pydeephaven.updateby

This module defines the UpdateByOperation and provides factory functions to create specific UpdateByOperations instances.

class BadDataBehavior(value)[source]

Bases: Enum

An Enum defining ways to handle invalid data during update-by operations.

POISON = 4

Allow the bad data to poison the result. This is only valid for use with NaN

RESET = 2

Reset the state for the bucket to None when invalid data is encountered

SKIP = 3

Skip and do not process the invalid data without changing state

THROW = 1

Throw an exception and abort processing when bad data is encountered

class DeltaControl(value)[source]

Bases: Enum

An Enum defining ways to handle null values during update-by Delta operations where delta operations return the difference between the current row and the previous row.

NULL_DOMINATES = 1

A valid value following a null value returns null

VALUE_DOMINATES = 2

A valid value following a null value returns the valid value

ZERO_DOMINATES = 3

A valid value following a null value returns zero

class MathContext(value)[source]

Bases: Enum

An Enum for predefined precision and rounding settings in numeric calculations.

DECIMAL128 = (34, 7)

a precision setting matching the IEEE 754R Decimal128 format, 34 digits, rounding is half-even

DECIMAL32 = (7, 7)

a precision setting matching the IEEE 754R Decimal32 format, 7 digits, rounding is half-even

DECIMAL64 = (16, 7)

a precision setting matching the IEEE 754R Decimal64 format, 16 digits, rounding is half-even

UNLIMITED = (0, 5)

unlimited precision arithmetic, rounding is half-up

class OperationControl(on_null=BadDataBehavior.SKIP, on_nan=BadDataBehavior.SKIP, big_value_context=MathContext.DECIMAL128)[source]

Bases: _UpdateByBase

A OperationControl represents control parameters for performing operations with the table UpdateByOperation.

Initializes an OperationControl for use with certain UpdateByOperation, such as EMAs.

Parameters:

  • on_null (BadDataBehavior) – the behavior for when null values are encountered, default is SKIP

  • on_nan (BadDataBehavior) – the behavior for when NaN values are encountered, default is SKIP

  • big_value_context (MathContext) – the context to use when processing arbitrary precision numeric values, default is DECIMAL128.

class UpdateByOperation(ub_column)[source]

Bases: _UpdateByBase

A UpdateByOperation represents an operator for the Table update-by operation.

cum_max(cols)[source]

Creates a cumulative maximum UpdateByOperation for the supplied column names.

Parameters:

cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performing the operation on all applicable columns.

Return type:

UpdateByOperation

Returns:

UpdateByOperation

cum_min(cols)[source]

Creates a cumulative minimum UpdateByOperation for the supplied column names.

Parameters:

cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

Return type:

UpdateByOperation

Returns:

UpdateByOperation

cum_prod(cols)[source]

Creates a cumulative product UpdateByOperation for the supplied column names.

Parameters:

cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

Return type:

UpdateByOperation

Returns:

UpdateByOperation

cum_sum(cols)[source]

Creates a cumulative sum UpdateByOperation for the supplied column names.

Parameters:

cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

Return type:

UpdateByOperation

Returns:

UpdateByOperation

delta(cols, delta_control=DeltaControl.NULL_DOMINATES)[source]

Creates a delta UpdateByOperation for the supplied column names. The Delta operation produces values by computing the difference between the current value and the previous value. When the current value is null, this operation will output null. When the current value is valid, the output will depend on the DeltaControl provided.

When delta_control is not provided or set to NULL_DOMINATES, a value following a null value returns null. When delta_control is set to VALUE_DOMINATES, a value following a null value returns the value. When delta_control is set to ZERO_DOMINATES, a value following a null value returns zero.

Parameters:

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

  • delta_control (DeltaControl) – defines how special cases should behave; when None, the default DeltaControl settings of VALUE_DOMINATES will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

ema_tick(decay_ticks, cols, op_control=None)[source]

Creates an EMA (exponential moving average) UpdateByOperation for the supplied column names, using ticks as the decay unit.

