cudf.Series.applymap#

Series.applymap(udf, out_dtype=None)#

Apply an elementwise function to transform the values in the Column.

The user function is expected to take one argument and return the result, which will be stored to the output Series. The function cannot reference globals except for other simple scalar objects.

Parameters
udffunction

Either a callable python function or a python function already decorated by numba.cuda.jit for call on the GPU as a device

out_dtypenumpy.dtype; optional

The dtype for use in the output. Only used for numba.cuda.jit decorated udf. By default, the result will have the same dtype as the source.

Returns
resultSeries

The mask and index are preserved.

Notes

The supported Python features are listed in

with these exceptions:

  • Math functions in cmath are not supported since libcudf does not have complex number support and output of cmath functions are most likely complex numbers.

  • These five functions in math are not supported since numba generates multiple PTX functions from them

    • math.sin()

    • math.cos()

    • math.tan()

    • math.gamma()

    • math.lgamma()

  • Series with string dtypes are not supported in applymap method.

  • Global variables need to be re-defined explicitly inside the udf, as numba considers them to be compile-time constants and there is no known way to obtain value of the global variable.

Examples

Returning a Series of booleans using only a literal pattern.

>>> import cudf
>>> s = cudf.Series([1, 10, -10, 200, 100])
>>> s.applymap(lambda x: x)
0      1
1     10
2    -10
3    200
4    100
dtype: int64
>>> s.applymap(lambda x: x in [1, 100, 59])
0     True
1    False
2    False
3    False
4     True
dtype: bool
>>> s.applymap(lambda x: x ** 2)
0        1
1      100
2      100
3    40000
4    10000
dtype: int64
>>> s.applymap(lambda x: (x ** 2) + (x / 2))
0        1.5
1      105.0
2       95.0
3    40100.0
4    10050.0
dtype: float64
>>> def cube_function(a):
...     return a ** 3
...
>>> s.applymap(cube_function)
0          1
1       1000
2      -1000
3    8000000
4    1000000
dtype: int64
>>> def custom_udf(x):
...     if x > 0:
...         return x + 5
...     else:
...         return x - 5
...
>>> s.applymap(custom_udf)
0      6
1     15
2    -15
3    205
4    105
dtype: int64

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