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Version: Python

How to use update_by

This guide will show you how to use the update_by table operation in your queries. update_by creates a new table with resultant columns containing aggregated calculations (referred to as UpdateByOperations) of columns in a source table. The calculations can be cumulative, windowed by rows (ticks), or windowed by time. The calculations are optionally done on a per-group basis, where groups are defined by one or more key columns.

Available UpdateByOperations

The calculations (UpdateByOperations) that can be performed with update_by are:

The use of update_by requires one or more of the calculations in the list above, as well as zero or more key columns to define groups. The resultant table contains all columns from the source table, as well as new columns if the output of the UpdateByOperation renames them. If no key columns are given, then the calculations are applied to all rows in the specified columns. If one or more key columns are given, the calculations are applied to each unique group in the key column(s).

Examples

Each of the following subsections illustrates how to use update_by.

A single UpdateByOperation with no grouping columns

The following example calculates the tick-based rolling sum of the X column in the source table. No key columns are provided, so a single group exists that contains all rows of the table.

from deephaven.updateby import rolling_sum_tick
from deephaven import empty_table

source = empty_table(20).update(["X = i"])

result = source.update_by(
ops=rolling_sum_tick(cols=["RollingSumX = X"], rev_ticks=3, fwd_ticks=0)
)

Multiple UpdateByOperations with no grouping columns

The following example builds on the previous by performing two UpdateByOperations in a single update_by. The cumulative minimum and maximum are calculated, and the range is derived from them.

from deephaven.updateby import cum_min, cum_max
from deephaven import empty_table

source = empty_table(20).update(["X = randomInt(0, 25)"])
result = source.update_by(
ops=[cum_min(cols=["MinX = X"]), cum_max(cols=["MaxX = X"])]
).update(["RangeX = MaxX - MinX"])

Multiple UpdateByOperations with a single grouping column

The following example builds on the previous by specifying a grouping column. The grouping column is Letter, which contains alternating letters A and B. As a result, the cumulative minimum, maximum, and range are calculated on a per-letter basis. The result table is split by letter via where to show this.

from deephaven.updateby import cum_min, cum_max
from deephaven import empty_table

source = empty_table(20).update(
["Letter = (i % 2 == 0) ? `A` : `B`", "X = randomInt(0, 25)"]
)
result = source.update_by(
ops=[cum_min(cols=["MinX = X"]), cum_max(cols=["MaxX = X"])], by=["Letter"]
).update(["RangeX = MaxX - MinX"])
result_a = result.where(["Letter == `A`"])
result_b = result.where(["Letter == `B`"])

A single UpdateByOperation applied to multiple columns with multiple grouping columns

The following example builds on the previous by applying a single UpdateByOperation to multiple columns as well as specifying multiple grouping columns. The grouping columns, Letter and Truth, contain alternating letters and random true/false values. Thus, groups are defined by unique combinations of letter and boolean. The result table is split by letter and truth value to show the unique groups.

from deephaven.updateby import rolling_sum_tick, cum_max, cum_min
from deephaven import empty_table

source = empty_table(20).update(
[
"Letter = (i % 2 == 0) ? `A` : `B`",
"Truth = randomBool()",
"X = randomInt(0, 25)",
"Y = randomInt(50, 75)",
]
)

rolling_sum_ops = rolling_sum_tick(
cols=["RollingSumX = X", "RollingSumY = Y"], rev_ticks=5, fwd_ticks=0
)
min_ops = cum_min(cols=["MinX = X", "MinY = Y"])
max_ops = cum_max(cols=["MaxX = X", "MaxY = Y"])

result = source.update_by(
ops=[rolling_sum_ops, min_ops, max_ops], by=["Letter", "Truth"]
).update(["RangeX = MaxX - MinX", "RangeY = MaxY - MinY"])
result_a_true = result.where(["Letter == `A`", "Truth == true"])
result_a_false = result.where(["Letter == `A`", "Truth == false"])
result_b_true = result.where(["Letter == `B`", "Truth == true"])
result_b_false = result.where(["Letter == `B`", "Truth == false"])

Applying an UpdateByOperation to all columns

The following example uses forward_fill to fill null values with the most recent previous non-null value. No columns are given to forward_fill, so the forward-fill is applied to all columns in the source table except for the specified key column(s). This also means that the X column is replaced in the result table by the forward-filled X values.

from deephaven.updateby import forward_fill
from deephaven.constants import NULL_INT
from deephaven import empty_table

source = empty_table(10).update(
[
"Letter = (i % 2 == 0) ? `A` : `B`",
"X = (i % 3 == 0) ? NULL_INT : i",
"Y = (i % 5 == 2) ? i : NULL_INT",
]
)

result = source.update_by(ops=forward_fill(cols=[]), by=["Letter"])

Tick-based windowed calculations

There are multiple UpdateByOperations that are windowed by ticks. When an operation is windowed, the window is defined when creating the operation.