The formula used is

a = e^(-1 / decay_ticks) ema_next = a * ema_last + (1 - a) * value

Parameters:

  • decay_ticks (float) – the decay rate in ticks

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

  • op_control (OperationControl) – defines how special cases should behave; when None, the default OperationControl settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

ema_time(ts_col, decay_time, cols, op_control=None)[source]

Creates an EMA (exponential moving average) UpdateByOperation for the supplied column names, using time as the decay unit.

The formula used is

a = e^(-dt / decay_time) ema_next = a * ema_last + (1 - a) * value

Args:

ts_col (str): the column in the source table to use for timestamps decay_time (Union[int, str]): the decay rate, can be expressed as an integer in nanoseconds or a time

interval string, e.g. “PT00:00:.001” or “PT5M”

cols (Union[str, List[str]]): the column(s) to be operated on, can include expressions to rename the output,

i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

op_control (OperationControl): defines how special cases should behave; when None, the default OperationControl

settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

emmax_tick(decay_ticks, cols, op_control=None)[source]

Creates an EM Max (exponential moving maximum) UpdateByOperation for the supplied column names, using ticks as the decay unit.

The formula used is

a = e^(-1 / decay_ticks) em_val_next = max(a * em_val_last, value)

Parameters:

  • decay_ticks (float) – the decay rate in ticks

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

  • op_control (OperationControl) – defines how special cases should behave; when None, the default OperationControl settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

emmax_time(ts_col, decay_time, cols, op_control=None)[source]

Creates an EM Max (exponential moving maximum) UpdateByOperation for the supplied column names, using time as the decay unit.

The formula used is

a = e^(-dt / decay_time) em_val_next = max(a * em_val_last, value)

Args:

ts_col (str): the column in the source table to use for timestamps decay_time (Union[int, str]): the decay rate, can be expressed as an integer in nanoseconds or a time

interval string, e.g. “PT00:00:.001” or “PT5M”

cols (Union[str, List[str]]): the column(s) to be operated on, can include expressions to rename the output,

i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

op_control (OperationControl): defines how special cases should behave; when None, the default OperationControl

settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

emmin_tick(decay_ticks, cols, op_control=None)[source]

Creates an EM Min (exponential moving minimum) UpdateByOperation for the supplied column names, using ticks as the decay unit.

The formula used is

a = e^(-1 / decay_ticks) em_val_next = min(a * em_val_last, value)

Parameters:

  • decay_ticks (float) – the decay rate in ticks

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

  • op_control (OperationControl) – defines how special cases should behave; when None, the default OperationControl settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

emmin_time(ts_col, decay_time, cols, op_control=None)[source]

Creates an EM Min (exponential moving minimum) UpdateByOperation for the supplied column names, using time as the decay unit.

The formula used is

a = e^(-dt / decay_time) em_val_next = min(a * em_val_last, value)

Args:

ts_col (str): the column in the source table to use for timestamps decay_time (Union[int, str]): the decay rate, can be expressed as an integer in nanoseconds or a time

interval string, e.g. “PT00:00:.001” or “PT5M”

cols (Union[str, List[str]]): the column(s) to be operated on, can include expressions to rename the output,

i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

op_control (OperationControl): defines how special cases should behave; when None, the default OperationControl

settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

ems_tick(decay_ticks, cols, op_control=None)[source]

Creates an EMS (exponential moving sum) UpdateByOperation for the supplied column names, using ticks as the decay unit.

The formula used is

a = e^(-1 / decay_ticks) ems_next = a * ems_last + value

Parameters:

  • decay_ticks (float) – the decay rate in ticks

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

  • op_control (OperationControl) – defines how special cases should behave; when None, the default OperationControl settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

ems_time(ts_col, decay_time, cols, op_control=None)[source]

Creates an EMS (exponential moving sum) UpdateByOperation for the supplied column names, using time as the decay unit.

