# Keep the corresponding value of another column after using groupby in a column

## Keep the corresponding value of another column after using groupby in a column

Contents

Problem Description:

After using groupby and agg to find the max and the min, I would like to keep the value of another column corresponding to said Max and Min.

What I have:

OrderMissingDate
Order 1Missing 12002
Order 1Missing 22003
Order 2Missing 32004
Order 2Missing 42005
Order 2Missing 52006
Order 3Missing 62007

What I do

``````calculation = df.groupby(by=('Order')).agg(Max=('Date','max'),Min=('Date','min')).reset_index()
calculation['difference'] = calculation['Max']-calculation['Min']
``````

What I get

OrderMinMaxdifference
Order 1200220031 year
Order 2200420062 years
Order 3200720070s

What I want,

OrderMinMaxdifferenceMissing MinMissing Max
Order 1200220031 yearMissing 1Missing 2
Order 2200420062 yearsMissing 3Missing 5
Order 3200720070sMissing 6Missing 6

## Solution – 1

I would use separate `groupby` and a `merge`:

``````g = df.groupby(by='Order')

(pd.merge(df.loc[g['Date'].idxmin()]
.rename({'Date': 'Min', 'Missing': 'Missing Min'}, axis=1),
df.loc[g['Date'].idxmax()]
.rename({'Date': 'Max', 'Missing': 'Missing Max'}, axis=1),
on='Order')
.assign(difference=lambda d: d['Max']-d['Min'])
)
``````

Output:

``````     Order Missing Min   Min Missing Max   Max  difference
0  Order 1   Missing 1  2002   Missing 2  2003           1
1  Order 2   Missing 3  2004   Missing 5  2006           2
2  Order 3   Missing 6  2007   Missing 6  2007           0
``````

## Solution – 2

maybe try to add additional group by calculations/columns, similiar to how you created "Max" and "Min" columns from "Date". (I apologize, I am not able to debug code right now).:

``````calculation = df.groupby(by=('Order')).agg(Max=('Date','max'),Min=('Date','min'), Missing_Min=('Missing','min'), Missing_Max=('Missing','max')).reset_index()
calculation['difference'] = calculation['Max']-calculation['Min']
``````

## Solution – 3

You need a `merge`. Two more lines in your code would get you what you want.

``````calculation["Missing Min"] = calculation.merge(
df, left_on=["Order", "Min"], right_on=["Order", "Date"]
)["Missing"]
calculation["Missing Max"] = calculation.merge(
df, left_on=["Order", "Max"], right_on=["Order", "Date"]
)["Missing"]
``````

print(calculation):

``````             Order   Max   Min  difference Missing Min Missing Max
0          Order 1   2003  2002           1  Missing 1   Missing 2
1          Order 2   2006  2004           2  Missing 3   Missing 5
2          Order 3   2007  2007           0  Missing 6   Missing 6
``````
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