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# pandas.Series.rpow

Series.rpow(other, level=None, fill_value=None, axis=0)

  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 object

      When a Series is provided, will align on indexes. For all other types, will behave the same as `==` but with possibly different results due to the other arguments.

    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:
    Series

      The result of the operation.

  See also

  `Series.pow`

    Element-wise Exponential power, see Python documentation for more details.

  Examples

      >>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
      >>> a
      a    1.0
      b    1.0
      c    1.0
      d    NaN
      dtype: float64
      >>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
      >>> b
      a    1.0
      b    NaN
      d    1.0
      e    NaN
      dtype: float64
      >>> a.pow(b, fill_value=0)
      a    1.0
      b    1.0
      c    1.0
      d    0.0
      e    NaN
      dtype: float64