The formula used is

a = e^(-dt / decay_time) eems_next = a * ems_last + value

Args:

ts_col (str): the column in the source table to use for timestamps decay_time (Union[int, str]): the decay rate, can be expressed as an integer in nanoseconds or a time

interval string, e.g. “PT00:00:.001” or “PT5M”

cols (Union[str, List[str]]): the column(s) to be operated on, can include expressions to rename the output,

i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

op_control (OperationControl): defines how special cases should behave; when None, the default OperationControl

settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

emstd_tick(decay_ticks, cols, op_control=None)[source]

Creates an EM Std (exponential moving standard deviation) UpdateByOperation for the supplied column names, using ticks as the decay unit.

The formula used is

a = e^(-1 / decay_ticks) variance = a * (prevVariance + (1 − a) * (x − prevEma)^2) ema = a * prevEma + x std = sqrt(variance)

Parameters:

  • decay_ticks (float) – the decay rate in ticks

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

  • op_control (OperationControl) – defines how special cases should behave; when None, the default OperationControl settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

emstd_time(ts_col, decay_time, cols, op_control=None)[source]

Creates an EM Std (exponential moving standard deviation) UpdateByOperation for the supplied column names, using time as the decay unit.

The formula used is

a = e^(-dt / timeDecay) variance = a * (prevVariance + (1 − a) * (x − prevEma)^2) ema = a * prevEma + x std = sqrt(variance)

Args:

ts_col (str): the column in the source table to use for timestamps decay_time (Union[int, str]): the decay rate, can be expressed as an integer in nanoseconds or a time

interval string, e.g. “PT00:00:.001” or “PT5M”

cols (Union[str, List[str]]): the column(s) to be operated on, can include expressions to rename the output,

i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

op_control (OperationControl): defines how special cases should behave; when None, the default OperationControl

settings as specified in __init__() will be used

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

forward_fill(cols)[source]

Creates a forward fill UpdateByOperation for the supplied column names. Null values in the column(s) are replaced by the last known non-null values. This operation is forward only.

Parameters:

cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the operation on all applicable columns.

Return type:

UpdateByOperation

Returns:

UpdateByOperation

rolling_avg_tick(cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling average UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows.

Here are some examples of window values:

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)

Parameters:

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling average operation on all columns.

  • rev_ticks (int) – the look-behind window size (in rows/ticks)

  • fwd_ticks (int) – the look-forward window size (int rows/ticks), default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_avg_time(ts_col, cols, rev_time, fwd_time=0)[source]

Creates a rolling average UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

Here are some examples of window values:

rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp (

inclusive)

rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the

current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following

the current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the

current row timestamp (inclusive), this is a purely backwards looking window

rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00”} - contains rows from 5m following through 10m

following the current row timestamp (inclusive), this is a purely forwards looking window

Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

  • rev_time (Union[int, str]) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”

  • fwd_time (Union[int, str]) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_count_tick(cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling count UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows.

Here are some examples of window values:

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)

Parameters:

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling count operation on all columns.

  • rev_ticks (int) – the look-behind window size (in rows/ticks)

  • fwd_ticks (int) – the look-forward window size (int rows/ticks), default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_count_time(ts_col, cols, rev_time, fwd_time=0)[source]

Creates a rolling count UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

Here are some examples of window values:

rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp (

inclusive)

rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the

current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following

the current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the

current row timestamp (inclusive), this is a purely backwards looking window

rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00”} - contains rows from 5m following through 10m

following the current row timestamp (inclusive), this is a purely forwards looking window

Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

  • rev_time (Union[int, str]) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”

  • fwd_time (Union[int, str]) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_formula_tick(formula, formula_param, cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling formula UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows.

User-defined formula can contain a combination of any of the following:
Built-in functions such as min, max, etc.
Mathematical arithmetic such as *, +, /, etc.
User-defined functions
Here are some examples of window values:
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)
Parameters:

  • formula (str) – the user defined formula to apply to each group.

  • formula_param (str) – the parameter name for the input column’s vector within the formula. If formula is max(each), then each is the formula_param.

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling formula operation on all columns.

