pivotMatches

Computes a presence matrix (similar to one-hot encoding) for the values in the specified columns of this DataFrame, returning a new DataFrame where:

• Columns represent all unique values from the selected columns (they become column groups corresponding to value combinations when using then, similar to pivot);

• Rows correspond to all unique combinations of values from the remaining columns; each combination is represented in dedicated key columns that store a distinct set of values for each row (similar to keys in GroupBy).

• Cells contain a Boolean value indicating whether a row with the corresponding combination of values (horizontal and vertical) exists in the DataFrame.

This function combines pivot, groupByOther, and matches operations into a single call.

This can include column groups and nested columns.

See Selecting Columns.

For more information: See pivotMatches on the documentation website.

See also:

• pivotCounts, which performs a similar operation but counts the number of matching rows instead of checking for their presence to produce a count matrix.

This pivotMatches Overload

Select or express pivot columns using the PivotDsl.

PivotDsl defines how key columns are selected and structured in a pivot:

• pivot with a single key column produces a Pivot containing one column for each unique key (i.e., key column unique values) with the corresponding group;

• pivot with multiple keys combined using and produces a Pivot with independent column groups for each key column, each having subcolumns with the keys corresponding to their unique values;

• pivot with multiple keys ordered using then produces a Pivot with nested column groups, representing a hierarchical structure of keys combinations from the pivoted columns — i.e., one group per unique key combination.

See Columns Selection DSL.

Examples

// Pivot by the "city" column
df.pivot { city }

// Independent pivot by "city" and "lastName" (from the "name" column group)
df.pivot { city and name.lastName }

// Hierarchical pivot by two columns with composite ("city", "lastName") keys
df.pivot { city then name.lastName }

Example

// Compute whether matching rows exist for all unique values of "city"
// and "name" (independently) across all possible combinations
// of values in the remaining columns.
df.pivotMatches { city and name }

Parameters

Computes a presence matrix (similar to one-hot encoding) for the values in the specified columns of this DataFrame, returning a new DataFrame where:

• Columns represent all unique values from the selected columns (they become column groups corresponding to value combinations when using then, similar to pivot);

• Rows correspond to all unique combinations of values from the remaining columns; each combination is represented in dedicated key columns that store a distinct set of values for each row (similar to keys in GroupBy).

• Cells contain a Boolean value indicating whether a row with the corresponding combination of values (horizontal and vertical) exists in the DataFrame.

This function combines pivot, groupByOther, and matches operations into a single call.

This can include column groups and nested columns.

See Selecting Columns.

For more information: See pivotMatches on the documentation website.

See also:

• pivotCounts, which performs a similar operation but counts the number of matching rows instead of checking for their presence to produce a count matrix.

This pivotMatches Overload

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

Example

// Compute whether matching rows exist for all unique values of "city"
// and "name" (independently) across all possible combinations
// of values in the remaining columns.
df.pivotMatches("city", "name")

Parameters

Computes a presence matrix (similar to one-hot encoding) for the values in the specified columns within each group of this GroupBy, returning a new DataFrame where:

• Columns represent all unique values from the selected columns (they become column groups corresponding to value combinations when using then, similar to pivot);

• Rows correspond to all unique combinations of values from the grouping columns; each combination is represented in dedicated key columns that store a distinct set of values for each row (similar to keys in GroupBy).

• Cells contain a Boolean value indicating whether a row with the corresponding combination of values (horizontal and vertical) exists in the DataFrame.

This function combines pivot and matches operations into a single call.

This can include column groups and nested columns.

See Selecting Columns.

For more information: See pivotMatches on the documentation website.

See also: pivotCounts, which performs a similar operation but counts the number of matching rows instead of checking for their presence.

This pivotMatches Overload

Select or express pivot columns using the PivotDsl.

