pyspark.pandas.DataFrame.transform¶

DataFrame.
transform
(func: Callable[[…], Series], axis: Union[int, str] = 0, *args: Any, **kwargs: Any) → DataFrame[source]¶ Call
func
on self producing a Series with transformed values and that has the same length as its input.See also Transform and apply a function.
Note
this API executes the function once to infer the type which is potentially expensive, for instance, when the dataset is created after aggregations or sorting.
To avoid this, specify return type in
func
, for instance, as below:>>> def square(x) > ps.Series[np.int32]: ... return x ** 2
pandasonSpark uses return type hints and does not try to infer the type.
Note
the series within
func
is actually multiple pandas series as the segments of the whole pandasonSpark series; therefore, the length of each series is not guaranteed. As an example, an aggregation against each series does work as a global aggregation but an aggregation of each segment. See below:>>> def func(x) > ps.Series[np.int32]: ... return x + sum(x)
 Parameters
 funcfunction
Function to use for transforming the data. It must work when pandas Series is passed.
 axisint, default 0 or ‘index’
Can only be set to 0 now.
 *args
Positional arguments to pass to func.
 **kwargs
Keyword arguments to pass to func.
 Returns
 DataFrame
A DataFrame that must have the same length as self.
 Raises
 ExceptionIf the returned DataFrame has a different length than self.
See also
DataFrame.aggregate
Only perform aggregating type operations.
DataFrame.apply
Invoke function on DataFrame.
Series.transform
The equivalent function for Series.
Examples
>>> df = ps.DataFrame({'A': range(3), 'B': range(1, 4)}, columns=['A', 'B']) >>> df A B 0 0 1 1 1 2 2 2 3
>>> def square(x) > ps.Series[np.int32]: ... return x ** 2 >>> df.transform(square) A B 0 0 1 1 1 4 2 4 9
You can omit type hints and let pandasonSpark infer its type.
>>> df.transform(lambda x: x ** 2) A B 0 0 1 1 1 4 2 4 9
For multiindex columns:
>>> df.columns = [('X', 'A'), ('X', 'B')] >>> df.transform(square) X A B 0 0 1 1 1 4 2 4 9
>>> (df * 1).transform(abs) X A B 0 0 1 1 1 2 2 2 3
You can also specify extra arguments.
>>> def calculation(x, y, z) > ps.Series[int]: ... return x ** y + z >>> df.transform(calculation, y=10, z=20) X A B 0 20 21 1 21 1044 2 1044 59069