+    pub fn get_column<T>(&self, col_name: &str) -> Option<&[T]>
+    where
+        T: Clone + PartialEq + fmt::Display + fmt::Debug + 'static + Send + Sync + Any,
+    {
+        let expected_type_id = TypeId::of::<T>();
+        if let Some(actual_type_id) = self.column_to_type.get(col_name) {
+            if *actual_type_id == expected_type_id {
+                if let Some(sub_frame_box) = self.frames_by_type.get(actual_type_id) {
+                    if let Some(frame) = sub_frame_box.as_any().downcast_ref::<Frame<T>>() {
+                        return Some(frame.column(col_name));
+                    }
+                }
+            }
+        }
+        None
+    }
+
+    /// Returns a HashMap representing a row, mapping column names to their string values.
+    pub fn get_row(&self, row_idx: usize) -> Option<HashMap<String, String>> {
+        if row_idx >= self.rows() {
+            return None;
+        }
+
+        let mut row_data = HashMap::new();
+        for col_name in &self.column_names {
+            if let Some(type_id) = self.column_to_type.get(col_name) {
+                if let Some(sub_frame_box) = self.frames_by_type.get(type_id) {
+                    let value = sub_frame_box.get_value_as_string(row_idx, col_name);
+                    row_data.insert(col_name.clone(), value);
+                }
+            }
+        }
+        Some(row_data)
+    }
+
+    pub fn add_column<T>(&mut self, col_name: &str, data: Vec<T>)
+    where
+        T: Clone + PartialEq + fmt::Display + fmt::Debug + 'static + Send + Sync + Any,
+    {
+        let type_id = TypeId::of::<T>();
+        let col_name_string = col_name.to_string();
+
+        // Check for duplicate column name across the entire DataFrame
+        if self.column_to_type.contains_key(&col_name_string) {
+            panic!(
+                "DataFrame::add_column: duplicate column name: '{}'",
+                col_name_string
+            );
+        }
+
+        // If this is the first column being added, set the DataFrame's index
+        if self.column_names.is_empty() {
+            self.index = RowIndex::Range(0..data.len());
+        } else {
+            // Ensure new column has the same number of rows as existing columns
+            if data.len() != self.index.len() {
+                panic!(
+                    "DataFrame::add_column: new column '{}' has {} rows, but existing columns have {} rows",
+                    col_name_string,
+                    data.len(),
+                    self.index.len()
+                );
+            }
+        }
+
+        // Check if a Frame of this type already exists
+        if let Some(sub_frame_box) = self.frames_by_type.get_mut(&type_id) {
+            // Downcast to the concrete Frame<T> and add the column
+            if let Some(frame) = sub_frame_box.as_any_mut().downcast_mut::<Frame<T>>() {
+                frame.add_column(col_name_string.clone(), data);
+            } else {
+                // This should ideally not happen if TypeId matches, but good for safety
+                panic!(
+                    "Type mismatch when downcasting existing SubFrame for TypeId {:?}",
+                    type_id
+                );
+            }
+        } else {
+            // No Frame of this type exists, create a new one
+            // The Frame::new constructor expects a Matrix and column names.
+            // We create a Matrix from a single column vector.
+            let new_frame = Frame::new(
+                crate::matrix::Matrix::from_cols(vec![data]),
+                vec![col_name_string.clone()],
+                Some(self.index.clone()), // Pass the DataFrame's index to the new Frame
+            );
+            self.frames_by_type.insert(type_id, Box::new(new_frame));
+        }
+
+        // Update column mappings and names
+        self.column_to_type.insert(col_name_string.clone(), type_id);
+        self.column_names.push(col_name_string);
+    }
+
+    /// Drops a column from the DataFrame.
+    /// Panics if the column does not exist.
+    pub fn drop_column(&mut self, col_name: &str) {
+        let col_name_string = col_name.to_string();
+
+        // 1. Get the TypeId associated with the column
+        let type_id = self
