stdFor

Returns the standard deviation of the values of each selected column of this DataFrame separately.

All primitive number types are supported: Byte, Short, Int, Long, Float, and Double. "Mixed" Number input is supported too, as long as it consists solely of those primitive numbers; its values are then first converted to their common type using UnifiedNumberTypeOptions.PRIMITIVES_ONLY, see number unification. Big numbers (BigInteger, BigDecimal) are not supported; convert them to a primitive number type first.

null values in the input are always ignored.

If the input contains NaN values, the result will be NaN, unless skipNaN is set to true.

The resulting standard deviation is always a Double and never null. Converting Long values to Double may lose precision for very large values.

For more information about the resulting types: See "std Type Conversion" on the documentation website.

Result cells for which there is nothing to compute the standard deviation of (for instance, because the input was empty or contained only null values) simply become Double.NaN. The same holds when there are no more than ddof values, so, by default, for a single value.

The columns are selected with the ColumnsForAggregateSelectionDsl — an extension of the Columns Selection DSL which lets you rename the result of a column with into and supply a default value for columns without any values.

See Selecting Columns.

See also:

• std() — the same, but for all suitable columns at once.

• std{ columns } — a single standard deviation of all values in the selected columns.

• meanFor — the mean of each selected column.

• The Std Operation — an overview of all std modes.

For more information: See std on the documentation website.

Example

// A single row with the standard deviation of the "age" values and of the "weight" values
df.stdFor { age and weight }
// The same, ignoring `NaN` values, and naming the results explicitly
df.stdFor(skipNaN = true) { age into "stdAge" and (weight into "stdWeight") }

Parameters

Returns the standard deviation of the values of each selected column of this DataFrame separately.

All primitive number types are supported: Byte, Short, Int, Long, Float, and Double. "Mixed" Number input is supported too, as long as it consists solely of those primitive numbers; its values are then first converted to their common type using UnifiedNumberTypeOptions.PRIMITIVES_ONLY, see number unification. Big numbers (BigInteger, BigDecimal) are not supported; convert them to a primitive number type first.

null values in the input are always ignored.

If the input contains NaN values, the result will be NaN, unless skipNaN is set to true.

The resulting standard deviation is always a Double and never null. Converting Long values to Double may lose precision for very large values.

For more information about the resulting types: See "std Type Conversion" on the documentation website.

Result cells for which there is nothing to compute the standard deviation of (for instance, because the input was empty or contained only null values) simply become Double.NaN. The same holds when there are no more than ddof values, so, by default, for a single value.

See Selecting Columns.

See also:

• std() — the same, but for all suitable columns at once.

• std{ columns } — a single standard deviation of all values in the selected columns.

• meanFor — the mean of each selected column.

• The Std Operation — an overview of all std modes.

For more information: See std on the documentation website.

Example

// A single row with the standard deviation of the "age" values and of the "weight" values
df.stdFor("age", "weight")

Parameters

Aggregates this GroupBy by computing the standard deviation of the values of each selected column separately, per group.

Returns a new DataFrame with one row per group, containing the group key columns and a column with the standard deviation for each selected column.

All primitive number types are supported: Byte, Short, Int, Long, Float, and Double. "Mixed" Number input is supported too, as long as it consists solely of those primitive numbers; its values are then first converted to their common type using UnifiedNumberTypeOptions.PRIMITIVES_ONLY, see number unification. Big numbers (BigInteger, BigDecimal) are not supported; convert them to a primitive number type first.

null values in the input are always ignored.

If the input contains NaN values, the result will be NaN, unless skipNaN is set to true.

The resulting standard deviation is always a Double and never null. Converting Long values to Double may lose precision for very large values.

For more information about the resulting types: See "std Type Conversion" on the documentation website.

Result cells for which there is nothing to compute the standard deviation of (for instance, because the input was empty or contained only null values) simply become Double.NaN. The same holds when there are no more than ddof values, so, by default, for a single value.

The columns are selected with the ColumnsForAggregateSelectionDsl — an extension of the Columns Selection DSL which lets you rename the result of a column with into and supply a default value for columns without any values.

See Selecting Columns.

See also:

• std() — the same, but for all suitable columns at once.

• std{ columns } — a single standard deviation of all values in the selected columns, per group.

• meanFor — the mean of each selected column, per group.

• aggregate — the general way to aggregate groups.

• The Std Operation — an overview of all std modes.