  • rev_ticks (int) – the look-behind window size (in rows/ticks)

  • fwd_ticks (int) – the look-forward window size (int rows/ticks), default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_formula_time(ts_col, formula, formula_param, cols, rev_time, fwd_time=0)[source]

Creates a rolling formula UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

User-defined formula can contain a combination of any of the following:
Built-in functions such as min, max, etc.
Mathematical arithmetic such as *, +, /, etc.
User-defined functions
Here are some examples of window values:
rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp
rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp ( inclusive)
rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the current row timestamp (inclusive)
rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following the current row timestamp (inclusive)
rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the current row timestamp (inclusive), this is a purely backwards looking window
rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00” - contains rows from 5m following through 10m following the current row timestamp (inclusive), this is a purely forwards looking window
Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • formula (str) – the user defined formula to apply to each group.

  • formula_param (str) – the parameter name for the input column’s vector within the formula. If formula is max(each), then each is the formula_param.

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling formula operation on all columns.

  • rev_time (int) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:00.001” or “PT5M”

  • fwd_time (int) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:00.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_group_tick(cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling group UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows. :rtype: UpdateByOperation

Here are some examples of window values:

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)

Args:
cols (Union[str, List[str]]): the column(s) to be operated on, can include expressions to rename the output,

i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

rev_ticks (int): the look-behind window size (in rows/ticks) fwd_ticks (int): the look-forward window size (int rows/ticks), default is 0

Returns:

an UpdateByOperation

Raises:

DHError

rolling_group_time(ts_col, cols, rev_time, fwd_time=0)[source]

Creates a rolling group UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

Here are some examples of window values:

rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp (

inclusive)

rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the

current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following

the current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the

current row timestamp (inclusive), this is a purely backwards looking window

rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00”} - contains rows from 5m following through 10m

following the current row timestamp (inclusive), this is a purely forwards looking window

Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

  • rev_time (Union[int, str]) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”

  • fwd_time (Union[int, str]) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_max_tick(cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling maximum UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows.

Here are some examples of window values:

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)

Parameters:

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling maximum operation on all columns.

  • rev_ticks (int) – the look-behind window size (in rows/ticks)

  • fwd_ticks (int) – the look-forward window size (int rows/ticks), default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_max_time(ts_col, cols, rev_time, fwd_time=0)[source]

Creates a rolling maximum UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

Here are some examples of window values:

rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp (

inclusive)

rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the

current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following

the current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the

current row timestamp (inclusive), this is a purely backwards looking window

rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00”} - contains rows from 5m following through 10m

following the current row timestamp (inclusive), this is a purely forwards looking window

Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

  • rev_time (Union[int, str]) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”

  • fwd_time (Union[int, str]) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_min_tick(cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling minimum UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows.

Here are some examples of window values:

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)

Parameters:

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling minimum operation on all columns.

  • rev_ticks (int) – the look-behind window size (in rows/ticks)

  • fwd_ticks (int) – the look-forward window size (int rows/ticks), default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_min_time(ts_col, cols, rev_time, fwd_time=0)[source]

Creates a rolling minimum UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

Here are some examples of window values:

rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp (

inclusive)

rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the

current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following

the current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the

current row timestamp (inclusive), this is a purely backwards looking window

rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00”} - contains rows from 5m following through 10m

following the current row timestamp (inclusive), this is a purely forwards looking window

Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

  • rev_time (Union[int, str]) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”

  • fwd_time (Union[int, str]) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_prod_tick(cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling product UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows.

Here are some examples of window values:

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)

Parameters:

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling product operation on all columns.

  • rev_ticks (int) – the look-behind window size (in rows/ticks)

  • fwd_ticks (int) – the look-forward window size (int rows/ticks), default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_prod_time(ts_col, cols, rev_time, fwd_time=0)[source]

Creates a rolling product UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

Here are some examples of window values:

rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp (

inclusive)

rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the

current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following

the current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the

current row timestamp (inclusive), this is a purely backwards looking window

rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00”} - contains rows from 5m following through 10m

following the current row timestamp (inclusive), this is a purely forwards looking window

Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

  • rev_time (Union[int, str]) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”

  • fwd_time (Union[int, str]) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_std_tick(cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling sample standard deviation UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows.

Sample standard deviation is computed using Bessel’s correction, which ensures that the sample variance will be an unbiased estimator of population variance.

