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# Splitting column of lists into multiple columns in Pandas

schedule Aug 10, 2023
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Consider the following DataFrame with some lists:

``` df = pd.DataFrame({"A":[1,[2,3],[]], "B":[4,5,6]})df A B0 1 41 [2,3] 52 [] 6 ```

# Solution

To split column `A`, which contains lists, into multiple columns:

``` pd.concat([df, df["A"].apply(pd.Series)], axis=1) A B 0 10 1 4 1.0 NaN1 [2,3] 5 2.0 3.02 [] 6 NaN NaN ```

# Explanation

We first expand the lists in column `A` (Series) horizontally using `apply(pd.Series)`:

``` df["A"].apply(pd.Series) 0 10 1.0 NaN1 2.0 3.02 NaN NaN ```

The return type here is `DataFrame`.

Next, we concatenate this DataFrame to the source DataFrame using the `concat(~)` method:

``` df = pd.concat([df,df["A"].apply(pd.Series)], axis=1)df A B 0 10 1 4 1.0 NaN1 [2,3] 5 2.0 3.02 [] 6 NaN NaN ```

Here, `axis=1` indicates that we want to concatenate our two DataFrames horizontally.

# Supplement - dropping columns

If you want to remove column `A` now that the lists have been expanded, use the `drop(~)` method like so:

``` df = df.drop("A", axis=1)df B 0 10 4 1.0 NaN1 5 2.0 3.02 6 NaN NaN ```

Here, `axis=1` indicates that we are dropping a column as opposed to a row.

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