For more information: See "groupBy statistics" on the documentation website., and See "GroupBy Aggregation Statistics" on the documentation website.

Example

// For each city, the standard deviation of the "age" values and of the "weight" values
df.groupBy { city }.stdFor { age and weight }

Parameters

Aggregates this GroupBy by computing the standard deviation of the values of each selected column separately, per group.

Returns a new DataFrame with one row per group, containing the group key columns and a column with the standard deviation for each selected column.

All primitive number types are supported: Byte, Short, Int, Long, Float, and Double. "Mixed" Number input is supported too, as long as it consists solely of those primitive numbers; its values are then first converted to their common type using UnifiedNumberTypeOptions.PRIMITIVES_ONLY, see number unification. Big numbers (BigInteger, BigDecimal) are not supported; convert them to a primitive number type first.

null values in the input are always ignored.

If the input contains NaN values, the result will be NaN, unless skipNaN is set to true.

The resulting standard deviation is always a Double and never null. Converting Long values to Double may lose precision for very large values.

For more information about the resulting types: See "std Type Conversion" on the documentation website.

Result cells for which there is nothing to compute the standard deviation of (for instance, because the input was empty or contained only null values) simply become Double.NaN. The same holds when there are no more than ddof values, so, by default, for a single value.

See Selecting Columns.

See also:

• std() — the same, but for all suitable columns at once.

• std{ columns } — a single standard deviation of all values in the selected columns, per group.

• meanFor — the mean of each selected column, per group.

• aggregate — the general way to aggregate groups.

• The Std Operation — an overview of all std modes.

For more information: See "groupBy statistics" on the documentation website., and See "GroupBy Aggregation Statistics" on the documentation website.

Example

// For each city, the standard deviation of the "age" values and of the "weight" values
df.groupBy { city }.stdFor("age", "weight")

Parameters

Aggregates this Pivot by computing the standard deviation of the values of each selected column separately, per group.

Returns a single DataRow with the pivot keys as (nested) columns, containing the standard deviation of each selected column of the corresponding group.

All primitive number types are supported: Byte, Short, Int, Long, Float, and Double. "Mixed" Number input is supported too, as long as it consists solely of those primitive numbers; its values are then first converted to their common type using UnifiedNumberTypeOptions.PRIMITIVES_ONLY, see number unification. Big numbers (BigInteger, BigDecimal) are not supported; convert them to a primitive number type first.

null values in the input are always ignored.

If the input contains NaN values, the result will be NaN, unless skipNaN is set to true.

The resulting standard deviation is always a Double and never null. Converting Long values to Double may lose precision for very large values.

For more information about the resulting types: See "std Type Conversion" on the documentation website.

Result cells for which there is nothing to compute the standard deviation of (for instance, because the input was empty or contained only null values) simply become Double.NaN. The same holds when there are no more than ddof values, so, by default, for a single value.

The columns are selected with the ColumnsForAggregateSelectionDsl — an extension of the Columns Selection DSL which lets you rename the result of a column with into and supply a default value for columns without any values.

See Selecting Columns, or check out the Pivot Grammar.

See also:

• std() — the same, but for all suitable columns at once.

• std{ columns } — a single standard deviation of all values in the selected columns, per group.

• Pivot aggregation — all other ways to aggregate a Pivot.

• The Std Operation — an overview of all std modes.

For more information: See "pivot statistics" on the documentation website., and See "Pivot Aggregation statistics" on the documentation website.

Example

// For each city, the standard deviation of the "age" values and of the "weight" values
df.pivot { city }.stdFor { age and weight }
// The same, but with the results grouped by aggregated column instead of by city
df.pivot { city }.stdFor(separate = true) { age and weight }

Parameters

Aggregates this Pivot by computing the standard deviation of the values of each selected column separately, per group.

Returns a single DataRow with the pivot keys as (nested) columns, containing the standard deviation of each selected column of the corresponding group.

All primitive number types are supported: Byte, Short, Int, Long, Float, and Double. "Mixed" Number input is supported too, as long as it consists solely of those primitive numbers; its values are then first converted to their common type using UnifiedNumberTypeOptions.PRIMITIVES_ONLY, see number unification. Big numbers (BigInteger, BigDecimal) are not supported; convert them to a primitive number type first.

null values in the input are always ignored.

If the input contains NaN values, the result will be NaN, unless skipNaN is set to true.

The resulting standard deviation is always a Double and never null. Converting Long values to Double may lose precision for very large values.

