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 and String column/index labels, 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: PLACEHOLDER

Install

Add the following to your Cargo.toml:

utah="0.0.1"

extern crate utah in lib.rs and you're good to go.

Examples

Create dataframes on the fly

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

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

Transform the dataframe

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

Chain operations

use utah::prelude::*;
let df: DataFrame<f64, String> = DataFrame::read_csv("test.csv").unwrap();       
let res : DataFrame<f64, String> = 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, OuterType> = dataframe!(
    {
        "name" =>  column!([InnerType::Str("Alice"),
                            InnerType::Str("Bob"),
                            InnerType::Str("Jane")]),
        "data" =>  column!([InnerType::Float(2.0),
                            InnerType::Empty(),
                            InnerType::Float(3.0)])
    });
let b: DataFrame<InnerType, OuterType> = DataFrame::read_csv("test.csv")?;
let res : DataFrame<InnerType, OuterType> = a.left_inner_join(&b).as_df()?;