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Fixing docs #134

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merged 3 commits into from
Oct 15, 2023
Merged

Fixing docs #134

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88514f9
Fixing docs
Bidek56 Oct 14, 2023
603156e
Adding title to Readme
Bidek56 Oct 14, 2023
1fb1734
Fixing doc for argSortBy
Bidek56 Oct 14, 2023
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2 changes: 2 additions & 0 deletions README.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
# Polars

Polars: Blazingly fast DataFrames in Rust, Python, Node.js, R and SQL

[![rust docs](https://docs.rs/polars/badge.svg)](https://docs.rs/polars/latest/polars/)
[![Build and test](https://github.com/pola-rs/polars/workflows/Build%20and%20test/badge.svg)](https://github.com/pola-rs/polars/actions)
[![](https://img.shields.io/crates/v/polars.svg)](https://crates.io/crates/polars)
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313 changes: 157 additions & 156 deletions polars/lazy/functions.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -235,11 +235,26 @@ export function all(): Expr {
return col("*");
}
/**
* __Find the indexes that would sort the columns.__
* ___
* Argsort by multiple columns. The first column will be used for the ordering.
* If there are duplicates in the first column, the second column will be used to determine the ordering
* and so on.
* Return the row indices that would sort the columns.
* @param exprs Column(s) to arg sort by. Accepts expression input.
* @param *more_exprs Additional columns to arg sort by, specified as positional arguments.
* @param descending Sort in descending order. When sorting by multiple columns, can be specified per column by passing a sequence of booleans.
* @example
* ```
* const df = pl.DataFrame({"a": [0, 1, 1, 0], "b": [3, 2, 3, 2],});
* df.select(pl.argSortBy(pl.col("a")));
* shape: (4, 1)
* ┌─────┐
* │ a │
* │ --- │
* │ u32 │
* ╞═════╡
* │ 0 │
* │ 3 │
* │ 1 │
* │ 2 │
* └─────┘
* ```
*/
export function argSortBy(
exprs: Expr[] | string[],
Expand Down Expand Up @@ -635,35 +650,34 @@ export function element(): Expr {
return col("");
}

/** Compute the bitwise AND horizontally across columns.

Parameters
----------
*exprs
Column(s) to use in the aggregation. Accepts expression input. Strings are
parsed as column names, other non-expression inputs are parsed as literals.

Examples
--------
>>> const df = pl.DataFrame(
... {
... "a": [false, false, true, true],
... "b": [false, true, null, true],
... "c": ["w", "x", "y", "z"],
... }
... )
>>> df.withColumns(pl.allHorizontal([pl.col("a"), pl.col("b")]))
shape: (4, 4)
┌───────┬───────┬─────┬───────┐
│ a ┆ b ┆ c ┆ all │
│ --- ┆ --- ┆ --- ┆ --- │
│ bool ┆ bool ┆ str ┆ bool │
╞═══════╪═══════╪═════╪═══════╡
│ false ┆ false ┆ w ┆ false │
│ false ┆ true ┆ x ┆ false │
│ true ┆ null ┆ y ┆ null │
│ true ┆ true ┆ z ┆ true │
└───────┴───────┴─────┴───────┘
/**
* Compute the bitwise AND horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
*
* @example
* ```
* >>> const df = pl.DataFrame(
* {
* "a": [false, false, true, true],
* "b": [false, true, null, true],
* "c": ["w", "x", "y", "z"],
* }
* )
* >>> df.withColumns(pl.allHorizontal([pl.col("a"), pl.col("b")]))
* shape: (4, 4)
* ┌───────┬───────┬─────┬───────┐
* │ a ┆ b ┆ c ┆ all │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ bool ┆ bool ┆ str ┆ bool │
* ╞═══════╪═══════╪═════╪═══════╡
* │ false ┆ false ┆ w ┆ false │
* │ false ┆ true ┆ x ┆ false │
* │ true ┆ null ┆ y ┆ null │
* │ true ┆ true ┆ z ┆ true │
* └───────┴───────┴─────┴───────┘
* ```
*/

export function allHorizontal(exprs: ExprOrString | ExprOrString[]): Expr {
Expand All @@ -674,37 +688,34 @@ export function allHorizontal(exprs: ExprOrString | ExprOrString[]): Expr {
return _Expr(pli.allHorizontal(exprs));
}

/*
Compute the bitwise OR horizontally across columns.

