min

Returns the minimum of the values in this DataColumn.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

Throws a NoSuchElementException when there is nothing left to compare, for instance when the input is empty or contains only null (or, if skipNaN is true, only null and NaN) values.

The result has the same type as the input values (minus nullability, if the input values were nullable).

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

See also:

• minOrNull — returns null instead of throwing for a column with nothing to compare.

• minOf — the minimum of the values a selector returns for each element.

• minBy — the element for which a selector returns the minimum value.

• max — the mirror operation.

• The Min Operation — an overview of all min modes.

For more information: See "min / max" on the documentation website.

Example

// The smallest age in the "age" column
df.age.min()
// The smallest weight in the "weight" column, ignoring `null` values
df.weight.min()

Parameters

Throws

Returns the minimum of the values of each suitable column of this DataFrame separately.

All columns whose values are mutually comparable are taken into account; the other columns are simply left out of the result.

Columns inside column groups are also excluded. To include those in the minimum, flatten the DataFrame first.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

See also:

• minFor — the same, but for an explicit selection of columns.

• min{ columns } — a single minimum of all values in the selected columns.

• max — the mirror operation.

• The Min Operation — an overview of all min modes.

For more information: See "min / max" on the documentation website.

Example

// A single row with the smallest value of each comparable column
// ("name"/"firstName", "name"/"lastName", "age", "city", "weight", and "isHappy")
df.flatten().min()

Parameters

Returns a single minimum of all the values in the selected columns of this DataFrame.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

Throws a NoSuchElementException when there is nothing left to compare, for instance when the input is empty or contains only null (or, if skipNaN is true, only null and NaN) values.

The result has the same type as the input values (minus nullability, if the input values were nullable).

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

This can include column groups and nested columns.

See also:

• minOrNull — returns null instead of throwing when there's nothing to compare.

• minFor — the minimum of each selected column separately.

• minOf — the minimum of the values a row expression returns for each row.

• max — the mirror operation.

• The Min Operation — an overview of all min modes.

For more information: See "min / max" on the documentation website.

Select or express columns using the Columns Selection DSL.

This DSL is initiated by a Columns Selector lambda, which operates in the context of the Columns Selection DSL and expects you to return a SingleColumn or ColumnSet (so, a ColumnsResolver). This is an entity formed by calling any (combination) of the functions in the DSL that is or can be resolved into one or more columns.

The Columns Selection DSL allows using Extension Properties for specifying columns type- and name-safe.

Check out: Columns Selection DSL Grammar

See Column Selectors on the documentation website.

For example:

df.min{ length andage }

df.min{ cols(1..5) }

df.min{ colsOf<Double>() }

Example

// The smallest of all values in the "age" and "weight" columns
df.min { age and weight }

Parameters

Throws

Returns a single minimum of all the values in the selected columns of this DataFrame.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

Throws a NoSuchElementException when there is nothing left to compare, for instance when the input is empty or contains only null (or, if skipNaN is true, only null and NaN) values.

The result has the same type as the input values (minus nullability, if the input values were nullable).

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

This can include column groups and nested columns.

See also:

• minOrNull — returns null instead of throwing when there's nothing to compare.

• minFor — the minimum of each selected column separately.

• minOf — the minimum of the values a row expression returns for each row.

• max — the mirror operation.

• The Min Operation — an overview of all min modes.

For more information: See "min / max" on the documentation website.

Select single or multiple columns using their names as Strings. (String API).

For example:

df.min("length", "age")

Example

// The smallest of all values in the "age" and "weight" columns
df.min("age", "weight")

Parameters

Throws

Aggregates this GroupBy by computing the minimum of the values of each suitable column separately, per group.

Returns a new DataFrame with one row per group, containing the group key columns and a column with the minimum for each suitable column. All columns whose values are mutually comparable are taken into account; the other columns are simply left out of the result.

Columns inside column groups are also excluded. To include those in the minimum, flatten the DataFrame first.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

See also:

• minFor — the same, but for an explicit selection of columns.

• min{ columns } — a single minimum of all values in the selected columns, per group.

• max — the mirror operation.

• aggregate — the general way to aggregate groups.

• The Min Operation — an overview of all min 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 smallest value of each comparable column
df.groupBy { city }.min()

Parameters

Aggregates this GroupBy by computing a single minimum of all the values in the selected columns, per group.

Returns a new DataFrame with one row per group, containing the group key columns and a single column with the minimum per group. That column is named name, or, if name is null, after the selected column if exactly one column is selected, and "min" otherwise.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

This can include column groups and nested columns.

See Selecting Columns.

See also:

• minFor — the minimum of each selected column separately, per group.

• minOf — the minimum of the values a row expression returns for each row of a group.

• max — the mirror operation.

• aggregate — the general way to aggregate groups.

