CODEWITHSUNDEEP
pythonpandasdataframedictionarynested
sargupta
sargupta

Reputation: 1033

how to create a Dataframe (multi-index) with sub-columns from a nested Dictionary?

I tried to create two sub-columns under one column. First I created a DataFrame from a Dictionary 

Destination = ['Milan', 'London','Lisbon','Milan','Mumbai', 'Lisbon', 'Porto', 'Lisbon']
Product_Revenue = [542,332,135,524,232, 522, 122, 122]

data_frame = {'Destination' : [i for i in Destination], 
                            'Revenue': [j for j in Product_Revenue]}
pd.DataFrame(data_frame)

Second, I wanted to add Booking as the main column so that Destination and Revenue becomes sub-column of it. But I got Destination and Revenue in the index and even as a list.

data_frame = {'Booking': {'Destination' : [i for i in Destination], 
                            'Revenue': [j for j in Product_Revenue]}}
pd.DataFrame(data_frame)

                             Booking

Destination    [Milan, London, Vienna, Milan, Mumbai, Paris, ...


Revenue     [542, 332, 135, 524, 232, 522, 122, 122]

The Destination and Revenue is showing in rows.

How can I write each value of both in parallel as the previous one?

A future goal is to add Users as the index. So, the final dataframe would be User Vs Booking Data where Destination and Price values would vary for each user.

Any help would be highly appreciated.

Upvotes: 1

Views: 446

Answers (1)

ecortazar
ecortazar

Reputation: 1422

The question is a bit confusing. If you are looking to build a DF with columns as multiindex where the first level is 'Booking' and the second level is [Destination, Revenue], the keys should be defined as tuples:

Destination = ['Milan', 'London','Lisbon','Milan','Mumbai', 'Lisbon', 'Porto', 'Lisbon']
Product_Revenue = [542,332,135,524,232, 522, 122, 122]

data_frame = {('Booking', 'Destination') : Destination, 
              ('Booking', 'Revenue'): Product_Revenue}

pd.DataFrame(data_frame)

Output:

Booking
Destination Revenue
0   Milan   542
1   London  332
2   Lisbon  135
3   Milan   524
4   Mumbai  232
5   Lisbon  522
6   Porto   122
7   Lisbon  122

Upvotes: 1

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