get

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

// although these can be shortened to just the colsOf<>{ }call

df.select{ colsOf<String>().cols{ "e" init.name() } }

df.select{ colsOf<String>()[{ it.any{ it == "Alice" } }]}

// identity call, same as all()

df.select{ colsOf<String>().cols() }

Filter vs. Cols:

If used with a predicate, cols {} functions exactly like filter {}. This is intentional, however; it is recommended to use filter {} on ColumnSets and cols {} on the rest.

Parameters

See also

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ cols{ "e" init.name() } }

df.select{ this[{ it.any{ it == "Alice" } }]}

// same as all()

df.select{ cols() }

Filter vs. Cols:

If used with a predicate, cols {} functions exactly like filter {}. This is intentional, however; it is recommended to use filter {} on ColumnSets and cols {} on the rest.

Parameters

See also

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ myColumnGroup.cols{ "e" init.name() } }

// same as allCols()

df.select{ myColumnGroup.cols() }

df.select{ myColumnGroup[{ ... }]}

Filter vs. Cols:

If used with a predicate, cols {} functions exactly like filter {}. This is intentional, however; it is recommended to use filter {} on ColumnSets and cols {} on the rest.

Parameters

See also

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "myGroupCol".cols{ "e" init.name() } }

df.select{ "myGroupCol"[{ it.any{ it == "Alice" } }]}

// same as allCols()

df.select{ "myGroupCol".cols() }

Filter vs. Cols:

If used with a predicate, cols {} functions exactly like filter {}. This is intentional, however; it is recommended to use filter {} on ColumnSets and cols {} on the rest.

Parameters

See also

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "pathTo"["myGroupCol"].cols{ "e" init.name() } }

df.select{ "pathTo"["myGroupCol"][{ it.any{ it == "Alice" } }]}

df.select{ "pathTo"["myGroupCol"].cols() } // identity call, same as allCols

Filter vs. Cols:

If used with a predicate, cols {} functions exactly like filter {}. This is intentional, however; it is recommended to use filter {} on ColumnSets and cols {} on the rest.

Parameters

See also

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ cols(colGroup.columnA, columnB) }

df.select{ this[colGroup.columnA, columnB]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ myColumnGroup.cols(columnA, columnB) }

df.select{ myColumnGroup[columnA, columnB]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "myColumnGroup".cols(columnA, columnB) }

df.select{ "myColumnGroup"[columnA, columnB]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "pathTo"["columnGroup"].cols(columnA, columnB) }

df.select{ "pathTo"["columnGroup"].cols("pathTo"["colA"], "pathTo"["colB"]) }

df.select{ "pathTo"["columnGroup"][columnA, columnB]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ cols("columnA", "columnB") }

df.select{ cols<String>("columnA", "columnB") }

df.select{ this["columnA", "columnB"]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ myColumnGroup.cols("columnA", "columnB") }

df.select{ myColumnGroup["columnA", "columnB"]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "columnGroup".cols("columnA", "columnB") }

df.select{ "columnGroup"["columnA", "columnB"]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "pathTo"["columnGroup"].cols("columnA", "columnB") }

df.select{ "pathTo"["columnGroup"]["columnA", "columnB"]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ cols("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ cols<String>("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ this["pathTo"["colA"], "pathTo"["colB"])]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ myColumnGroup.cols("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ myColumnGroup.cols<String>("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ myColumnGroup["pathTo"["colA"], "pathTo"["colB"])]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "columnGroup".cols("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ "columnGroup".cols<String>("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ "columnGroup"[""pathTo"["colA"], "pathTo"["colB"])]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "pathTo"["columnGroup"].cols("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ "pathTo"["columnGroup"].cols<String>("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ "pathTo"["columnGroup"]["pathTo"["colA"], "pathTo"["colB"])]}

Parameters

Throws

Cols: Columns by Indices

Retrieves one or multiple columns from this in the form of a ColumnSet by their indices. If any of the indices are out of bounds, an IndexOutOfBoundsException is thrown.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.select{ cols(1, 3, 2) }

df.select{ colsOf<Int>()[5, 1, 2]}

df.select{ "myColumnGroup".cols(0, 2) }

Examples for this overload:

df.select{ colsOf<Int>().cols(1, 3) }

df.select{ all()[5, 1]}

Parameters

Throws

Cols: Columns by Index Range

Retrieves multiple columns from this in the form of a ColumnSet by a range of indices. If any of the indices in the range are out of bounds, an IndexOutOfBoundsException is thrown.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.select{ cols(1..3) }

df.select{ colsOf<Int>()[1..5]}

df.select{ "myColGroup".cols(0..2) }

Examples for this overload:

df.select{ colsOf<Int>().cols(1..3) }

df.select{ all()[1..5]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ Type::columnGroup.cols{ "e" init.name() } }

df.select{ Type::columnGroup[{ it.any{ it == "Alice" } }]}

// same as allCols()

df.select{ Type::columnGroup.cols() }

Filter vs. Cols:

If used with a predicate, cols {} functions exactly like filter {}. This is intentional, however; it is recommended to use filter {} on ColumnSets and cols {} on the rest.

Parameters

See also

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ Type::myColumnGroup.cols(columnA, columnB) }

df.select{ DataSchemaType::myColumnGroup["pathTo"["colA"], "pathTo"["colB"]]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ Type::myColumnGroup.cols("columnA", "columnB") }

df.select{ DataSchemaType::myColumnGroup["columnA", "columnB"]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ Type::myColumnGroup.cols("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ Type::myColumnGroup.cols<String>("pathTo"["colA"], "pathTo"["colB"])) }

df.select{ DataSchemaType::myColumnGroup["columnA", "columnB"]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ cols(Type::colA, Type::colB) }

df.select{ this[Type::colA, Type::colB]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ myColumnGroup.cols(Type::colA, Type::colB) }

df.select{ myColumnGroup[Type::colA, Type::colB]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "myColumnGroup".cols(Type::colA, Type::colB) }

df.select{ "myColumnGroup"[Type::colA, Type::colB]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ Type::myColumnGroup.cols(Type::colA, Type::colB) }

df.select{ Type::myColumnGroup[Type::colA, Type::colB]}

Parameters

Throws

Cols

Creates a subset of columns (ColumnSet) from this.

You can use either a ColumnFilter, or any of the vararg overloads for any Access APIs. The function can be both typed and untyped (in case you're supplying a column name, -path, or index (range)).

This function operates solely on columns at the top-level.

Aside from calling cols directly, you can also use the get operator in most cases.

For more information: See cols on the documentation website.

Check out: Grammar

For example:

df.remove{ cols{ it.hasNulls() } }

df.select{ myGroupCol.cols(columnA, columnB) }

df.select{ colsOf<String>()[1, 3, 5]}

Examples for this overload:

df.select{ "pathTo"["columnGroup"].cols(Type::colA, Type::colB) }

df.select{ "pathTo"["columnGroup"][Type::colA, Type::colB]}

Parameters

Throws