Unlocking the hidden gems: Discovering values in one column based on the minimum value from another column within each group using

I am struggling with a dataframe that looks like the following:

https://i.stack.imgur.com/Ays3S.png

My goal is to create a new column that holds the quota of the minimum scale_qty for each group formed by plant, material. Here is the desired outcome:

https://i.stack.imgur.com/XLG4K.png

Up to this point, I have only been able to extract rows containing the minimum scale_qty or determine the minimum value itself. I am stuck on how to calculate the quota for each of those instances.

g = df.groupby(['plant', 'material']) df['min_scale_qty'] = g['scale_qty'].transform(min)

Your assistance in solving this would be greatly appreciated. Thank you!

pythonpandasdataframegroup-by

Answer №1

To create a new column called 'quota_of_min_scale_qty', you can use the pandas.Series.idxmin function in combination with the transform method.

df['quota_of_min_scale_qty'] = (
    df.loc[
        df
        .groupby(['plant', 'material'])['scale_qty']
        .transform(lambda x: x.idxmin()), 'quota']
    .values
)

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