Crate rustframes 

RustFrames

A blazing-fast, memory-safe alternative to NumPy + Pandas, written in Rust.

RustFrames provides:

• N-dimensional arrays with broadcasting support
• DataFrame operations with groupby, joins, and filtering
• Linear algebra operations (matrix multiplication, decompositions, etc.)
• CSV and JSON I/O with automatic type inference
• Memory-safe operations with zero-cost abstractions

Quick Start

Arrays

use rustframes::array::Array;

// Create a 2D array
let arr = Array::from_vec(vec![1.0, 2.0, 3.0, 4.0], vec![2, 2]);

// Element-wise operations with broadcasting
let scalar_mult = &arr * 2.0;

// Linear algebra
let identity = Array::<f64>::ones(vec![2, 2]);
let product = arr.dot(&identity);

// Reductions
println!("Sum: {}", arr.sum());
println!("Mean: {}", arr.mean());

DataFrames

use rustframes::dataframe::{DataFrame, Series};

// Create DataFrame
let df = DataFrame::new(vec![
    ("name".to_string(), Series::from(vec!["Alice", "Bob", "Charlie"])),
    ("age".to_string(), Series::from(vec![25, 30, 35])),
    ("score".to_string(), Series::from(vec![85.5, 92.0, 78.5])),
]);

// Operations
let filtered = df.filter(&[true, false, true]);
let sorted = df.sort_by("age", true);
let grouped = df.groupby("age").mean();

// I/O
let df_from_csv = DataFrame::from_csv("tests/data/test.csv")?;
df.to_csv("output.csv")?;

Re-exports

pub use array::Array;

pub use dataframe::core::JoinType;

pub use dataframe::DataFrame;

pub use dataframe::Series;

Modules

array

dataframe