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title var_by

var_by returns the sample variance for each group. Null values are ignored.

Sample variance is calculated using the Bessel correction, which ensures it is an unbiased estimator of population variance under some conditions.

Caution

Applying this aggregation to a column where the sample variance cannot be computed will result in an error. For example, the sample variance is not defined for a column of string values.

Syntax

table.var_by(by: Union[str, list[str]]) -> Table

Parameters

The column(s) by which to group data.

  • [] returns the sample variance for all non-key columns (default).
  • "X" will output the sample variance of each group in column X.
  • "X", "Y" will output the sample variance of each group designated from the X and Y columns.

Returns

A new table containing the sample variance for each group.

How to calculate sample variance

Sample variance is a measure of the average dispersion of data values from the mean. Unlike sample standard deviation, it is not on the same scale as the data, meaning that sample variance cannot be readily interpreted in the same units as the data. The formula for sample variance is as follows:

$$ s = \frac{\sum_{i=1}^{n}{(x_i - \bar{x})^2}}{n-1} $$

Examples

In this example, var_by returns the sample variance of the whole table. Because the sample variance cannot be computed for the string columns X and Y, these columns are dropped before applying var_by.

from deephaven import new_table
from deephaven.column import string_col, int_col

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.drop_columns(cols=["X", "Y"]).var_by()

In this example, var_by returns the sample variance, as grouped by X. Because the sample variance cannot be computed for the string column Y, this column is dropped before applying var_by.

from deephaven import new_table
from deephaven.column import string_col, int_col

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.drop_columns(cols=["Y"]).var_by(by=["X"])

In this example, var_by returns the sample variance, as grouped by X and Y.

from deephaven import new_table
from deephaven.column import string_col, int_col

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.var_by(by=["X", "Y"])

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