Here are some examples of window values:

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)

Parameters:

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sample standard deviation operation on all columns.

  • rev_ticks (int) – the look-behind window size (in rows/ticks)

  • fwd_ticks (int) – the look-forward window size (int rows/ticks), default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_std_time(ts_col, cols, rev_time, fwd_time=0)[source]

Creates a rolling sample standard deviation UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

Sample standard deviation is computed using Bessel’s correction, which ensures that the sample variance will be an unbiased estimator of population variance.

Here are some examples of window values:

rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp (

inclusive)

rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the

current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following

the current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the

current row timestamp (inclusive), this is a purely backwards looking window

rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00”} - contains rows from 5m following through 10m

following the current row timestamp (inclusive), this is a purely forwards looking window

Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

  • rev_time (Union[int, str]) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”

  • fwd_time (Union[int, str]) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_sum_tick(cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling sum UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows.

Here are some examples of window values:

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)

Parameters:

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

  • rev_ticks (int) – the look-behind window size (in rows/ticks)

  • fwd_ticks (int) – the look-forward window size (int rows/ticks), default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_sum_time(ts_col, cols, rev_time, fwd_time=0)[source]

Creates a rolling sum UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

Here are some examples of window values:

rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp

(inclusive)

rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the

current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following

the current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the

current row timestamp (inclusive), this is a purely backwards looking window

rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00”} - contains rows from 5m following through 10m

following the current row timestamp (inclusive), this is a purely forwards looking window

Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling sum operation on all columns.

  • rev_time (Union[int, str]) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”

  • fwd_time (Union[int, str]) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_wavg_tick(wcol, cols, rev_ticks, fwd_ticks=0)[source]

Creates a rolling weighted average UpdateByOperation for the supplied column names, using ticks as the windowing unit. Ticks are row counts, and you may specify the reverse and forward window in number of rows to include. The current row is considered to belong to the reverse window but not the forward window. Also, negative values are allowed and can be used to generate completely forward or completely reverse windows.

Here are some examples of window values:

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)

Parameters:

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling weighted average operation on all columns.

  • wcol (str) – the column containing the weight values

  • rev_ticks (int) – the look-behind window size (in rows/ticks)

  • fwd_ticks (int) – the look-forward window size (int rows/ticks), default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –

rolling_wavg_time(ts_col, wcol, cols, rev_time, fwd_time=0)[source]

Creates a rolling weighted average UpdateByOperation for the supplied column names, using time as the windowing unit. This function accepts nanoseconds or time strings as the reverse and forward window parameters. Negative values are allowed and can be used to generate completely forward or completely reverse windows. A row containing a null in the timestamp column belongs to no window and will not be considered in the windows of other rows; its output will be null.

Here are some examples of window values:

rev_time = 0, fwd_time = 0 - contains rows that exactly match the current row timestamp rev_time = “PT00:10:00”, fwd_time = “0” - contains rows from 10m before through the current row timestamp (

inclusive)

rev_time = 0, fwd_time = 600_000_000_000 - contains rows from the current row through 10m following the

current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “PT00:10:00” - contains rows from 10m before through 10m following

the current row timestamp (inclusive)

rev_time = “PT00:10:00”, fwd_time = “-PT00:05:00” - contains rows from 10m before through 5m before the

current row timestamp (inclusive), this is a purely backwards looking window

rev_time = “-PT00:05:00”, fwd_time = “PT00:10:00”} - contains rows from 5m following through 10m

following the current row timestamp (inclusive), this is a purely forwards looking window

Parameters:

  • ts_col (str) – the timestamp column for determining the window

  • cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the rolling weighted average operation on all columns.

  • wcol (str) – the column containing the weight values

  • rev_time (Union[int, str]) – the look-behind window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”

  • fwd_time (Union[int, str]) – the look-ahead window size, can be expressed as an integer in nanoseconds or a time interval string, e.g. “PT00:00:.001” or “PT5M”, default is 0

Return type:

UpdateByOperation

Returns:

an UpdateByOperation

Raises:

DHError –