For more information about the resulting types: See "std Type Conversion" on the documentation website.

Result cells for which there is nothing to compute the standard deviation of (for instance, because the input was empty or contained only null values) simply become Double.NaN. The same holds when there are no more than ddof values, so, by default, for a single value.

See Selecting Columns, or check out the Pivot Grammar.

See also:

• std() — the same, but for all suitable columns at once.

• std{ columns } — a single standard deviation of all values in the selected columns, per group.

• Pivot aggregation — all other ways to aggregate a Pivot.

• The Std Operation — an overview of all std modes.

For more information: See "pivot statistics" on the documentation website., and See "Pivot Aggregation statistics" on the documentation website.

Example

// For each city, the standard deviation of the "age" values and of the "weight" values
df.pivot { city }.stdFor("age", "weight")

Parameters

Aggregates this PivotGroupBy by computing the standard deviation of the values of each selected column separately, per group.

Returns a DataFrame where each cell contains the standard deviation of each selected column of the group corresponding to that pivot key (column) and groupBy key (row).

All primitive number types are supported: Byte, Short, Int, Long, Float, and Double. "Mixed" Number input is supported too, as long as it consists solely of those primitive numbers; its values are then first converted to their common type using UnifiedNumberTypeOptions.PRIMITIVES_ONLY, see number unification. Big numbers (BigInteger, BigDecimal) are not supported; convert them to a primitive number type first.

null values in the input are always ignored.

If the input contains NaN values, the result will be NaN, unless skipNaN is set to true.

The resulting standard deviation is always a Double and never null. Converting Long values to Double may lose precision for very large values.

For more information about the resulting types: See "std Type Conversion" on the documentation website.

Result cells for which there exists a group, but there is nothing to compute the standard deviation of (for instance, because the group was empty or contained only null values) simply become Double.NaN. The same holds when there are no more than ddof values, so, by default, for a single value.

For empty pivot intersections, null or the set default is used.

The columns are selected with the ColumnsForAggregateSelectionDsl — an extension of the Columns Selection DSL which lets you rename the result of a column with into and supply a default value for columns without any values.

See Selecting Columns, or check out the PivotGroupBy Grammar.

See also:

• std() — the same, but for all suitable columns at once.

• std{ columns } — a single standard deviation of all values in the selected columns, per group.

• PivotGroupBy aggregation — all other ways to aggregate a PivotGroupBy.

• The Std Operation — an overview of all std modes.

For more information: See "pivot statistics" on the documentation website., and See "Pivot Aggregation statistics" on the documentation website.

Example

// Per city and last name, the standard deviation of the "age" values and of the "weight" values
df.pivot { city }.groupBy { name.lastName }.stdFor { age and weight }

Parameters

Aggregates this PivotGroupBy by computing the standard deviation of the values of each selected column separately, per group.

Returns a DataFrame where each cell contains the standard deviation of each selected column of the group corresponding to that pivot key (column) and groupBy key (row).

All primitive number types are supported: Byte, Short, Int, Long, Float, and Double. "Mixed" Number input is supported too, as long as it consists solely of those primitive numbers; its values are then first converted to their common type using UnifiedNumberTypeOptions.PRIMITIVES_ONLY, see number unification. Big numbers (BigInteger, BigDecimal) are not supported; convert them to a primitive number type first.

null values in the input are always ignored.

If the input contains NaN values, the result will be NaN, unless skipNaN is set to true.

The resulting standard deviation is always a Double and never null. Converting Long values to Double may lose precision for very large values.

For more information about the resulting types: See "std Type Conversion" on the documentation website.

Result cells for which there exists a group, but there is nothing to compute the standard deviation of (for instance, because the group was empty or contained only null values) simply become Double.NaN. The same holds when there are no more than ddof values, so, by default, for a single value.

For empty pivot intersections, null or the set default is used.

See Selecting Columns, or check out the PivotGroupBy Grammar.

See also:

• std() — the same, but for all suitable columns at once.

• std{ columns } — a single standard deviation of all values in the selected columns, per group.

• PivotGroupBy aggregation — all other ways to aggregate a PivotGroupBy.

• The Std Operation — an overview of all std modes.

For more information: See "pivot statistics" on the documentation website., and See "Pivot Aggregation statistics" on the documentation website.

Example

// Per city and last name, the standard deviation of the "age" values and of the "weight" values
df.pivot { city }.groupBy { name.lastName }.stdFor("age", "weight")

Parameters