Crate polars_lazy

Lazy API of Polars

The lazy API of Polars supports a subset of the eager API. Apart from the distributed compute, it is very similar to Apache Spark. You write queries in a domain specific language. These queries translate to a logical plan, which represent your query steps. Before execution this logical plan is optimized and may change the order of operations if this will increase performance. Or implicit type casts may be added such that execution of the query won’t lead to a type error (if it can be resolved).

Lazy DSL

The lazy API of polars replaces the eager DataFrame with the LazyFrame, through which the lazy API is exposed. The LazyFrame represents a logical execution plan: a sequence of operations to perform on a concrete data source. These operations are not executed until we call collect. This allows polars to optimize/reorder the query which may lead to faster queries or fewer type errors.

In general, a LazyFrame requires a concrete data source — a DataFrame, a file on disk, etc. — which polars-lazy then applies the user-specified sequence of operations to. To obtain a LazyFrame from an existing DataFrame, we call the lazy method on the DataFrame. A LazyFrame can also be obtained through the lazy versions of file readers, such as LazyCsvReader.

The other major component of the polars lazy API is Expr, which represents an operation to be performed on a LazyFrame, such as mapping over a column, filtering, or groupby-aggregation. Expr and the functions that produce them can be found in the dsl module.

Most operations on a LazyFrame consume the LazyFrame and return a new LazyFrame with the updated plan. If you need to use the same LazyFrame multiple times, you should clone it, and optionally cache it beforehand.

Examples

Adding a new column to a lazy DataFrame

 #[macro_use] extern crate polars_core;
 use polars_core::prelude::*;
 use polars_lazy::prelude::*;

 let df = df! {
     "column_a" => &[1, 2, 3, 4, 5],
     "column_b" => &["a", "b", "c", "d", "e"]
 }.unwrap();

 let new = df.lazy()
     // Note the reverse here!!
     .reverse()
     .with_column(
         // always rename a new column
         (col("column_a") * lit(10)).alias("new_column")
     )
     .collect()
     .unwrap();

 assert!(new.column("new_column")
     .unwrap()
     .equals(
         &Column::new("new_column".into(), &[50, 40, 30, 20, 10])
     )
 );

Modifying a column based on some predicate

 #[macro_use] extern crate polars_core;
 use polars_core::prelude::*;
 use polars_lazy::prelude::*;

 let df = df! {
     "column_a" => &[1, 2, 3, 4, 5],
     "column_b" => &["a", "b", "c", "d", "e"]
 }.unwrap();

 let new = df.lazy()
     .with_column(
         // value = 100 if x < 3 else x
         when(
             col("column_a").lt(lit(3))
         ).then(
             lit(100)
         ).otherwise(
             col("column_a")
         ).alias("new_column")
     )
     .collect()
     .unwrap();

 assert!(new.column("new_column")
     .unwrap()
     .equals(
         &Column::new("new_column".into(), &[100, 100, 3, 4, 5])
     )
 );

Groupby + Aggregations

 use polars_core::prelude::*;
 use polars_core::df;
 use polars_lazy::prelude::*;
 use arrow::legacy::prelude::QuantileMethod;

 fn example() -> PolarsResult<DataFrame> {
     let df = df!(
         "date" => ["2020-08-21", "2020-08-21", "2020-08-22", "2020-08-23", "2020-08-22"],
         "temp" => [20, 10, 7, 9, 1],
         "rain" => [0.2, 0.1, 0.3, 0.1, 0.01]
     )?;

     df.lazy()
     .group_by([col("date")])
     .agg([
         col("rain").min().alias("min_rain"),
         col("rain").sum().alias("sum_rain"),
         col("rain").quantile(lit(0.5), QuantileMethod::Nearest).alias("median_rain"),
     ])
     .sort(["date"], Default::default())
     .collect()
 }

Calling any function

Below we lazily call a custom closure of type Series => Result<Series>. Because the closure changes the type/variant of the Series we also define the return type. This is important because due to the laziness the types should be known beforehand. Note that by applying these custom functions you have access to the whole eager API of the Series/ChunkedArrays.

 #[macro_use] extern crate polars_core;
 use polars_core::prelude::*;
 use polars_lazy::prelude::*;

 let df = df! {
     "column_a" => &[1, 2, 3, 4, 5],
     "column_b" => &["a", "b", "c", "d", "e"]
 }.unwrap();

 let new = df.lazy()
     .with_column(
         col("column_a")
         // apply a custom closure Series => Result<Series>
         .map(|_s| {
             Ok(Some(Column::new("".into(), &[6.0f32, 6.0, 6.0, 6.0, 6.0])))
         },
         // return type of the closure
         GetOutput::from_type(DataType::Float64)).alias("new_column")
     )
     .collect()
     .unwrap();

Joins, filters and projections

In the query below we do a lazy join and afterwards we filter rows based on the predicate a < 2. And last we select the columns "b" and "c_first". In an eager API this query would be very suboptimal because we join on DataFrames with more columns and rows than needed. In this case the query optimizer will do the selection of the columns (projection) and the filtering of the rows (selection) before the join, thereby reducing the amount of work done by the query.

fn example(df_a: DataFrame, df_b: DataFrame) -> LazyFrame {
    df_a.lazy()
    .left_join(df_b.lazy(), col("b_left"), col("b_right"))
    .filter(
        col("a").lt(lit(2))
    )
    .group_by([col("b")])
    .agg(
        vec![col("b").first().alias("first_b"), col("c").first().alias("first_c")]
     )
    .select(&[col("b"), col("c_first")])
}

If we want to do an aggregation on all columns we can use the wildcard operator * to achieve this.

fn aggregate_all_columns(df_a: DataFrame) -> LazyFrame {
    df_a.lazy()
    .group_by([col("b")])
    .agg(
        vec![col("*").first()]
     )
}

Modules

• dsl
  Domain specific language for the Lazy API.
• frame
  Lazy variant of a DataFrame.
• physical_plan
• prelude

Macros

• fallible
  Helper to delay a failing method until the query plan is collected