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Version: Python

rollup

The Deephaven rollup method creates a rollup table from a source table given zero or more aggregations and zero or more grouping columns to create a hierarchy from.

Syntax​

result = source.rollup(
    aggs: list[Aggregation],
    by: list[str] = None,
    include_constituents: bool = False,
) -> RollupTable

Parameters​

ParameterTypeDescription
aggslist[Aggregation]

A list of aggregations. If None, no aggregations are performed.

bylist[str]

Zero or more column names to group on and create hierarchy from. If None, no hierarchy is created.

include_constituents optionalbool

Whether or not to include constituent rows at the leaf level. Default is False.

Returns​

A rollup table.

Examples​

The following example creates two rollup tables from a source table of insurance expense data. The first performs no aggregations, but creates heirarchy from the region and age columns. The second performs two aggregations: the aggregated average of the bmi and expenses columns are calculated, then the same by columns are given as the first. The optional argument include_constituents is set to True so that members of the lowest-level nodes (individual cells) can be expanded.

from deephaven import read_csv, agg

insurance = read_csv(
    "https://media.githubusercontent.com/media/deephaven/examples/main/Insurance/csv/insurance.csv"
)

agg_list = [agg.avg(cols=["bmi", "expenses"])]
by_list = ["region", "age"]

test_rollup = insurance.rollup(aggs=None, by=by_list, include_constituents=True)
insurance_rollup = insurance.rollup(
    aggs=agg_list, by=by_list, include_constituents=True
)

The following example creates a rollup table from real-time source data. The source data updates 10,000 times per second. The result rollup table can be expanded by the N column to show unique values of M for each N. The aggregated average and sum are calculated for the Value and Weight, respectively.

from deephaven import empty_table, time_table
from deephaven import agg

agg_list = [agg.avg(cols=["Value"]), agg.sum_(cols=["Weight"])]
by_list = ["N", "M"]

rows = empty_table(1_000_000).update_view(["Group = i", "N = i % 347", "M = i % 29"])
changes = (
    time_table("PT0.0001S")
    .view(
        [
            "Group = i % 1_000_000",
            "LastModified = Timestamp",
            "Value = (i * Math.sin(i)) % 6977",
            "Weight = (i * Math.sin(i)) % 7151",
        ]
    )
    .last_by("Group")
)

source = rows.join(changes, "Group")

result = source.rollup(aggs=agg_list, by=by_list)

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