+            .column_to_type
+            .remove(&col_name_string)
+            .unwrap_or_else(|| {
+                panic!(
+                    "DataFrame::drop_column: column '{}' not found",
+                    col_name_string
+                );
+            });
+
+        // 2. Remove the column name from the ordered list
+        self.column_names.retain(|name| name != &col_name_string);
+
+        // 3. Find the Frame object and delete the column from it
+        if let Some(sub_frame_box) = self.frames_by_type.get_mut(&type_id) {
+            sub_frame_box.delete_column_from_frame(&col_name_string);
+
+            // 4. If the Frame object for this type becomes empty, remove it from frames_by_type
+            if sub_frame_box.get_frame_cols() == 0 {
+                self.frames_by_type.remove(&type_id);
+            }
+        } else {
+            // This should not happen if column_to_type was consistent
+            panic!(
+                "DataFrame::drop_column: internal error, no frame found for type_id {:?}",
+                type_id
+            );
+        }
+    }
+}
+
+impl fmt::Display for DataFrame {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        // Display column headers
+        for col_name in self.column_names.iter().take(DEFAULT_DISPLAY_COLS) {
+            write!(f, "{:<15}", col_name)?;
+        }
+        if self.column_names.len() > DEFAULT_DISPLAY_COLS {
+            write!(f, "...")?;
+        }
+        writeln!(f)?;
+
+        // Display data rows
+        let mut displayed_rows = 0;
+        for i in 0..self.index.len() {
+            if displayed_rows >= DEFAULT_DISPLAY_ROWS {
+                writeln!(f, "...")?;
+                break;
+            }
+            for col_name in self.column_names.iter().take(DEFAULT_DISPLAY_COLS) {
+                if let Some(type_id) = self.column_to_type.get(col_name) {
+                    if let Some(sub_frame_box) = self.frames_by_type.get(type_id) {
+                        write!(f, "{:<15}", sub_frame_box.get_value_as_string(i, col_name))?;
+                    } else {
+                        // This case indicates an inconsistency: column_to_type has an entry,
+                        // but frames_by_type doesn't have the corresponding Frame.
+                        write!(f, "{:<15}", "[ERROR]")?;
+                    }
+                } else {
+                    // This case indicates an inconsistency: column_names has an entry,
+                    // but column_to_type doesn't have the corresponding column.
+                    write!(f, "{:<15}", "[ERROR]")?;
+                }
+            }
+            if self.column_names.len() > DEFAULT_DISPLAY_COLS {
+                write!(f, "...")?;
+            }
+            writeln!(f)?;
+            displayed_rows += 1;
+        }
+        Ok(())
+    }
+}
+
+impl fmt::Debug for DataFrame {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        f.debug_struct("DataFrame")
+            .field("column_names", &self.column_names)
+            .field("index", &self.index)
+            .field("column_to_type", &self.column_to_type)
+            .field("frames_by_type", &self.frames_by_type)
+            .finish()
+    }
+}
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::frame::Frame;
+    use crate::matrix::Matrix;
+
+    #[test]
+    fn test_dataframe_new() {
+        let df = DataFrame::new();
+        assert_eq!(df.rows(), 0);
+        assert_eq!(df.cols(), 0);
+        assert!(df.get_column_names().is_empty());
+        assert!(df.frames_by_type.is_empty());
+        assert!(df.column_to_type.is_empty());
+    }
+
+    #[test]
+    fn test_dataframe_add_column_initial() {
+        let mut df = DataFrame::new();
+        let data = vec![1, 2, 3];
+        df.add_column("col_int", data.clone());
+
+        assert_eq!(df.rows(), 3);
+        assert_eq!(df.cols(), 1);
+        assert_eq!(df.get_column_names(), &vec!["col_int".to_string()]);
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>()));
+        assert_eq!(df.column_to_type.get("col_int"), Some(&TypeId::of::<i32>()));
+
+        // Verify the underlying frame
+        let sub_frame_box = df.frames_by_type.get(&TypeId::of::<i32>()).unwrap();
