Converting a Pandas GroupBy output from Series to DataFrame

Question

I'm starting with input data like this df1 = pandas.DataFrame( { "Name" : ["Alice", "Bob", "Mallory", "Mallory", "Bob" , "Mallory"] , "City" : ["Seattle", "Seattle", "Portland", "Seattle", "Seattle", "Portland"] } ) Which when printed appears like this: City Name 0 Seattle Alice 1 Seattle Bob 2 Portland Mallory 3 Seattle Mallory 4 Seattle Bob 5 Portland Mallory Grouping is simple enough: g1 = df1.groupby( [ "Name", "City"] ).count() and printing yields a GroupBy object: City Name Name City Alice Seattle 1 1 Bob Seattle 2 2 Mallory Portland 2 2 Seattle 1 1 But what I want eventually is another DataFrame object that contains all the rows in the GroupBy object. In other words, I want to get the following result: City Name Name City Alice Seattle 1 1 Bob Seattle 2 2 Mallory Portland 2 2 Mallory Seattle 1 1 I can't quite see how to accomplish this in the pandas documentation. Any hints would be welcome. Select the correct answer from above options

Answer 1

You can simply use .reset_index() method with .groupby() function for your problem. For example: In [1]: DataFrame({'count' : df1.groupby( [ "Name", "City"] ).size()}).reset_index() Out[1]: Name City count 0 Alice Seattle 1 1 Bob Seattle 2 2 Mallory Portland 2 3 Mallory Seattle 1 Or you can use: In[2]: df1.groupby( [ "Name", "City"] ).size().to_frame(name = 'count').reset_index() Out[2]: Name City count 0 Alice Seattle 1 1 Bob Seattle 2 2 Mallory Portland 2 3 Mallory Seattle 1 Hope this answer helps.