PivotDsl defines how key columns are selected and structured in a pivot:

• pivot with a single key column produces a Pivot containing one column for each unique key (i.e., key column unique values) with the corresponding group;

• pivot with multiple keys combined using and produces a Pivot with independent column groups for each key column, each having subcolumns with the keys corresponding to their unique values;

• pivot with multiple keys ordered using then produces a Pivot with nested column groups, representing a hierarchical structure of keys combinations from the pivoted columns — i.e., one group per unique key combination.

See Columns Selection DSL.

Examples

// Pivot by the "city" column
df.pivot { city }

// Independent pivot by "city" and "lastName" (from the "name" column group)
df.pivot { city and name.lastName }

// Hierarchical pivot by two columns with composite ("city", "lastName") keys
df.pivot { city then name.lastName }

Example

// Compute whether matching rows exist for all unique values of "city"
// and "name" (independently) across all grouping key combinations
gb.pivotMatches { city and name }

Parameters

Computes a presence matrix (similar to one-hot encoding) for the values in the specified columns within each group of this GroupBy, returning a new DataFrame where:

• Columns represent all unique values from the selected columns (they become column groups corresponding to value combinations when using then, similar to pivot);

• Rows correspond to all unique combinations of values from the grouping columns; each combination is represented in dedicated key columns that store a distinct set of values for each row (similar to keys in GroupBy).

• Cells contain a Boolean value indicating whether a row with the corresponding combination of values (horizontal and vertical) exists in the DataFrame.

This function combines pivot and matches operations into a single call.

This can include column groups and nested columns.

See Selecting Columns.

For more information: See pivotMatches on the documentation website.

See also: pivotCounts, which performs a similar operation but counts the number of matching rows instead of checking for their presence.

This pivotMatches Overload

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

Example

// Compute whether matching rows exist for all unique values of "city"
// and "name" (independently) across all grouping key combinations
df.pivotMatches("city", "name")

Parameters

Computes the pivotMatches statistic for the selected columns within each group and adds it to the aggregate result.

This is a shortcut for combining pivot and matches.

The resulting DataFrame columns are inserted into the final DataFrame returned by aggregate. The resulting column name can be specified using into.

See GroupBy.pivotMatches for more details.

For more information: See pivotMatches on the documentation website.

See also: pivot, pivotCounts.

This pivotMatches overload

Select or express pivot columns using the PivotDsl.

PivotDsl defines how key columns are selected and structured in a pivot:

• pivot with a single key column produces a Pivot containing one column for each unique key (i.e., key column unique values) with the corresponding group;

• pivot with multiple keys combined using and produces a Pivot with independent column groups for each key column, each having subcolumns with the keys corresponding to their unique values;

• pivot with multiple keys ordered using then produces a Pivot with nested column groups, representing a hierarchical structure of keys combinations from the pivoted columns — i.e., one group per unique key combination.

See Columns Selection DSL.

Examples

// Pivot by the "city" column
df.pivot { city }

// Independent pivot by "city" and "lastName" (from the "name" column group)
df.pivot { city and name.lastName }

// Hierarchical pivot by two columns with composite ("city", "lastName") keys
df.pivot { city then name.lastName }

Example

df.groupBy { name.firstName }.aggregate {
    // Compute whether matching rows exist for all unique values of "city"
    // across all "name.firstName" key values and adds it to the aggregation result
    pivotMatches { city }
}

Parameters

Computes the pivotMatches statistic for the selected columns within each group and adds it to the aggregate result.

This is a shortcut for combining pivot and matches.

The resulting DataFrame columns are inserted into the final DataFrame returned by aggregate. The resulting column name can be specified using into.

See GroupBy.pivotMatches for more details.

For more information: See pivotMatches on the documentation website.

See also: pivot, pivotCounts.

This pivotMatches overload

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

Example

df.groupBy("firstName").aggregate {
    // Compute whether matching rows exist for all unique values of "city"
    // across all "firstName" key values and adds it to the aggregation result
    pivotMatches("city")
}

Parameters