+        let frame = sub_frame_box.as_any().downcast_ref::<Frame<i32>>().unwrap();
+        assert_eq!(frame.rows(), 3);
+        assert_eq!(frame.cols(), 1);
+        assert_eq!(frame.columns(), &vec!["col_int".to_string()]);
+    }
+
+    #[test]
+    fn test_dataframe_add_column_same_type() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int1", vec![1, 2, 3]);
+        df.add_column("col_int2", vec![4, 5, 6]);
+
+        assert_eq!(df.rows(), 3);
+        assert_eq!(df.cols(), 2);
+        assert_eq!(
+            df.get_column_names(),
+            &vec!["col_int1".to_string(), "col_int2".to_string()]
+        );
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>()));
+        assert_eq!(
+            df.column_to_type.get("col_int1"),
+            Some(&TypeId::of::<i32>())
+        );
+        assert_eq!(
+            df.column_to_type.get("col_int2"),
+            Some(&TypeId::of::<i32>())
+        );
+
+        // Verify the underlying frame
+        let sub_frame_box = df.frames_by_type.get(&TypeId::of::<i32>()).unwrap();
+        let frame = sub_frame_box.as_any().downcast_ref::<Frame<i32>>().unwrap();
+        assert_eq!(frame.rows(), 3);
+        assert_eq!(frame.cols(), 2);
+        assert_eq!(
+            frame.columns(),
+            &vec!["col_int1".to_string(), "col_int2".to_string()]
+        );
+    }
+
+    #[test]
+    fn test_dataframe_add_column_different_type() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int", vec![1, 2, 3]);
+        df.add_column("col_float", vec![1.1, 2.2, 3.3]);
+        df.add_column(
+            "col_string",
+            vec!["a".to_string(), "b".to_string(), "c".to_string()],
+        );
+
+        assert_eq!(df.rows(), 3);
+        assert_eq!(df.cols(), 3);
+        assert_eq!(
+            df.get_column_names(),
+            &vec![
+                "col_int".to_string(),
+                "col_float".to_string(),
+                "col_string".to_string()
+            ]
+        );
+
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>()));
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<f64>()));
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<String>()));
+
+        assert_eq!(df.column_to_type.get("col_int"), Some(&TypeId::of::<i32>()));
+        assert_eq!(
+            df.column_to_type.get("col_float"),
+            Some(&TypeId::of::<f64>())
+        );
+        assert_eq!(
+            df.column_to_type.get("col_string"),
+            Some(&TypeId::of::<String>())
+        );
+
+        // Verify underlying frames
+        let int_frame = df
+            .frames_by_type
+            .get(&TypeId::of::<i32>())
+            .unwrap()
+            .as_any()
+            .downcast_ref::<Frame<i32>>()
+            .unwrap();
+        assert_eq!(int_frame.columns(), &vec!["col_int".to_string()]);
+
+        let float_frame = df
+            .frames_by_type
+            .get(&TypeId::of::<f64>())
+            .unwrap()
+            .as_any()
+            .downcast_ref::<Frame<f64>>()
+            .unwrap();
+        assert_eq!(float_frame.columns(), &vec!["col_float".to_string()]);
+
+        let string_frame = df
+            .frames_by_type
+            .get(&TypeId::of::<String>())
+            .unwrap()
+            .as_any()
+            .downcast_ref::<Frame<String>>()
+            .unwrap();
+        assert_eq!(string_frame.columns(), &vec!["col_string".to_string()]);
+    }
+
+    #[test]
+    fn test_dataframe_get_column() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int", vec![1, 2, 3]);
+        df.add_column("col_float", vec![1.1, 2.2, 3.3]);
+        df.add_column(
+            "col_string",
+            vec!["a".to_string(), "b".to_string(), "c".to_string()],
+        );
+
+        // Test getting existing columns with correct type
+        assert_eq!(
+            df.get_column::<i32>("col_int").unwrap(),
+            vec![1, 2, 3].as_slice()
+        );
+        assert_eq!(
+            df.get_column::<f64>("col_float").unwrap(),
+            vec![1.1, 2.2, 3.3].as_slice()
+        );
+        assert_eq!(
+            df.get_column::<String>("col_string").unwrap(),
+            vec!["a".to_string(), "b".to_string(), "c".to_string()].as_slice()
+        );
+
+        // Test getting non-existent column