Parameters
----------
*exprs
Column(s) to use in the aggregation. Accepts expression input. Strings are
parsed as column names, other non-expression inputs are parsed as literals.

Examples
--------
>>> const df = pl.DataFrame(
... {
... "a": [false, false, true, null],
... "b": [false, true, null, null],
... "c": ["w", "x", "y", "z"],
... }
... )
>>> df.withColumns(pl.anyHorizontal([pl.col("a"), pl.col("b")]))
shape: (4, 4)
┌───────┬───────┬─────┬───────┐
│ a ┆ b ┆ c ┆ any │
│ --- ┆ --- ┆ --- ┆ --- │
│ bool ┆ bool ┆ str ┆ bool │
╞═══════╪═══════╪═════╪═══════╡
│ false ┆ false ┆ w ┆ false │
│ false ┆ true ┆ x ┆ true │
│ true ┆ null ┆ y ┆ true │
│ null ┆ null ┆ z ┆ null │
└───────┴───────┴─────┴───────┘
*/
/**
* Compute the bitwise OR horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [false, false, true, null],
* ... "b": [false, true, null, null],
* ... "c": ["w", "x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.anyHorizontal([pl.col("a"), pl.col("b")]))
* shape: (4, 4)
* ┌───────┬───────┬─────┬───────┐
* │ a ┆ b ┆ c ┆ any │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ bool ┆ bool ┆ str ┆ bool │
* ╞═══════╪═══════╪═════╪═══════╡
* │ false ┆ false ┆ w ┆ false │
* │ false ┆ true ┆ x ┆ true │
* │ true ┆ null ┆ y ┆ true │
* │ null ┆ null ┆ z ┆ null │
* └───────┴───────┴─────┴───────┘
* ```
*/

export function anyHorizontal(exprs: ExprOrString | ExprOrString[]): Expr {
exprs = Array.isArray(exprs) ? exprs : [exprs];
Expand All @@ -714,73 +725,67 @@ export function anyHorizontal(exprs: ExprOrString | ExprOrString[]): Expr {
return _Expr(pli.anyHorizontal(exprs));
}

/*
Get the maximum value horizontally across columns.

Parameters
----------
*exprs
Column(s) to use in the aggregation. Accepts expression input. Strings are
parsed as column names, other non-expression inputs are parsed as literals.

Examples
--------
>>> const df = pl.DataFrame(
... {
... "a": [1, 8, 3],
... "b": [4, 5, null],
... "c": ["x", "y", "z"],
... }
... )
>>> df.withColumns(pl.maxHorizontal(pl.col("a"), pl.col("b")))
shape: (3, 4)
┌─────┬──────┬─────┬─────┐
│ a ┆ b ┆ c ┆ max │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str ┆ i64 │
╞═════╪══════╪═════╪═════╡
│ 1 ┆ 4 ┆ x ┆ 4 │
│ 8 ┆ 5 ┆ y ┆ 8 │
│ 3 ┆ null ┆ z ┆ 3 │
└─────┴──────┴─────┴─────┘
*/
/**
* Get the maximum value horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [1, 8, 3],
* ... "b": [4, 5, null],
* ... "c": ["x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.maxHorizontal(pl.col("a"), pl.col("b")))
* shape: (3, 4)
* ┌─────┬──────┬─────┬─────┐
* │ a ┆ b ┆ c ┆ max │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ str ┆ i64 │
* ╞═════╪══════╪═════╪═════╡
* │ 1 ┆ 4 ┆ x ┆ 4 │
* │ 8 ┆ 5 ┆ y ┆ 8 │
* │ 3 ┆ null ┆ z ┆ 3 │
* └─────┴──────┴─────┴─────┘
* ```
*/
export function maxHorizontal(exprs: ExprOrString | ExprOrString[]): Expr {
exprs = Array.isArray(exprs) ? exprs : [exprs];

exprs = selectionToExprList(exprs);

return _Expr(pli.maxHorizontal(exprs));
}
/*
Get the minimum value horizontally across columns.

