cudf.Series.apply#

Series.apply(func, convert_dtype=True, args=(), **kwargs)#

Apply a scalar function to the values of a Series. Similar to pandas.Series.apply.

apply relies on Numba to JIT compile func. Thus the allowed operations within func are limited to those supported by the CUDA Python Numba target. For more information, see the cuDF guide to user defined functions.

Parameters
funcfunction

Scalar Python function to apply.

convert_dtypebool, default True

In cuDF, this parameter is always True. Because cuDF does not support arbitrary object dtypes, the result will always be the common type as determined by numba based on the function logic and argument types. See examples for details.

argstuple

Positional arguments passed to func after the series value.

**kwargs

Not supported

Notes

UDFs are cached in memory to avoid recompilation. The first call to the UDF will incur compilation overhead. func may call nested functions that are decorated with the decorator numba.cuda.jit(device=True), otherwise numba will raise a typing error.

Examples

Apply a basic function to a series >>> sr = cudf.Series([1,2,3]) >>> def f(x): … return x + 1 >>> sr.apply(f) 0 2 1 3 2 4 dtype: int64

Apply a basic function to a series with nulls:

>>> sr = cudf.Series([1,cudf.NA,3])
>>> def f(x):
...     return x + 1
>>> sr.apply(f)
0       2
1    <NA>
2       4
dtype: int64

Use a function that does something conditionally, based on if the value is or is not null:

>>> sr = cudf.Series([1,cudf.NA,3])
>>> def f(x):
...     if x is cudf.NA:
...         return 42
...     else:
...         return x - 1
>>> sr.apply(f)
0     0
1    42
2     2
dtype: int64

Results will be upcast to the common dtype required as derived from the UDFs logic. Note that this means the common type will be returned even if such data is passed that would not result in any values of that dtype:

>>> sr = cudf.Series([1,cudf.NA,3])
>>> def f(x):
...     return x + 1.5
>>> sr.apply(f)
0     2.5
1    <NA>
2     4.5
dtype: float64