+        assert_eq!(df.get_column::<i32>("non_existent"), None);
+
+        // Test getting existing column with incorrect type
+        assert_eq!(df.get_column::<f64>("col_int"), None);
+        assert_eq!(df.get_column::<i32>("col_float"), None);
+    }
+
+    #[test]
+    fn test_dataframe_get_row() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int", vec![1, 2, 3]);
+        df.add_column("col_float", vec![1.1, 2.2, 3.3]);
+        df.add_column(
+            "col_string",
+            vec!["a".to_string(), "b".to_string(), "c".to_string()],
+        );
+
+        // Test getting an existing row
+        let row0 = df.get_row(0).unwrap();
+        assert_eq!(row0.get("col_int"), Some(&"1".to_string()));
+        assert_eq!(row0.get("col_float"), Some(&"1.1".to_string()));
+        assert_eq!(row0.get("col_string"), Some(&"a".to_string()));
+
+        let row1 = df.get_row(1).unwrap();
+        assert_eq!(row1.get("col_int"), Some(&"2".to_string()));
+        assert_eq!(row1.get("col_float"), Some(&"2.2".to_string()));
+        assert_eq!(row1.get("col_string"), Some(&"b".to_string()));
+
+        // Test getting an out-of-bounds row
+        assert_eq!(df.get_row(3), None);
+    }
+
+    #[test]
+    #[should_panic(expected = "DataFrame::add_column: duplicate column name: 'col_int'")]
+    fn test_dataframe_add_column_duplicate_name() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int", vec![1, 2, 3]);
+        df.add_column("col_int", vec![4, 5, 6]);
+    }
+
+    #[test]
+    #[should_panic(
+        expected = "DataFrame::add_column: new column 'col_int2' has 2 rows, but existing columns have 3 rows"
+    )]
+    fn test_dataframe_add_column_mismatched_rows() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int1", vec![1, 2, 3]);
+        df.add_column("col_int2", vec![4, 5]);
+    }
+
+    #[test]
+    fn test_dataframe_display() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int", vec![1, 2, 3, 4, 5, 6]);
+        df.add_column("col_float", vec![1.1, 2.2, 3.3, 4.4, 5.5, 6.6]);
+        df.add_column(
+            "col_string",
+            vec![
+                "a".to_string(),
+                "b".to_string(),
+                "c".to_string(),
+                "d".to_string(),
+                "e".to_string(),
+                "f".to_string(),
+            ],
+        );
+
+        let expected_output = "\
+col_int        col_float      col_string     
+1              1.1            a              
+2              2.2            b              
+3              3.3            c              
+4              4.4            d              
+5              5.5            e              
+...
+";
+        assert_eq!(format!("{}", df), expected_output);
+    }
+
+    #[test]
+    fn test_dataframe_debug() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int", vec![1, 2, 3]);
+        df.add_column("col_float", vec![1.1, 2.2, 3.3]);
+
+        let debug_output = format!("{:?}", df);
+        assert!(debug_output.contains("DataFrame {"));
+        assert!(debug_output.contains("column_names: [\"col_int\", \"col_float\"]"));
+        assert!(debug_output.contains("index: Range(0..3)"));
+        assert!(debug_output.contains("column_to_type: {"));
+        assert!(debug_output.contains("frames_by_type: {"));
+    }
+
+    #[test]
+    fn test_dataframe_drop_column_single_type() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int1", vec![1, 2, 3]);
+        df.add_column("col_int2", vec![4, 5, 6]);
+        df.add_column("col_float", vec![1.1, 2.2, 3.3]);
+
+        assert_eq!(df.cols(), 3);
+        assert_eq!(
+            df.get_column_names(),
+            &vec![
+                "col_int1".to_string(),
+                "col_int2".to_string(),
+                "col_float".to_string()
+            ]
+        );
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>()));
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<f64>()));
+
+        df.drop_column("col_int1");
+
+        assert_eq!(df.cols(), 2);
+        assert_eq!(
+            df.get_column_names(),
+            &vec!["col_int2".to_string(), "col_float".to_string()]
+        );
+        assert!(df.column_to_type.get("col_int1").is_none());
