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# pandas.Series.combine

Series.combine(other, func, fill_value=None)

  Combine the Series with a Series or scalar according to func.

  Combine the Series and other using func to perform elementwise selection for combined Series. fill_value is assumed when value is missing at some index from one of the two objects being combined.

  Parameters:
    otherSeries or scalar

      The value(s) to be combined with the Series.

    funcfunction

      Function that takes two scalars as inputs and returns an element.

    fill_valuescalar, optional

      The value to assume when an index is missing from one Series or the other. The default specifies to use the appropriate NaN value for the underlying dtype of the Series.

  Returns:
    Series

      The result of combining the Series with the other object.

  See also

  `Series.combine_first`

    Combine Series values, choosing the calling Series’ values first.

  Examples

  Consider 2 Datasets `s1` and `s2` containing highest clocked speeds of different birds.

      >>> s1 = pd.Series({'falcon': 330.0, 'eagle': 160.0})
      >>> s1
      falcon    330.0
      eagle     160.0
      dtype: float64
      >>> s2 = pd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})
      >>> s2
      falcon    345.0
      eagle     200.0
      duck       30.0
      dtype: float64

  Now, to combine the two datasets and view the highest speeds of the birds across the two datasets

      >>> s1.combine(s2, max)
      duck        NaN
      eagle     200.0
      falcon    345.0
      dtype: float64

  In the previous example, the resulting value for duck is missing, because the maximum of a NaN and a float is a NaN. So, in the example, we set `fill_value=0`, so the maximum value returned will be the value from some dataset.

      >>> s1.combine(s2, max, fill_value=0)
      duck       30.0
      eagle     200.0
      falcon    345.0
      dtype: float64