Crate polars

Polars: DataFrames in Rust

Polars is a DataFrame library for Rust. It is based on Apache Arrows memory model. This means that operations on Polars array's (called Series or ChunkedArray<T> {if the type T is known}) are optimally aligned cache friendly operations and SIMD. Sadly, Apache Arrow needs nightly Rust, which means that Polars cannot run on stable.

Read more in the pages of the DataFrame, Series, and ChunkedArray data structures.

Read and write CSV/ JSON

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()
}

For more IO examples see:

• the csv module
• the json module
• the IPC module

Joins

use polars::prelude::*;

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

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

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

+------+------+------+
| days | temp | rain |
| ---  | ---  | ---  |
| i32  | f64  | f64  |
+======+======+======+
| 0    | 22.1 | null |
+------+------+------+
| 1    | 19.9 | 0.1  |
+------+------+------+
| 2    | 7    | 0.2  |
+------+------+------+
| 3    | 2    | null |
+------+------+------+
| 4    | 3    | null |
+------+------+------+

GroupBys

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")
     .into_iter()
     .map(|optional_v| {
         match optional_v {
             Some(v) => Some(v * v),
             None => None, // null value
         }
 }).collect();

Apply custom closures

Besides running custom iterators, custom closures can be applied on the values of ChunkedArray by using the apply method. This method accepts a closure that will be applied on all values of Option<T> that are non null. Note that this is the fastest way to apply a custom closure on ChunkedArray's.

let s: Series = Series::new("values", [Some(1.0), None, Some(3.0)]);
// null values are ignored automatically
let squared = s.f64()
    .unwrap()
    .apply(|value| value.powf(2.0))
    .into_series();

assert_eq!(Vec::from(squared.f64().unwrap()), &[Some(1.0), None, Some(9.0)])

Comparisons

use polars::prelude::*;
use itertools::Itertools;
let s = Series::new("dollars", &[1, 2, 3]);
let mask = s.eq(1);
let valid = [true, false, false].iter();

assert_eq!(Vec::from(mask.bool().unwrap()), &[Some(true), Some(false), Some(false)]);

And more...

• DataFrame
• Series
• ChunkedArray
  • Operations implemented by Traits
• Time/ DateTime utilities
• Groupby and aggregations

Features

Additional cargo features:

• pretty (default)
  • pretty printing of DataFrames
• simd
  • SIMD operations
• temporal
  • Conversions between Chrono and Polars for temporal data

Modules

chunked_array	The typed heart of every Series column.
datatypes	Data types supported by Polars.
doc	Other documentation
error
frame	DataFrame module.
prelude	Everything you need to get started with Polars.
series	Type agnostic columnar data structure.
testing	Testing utilities.

Macros

apply_method_all_series
apply_method_numeric_series
apply_method_numeric_series_and_return
apply_operand_on_chunkedarray_by_iter
exec_concurrent
match_arrow_data_type_apply_macro