Acquainted from the beginning
Radiant of recordsdata wrangling habits, Polars exposes a total Python
API, including the elephantine jam of points to manipulate DataFrames
the utilization of an expression language that will empower you to plot
readable and performant code.
DataFrames to the Rust ecosystem
Polars is written in Rust, uncompromising in its picks to
provide a feature-total DataFrame API to the Rust ecosystem.
Utilize it as a DataFrame library or as question engine backend to your
recordsdata items.
Welcome to love a flash recordsdata wrangling
Polars is a lightning like a flash DataFrame library/in-memory question engine.
Its embarrassingly parallel execution, cache efficient algorithms
and expressive API makes it perfect for efficient recordsdata wrangling,
recordsdata pipelines, hasty APIs and up to now more.
Polars is ready as like a flash as it gets, assemble out regarding the ends within the H2O.ai
benchmark.
Rust
Below a like a flash demonstration of Polars API in Rust.
command polars::prelude::*;
fn example() -> Consequence {
LazyCsvReader::unique("foo.csv".into())
.dwell()
.filter(col("bar").gt(lit(100)))
.groupby(vec![col("ham")])
.agg(vec![col("spam").sum(), col("ham").sort(false).first()])
.decide up()
} Python
Below a like a flash demonstration of Polars API in Python.
import polars as pl
q = (
pl.scan_csv("iris.csv")
.filter(pl.col("sepal_length") > 5)
.groupby("species")
.agg(pl.all().sum())
)
df = q.decide up()