Crate polars

Polars DataFrames in Rust

WIP

Read csv

use polars::prelude::*;
use std::fs::File;

fn example() -> Result<DataFrame> {
    let file = File::open("iris.csv").expect("could not open file");

    CsvReader::new(file)
            .infer_schema(None)
            .has_header(true)
            .finish()
}

Join

use polars::prelude::*;

// Create first df.
let s0 = Series::init("days", [0, 1, 2, 3, 4].as_ref());
let s1 = Series::init("temp", [22.1, 19.9, 7., 2., 3.].as_ref());
let temp = DataFrame::new_from_columns(vec![s0, s1]).unwrap();

// Create second df.
let s0 = Series::init("days", [1, 2].as_ref());
let s1 = Series::init("rain", [0.1, 0.2].as_ref());
let rain = DataFrame::new_from_columns(vec![s0, s1]).unwrap();

// Left join on days column.
let joined = temp.left_join(&rain, "days", "days");
println!("{}", joined.unwrap())

GroupBy

use polars::prelude::*;
fn groupby_sum(df: &DataFrame) -> Result<DataFrame> {
    df.groupby("column_name")?
    .select("agg_column_name")
    .sum()
}

Arithmetic

use polars::prelude::*;
let s: Series = [1, 2, 3].iter().collect();
let s_squared = &s * &s;

Rust iterators

use polars::prelude::*;

let s: Series = [1, 2, 3].iter().collect();
let s_squared: Series = s.i32()
     .expect("datatype mismatch")
     .iter()
     .map(|optional_v| {
         match optional_v {
             Some(v) => Some(v * v),
             None => None, // null value
         }
 }).collect();

Comparison

use polars::prelude::*;
use itertools::Itertools;
let s = Series::init("dollars", [1, 2, 3].as_ref());
let mask = s.eq(1).expect("could not compare types");
let valid = [true, false, false].iter();
assert!(mask
    .iter()
    .map(|opt_bool| opt_bool.unwrap()) // option, because series can be null
    .zip(valid)
    .all(|(a, b)| a == *b))

Read more in the DataFrame and Series modules.

Modules

datatypes
error
frame
prelude
series
testing

Macros

apply_method_all_series
apply_method_arrowprimitive_series