Version: Python

# var_by

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

##### caution

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

## Syntax​

table.var_by(by: List[str]=[])

## Parameters​

ParameterTypeDescription
by optionalList[str]

The column(s) by which to group data.

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

## Returns​

A new table containing the variance for each group.

## How to calculate variance​

1. Find the mean of the data set. Add all data values and divide by the sample size $n$.
$\bar{x} = \frac{\sum_{i=1}^{n}{x_i}}{n}$
1. Find the squared difference from the mean for each data value. Subtract the mean from each data value and square the result.
$(x_i - \bar{x})^2$
1. Find the sum of all the squared differences. The sum of squares is all the squared differences added together.
$SS = \sum_{i=1}^{n}{(x_i - \bar{x})^2}$
1. Calculate the variance. Variance is the sum of squares divided by the number of data points. The formula for variance for a sample set of data is:
$s^2 = \frac{\Sigma (x_i - \bar{x})^2 }{n-1}$

## Examples​

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

from deephaven import new_tablefrom deephaven.column import string_col, int_colsource = 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 variance, as grouped by X. Because the variance can not be computed for the string column Y, this column is dropped before applying var_by.

from deephaven import new_tablefrom deephaven.column import string_col, int_colsource = 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 variance, as grouped by X and Y.

from deephaven import new_tablefrom deephaven.column import string_col, int_colsource = 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"])