# Pandas: Combine Functions

`pandas`

has two handy functions for combining DataFrames:

- The
`combine`

function performs a column-wise combine of one DataFrame with another:

```
# Combine using a simple function that chooses the smaller column.
>>> def take_smaller(s1, s2):
return s1 if s1.sum() < s2.sum() else s2
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df1
A B
0 0 4
1 0 4
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df2
A B
0 1 3
1 1 3
>>> df1.combine(df2, take_smaller)
A B
0 0 3
1 0 3
# Combine using a true element-wise combine function.
>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df1
A B
0 5 2
1 0 4
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df2
A B
0 1 3
1 1 3
>>> df1.combine(df2, np.minimum)
A B
0 1 2
1 0 3
```

- The
`combine_first`

function combines the two DataFrames by filling null values in one DataFrame with non-null values from the other:

```
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df1
A B
0 NaN 4.0
1 0.0 NaN
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df2
B C
1 3 1
2 3 1
>>> df1.combine_first(df2)
A B C
0 NaN 4.0 NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
```

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