python - Series to Dataframe conversion with column indexing -

there series called location_ratings below :

location_ratings = location['location'].value_counts() 

below location_ratings's sample output:

 location  brazil                                       180  alaska                                       175  russia                                       171  colombia                                     146  canada                                       144  california                                   142  france                                       130  england                                      104  india                                         97  indonesia                                     84  china                                         83 

there 2 values 1 location , other 1 numeric value (ratings).

i want separate them 2 new columns, 1 should 'location' other should 'ratings' in dataframe.

i tried converting series dataframe , resetting index using below code failed expected result.

failed attempt 1 :

d1 = location_ratings.to_frame().reset_index().t 

failed attempt 2 :

d1 = location_ratings.to_frame(). d1.columns = ['location', 'ratings'] 

you can first change index name rename_axis (new in pandas 0.18.0) , reset_index:

location = pd.dataframe({'location': {0: 'brazil', 1: 'brazil', 2: 'brazil', 3: 'brazil', 4: 'brazil', 5: 'alaska', 6: 'alaska', 7: 'alaska', 8: 'alaska'}}) print (location)   location 0   brazil 1   brazil 2   brazil 3   brazil 4   brazil 5   alaska 6   alaska 7   alaska 8   alaska  location_ratings = location['location'].value_counts().rename_axis('location') print (location_ratings) location brazil    5 alaska    4 name: location, dtype: int64  d1 = location_ratings.reset_index(name='ratings') print (d1)   location  ratings 0   brazil        5 1   alaska        4 

another solution assigning new column names:

d1 = location_ratings.reset_index() d1.columns = ['location', 'ratings'] print (d1)   location  ratings 0   brazil        5 1   alaska        4