Pandas Rolling second Highest Value based on another column

It is not the most elegant solution, but I would do the following:

1- Load Dataset

import numpy as npimport pandas as pddata={'Person':['a','a','a','a','a','b','b','b','b','b','b'],     'Sales':['50','60','90','30','33','100','600','80','90','400','550'],     'Price':['10','12','8','10','12','10','13','16','14','12','10']}data=pd.DataFrame(data)data['Sales'] = data['Sales'].astype(float)

2- Use Groupby and expanding together:

data['2nd_sales'] = data.groupby('Person')['Sales'].expanding(min_periods=2) \                                  .apply(lambda x: x.nlargest(2).values[-1]).values

3- Calculate the Second_Highest_Price:

data['Second_Highest_Price'] = np.where((data['Sales'].shift() == data['2nd_sales']), data['Price'].shift(),                                (np.where((data['Sales'] == data['2nd_sales']), data['Price'], np.nan)))data['Second_Highest_Price'] = data.groupby('Person')['Second_Highest_Price'].ffill()

Output:

data['Second_Highest_Price'].valuesarray([nan, '10', '12', '12', '12', nan, '10', '10', '10', '12', '10'],      dtype=object)