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>())); // Frame<i32> should still exist
+        let int_frame = df
+            .frames_by_type
+            .get(&TypeId::of::<i32>())
+            .unwrap()
+            .as_any()
+            .downcast_ref::<Frame<i32>>()
+            .unwrap();
+        assert_eq!(int_frame.columns(), &vec!["col_int2".to_string()]);
+
+        df.drop_column("col_int2");
+
+        assert_eq!(df.cols(), 1);
+        assert_eq!(df.get_column_names(), &vec!["col_float".to_string()]);
+        assert!(df.column_to_type.get("col_int2").is_none());
+        assert!(!df.frames_by_type.contains_key(&TypeId::of::<i32>())); // Frame<i32> should be removed
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<f64>()));
+    }
+
+    #[test]
+    fn test_dataframe_drop_column_mixed_types() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int", vec![1, 2, 3]);
+        df.add_column("col_float", vec![1.1, 2.2, 3.3]);
+        df.add_column(
+            "col_string",
+            vec!["a".to_string(), "b".to_string(), "c".to_string()],
+        );
+
+        assert_eq!(df.cols(), 3);
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>()));
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<f64>()));
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<String>()));
+
+        df.drop_column("col_float");
+
+        assert_eq!(df.cols(), 2);
+        assert_eq!(
+            df.get_column_names(),
+            &vec!["col_int".to_string(), "col_string".to_string()]
+        );
+        assert!(df.column_to_type.get("col_float").is_none());
+        assert!(!df.frames_by_type.contains_key(&TypeId::of::<f64>())); // Frame<f64> should be removed
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>()));
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<String>()));
+
+        df.drop_column("col_int");
+        df.drop_column("col_string");
+
+        assert_eq!(df.cols(), 0);
+        assert!(df.get_column_names().is_empty());
+        assert!(df.frames_by_type.is_empty());
+        assert!(df.column_to_type.is_empty());
+    }
+
+    #[test]
+    #[should_panic(expected = "DataFrame::drop_column: column 'non_existent' not found")]
+    fn test_dataframe_drop_column_non_existent() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int", vec![1, 2, 3]);
+        df.drop_column("non_existent");
+    }
+
+    #[test]
+    fn test_dataframe_add_column_reuses_existing_frame() {
+        let mut df = DataFrame::new();
+        df.add_column("col_int1", vec![1, 2, 3]);
+        df.add_column("col_float1", vec![1.1, 2.2, 3.3]);
+
+        // Initially, there should be two frames (one for i32, one for f64)
+        assert_eq!(df.frames_by_type.len(), 2);
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>()));
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<f64>()));
+
+        // Add another integer column
+        df.add_column("col_int2", vec![4, 5, 6]);
+
+        // The number of frames should still be 2, as the existing i32 frame should be reused
+        assert_eq!(df.frames_by_type.len(), 2);
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>()));
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<f64>()));
+
+        // Verify the i32 frame now contains both integer columns
+        let int_frame = df.frames_by_type.get(&TypeId::of::<i32>()).unwrap().as_any().downcast_ref::<Frame<i32>>().unwrap();
+        assert_eq!(int_frame.columns(), &vec!["col_int1".to_string(), "col_int2".to_string()]);
+        assert_eq!(int_frame.cols(), 2);
+
+        // Add another float column
+        df.add_column("col_float2", vec![4.4, 5.5, 6.6]);
+
+        // The number of frames should still be 2, as the existing f64 frame should be reused
+        assert_eq!(df.frames_by_type.len(), 2);
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<i32>()));
+        assert!(df.frames_by_type.contains_key(&TypeId::of::<f64>()));
+
+        // Verify the f64 frame now contains both float columns
+        let float_frame = df.frames_by_type.get(&TypeId::of::<f64>()).unwrap().as_any().downcast_ref::<Frame<f64>>().unwrap();
+        assert_eq!(float_frame.columns(), &vec!["col_float1".to_string(), "col_float2".to_string()]);
+        assert_eq!(float_frame.cols(), 2);
+    }
+}

