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//!
//! # Polars Lazy cookbook
//!
//! This page should serve a cookbook to quickly get you started with polars' query engine.
//! The lazy API allows you to create complex well performing queries on top of Polars eager.
//!
//! ## Tree Of Contents
//!
//! * [Start a lazy computation](#start-a-lazy-computation)
//! * [Filter](#filter)
//! * [Sort](#sort)
//! * [GroupBy](#groupby)
//! * [Joins](#joins)
//! * [Conditionally apply](#conditionally-apply)
//!
//! ## Start a lazy computation
//!
//! ```
//! use polars::prelude::*;
//! use polars::df;
//!
//! # fn example() -> Result<()> {
//! let df = df![
//! "a" => [1, 2, 3],
//! "b" => [None, Some("a"), Some("b")]
//! ]?;
//! // from an eager DataFrame
//! let lf: LazyFrame = df.lazy();
//!
//! // scan a csv file lazily
//! let lf: LazyFrame = LazyCsvReader::new("some_path".into())
//! .has_header(true)
//! .finish();
//!
//! // scan a parquet file lazily
//! let lf: LazyFrame = LazyFrame::new_from_parquet("some_path".into(), None, true);
//!
//! # Ok(())
//! # }
//! ```
//!
//! ## Filter
//! ```
//! use polars::prelude::*;
//! use polars::df;
//!
//! # fn example() -> Result<()> {
//! let df = df![
//! "a" => [1, 2, 3],
//! "b" => [None, Some("a"), Some("b")]
//! ]?;
//!
//! let filtered = df.lazy()
//! .filter(col("a").gt(lit(2)))
//! .collect()?;
//!
//! // filtered:
//!
//! // ╭─────┬─────╮
//! // │ a ┆ b │
//! // │ --- ┆ --- │
//! // │ i64 ┆ str │
//! // ╞═════╪═════╡
//! // │ 3 ┆ "c" │
//! // ╰─────┴─────╯
//!
//! # Ok(())
//! # }
//! ```
//!
//! ## Sort
//! ```
//! use polars::prelude::*;
//! use polars::df;
//!
//! # fn example() -> Result<()> {
//! let df = df![
//! "a" => [1, 2, 3],
//! "b" => ["a", "a", "b"]
//! ]?;
//! // sort this DataFrame by multiple columns
//!
//! // ordering of the columns
//! let reverse = vec![true, false];
//!
//! let sorted = df.lazy()
//! .sort_by_exprs(vec![col("b"), col("a")], reverse)
//! .collect()?;
//!
//! // sorted:
//!
//! // ╭─────┬─────╮
//! // │ a ┆ b │
//! // │ --- ┆ --- │
//! // │ i64 ┆ str │
//! // ╞═════╪═════╡
//! // │ 1 ┆ "a" │
//! // ├╌╌╌╌╌┼╌╌╌╌╌┤
//! // │ 2 ┆ "a" │
//! // ├╌╌╌╌╌┼╌╌╌╌╌┤
//! // │ 3 ┆ "b" │
//! // ╰─────┴─────╯
//!
//! # Ok(())
//! # }
//! ```
//!
//! ## Groupby
//!
//! This example is from the polars [user guide](https://pola-rs.github.io/polars-book/user-guide/howcani/df/groupby.html).
//!
//! ```
//! use polars::prelude::*;
//! # fn example() -> Result<()> {
//!
//! let df = LazyCsvReader::new("reddit.csv".into())
//! .has_header(true)
//! .with_delimiter(b',')
//! .finish()
//! .groupby(vec![col("comment_karma")])
//! .agg(vec![col("name").n_unique().alias("unique_names"), col("link_karma").max()])
//! // take only 100 rows.
//! .fetch(100)?;
//! # Ok(())
//! # }
//! ```
//!
//! ## Joins
//!
//! ```
//! use polars::prelude::*;
//! use polars::df;
//! # fn example() -> Result<()> {
//! let df_a = df![
//! "a" => [1, 2, 1, 1],
//! "b" => ["a", "b", "c", "c"],
//! "c" => [0, 1, 2, 3]
//! ]?;
//!
//! let df_b = df![
//! "foo" => [1, 1, 1],
//! "bar" => ["a", "c", "c"],
//! "ham" => ["let", "var", "const"]
//! ]?;
//!
//! let lf_a = df_a.clone().lazy();
//! let lf_b = df_b.clone().lazy();
//!
//! let joined = lf_a.join(lf_b, vec![col("a")], vec![col("foo")], JoinType::Outer).collect()?;
//! // joined:
//!
