**Examples**

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

In [1]:

```
import numpy as np
import pandas as pd
```

In [2]:

```
s1 = pd.Series({'eagle': 320.0, 'parrot': 120.0})
s1
```

Out[2]:

In [3]:

```
s2 = pd.Series({'eagle': 335.0, 'parrot': 180.0, 'sparrow': 100.0})
s2
```

Out[3]:

In [4]:

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

In [ ]:

```
s1.combine(s2, max)
```

In the previous example, the resulting value for sparrow 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.

In [ ]:

```
s1.combine(s2, max, fill_value=0)
```