Utah

Utah is a Rust crate backed by ndarray for type-conscious, tabular data manipulation with an expressive, functional interface.

Note: This crate works on stable. However, if you are working with dataframes with f64 data, use nightly, because you will get the performance benefits of specialization.

API currently in development and subject to change.

For an in-depth introduction to the mechanics of this crate, as well as future goals, read this blog post.

Install

Add the following to your Cargo.toml:

utah="0.1.2"

And add the following to your lib.rs or main.rs

#[macro_use]
extern crate utah

Documentation

Check out docs.rs for latest documentation.

Examples

Create dataframes on the fly

use utah::prelude::*;
let df = DataFrame<f64> = dataframe!(
    {
        "a" =>  col!([2., 3., 2.]),
        "b" =>  col!([2., NAN, 2.])
    });

let a = arr2(&[[2.0, 7.0], [3.0, 4.0]]);
let df : Result<DataFrame<f64>> = DataFrame::new(a).index(&["1", "2"]);

Transform the dataframe

use utah::prelude::*;
let df: DataFrame<f64> = DataFrame::read_csv("test.csv").unwrap();       
let res : DataFrame<f64> = df.remove(&["a", "c"], UtahAxis::Column).as_df()?;

Chain operations

use utah::prelude::*;
let df: DataFrame<f64> = DataFrame::read_csv("test.csv").unwrap();       
let res : DataFrame<f64> = df.df_iter(UtahAxis::Row)
                                     .remove(&["1"])
                                     .select(&["2"])
                                     .append("8", new_data.view())
                                     .sumdf()
                                     .as_df()?;

Support mixed types

use utah::prelude::*;
let a = DataFrame<InnerType> = dataframe!(
    {
        "name" =>  col!([InnerType::Str("Alice"),
                            InnerType::Str("Bob"),
                            InnerType::Str("Jane")]),
        "data" =>  col!([InnerType::Float(2.0),
                            InnerType::Empty(),
                            InnerType::Float(3.0)])
    });
let b: DataFrame<InnerType> = DataFrame::read_csv("test.csv").unwrap();
let res : DataFrame<InnerType> = a.concat(&b).as_df()?;