For all tick-based windowed calculations, the window size and location relative to the current row are defined by two input parameters: rev_ticks and fwd_ticks. The former defines how far backwards the window goes, whereas the latter defines how far forwards it goes. rev_ticks is inclusive of the current row: rev_ticks = 1 means the window starts at the current row. fwd_ticks is not inclusive of the current row: fwd_ticks = 0 means the window ends at the current row. Both of these values can be either positive or negative. The bulleted list below gives several examples of these two parameters and the rolling window they create.

  • rev_ticks = 1, fwd_ticks = 0 - Contains only the current row.
  • rev_ticks = 10, fwd_ticks = 0 - Contains 9 previous rows and the current row.
  • rev_ticks = 0, fwd_ticks = 10 - Contains the following 10 rows; excludes the current row.
  • rev_ticks = 10, fwd_ticks = 10 - Contains the previous 9 rows, the current row and the 10 rows following.
  • rev_ticks = 10, fwd_ticks = -5 - Contains 5 rows, beginning at 9 rows before, ending at 5 rows before the current row (inclusive).
  • rev_ticks = 11, fwd_ticks = -1 - Contains 10 rows, beginning at 10 rows before, ending at 1 row before the current row (inclusive).
  • rev_ticks = -5, fwd_ticks = 10 - Contains 5 rows, beginning 5 rows following, ending at 10 rows following the current row (inclusive).

The following example:

  • Creates a static source table with two columns.
  • Calculates the rolling sum of X grouped by Letter.
    • Three rolling sums are calculated using a window before, containing, and after to the current row.
  • Splits the result table by letter via where to show how the windowed calculations are performed on a per-group basis.
from deephaven.updateby import rolling_sum_tick
from deephaven import empty_table

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

op_contains = rolling_sum_tick(cols=["ContainsX = X"], rev_ticks=1, fwd_ticks=1)
op_before = rolling_sum_tick(cols=["PriorX = X"], rev_ticks=3, fwd_ticks=-1)
op_after = rolling_sum_tick(cols=["PosteriorX = X"], rev_ticks=-1, fwd_ticks=3)

result = source.update_by(ops=[op_contains, op_before, op_after], by=["Letter"])
result_a = result.where(["Letter == `A`"])
result_b = result.where(["Letter == `B`"])

Time-based windowed calculations

There are multiple UpdateByOperations that are windowed by time. When an operation is windowed, the window is defined when creating the operation. These operations require the source table to contain a column of date-times.

For all time-based windowed calculations, the window size and location relative to the current row are defined by two input parameters: fwd_time and rev_time. The former defines how far forward the window goes, whereas the latter defines how far backwards it goes. These parameters parameter can be given as an integer number of nanoseconds or a string in the form HH:MM:SS.dddd. The bulleted list below explains how window sizes vary based on the two parameters:

  • rev_time = "PT00:00:00", fwd_time = "PT00:00:00" - Contains rows that exactly match the current timestamp.
  • rev_time = "PT00:10:00", fwd_time = "PT00:00:00" - Contains rows from 10m earlier through the current timestamp (inclusive).
  • rev_time = "PT00:00:00", fwd_time = "PT00:10:00" - Contains rows from the current timestamp through 10m following the current row timestamp (inclusive).
  • rev_time = int(60e9), fwd_time = int(60e9) - Contains rows from 1m earlier through 1m3 following the current timestamp (inclusive).
  • rev_time = "PT00:10:00", fwd_time = "-PT00:05:00" - Contains rows from 10m earlier through 5m before the current timestamp (inclusive). This is a purely backwards-looking window.
  • rev_time = int(-5e9), fwd_time = int(10e9) - Contains rows from 5s following through 10s following the current timestamp (inclusive). This is a purely forwards-looking window.

The following example:

  • Creates a static source table with three columns.
  • Calculates the rolling sum of X grouped by Letter.
    • Three rolling sums are calculated using a window before, containing, and after the current timestamp.
  • Splits the result table by letter via where to show how the windowed calculations are performed on a per-group basis.
from deephaven.updateby import rolling_sum_time
from deephaven.time import to_j_instant
from deephaven import empty_table

base_time = to_j_instant("2023-01-01T00:00:00 ET")

source = empty_table(20).update(
["Timestamp = base_time + i * SECOND", "X = i", "Letter = (i % 2 == 0) ? `A` : `B`"]
)

op_before = rolling_sum_time(
ts_col="Timestamp", cols=["PriorX = X"], rev_time="PT00:00:03", fwd_time=int(-1e9)
)
op_contains = rolling_sum_time(
ts_col="Timestamp",
cols=["ContainsX = X"],
rev_time="PT00:00:01",
fwd_time="PT00:00:01",
)
op_after = rolling_sum_time(
ts_col="Timestamp",
cols=["PosteriorX = X"],
rev_time="-PT00:00:01",
fwd_time=int(3e9),
)

result = source.update_by(ops=[op_before, op_contains, op_after], by=["Letter"])
result_a = result.where(["Letter == `A`"])
result_b = result.where(["Letter == `B`"])

Handling erroneous data

It's common for tables to contain null, NaN, or other erroneous values. Certain update_by operations can be told how to handle these through the use of the op_control input parameter. They are:

To see how erroneous data can be handled differently, see the OperationControl reference guide.