std
agg.std
returns an aggregator that computes the sample standard deviation of values, within an aggregation group, for each input column.
Sample standard deviation is calculated as the square root of the Besselcorrected sample variance, which can be shown to be an unbiased estimator of population variance under some conditions. However, sample standard deviation is a biased estimator of population standard deviation.
Syntax
std(cols: Union[str, list[str]]) > Aggregation
Parameters
Parameter  Type  Description 

cols  Union[str, list[str]]  The source column(s) for the calculations.

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 an error.
Returns
An aggregator that computes the sample standard deviation of values, within an aggregation group, for each input column.
Examples
In this example, agg.std
returns the sample standard deviation of values of Number
as grouped by X
.
from deephaven import new_table
from deephaven.column import string_col, int_col, double_col
from deephaven import agg as agg
source = new_table(
[
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "N", "O", "N", "P", "M", "O", "P", "M"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
]
)
result = source.agg_by([agg.std(cols=["Number"])], by=["X"])
 source
 result
In this example, agg.std
returns the sample standard deviation of values of Number
(renamed to Std
), as grouped by X
.
from deephaven import new_table
from deephaven.column import string_col, int_col, double_col
from deephaven import agg as agg
source = new_table(
[
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "N", "O", "N", "P", "M", "O", "P", "M"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
]
)
result = source.agg_by([agg.std(cols=["Std = Number"])], by=["X"])
 source
 result
In this example, agg.std
returns the sample standard deviation of values of Number
(renamed to Std
), as grouped by X
and Y
.
from deephaven import new_table
from deephaven.column import string_col, int_col, double_col
from deephaven import agg as agg
source = new_table(
[
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "N", "O", "N", "P", "M", "O", "P", "M"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
]
)
result = source.agg_by([agg.std(cols=["Std = Number"])], by=["X", "Y"])
 source
 result
In this example, agg.first
returns the sample standard deviation of values of Number
, and agg.median
returns the median value of Number
, as grouped by X
.
from deephaven import new_table
from deephaven.column import string_col, int_col, double_col
from deephaven import agg as agg
source = new_table(
[
string_col("X", ["A", "B", "A", "C", "B", "A", "B", "B", "C"]),
string_col("Y", ["M", "P", "O", "N", "P", "M", "O", "P", "N"]),
int_col("Number", [55, 76, 20, 130, 230, 50, 73, 137, 214]),
]
)
result = source.agg_by(
[agg.std(cols=["Std = Number"]), agg.median(cols=["Median = Number"])], by=["X"]
)
 source
 result