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# Sorting values within groups in Pandas

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Data Aggregation Cookbook
schedule Jul 1, 2022
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# Grouping by single column

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[6,3,5,4],"B":[3,4,8,2],"group":["a","b","a","b"]})df A B group0 6 3 a1 3 4 b2 5 8 a3 4 2 b ```

## Using sort_values method

To get the row with the smallest value for `A` in each group:

``` df.sort_values("A").groupby("group").head(1) A B group1 3 4 b2 5 8 a ```

If you want the largest value instead, set `ascending=False` for `sort_values(~)`.

## Explanation

Here, we are first sorting `df` by column `A` using `sort_values(~)`:

``` df.sort_values("A")   # ascending=True A B group1 3 4 b3 4 2 b2 5 8 a0 6 3 a ```

We then group by column `group`, and fetch the first row of each group using `head(1)` (you could also use `first()`). This is guaranteed to work because `groupby(~)` preserves the ordering of the rows, that is, the order of the rows computed by `sort_values(~)` will be respected.

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