//! // ╭─────┬─────┬─────┬──────┬─────────╮
//! // │ b ┆ c ┆ a ┆ bar ┆ ham │
//! // │ --- ┆ --- ┆ --- ┆ --- ┆ --- │
//! // │ str ┆ i64 ┆ i64 ┆ str ┆ str │
//! // ╞═════╪═════╪═════╪══════╪═════════╡
//! // │ "a" ┆ 0 ┆ 1 ┆ "a" ┆ "let" │
//! // ├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
//! // │ "a" ┆ 0 ┆ 1 ┆ "c" ┆ "var" │
//! // ├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
//! // │ "a" ┆ 0 ┆ 1 ┆ "c" ┆ "const" │
//! // ├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
//! // │ "b" ┆ 1 ┆ 2 ┆ null ┆ null │
//! // ├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
//! // │ "c" ┆ 2 ┆ 1 ┆ null ┆ null │
//! // ├╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌┤
//! // │ "c" ┆ 3 ┆ 1 ┆ null ┆ null │
//! // ╰─────┴─────┴─────┴──────┴─────────╯
//!
//! // other join syntax options
//! # let lf_a = df_a.clone().lazy();
//! # let lf_b = df_b.clone().lazy();
//! let inner = lf_a.inner_join(lf_b, col("a"), col("foo")).collect()?;
//!
//! # let lf_a = df_a.clone().lazy();
//! # let lf_b = df_b.clone().lazy();
//! let left = lf_a.left_join(lf_b, col("a"), col("foo")).collect()?;
//!
//! # let lf_a = df_a.clone().lazy();
//! # let lf_b = df_b.clone().lazy();
//! let outer = lf_a.outer_join(lf_b, col("a"), col("foo")).collect()?;
//!
//! # let lf_a = df_a.clone().lazy();
//! # let lf_b = df_b.clone().lazy();
//! let joined_with_builder = lf_a.join_builder()
//! .with(lf_b)
//! .left_on(vec![col("a")])
//! .right_on(vec![col("foo")])
//! .how(JoinType::Inner)
//! .force_parallel(true)
//! .finish()
//! .collect()?;
//!
//! # Ok(())
//! # }
//! ```
//!
//! ## Conditionally apply
//! If we want to create a new column based on some condition, we can use the `.when()/.then()/.otherwise()` expressions.
//!
//! * `when` - accpets a predicate epxression
//! * `then` - expression to use when `predicate == true`
//! * `otherwise` - expression to use when `predicate == false`
//!
//! ```
//! use polars::prelude::*;
//! use polars::df;
//! # fn example() -> Result<()> {
//! let df = df![
//! "range" => [1, 2, 3, 4, 5, 6, 8, 9, 10],
//! "left" => (0..10).map(|_| Some("foo")).collect::<Vec<_>>(),
//! "right" => (0..10).map(|_| Some("bar")).collect::<Vec<_>>()
//! ]?;
//!
//! let new = df.lazy()
//! .with_column(when(col("range").gt_eq(lit(5)))
//! .then(col("left"))
//! .otherwise(col("right")).alias("foo_or_bar")
//! ).collect()?;
//!
//! // new:
//!
//! // ╭───────┬───────┬───────┬────────────╮
//! // │ range ┆ left ┆ right ┆ foo_or_bar │
//! // │ --- ┆ --- ┆ --- ┆ --- │
//! // │ i64 ┆ str ┆ str ┆ str │
//! // ╞═══════╪═══════╪═══════╪════════════╡
//! // │ 0 ┆ "foo" ┆ "bar" ┆ "bar" │
//! // ├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
//! // │ 1 ┆ "foo" ┆ "bar" ┆ "bar" │
//! // ├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
//! // │ 2 ┆ "foo" ┆ "bar" ┆ "bar" │
//! // ├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
//! // │ 3 ┆ "foo" ┆ "bar" ┆ "bar" │
//! // ├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
//! // │ ... ┆ ... ┆ ... ┆ ... │
//! // ├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
//! // │ 5 ┆ "foo" ┆ "bar" ┆ "foo" │
//! // ├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
//! // │ 6 ┆ "foo" ┆ "bar" ┆ "foo" │
//! // ├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
//! // │ 7 ┆ "foo" ┆ "bar" ┆ "foo" │
//! // ├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
//! // │ 8 ┆ "foo" ┆ "bar" ┆ "foo" │
//! // ├╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
//! // │ 9 ┆ "foo" ┆ "bar" ┆ "foo" │
//! // ╰───────┴───────┴───────┴────────────╯
//!
//! # Ok(())
//! # }
//! ```