polars.DataFrame.fold#
- DataFrame.fold(operation: Callable[[Series, Series], Series]) Series [source]#
Apply a horizontal reduction on a DataFrame.
This can be used to effectively determine aggregations on a row level, and can be applied to any DataType that can be supercasted (casted to a similar parent type).
An example of the supercast rules when applying an arithmetic operation on two DataTypes are for instance:
Int8 + String = String
Float32 + Int64 = Float32
Float32 + Float64 = Float64
- Parameters:
- operation
function that takes two
Series
and returns aSeries
.
Examples
A horizontal sum operation:
>>> df = pl.DataFrame( ... { ... "a": [2, 1, 3], ... "b": [1, 2, 3], ... "c": [1.0, 2.0, 3.0], ... } ... ) >>> df.fold(lambda s1, s2: s1 + s2) shape: (3,) Series: 'a' [f64] [ 4.0 5.0 9.0 ]
A horizontal minimum operation:
>>> df = pl.DataFrame({"a": [2, 1, 3], "b": [1, 2, 3], "c": [1.0, 2.0, 3.0]}) >>> df.fold(lambda s1, s2: s1.zip_with(s1 < s2, s2)) shape: (3,) Series: 'a' [f64] [ 1.0 1.0 3.0 ]
A horizontal string concatenation:
>>> df = pl.DataFrame( ... { ... "a": ["foo", "bar", None], ... "b": [1, 2, 3], ... "c": [1.0, 2.0, 3.0], ... } ... ) >>> df.fold(lambda s1, s2: s1 + s2) shape: (3,) Series: 'a' [str] [ "foo11.0" "bar22.0" null ]
A horizontal boolean or, similar to a row-wise .any():
>>> df = pl.DataFrame( ... { ... "a": [False, False, True], ... "b": [False, True, False], ... } ... ) >>> df.fold(lambda s1, s2: s1 | s2) shape: (3,) Series: 'a' [bool] [ false true true ]