avg
agg.avg
returns an aggregator that computes the average (mean) of values, within an aggregation group, for each input column.
Syntax
avg(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 average (mean) of values, within an aggregation group, for each input column.
Examples
In this example, agg.avg
returns the average 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", "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.avg(cols=["Number"])], by=["X"])
 source
 result
In this example, agg.avg
returns the average value of Number
(renamed to Avg
), 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.avg(cols=["Avg = Number"])], by=["X"])
 source
 result
In this example, agg.avg
returns the average value of Number
, 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", "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.avg(cols=["Avg = Number"])], by=["X", "Y"])
 source
 result
In this example, agg.avg
returns the average value of Number
, and agg.std
returns the sample standard deviation 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
agg_list = [agg.avg(cols=["AvgNumber = Number"]), agg.std(cols=["StdNumber = Number"])]
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(aggs=agg_list, by=["X"])
 source
 result