src/dataframe/mod.rs

@@ -0,0 +1,4 @@
+//! This module provides the DataFrame structure for handling tabular data with mixed types.
+pub mod df;
+
+pub use df::{DataFrame, SubFrame};

src/frame/base.rs

@@ -1,19 +1,3 @@
-//! Core data-frame structures such as [`Frame`] and [`RowIndex`].
-//!
-//! The [`Frame`] type stores column-labelled data with an optional row index
-//! and builds upon the [`crate::matrix::Matrix`] type.
-//!
-//! # Examples
-//!
-//! ```
-//! use rustframe::frame::{Frame, RowIndex};
-//! use rustframe::matrix::Matrix;
-//!
-//! let data = Matrix::from_cols(vec![vec![1, 2], vec![3, 4]]);
-//! let frame = Frame::new(data, vec!["L", "R"], Some(RowIndex::Int(vec![10, 20])));
-//! assert_eq!(frame.columns(), &["L", "R"]);
-//! assert_eq!(frame.index(), &RowIndex::Int(vec![10, 20]));
-//! ```
 use crate::matrix::Matrix;
 use chrono::NaiveDate;
 use std::collections::HashMap;
@@ -332,7 +316,7 @@ impl<T: Clone + PartialEq> Frame<T> {
         )
     }
 
-    /// Returns an immutable slice of the specified column's data.
+    /// Returns an immutable slice of the specified column's data by name.
     /// Panics if the column name is not found.
     pub fn column(&self, name: &str) -> &[T] {
         let idx = self
@@ -341,7 +325,13 @@ impl<T: Clone + PartialEq> Frame<T> {
         self.matrix.column(idx)
     }
 
-    /// Returns a mutable slice of the specified column's data.
+    /// Returns an immutable slice of the specified column's data by its physical index.
+    /// Panics if the index is out of bounds.
+    pub fn column_by_physical_idx(&self, idx: usize) -> &[T] {
+        self.matrix.column(idx)
+    }
+
+    /// Returns a mutable slice of the specified column's data by name.
     /// Panics if the column name is not found.
     pub fn column_mut(&mut self, name: &str) -> &mut [T] {
         let idx = self
@@ -350,6 +340,12 @@ impl<T: Clone + PartialEq> Frame<T> {
         self.matrix.column_mut(idx)
     }
 
+    /// Returns a mutable slice of the specified column's data by its physical index.
+    /// Panics if the index is out of bounds.
+    pub fn column_mut_by_physical_idx(&mut self, idx: usize) -> &mut [T] {
+        self.matrix.column_mut(idx)
+    }
+
     // Row access methods
 
     /// Returns an immutable view of the row for the given integer key.