• The Min Operation — an overview of all min 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 smallest of all values in the "age" and "weight" columns,
// in a column called "minValue"
df.groupBy { city }.min("minValue") { age and weight }

Parameters

Aggregates this GroupBy by computing a single minimum of all the values in the selected columns, per group.

Returns a new DataFrame with one row per group, containing the group key columns and a single column with the minimum per group. That column is named name, or, if name is null, after the selected column if exactly one column is selected, and "min" otherwise.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

This can include column groups and nested columns.

See Selecting Columns.

See also:

• minFor — the minimum of each selected column separately, per group.

• minOf — the minimum of the values a row expression returns for each row of a group.

• max — the mirror operation.

• aggregate — the general way to aggregate groups.

• The Min Operation — an overview of all min 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 smallest of all values in the "age" and "weight" columns,
// in a column called "minValue"
df.groupBy { city }.min("age", "weight", name = "minValue")

Parameters

Aggregates this Pivot by computing the minimum of the values of each suitable column separately, per group.

Returns a single DataRow with the pivot keys as (nested) columns, containing the minimum of each suitable column of the corresponding group. All columns whose values are mutually comparable are taken into account; the other columns are simply left out of the result.

Columns inside column groups are also excluded. To include those in the minimum, flatten the DataFrame first.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

Check out the Pivot Grammar.

See also:

• minFor — the same, but for an explicit selection of columns.

• min{ columns } — a single minimum of all values in the selected columns, per group.

• max — the mirror operation.

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

• The Min Operation — an overview of all min 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 smallest value of each comparable column
df.pivot { city }.min()

Parameters

Aggregates this Pivot by computing a single minimum of all the values in the selected columns, per group.

Returns a single DataRow with the pivot keys as (nested) columns, containing the smallest value among all the values in the selected columns of the corresponding group.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

This can include column groups and nested columns.

See Selecting Columns, or check out the Pivot Grammar.

See also:

• min() — the minimum of each suitable column separately, per group.

• minFor — the minimum of each selected column separately, per group.

• max — the mirror operation.

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

• The Min Operation — an overview of all min 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 smallest of all values in the "age" and "weight" columns
df.pivot { city }.min { age and weight }

Parameters

Aggregates this Pivot by computing a single minimum of all the values in the selected columns, per group.

Returns a single DataRow with the pivot keys as (nested) columns, containing the smallest value among all the values in the selected columns of the corresponding group.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

This can include column groups and nested columns.

See Selecting Columns, or check out the Pivot Grammar.

See also:

• min() — the minimum of each suitable column separately, per group.

• minFor — the minimum of each selected column separately, per group.

• max — the mirror operation.

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

• The Min Operation — an overview of all min 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 smallest of all values in the "age" and "weight" columns
df.pivot { city }.min("age", "weight")

Parameters

Aggregates this PivotGroupBy by computing the minimum of the values of each suitable column separately, per group.

Returns a DataFrame where each cell contains the minimum of each suitable column of the group corresponding to that pivot key (column) and groupBy key (row). All columns whose values are mutually comparable are taken into account; the other columns are simply left out of the result.

Columns inside column groups are also excluded. To include those in the minimum, flatten the DataFrame first.

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

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

Check out the PivotGroupBy Grammar.

See also:

• minFor — the same, but for an explicit selection of columns.

• min{ columns } — a single minimum of all values in the selected columns, per group.

• max — the mirror operation.

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

• The Min Operation — an overview of all min 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 smallest value of each comparable column
df.pivot { city }.groupBy { name.lastName }.min()

Parameters

Aggregates this PivotGroupBy by computing a single minimum of all the values in the selected columns, per group.

Returns a DataFrame where each cell contains the smallest value among all the values in the selected columns of the group corresponding to that pivot key (column) and groupBy key (row).

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

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:

• min() — the minimum of each suitable column separately, per group.

• minFor — the minimum of each selected column separately, per group.

• max — the mirror operation.

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

• The Min Operation — an overview of all min 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 smallest of all values in the "age" and "weight" columns
df.pivot { city }.groupBy { name.lastName }.min { age and weight }

Parameters

Aggregates this PivotGroupBy by computing a single minimum of all the values in the selected columns, per group.

Returns a DataFrame where each cell contains the smallest value among all the values in the selected columns of the group corresponding to that pivot key (column) and groupBy key (row).

Only self-comparable values are supported: values of a type T : Comparable<T> that are mutually comparable (like strings, primitive numbers, or dates). This includes all primitive number types, but no mix of different number types, which would result in a ClassCastException.

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.

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

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

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:

• min() — the minimum of each suitable column separately, per group.

• minFor — the minimum of each selected column separately, per group.

• max — the mirror operation.

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

• The Min Operation — an overview of all min 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 smallest of all values in the "age" and "weight" columns
df.pivot { city }.groupBy { name.lastName }.min("age", "weight")

Parameters