bodo.pandas.BodoSeries.map

BodoSeries.map(arg, na_action=None, engine="bodo") -> BodoSeries

Map values of a BodoSeries according to a mapping.

If arg is a function, bodo.jit will be applied to arg. If this JIT compilation fails for any reason, the mapping function will be run as a normal Python function. If the compilation succeeds, the JIT compiled function will be used for the map and the overheads associated with running Python code from within the execution pipeline are avoided.

Note

Calling BodoSeries.map will immediately execute a plan if this JIT compilation fails to generate a small sample of the BodoSeries and then call pandas.Series.map on the sample to infer output types before proceeding with lazy evaluation.

Note

Functions passed to arg (whether explicitly wrapper with a JIT decorator or not) may not use Numba's with objmode context. Doing so will result in a runtime exception.

Parameters

arg : function, collections.abc.Mapping subclass or Series: Mapping correspondence. function may be a Python function or a dispatcher generated through numba.jit or bodo.jit.

na_actions : {None, ‘ignore’}, default None: If 'ignore' then NaN values will be propagated without passing them to the mapping correspondence.

engine : {'bodo', 'python'}, default 'bodo': The engine to use to compute the UDF. By default, engine='bodo' will apply bodo.jit to arg with fallback to Python as described above. Use engine='python' to avoid any jit compilation.

Returns

BodoSeries

Example

import bodo.pandas as bd

bdf = bd.DataFrame(
    {
        "A": bd.array([1, 2, 3, 7] * 3, "Int64"),
        "B": ["A1", "B1", "C1", "Abc"] * 3,
        "C": bd.array([4, 5, 6, -1] * 3, "Int64"),
    }
)

bodo_ser = bdf.A.map(lambda x: x ** 2)
print(type(bodo_ser))
print(bodo_ser)

Output:

<class 'bodo.pandas.series.BodoSeries'>
0      1
1      4
2      9
3     49
4      1
5      4
6      9
7     49
8      1
9      4
10     9
11    49
Name: A, dtype: int64[pyarrow]

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