colsAtAnyDepth

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ colGroups().colsAtAnyDepth().filter { "Alice" Iterable.containsit.values() } }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ colsAtAnyDepth().filter { "Alice" Iterable.containsit.values() }.first() }

df.select{ colsAtAnyDepth().filter { !it.isColumnGroup} }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ myColGroup.colsAtAnyDepth().filter { "Alice" Iterable.containsit.values() } }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ "myColumnGroup".colsAtAnyDepth().filter { "Alice" Iterable.containsit.values() } }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ "pathTo"["myGroupCol"].colsAtAnyDepth().filter { "Alice" Iterable.containsit.values() } }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

this.colsAtAnyDepth().filter(predicate)

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ colGroups().colsAtAnyDepth{ "Alice" Iterable.containsit.values() } }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

this.colsAtAnyDepth().filter(predicate)

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ colsAtAnyDepth{ "Alice" Iterable.containsit.values() }.first() }

df.select{ colsAtAnyDepth{ !it.isColumnGroup} }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

this.colsAtAnyDepth().filter(predicate)

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ myColGroup.colsAtAnyDepth{ "Alice" Iterable.containsit.values() } }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

this.colsAtAnyDepth().filter(predicate)

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ "myColumnGroup".colsAtAnyDepth{ "Alice" Iterable.containsit.values() } }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ Type::myColumnGroup.colsAtAnyDepth{ "Alice" Iterable.containsit.values() } }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also

colsAtAnyDepth().filter(predicate)

Cols At Any Depth

Returns all columns in this at any depth (so also inside Column Groups)

This function can also be followed by another ColumnSet filter function like colsOf, single, or valueCols.

For more information: See colsAtAnyDepth on the documentation website.

Check out: Grammar

For example:

// Depth-first search to a column containing the value "Alice"

df.select{ colsAtAnyDepth().filter{ "Alice" Iterable.containsit.values() }.first() }

// The columns at any depth excluding the top-level

df.select{ colGroups().colsAtAnyDepth() }

// All value- and frame columns at any depth

df.select{ colsAtAnyDepth().filter{ !it.isColumnGroup} }

// All value columns at any depth nested under a column group named "myColGroup"

df.select{ myColGroup.colsAtAnyDepth().valueCols() }

Examples for this overload:

df.select{ "pathTo"["myGroupCol"].colsAtAnyDepth{ "Alice" Iterable.containsit.values() } }

Converting from deprecated syntax:

dfs { condition } -> colsAtAnyDepth().filter{ condition }

allDfs(includeGroups = false) -> colsAtAnyDepth().filter{ includeGroups || !it.isColumnGroup() }

dfsOf<Type> { condition } -> colsAtAnyDepth().colsOf<Type> { condition }

cols{ condition }.recursively() -> colsAtAnyDepth().filter{ condition }

first{ condition }.rec() -> colsAtAnyDepth{ condition }.first()

all().recursively() -> colsAtAnyDepth()

See also