polars 0.6.0

DataFrame library
Documentation

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 read file");

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

For more IO examples see:

Joins

use polars::prelude::*;

fn join() -> Result<DataFrame> {
// 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])?;

// 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])?;

// Left join on days column.
temp.left_join(&rain, "days", "days")
}

println!("{}", join().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 |
+------+------+------+

Groupby's | aggregations | pivots

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.

# use polars::prelude::*;
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), &[Some(true), Some(false), Some(false)]);

Temporal data types

# use polars::prelude::*;
let dates = &[
"2020-08-21",
"2020-08-21",
"2020-08-22",
"2020-08-23",
"2020-08-22",
];
// date format
let fmt = "%Y-%m-%d";
// create date series
let s0 = Date32Chunked::parse_from_str_slice("date", dates, fmt)
.into_series();

And more...

Features

Additional cargo features:

  • pretty (default)
  • pretty printing of DataFrames
  • temporal (default)
  • Conversions between Chrono and Polars for temporal data
  • simd
  • SIMD operations
  • paquet
  • Read Apache Parquet format
  • random
  • Generate array's with randomly sampled values
  • ndarray
  • Convert from DataFrame to ndarray
  • parallel
  • Parallel variants of operation