nodejs-polars
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    Interface GroupBy

    Starts a new GroupBy operation.

    interface GroupBy {
        "[inspect]"(): string;
        agg(...columns: pl.Expr[]): pl.DataFrame;
        agg(
            columns: Record<string, keyof pl.Expr | (keyof pl.Expr)[]>,
        ): pl.DataFrame;
        first(): pl.DataFrame;
        groups(): pl.DataFrame;
        head(n?: number): pl.DataFrame;
        last(): pl.DataFrame;
        len(): pl.DataFrame;
        max(): pl.DataFrame;
        mean(): pl.DataFrame;
        median(): pl.DataFrame;
        min(): pl.DataFrame;
        nUnique(): pl.DataFrame;
        pivot(pivotCol: string, valuesCol: string): PivotOps;
        pivot(__namedParameters: { pivotCol: string; valuesCol: string }): PivotOps;
        quantile(quantile: number): pl.DataFrame;
        sum(): pl.DataFrame;
        tail(n?: number): pl.DataFrame;
        toString(): string;
    }
    Index
    [inspect] agg first groups head last len max mean median min nUnique pivot quantile sum tail toString

    • Compute aggregations for each group of a group by operation.

      Parameters

      • ...columns: pl.Expr[]

        Aggregations to compute for each group of the group by operation, specified as positional arguments.

      Returns pl.DataFrame

      const df = pl.DataFrame({
         foo: [1, 2, 2, 3, 3],
         ham: [6.0, 6, 7, 8.0, 8.0],
         bar: ["a", "b", "c", "c", "c"],
         spam: ["a", "b", "c", "c", "c"],
       });
      // use lazy api rest parameter style
      > df.groupBy('foo', 'bar').agg(pl.count('ham'), pl.col('spam')).sort(["foo", "bar"]);
      shape: (4, 4)
       ┌─────┬─────┬─────┬────────────┐
       │ foo ┆ bar ┆ ham ┆ spam       │
       │ --- ┆ --- ┆ --- ┆ ---        │
       │ f64 ┆ str ┆ u32 ┆ list[str]  │
       ╞═════╪═════╪═════╪════════════╡
       │ 1.0 ┆ a   ┆ 1   ┆ ["a"]      │
       │ 2.0 ┆ b   ┆ 1   ┆ ["b"]      │
       │ 2.0 ┆ c   ┆ 1   ┆ ["c"]      │
       │ 3.0 ┆ c   ┆ 2   ┆ ["c", "c"] │
       └─────┴─────┴─────┴────────────┘

      > df.groupBy("bar").agg(pl.col("foo"), pl.col("ham"), pl.col("spam") ).sort("bar");
      shape: (3, 4)
       ┌─────┬─────────────────┬─────────────────┬─────────────────┐
       │ bar ┆ foo             ┆ ham             ┆ spam            │
       │ --- ┆ ---             ┆ ---             ┆ ---             │
       │ str ┆ list[f64]       ┆ list[f64]       ┆ list[str]       │
       ╞═════╪═════════════════╪═════════════════╪═════════════════╡
       │ a   ┆ [1.0]           ┆ [6.0]           ┆ ["a"]           │
       │ b   ┆ [2.0]           ┆ [6.0]           ┆ ["b"]           │
       │ c   ┆ [2.0, 3.0, 3.0] ┆ [7.0, 8.0, 8.0] ┆ ["c", "c", "c"] │
       └─────┴─────────────────┴─────────────────┴─────────────────┘
      > const h = pl.col("ham");
      > df.groupBy("bar").agg(h.sum().as("sum_ham"), h.min().as("min_ham"), h.max().as("max_ham"));
      shape: (3, 4)
       ┌─────┬─────────┬─────────┬─────────┐
       │ bar ┆ sum_ham ┆ min_ham ┆ max_ham │
       │ --- ┆ ---     ┆ ---     ┆ ---     │
       │ str ┆ f64     ┆ f64     ┆ f64     │
       ╞═════╪═════════╪═════════╪═════════╡
       │ a   ┆ 6.0     ┆ 6.0     ┆ 6.0     │
       │ b   ┆ 6.0     ┆ 6.0     ┆ 6.0     │
       │ c   ┆ 23.0    ┆ 7.0     ┆ 8.0     │
       └─────┴─────────┴─────────┴─────────┘

    • Parameters

      Returns pl.DataFrame

    • Return first n rows of each group.

      Parameters

      • Optionaln: number

        Number of values of the group to select

      Returns pl.DataFrame

      > df = pl.DataFrame({
      >   "letters": ["c", "c", "a", "c", "a", "b"],
      >   "nrs": [1, 2, 3, 4, 5, 6]
      > })
      > df
      shape: (6, 2)
      ╭─────────┬─────╮
      │ letters ┆ nrs │
      │ ---     ┆ --- │
      │ str     ┆ i64 │
      ╞═════════╪═════╡
      │ "c"     ┆ 1   │
      ├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
      │ "c"     ┆ 2   │
      ├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
      │ "a"     ┆ 3   │
      ├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
      │ "c"     ┆ 4   │
      ├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
      │ "a"     ┆ 5   │
      ├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
      │ "b"     ┆ 6   │
      ╰─────────┴─────╯
      > df.groupby("letters")
      >   .head(2)
      >   .sort("letters");
      > >>
      shape: (5, 2)
      ╭─────────┬─────╮
      │ letters ┆ nrs │
      │ ---     ┆ --- │
      │ str     ┆ i64 │
      ╞═════════╪═════╡
      │ "a"     ┆ 3   │
      ├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
      │ "a"     ┆ 5   │
      ├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
      │ "b"     ┆ 6   │
      ├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
      │ "c"     ┆ 1   │
      ├╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌┤
      │ "c"     ┆ 2   │
      ╰─────────┴─────╯

    • Return the number of rows in each group.

      Returns pl.DataFrame

      > df = pl.DataFrame({a: ["Apple", "Apple", "Orange"], b: [1, None, 2]});
        > df.groupBy("a").len()
       shape: (2, 2)
       ┌────────┬─────┐
       │ a      ┆ len │
       │ ---    ┆ --- │
       │ str    ┆ u32 │
       ╞════════╪═════╡
       │ Apple  ┆ 2   │
       │ Orange ┆ 1   │
       └────────┴─────┘

    • Do a pivot operation based on the group key, a pivot column and an aggregation function on the values column.

      Parameters

      • pivotCol: string

        Column to pivot.

      • valuesCol: string

        Column that will be aggregated.

      Returns PivotOps

    • Parameters

      • __namedParameters: { pivotCol: string; valuesCol: string }

      Returns PivotOps

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