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 Expr) | (keyof Expr)[]>): pl.DataFrame;
        aggList(): 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(__namedParameters: { pivotCol: string; valuesCol: string }): PivotOps;
        pivot(pivotCol: string, valuesCol: string): PivotOps;
        quantile(quantile: number): pl.DataFrame;
        sum(): pl.DataFrame;
        tail(n?: number): pl.DataFrame;
        toString(): string;
    }
    Index

    Methods

    [inspect] agg aggList first groups head last len max mean median min nUnique pivot quantile sum tail toString

    Methods

    • Use multiple aggregations on columns. This can be combined with complete lazy API and is considered idiomatic polars.

      Parameters

      • ...columns: pl.Expr[]

        map of 'col' -> 'agg'

        • using lazy API (recommended): [col('foo').sum(), col('bar').min()]
        • using multiple aggs per column: {'foo': ['sum', 'numUnique'], 'bar': ['min'] }
        • using single agg per column: {'foo': ['sum'], 'bar': 'min' }

      Returns pl.DataFrame

      // use lazy api rest parameter style
      > df.groupBy('foo', 'bar')
      >   .agg(pl.sum('ham'), col('spam').tail(4).sum())

      // use lazy api array style
      > df.groupBy('foo', 'bar')
      >   .agg([pl.sum('ham'), col('spam').tail(4).sum()])

      // use a mapping
      > df.groupBy('foo', 'bar')
      >   .agg({'spam': ['sum', 'min']})

    • Parameters

      • columns: Record<string, (keyof Expr) | (keyof Expr)[]>

      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   │
      ╰─────────┴─────╯

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

      Parameters

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

      Returns PivotOps

    • Parameters

      • pivotCol: string
      • valuesCol: string

      Returns PivotOps

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