src/frame/mod.rs

@@ -1,21 +1,3 @@
-//! High-level interface for working with columnar data and row indices.
-//!
-//! The [`Frame`](crate::frame::Frame) type combines a matrix with column labels and a typed row
-//! index, similar to data frames in other data-analysis libraries.
-//!
-//! # Examples
-//!
-//! ```
-//! use rustframe::frame::{Frame, RowIndex};
-//! use rustframe::matrix::Matrix;
-//!
-//! // Build a frame from two columns labelled "A" and "B".
-//! 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);
-//!
-//! assert_eq!(frame["A"], vec![1.0, 2.0]);
-//! assert_eq!(frame.index(), &RowIndex::Range(0..2));
-//! ```
 pub mod base;
 pub mod ops;
 

src/frame/ops.rs

@@ -1,16 +1,3 @@
-//! Trait implementations that allow [`Frame`] to reuse matrix operations.
-//!
-//! These modules forward numeric and boolean aggregation methods from the
-//! underlying [`Matrix`](crate::matrix::Matrix) type so that they can be called
-//! directly on a [`Frame`].
-//!
-//! ```
-//! use rustframe::frame::Frame;
-//! use rustframe::matrix::{Matrix, SeriesOps};
-//!
-//! let frame = Frame::new(Matrix::from_cols(vec![vec![1.0, 2.0]]), vec!["A"], None);
-//! assert_eq!(frame.sum_vertical(), vec![3.0]);
-//! ```
 use crate::frame::Frame;
 use crate::matrix::{Axis, BoolMatrix, BoolOps, FloatMatrix, SeriesOps};
 

src/lib.rs

@@ -1,5 +1,8 @@
 #![doc = include_str!("../README.md")]
 
+/// Documentation for the [`crate::dataframe`] module.
+pub mod dataframe;
+
 /// Documentation for the [`crate::matrix`] module.
 pub mod matrix;
 

src/matrix/boolops.rs

@@ -1,14 +1,3 @@
-//! Logical reductions for boolean matrices.
-//!
-//! The [`BoolOps`] trait mirrors common boolean aggregations such as `any` and
-//! `all` over rows or columns of a [`BoolMatrix`].
-//!
-//! ```
-//! use rustframe::matrix::{BoolMatrix, BoolOps};
-//!
-//! let m = BoolMatrix::from_vec(vec![true, false], 2, 1);
-//! assert!(m.any());
-//! ```
 use crate::matrix::{Axis, BoolMatrix};
 
 /// Boolean operations on `Matrix<bool>`

src/matrix/mod.rs

@@ -1,18 +1,3 @@
-//! Core matrix types and operations.
-//!
-//! The [`Matrix`](crate::matrix::Matrix) struct provides a simple column‑major 2D array with a
-//! suite of numeric helpers. Additional traits like [`SeriesOps`](crate::matrix::SeriesOps) and
-//! [`BoolOps`](crate::matrix::BoolOps) extend functionality for common statistics and logical reductions.
-//!
-//! # Examples
-//!
-//! ```
-//! use rustframe::matrix::Matrix;
-//!
-//! let m = Matrix::from_cols(vec![vec![1, 2], vec![3, 4]]);
-//! assert_eq!(m.shape(), (2, 2));
-//! assert_eq!(m[(0,1)], 3);
-//! ```
 pub mod boolops;
 pub mod mat;
 pub mod seriesops;

src/matrix/seriesops.rs

@@ -1,14 +1,3 @@
-//! Numeric reductions and transformations over matrix axes.
-//!
-//! [`SeriesOps`] provides methods like [`SeriesOps::sum_vertical`] or
-//! [`SeriesOps::map`] that operate on [`FloatMatrix`] values.
-//!
-//! ```
-//! use rustframe::matrix::{Matrix, SeriesOps};
-//!
-//! let m = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
-//! assert_eq!(m.sum_horizontal(), vec![4.0, 6.0]);
-//! ```
 use crate::matrix::{Axis, BoolMatrix, FloatMatrix};
 
 /// "Series-like" helpers that work along a single axis.

