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//! # Polars: *<small>DataFrames in Rust</small>* //! //! Read more in the [DataFrame](frame/struct.DataFrame.html), [Series](series/enum.Series.html), and //! [ChunkedArray](chunked_array/struct.ChunkedArray.html) 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](frame/ser/csv/index.html) //! * [the json module](frame/ser/json/index.html) //! * [the IPC module](frame/ser/ipc/index.html) //! //! //! ## 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()) //! ``` //! //! ```text //! +------+------+------+ //! | 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](chunked_array/struct.ChunkedArray.html) //! by using the [apply](chunked_array/apply/trait.Apply.html) 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.bool().unwrap()), &[Some(true), Some(false), Some(false)]); //! ``` //! //! ## And more... //! //! * [DataFrame](frame/struct.DataFrame.html) //! * [Series](series/enum.Series.html) //! * [ChunkedArray](chunked_array/struct.ChunkedArray.html) //! //! ## Features //! //! Additional cargo features: //! //! * `pretty` (default) //! - pretty printing of DataFrames //! * `simd` //! - SIMD operations #![allow(dead_code)] #![feature(iterator_fold_self)] #[macro_use] pub mod series; #[macro_use] pub(crate) mod utils; pub mod chunked_array; pub mod datatypes; #[cfg(feature = "docs")] pub mod doc; pub mod error; mod fmt; pub mod frame; pub mod prelude; pub mod testing;