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Dataframe structure and operations in Rust

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- - bench_creation.rs
  - bench_sum.rs
  - mod.rs
- - aggregate.rs
  - interact.rs
  - mod.rs
  - process.rs
  - transform.rs
- - generic_dataframe.rs
  - generic_hashmap.rs
  - mixed_dataframe_experimentation.rs
  - specialization.rs
- - impl_constructor_generic.rs
  - impl_empty.rs
  - impl_ops_f64_string.rs
  - impl_ops_generic.rs
  - mod.rs
- - mod.rs
  - test.csv
  - tests.rs
- - error.rs
  - macros.rs
  - mod.rs
  - readcsv.rs
  - traits.rs
  - types.rs
- dataframe.rs
- lib.rs
- mixedtypes.rs
- prelude.rs
- test_main.rs
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README.md

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# Utah

[![Build Status](https://travis-ci.org/pegasos1/utah.svg?branch=master)](https://travis-ci.org/pegasos1/utah)

[**Utah**](http://crates.io/crates/utah) is a Rust crate backed by [ndarray](https://github.com/bluss/rust-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](http://kernelmachine.github.io/2016/12/28/Introducing-Utah/) 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](http://docs.rs/utah) for latest documentation.

## Examples

#### Create dataframes on the fly

```rust
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

```rust
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

```rust
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

```rust
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()?;
```

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