src/random/crypto.rs

@@ -1,13 +1,3 @@
-//! Cryptographically secure random number generator.
-//!
-//! On Unix systems this reads from `/dev/urandom`; on Windows it uses the
-//! system's preferred CNG provider.
-//!
-//! ```
-//! use rustframe::random::{crypto_rng, Rng};
-//! let mut rng = crypto_rng();
-//! let _v = rng.next_u64();
-//! ```
 #[cfg(unix)]
 use std::{fs::File, io::Read};
 

src/random/mod.rs

@@ -1,18 +1,3 @@
-//! Random number generation utilities.
-//!
-//! Provides both a simple pseudo-random generator [`Prng`](crate::random::Prng) and a
-//! cryptographically secure alternative [`CryptoRng`](crate::random::CryptoRng). The
-//! [`SliceRandom`](crate::random::SliceRandom) trait offers shuffling of slices using any RNG
-//! implementing [`Rng`](crate::random::Rng).
-//!
-//! ```
-//! use rustframe::random::{rng, SliceRandom};
-//!
-//! let mut rng = rng();
-//! let mut data = [1, 2, 3, 4];
-//! data.shuffle(&mut rng);
-//! assert_eq!(data.len(), 4);
-//! ```
 pub mod crypto;
 pub mod prng;
 pub mod random_core;

src/random/prng.rs

@@ -1,11 +1,3 @@
-//! A tiny XorShift64-based pseudo random number generator.
-//!
-//! ```
-//! use rustframe::random::{rng, Rng};
-//! let mut rng = rng();
-//! let x = rng.next_u64();
-//! assert!(x >= 0);
-//! ```
 use std::time::{SystemTime, UNIX_EPOCH};
 
 use crate::random::Rng;

src/random/random_core.rs

@@ -1,11 +1,3 @@
-//! Core traits for random number generators and sampling ranges.
-//!
-//! ```
-//! use rustframe::random::{rng, Rng};
-//! let mut r = rng();
-//! let value: f64 = r.random_range(0.0..1.0);
-//! assert!(value >= 0.0 && value < 1.0);
-//! ```
 use std::f64::consts::PI;
 use std::ops::Range;
 

src/random/seq.rs

@@ -1,11 +1,3 @@
-//! Extensions for shuffling slices with a random number generator.
-//!
-//! ```
-//! use rustframe::random::{rng, SliceRandom};
-//! let mut data = [1, 2, 3];
-//! data.shuffle(&mut rng());
-//! assert_eq!(data.len(), 3);
-//! ```
 use crate::random::Rng;
 
 /// Trait for randomizing slices.

src/utils/dateutils/dates.rs

@@ -1,10 +1,3 @@
-//! Generation and manipulation of calendar date sequences.
-//!
-//! ```
-//! use rustframe::utils::dateutils::dates::{DateFreq, DatesList};
-//! let list = DatesList::new("2024-01-01".into(), "2024-01-03".into(), DateFreq::Daily);
-//! assert_eq!(list.count().unwrap(), 3);
-//! ```
 use chrono::{Datelike, Duration, NaiveDate, Weekday};
 use std::collections::HashMap;
 use std::error::Error;

src/utils/dateutils/mod.rs

@@ -1,13 +1,3 @@
-//! Generators for sequences of calendar and business dates.
-//!
-//! See [`dates`] for all-day calendars and [`bdates`] for business-day aware
-//! variants.
-//!
-//! ```
-//! use rustframe::utils::dateutils::{DatesList, DateFreq};
-//! let list = DatesList::new("2024-01-01".into(), "2024-01-02".into(), DateFreq::Daily);
-//! assert_eq!(list.count().unwrap(), 2);
-//! ```
 pub mod bdates;
 pub mod dates;
 

src/utils/mod.rs

@@ -1,14 +1,3 @@
-//! Assorted helper utilities.
-//!
-//! Currently this module exposes date generation utilities in [`dateutils`](crate::utils::dateutils),
-//! including calendar and business date sequences.
-//!
-//! ```
-//! use rustframe::utils::DatesList;
-//! use rustframe::utils::DateFreq;
-//! let dates = DatesList::new("2024-01-01".into(), "2024-01-03".into(), DateFreq::Daily);
-//! assert_eq!(dates.count().unwrap(), 3);
-//! ```
 pub mod dateutils;
 
 pub use dateutils::{BDateFreq, BDatesGenerator, BDatesList};