meanFor

Returns the mean 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 average 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 "mean Type Conversion" on the documentation website.

Result cells for which there is nothing to average (for instance, because the input was empty or contained only null values) simply become Double.NaN. 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.

This can include column groups and nested columns.

See Selecting Columns.

See also:

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

• mean{ columns } — a single mean of means of the selected columns.

• sumFor — the sum of each selected column separately.

• The Mean Operation — an overview of all mean modes.

For more information: See mean on the documentation website.

Example

// A single row with the mean of the "age" values and the mean of the "weight" values
df.meanFor { age and weight }
// The same, ignoring `NaN` values, and naming the results explicitly
df.meanFor(skipNaN = true) { age into "meanAge" and (weight into "meanWeight") }

Parameters

Returns the mean 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 average 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 "mean Type Conversion" on the documentation website.

Result cells for which there is nothing to average (for instance, because the input was empty or contained only null values) simply become Double.NaN.

This can include column groups and nested columns.

See Selecting Columns.

See also:

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

• mean{ columns } — a single mean of means of the selected columns.

• sumFor — the sum of each selected column separately.

• The Mean Operation — an overview of all mean modes.

For more information: See mean on the documentation website.

Example

// A single row with the mean of the "age" values and the mean of the "weight" values
df.meanFor("age", "weight")

Parameters

Aggregates this GroupBy by computing the mean 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 mean 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 average 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 "mean Type Conversion" on the documentation website.

Result cells for which there is nothing to average (for instance, because the input was empty or contained only null values) simply become Double.NaN.

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.

This can include column groups and nested columns.

See Selecting Columns.

See also:

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

• mean{ columns } — a single mean of means of the selected columns, per group.

• sumFor — the sum of each selected column, per group.

• aggregate — the general way to aggregate groups.

• The Mean Operation — an overview of all mean 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 mean of the "age" values and the mean of the "weight" values
df.groupBy { city }.meanFor { age and weight }

Parameters

Aggregates this GroupBy by computing the mean 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 mean 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 average 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 "mean Type Conversion" on the documentation website.

Result cells for which there is nothing to average (for instance, because the input was empty or contained only null values) simply become Double.NaN.

This can include column groups and nested columns.

See Selecting Columns.

See also:

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

• mean{ columns } — a single mean of means of the selected columns, per group.

• sumFor — the sum of each selected column, per group.

• aggregate — the general way to aggregate groups.

• The Mean Operation — an overview of all mean 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 mean of the "age" values and the mean of the "weight" values
df.groupBy { city }.meanFor("age", "weight")

Parameters

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

Returns a single DataRow with the pivot keys as (nested) columns, containing the mean 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 average 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 "mean Type Conversion" on the documentation website.

Result cells for which there is nothing to average (for instance, because the input was empty or contained only null values) simply become Double.NaN. 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.

This can include column groups and nested columns.

See Selecting Columns, or check out the Pivot Grammar.

See also:

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

• mean{ columns } — a single mean of means of the selected columns, per group.

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

• The Mean Operation — an overview of all mean 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 mean of the "age" values and the mean of the "weight" values
df.pivot { city }.meanFor { age and weight }
// The same, but with the results grouped by aggregated column instead of by city
df.pivot { city }.meanFor(separate = true) { age and weight }

Parameters

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

Returns a single DataRow with the pivot keys as (nested) columns, containing the mean 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 average 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 "mean Type Conversion" on the documentation website.

Result cells for which there is nothing to average (for instance, because the input was empty or contained only null values) simply become Double.NaN.

This can include column groups and nested columns.

See Selecting Columns, or check out the Pivot Grammar.

See also:

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

• mean{ columns } — a single mean of means of the selected columns, per group.

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

• The Mean Operation — an overview of all mean 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 mean of the "age" values and the mean of the "weight" values
df.pivot { city }.meanFor("age", "weight")

Parameters

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

Returns a DataFrame where each cell contains the mean 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 average 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 "mean Type Conversion" on the documentation website.

Result cells for which there exists a group, but there is nothing to average (for instance, because the group was empty or contained only null values) simply become Double.NaN.

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.

This can include column groups and nested columns.

See Selecting Columns, or check out the PivotGroupBy Grammar.

See also:

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

• mean{ columns } — a single mean of means of the selected columns, per group.

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

• The Mean Operation — an overview of all mean 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 mean of the "age" values and the mean of the "weight" values
df.pivot { city }.groupBy { name.lastName }.meanFor { age and weight }

Parameters

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

Returns a DataFrame where each cell contains the mean 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 average 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 "mean Type Conversion" on the documentation website.

Result cells for which there exists a group, but there is nothing to average (for instance, because the group was empty or contained only null values) simply become Double.NaN.

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

This can include column groups and nested columns.

See Selecting Columns, or check out the PivotGroupBy Grammar.

See also:

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

• mean{ columns } — a single mean of means of the selected columns, per group.

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

• The Mean Operation — an overview of all mean 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 mean of the "age" values and the mean of the "weight" values
df.pivot { city }.groupBy { name.lastName }.meanFor("age", "weight")

Parameters