pandas.Series.combine — pandas 0.24.0rc1 documentation (original) (raw)
Series. combine(other, func, fill_value=None)[source]¶
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: | other : Series or scalar The value(s) to be combined with the Series. func : function Function that takes two scalars as inputs and returns an element. fill_value : scalar, 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. |
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