modin.pandas.Series.rpow

Series.rpow(other, level=None, fill_value=None, axis=0)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/series_overrides.py#L798-L806)

Return Exponential power of series and other, element-wise (binary operator rpow).

Equivalent to other ** series, but with support to substitute a fill_value for missing data in either one of the inputs.

Parameters:

  • other (Series or scalar value) –

  • level (int or name) – Broadcast across a level, matching Index values on the passed MultiIndex level.

  • fill_value (None or float value, default None (NaN)) – Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result of filling (at that location) will be missing.

  • axis ({0 or 'index'}) – Unused. Parameter needed for compatibility with DataFrame.

Returns:

The result of the operation.

Return type:

Series

See also

Series.pow

Element-wise Exponential power, see Python documentation (https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types) for more details.

Caution

In Snowpark pandas API, zero raised to negative powers produces inf value as the result while pandas raises a ValueError. In Snowpark pandas API, None raised to 0 and 1 raised to None produce 1.0 as the result. pandas produces np.nan for both cases.

Caution

In Snowpark pandas API, whenever a binary operation involves a NULL value, the result will preserve NULL values, e.g. NULL.rpow(<other>) will yield NULL.

Caution

Snowpark pandas API denotes invalid numeric results with None while pandas uses NaN.

Caution

Snowpark pandas API does not support fill_value and level except for default values.

Examples

>>> a = pd.Series([1, -2, 0, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a    1.0
b   -2.0
c    0.0
d    NaN
dtype: float64
>>> b = pd.Series([-2, 1, 3, np.nan, 1], index=['a', 'b', 'c', 'd', 'f'])
>>> b
a   -2.0
b    1.0
c    3.0
d    NaN
f    1.0
dtype: float64
>>> a.rpow(b)
a   -2.0
b    1.0
c    1.0
d    NaN
f    1.0
dtype: float64
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