Parameters
----------
*exprs
Column(s) to use in the aggregation. Accepts expression input. Strings are
parsed as column names, other non-expression inputs are parsed as literals.

Examples
--------
>>> const df = pl.DataFrame(
... {
... "a": [1, 8, 3],
... "b": [4, 5, null],
... "c": ["x", "y", "z"],
... }
... )
>>> df.withColumns(pl.minHorizontal(pl.col("a"), pl.col("b")))
shape: (3, 4)
┌─────┬──────┬─────┬─────┐
│ a ┆ b ┆ c ┆ min │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str ┆ i64 │
╞═════╪══════╪═════╪═════╡
│ 1 ┆ 4 ┆ x ┆ 1 │
│ 8 ┆ 5 ┆ y ┆ 5 │
│ 3 ┆ null ┆ z ┆ 3 │
└─────┴──────┴─────┴─────┘
*/
/**
* Get the minimum value horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [1, 8, 3],
* ... "b": [4, 5, null],
* ... "c": ["x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.minHorizontal(pl.col("a"), pl.col("b")))
* shape: (3, 4)
* ┌─────┬──────┬─────┬─────┐
* │ a ┆ b ┆ c ┆ min │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ str ┆ i64 │
* ╞═════╪══════╪═════╪═════╡
* │ 1 ┆ 4 ┆ x ┆ 1 │
* │ 8 ┆ 5 ┆ y ┆ 5 │
* │ 3 ┆ null ┆ z ┆ 3 │
* └─────┴──────┴─────┴─────┘
* ```
*/

export function minHorizontal(exprs: ExprOrString | ExprOrString[]): Expr {
exprs = Array.isArray(exprs) ? exprs : [exprs];
Expand All @@ -790,37 +795,33 @@ export function minHorizontal(exprs: ExprOrString | ExprOrString[]): Expr {
return _Expr(pli.minHorizontal(exprs));
}

/*
Sum all values horizontally across columns.

Parameters
----------
*exprs
Column(s) to use in the aggregation. Accepts expression input. Strings are
parsed as column names, other non-expression inputs are parsed as literals.

Examples
--------
>>> const df = pl.DataFrame(
... {
... "a": [1, 8, 3],
... "b": [4, 5, null],
... "c": ["x", "y", "z"],
... }
... )
>>> df.withColumns(pl.sumHorizontal(pl.col("a"), ol.col("b")))
shape: (3, 4)
┌─────┬──────┬─────┬──────┐
│ a ┆ b ┆ c ┆ sum │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ str ┆ i64 │
╞═════╪══════╪═════╪══════╡
│ 1 ┆ 4 ┆ x ┆ 5 │
│ 8 ┆ 5 ┆ y ┆ 13 │
│ 3 ┆ null ┆ z ┆ null │
└─────┴──────┴─────┴──────┘

*/
/**
* Sum all values horizontally across columns.
* @param *exprs
* Column(s) to use in the aggregation. Accepts expression input. Strings are
* parsed as column names, other non-expression inputs are parsed as literals.
* @example
* ```
* >>> const df = pl.DataFrame(
* ... {
* ... "a": [1, 8, 3],
* ... "b": [4, 5, null],
* ... "c": ["x", "y", "z"],
* ... }
* ... )
* >>> df.withColumns(pl.sumHorizontal(pl.col("a"), ol.col("b")))
* shape: (3, 4)
* ┌─────┬──────┬─────┬──────┐
* │ a ┆ b ┆ c ┆ sum │
* │ --- ┆ --- ┆ --- ┆ --- │
* │ i64 ┆ i64 ┆ str ┆ i64 │
* ╞═════╪══════╪═════╪══════╡
* │ 1 ┆ 4 ┆ x ┆ 5 │
* │ 8 ┆ 5 ┆ y ┆ 13 │
* │ 3 ┆ null ┆ z ┆ null │
* └─────┴──────┴─────┴──────┘
* ```
*/
export function sumHorizontal(exprs: ExprOrString | ExprOrString[]): Expr {
exprs = Array.isArray(exprs) ? exprs : [exprs];

Expand Down