formula

formula returns an aggregator that computes a user-defined formula aggregation across specified columns.

Syntax

formula(formula: str, formula_param: Optional[str] = None, cols: List[str]) -> Aggregation

Parameters

ParameterTypeDescription
formulastr

The user-defined formula to apply to each group. The formula can contain a combination of any of the following:

  • Built-in functions such as min, max, etc.
  • Mathematical arithmetic such as *, +, /, etc.
  • Python functions

If formula_param is not None, the formula can only be applied to one column at a time, and it is applied to the specified formula_param. If formula_param is None, the formula is applied to any column or literal value present in the formula. The use of formula_param is deprecated.

Key column(s) can be used as input to the formula. When this happens, key values are treated as scalars.

formula_paramstr

The parameter name within the formula. If formula_param is each, then formula must contain each. For example, max(each), min(each), etc. Use of this parameter is deprecated.

colslist[str]

The source column(s) for the calculations.

  • ["X"] applies the formula to each value in the X column for each group.
  • ["Y = X"] applies the formula to each value in the X column for each group and renames it to Y.
  • ["X, A = B"] applies the formula to each value in the X column for each group and the formula to each value in the B column while renaming it to A.

Caution

If an aggregation does not rename the resulting column, the aggregation column will appear in the output table, not the input column. If multiple aggregations on the same column do not rename the resulting columns, an error will result, because the aggregations are trying to create multiple columns with the same name. For example, in table.agg_by([agg.sum_(cols=[“X”]), agg.avg(cols=["X"]), both the sum and the average aggregators produce column X, which results in a name conflict error.

Returns

An aggregator that computes a user-defined formula within an aggregation group, for each input column.

Examples

The following example uses agg.formula to calculate several formula aggregations by the Letter column. Since formula_param is None, the formulas are applied to any column or literal value present in the formula. The specified formulas operate on zero, one, two, and three different columns at a time.

from deephaven import empty_table
from deephaven import agg

source = empty_table(20).update(
    ["X = i", "Y = 2 * i", "Z = 3 * i", "Letter = (X % 2 == 0) ? `A` : `B`"]
)

result = source.agg_by(
    [
        agg.formula(formula="OutA = sqrt(5.0)"),
        agg.formula(formula="OutB = min(X)"),
        agg.formula(formula="OutC = min(X) + max(Y)"),
        agg.formula(formula="OutD = sum(X + Y + Z)"),
    ],
    by=["Letter"],
)

In this example, agg.formula is used to calculate the aggregate minimum across several columns by the Letter column. The example uses formula_param to tell Deephaven that the formula applies to each.

from deephaven import empty_table
from deephaven import agg

source = empty_table(20).update(
    ["X = i", "Y = 2 * i", "Z = 3 * i", "Letter = (X % 2 == 0) ? `A` : `B`"]
)

result = source.agg_by(
    agg.formula(
        formula="min(each)",
        formula_param="each",
        cols=["MinX = X", "MinY = Y", "MinZ = Z"],
    ),
    by=["Letter"],
)

In this example, agg.formula is used to calculate the aggregated average across several columns by the Letter and Color column. Since the formula is applied to one column at a time, the each parameter is used to refer to the current column being processed.

from deephaven import empty_table
from deephaven import agg

colors = ["Red", "Blue", "Green"]

formulas = [
    "X = 0.1 * i",
    "Y1 = Math.pow(X, 2)",
    "Y2 = Math.sin(X)",
    "Y3 = Math.cos(X)",
]
grouping_cols = ["Letter = (i % 2 == 0) ? `A` : `B`", "Color = (String)colors[i % 3]"]

source = empty_table(40).update(formulas + grouping_cols)

my_agg = [
    agg.formula(
        formula="avg(k)",
        formula_param="k",
        cols=[f"AvgY{idx} = Y{idx}" for idx in range(1, 4)],
    )
]

result = source.agg_by(aggs=my_agg, by=["Letter", "Color"])

In this example, agg.formula is used to calculate the aggregate sum of squares across each of the X, Y, and Z columns by the Letter column. Note that since the formula is applied to one column at a time, the each parameter is used to refer to the current column being processed.

from deephaven import empty_table
from deephaven import agg

source = empty_table(20).update(
    ["X = i", "Y = 2 * i", "Z = 3 * i", "Letter = (X % 2 == 0) ? `A` : `B`"]
)

my_agg = [
    agg.formula(
        formula="sum(each * each)",
        formula_param="each",
        cols=["SumSqrX = X", "SumSqrY = Y", "SumSqrZ = Z"],
    )
]

result = source.agg_by(my_agg, by=["Letter"])

In this example, agg.formula calls a user-defined function, range, to calculate the aggregate range of the X, Y, and Z columns by Letter. Note that since the formula is applied to one column at a time, the each parameter is used to refer to the current column being processed.

Caution

Type hints will not set the column type when used in agg.formula. Use an explicit typecast in the formula string to ensure the resultant column(s) are of the correct type.

from deephaven import empty_table
from deephaven import agg


def range(each):
    return max(each) - min(each)


source = empty_table(20).update(
    ["X = i", "Y = 2 * i", "Z = 3 * i", "Letter = (X % 2 == 0) ? `A` : `B`"]
)

result = source.agg_by(
    [
        agg.formula(
            formula="(int)range(each)",
            formula_param="each",
            cols=["RangeX = X", "RangeY = Y", "RangeZ = Z"],
        )
    ],
    by=["Letter"],
)