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# Returning multiple columns using the apply function in Pandas

schedule Aug 12, 2023
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To return multiple columns using the `apply(~)` function in Pandas, make the parameter function return a `Series`.

As an example, consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4]})df A0 31 4 ```

# Solution

To create columns `B` and `C`, return a `Series` for the parameter function:

``` def foo(val): return pd.Series({ # returns a row with column labels B and C "B": val * -2, "C": val * 3 })df["A"].apply(foo) # returns a DataFrame B C0 -6 91 -8 12 ```

Here, `foo` is called twice, once for each value in `df`.

# Supplement - merging with the original DataFrame

You can also concatenate the newly created columns with `df` using Pandas' `concat(~)` method:

``` pd.concat([df, df["A"].apply(foo)], axis=1) A B C0 3 -6 91 4 -8 12 ```

Here, `axis=1` indicates that we want to concatenate the two DataFrames horizontally, as opposed to vertically.

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