Provides multiple-dtype columner storage, known as DataFrame in pandas/R

Related tags

Data processing brassfibre
Overview

brassfibre

Provides multiple-dtype columner storage, known as DataFrame in pandas/R.

Release Documentation Build Status

Series

Single-dtype 1-dimentional vector with label (index).

Creation

extern crate brassfibre;
use brassfibre::prelude::*;

let values: Vec<i64> = vec![1, 2, 3, 4, 3];
let index: Vec<i64> = vec![10, 20, 30, 40, 50];
let s = Series::<i64, i64>::new(values, index);

println!("{:?}", &s);
// 10 1
// 20 2
// 30 3
// 40 4
// 50 3

Selection

println!("{:?}", &s.locs(&vec![10, 40, 50]));
// 10 1
// 40 4
// 50 3

println!("{:?}", &s.ilocs(&vec![2, 3, 4]));
// 30 3
// 40 4
// 50 3

Calculation

println!("{:?}", &(&b + 5));
// 10 2
// 20 3
// 30 4
// 40 5
// 50 4

println!("{:?}", &s.sum());
// 13

println!("{:?}", &s.describe());
// count                 5
//  mean               2.6
//   std 1.019803902718557
//   min                 1
//   max                 4

println!("{:?}", s.value_counts());
// 3 2
// 2 1
// 1 1
// 4 1

Group By

let sg = s.groupby(vec![1, 1, 1, 2, 2]);

println!("{:?}", sg.get_group(&1));
// 10 1
// 20 2
// 30 3

println!("{:?}", sg.sum());
// 1 6
// 2 7

DataFrame

Multiple-dtype 2-dimentional vector with labels (index and columns).

Creation

#[macro_use]
extern crate brassfibre;
use brassfibre::prelude::*;

let values = vec![array![1, 2, 3, 4, 5],
                  array![6.1, 7.1, 8.1, 9.1, 10.1],
                  array![11, 12, 13, 14, 15]];
let df = DataFrame::from_vec(values,
                             vec![10, 20, 30, 40, 50],
                             vec!["X", "YYY", "ZZ"]);
println!("{:?}", &df);
//    X  YYY ZZ
// 10 1  6.1 11
// 20 2  7.1 12
// 30 3  8.1 13
// 40 4  9.1 14
// 50 5 10.1 15

Selection

by multiple index labels

println!("{:?}", &df.locs(&vec![20, 30, 40]));
//    X YYY ZZ
// 20 2 7.1 12
// 30 3 8.1 13
// 40 4 9.1 14

by multiple index locations

println!("{:?}", &df.ilocs(&vec![0, 2, 1]));
//    X YYY ZZ
// 10 1 6.1 11
// 30 3 8.1 13
// 20 2 7.1 12

Group By

get group

let dg = df.groupby(vec!["A", "A", "B", "A", "B"]);
println!("{:?}", &dg.get_group(&"A"));
//    X YYY ZZ
// 10 1 6.1 11
// 20 2 7.1 12
// 40 4 9.1 14

Reshaping

inner join

let values2 = vec![array![1.1, 2.1, 3.1],
                   array![6, 7, 8]];
let df2 = DataFrame::from_vec(values2,
                              vec![20, 30, 40],
                              vec!["X2", "Y2"]);
let j = df.join_inner(&df2);
println!("{:?}", &j);
//    X YYY ZZ  X2 Y2
// 20 2 7.1 12 1.1  6
// 30 3 8.1 13 2.1  7
// 40 4 9.1 14 3.1  8
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Comments
  • Fixes some issues found by clippy

    Fixes some issues found by clippy

    Fixes some issues found by clippy. The only changes in the usage of the library is that group_by now uses a slice instead of a Vec. I think this is a good change, no need for a Vec here

    opened by JordiPolo 1
  • Updates of other crates and fixes

    Updates of other crates and fixes

    • Fixes warnings (unused parameters and variables)
    • Updates dependencies and related code. Most notably csv to 1.0 which does not use rusc-serialize anymore. And that brought me to update rust-nullvec also.

    This will not compile till this is merged https://github.com/sinhrks/rust-nullvec/pull/6 and nullvec 0.2.0 is released

    opened by JordiPolo 1
  • A better way to access individual elements?

    A better way to access individual elements?

    Currently, to get a single element out of a Dataframe, I use a macro like the following:

    macro_rules! get_df_value {
    ($df: ident, $what: expr, $x: expr) => {
        {
            let x = $df.gets(&[String::from($what)]);
            x.values[0].iloc(&$x).into()
        }
    }
}

    Unfortunately, that isn't the full story, since type inference doesn't always work with this, so sometimes, I need to additionally do the following:

    let t1: f64 = get_df_value!(data, "col1", row);
let t2: f64 = get_df_value!(data, "col2", row);
println!("{}", (t1 - t2).abs());

    This is a lot to just access and use single values, which I would assume is a very common action. At least, it is very common for me.

    Best, Rob

    opened by rennis250 2
Releases(v0.1.0)
Owner
Sinhrks
Sinhrks
GitHub
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