Transform a Counter object into a Pandas DataFrame

Question

I used Counter on a list to compute this variable:

final = Counter(event_container) 

print final gives:

Counter({'fb_view_listing': 76, 'fb_homescreen': 63, 'rt_view_listing': 50, 'rt_home_start_app': 46, 'fb_view_wishlist': 39, 'fb_view_product': 37, 'fb_search': 29, 'rt_view_product': 23, 'fb_view_cart': 22, 'rt_search': 12, 'rt_view_cart': 12, 'add_to_cart': 2, 'create_campaign': 1, 'fb_connect': 1, 'sale': 1, 'guest_sale': 1, 'remove_from_cart': 1, 'rt_transaction_confirmation': 1, 'login': 1}) 

Now I want to convert final into a Pandas DataFrame, but when I'm doing:

final_df = pd.DataFrame(final) 

but I got an error.

I guess final is not a proper dictionary, so how can I convert final to a dictionary? Or is it an other way to convert final to a DataFrame?

Answer 1

You can construct using from_dict and pass param orient='index', then call reset_index so you get a 2 column df:

In [40]: from collections import Counter d = Counter({'fb_view_listing': 76, 'fb_homescreen': 63, 'rt_view_listing': 50, 'rt_home_start_app': 46, 'fb_view_wishlist': 39, 'fb_view_product': 37, 'fb_search': 29, 'rt_view_product': 23, 'fb_view_cart': 22, 'rt_search': 12, 'rt_view_cart': 12, 'add_to_cart': 2, 'create_campaign': 1, 'fb_connect': 1, 'sale': 1, 'guest_sale': 1, 'remove_from_cart': 1, 'rt_transaction_confirmation': 1, 'login': 1}) df = pd.DataFrame.from_dict(d, orient='index').reset_index() df  Out[40]:                           index   0 0                         login   1 1   rt_transaction_confirmation   1 2                  fb_view_cart  22 3                    fb_connect   1 4               rt_view_product  23 5                     fb_search  29 6                          sale   1 7               fb_view_listing  76 8                   add_to_cart   2 9                  rt_view_cart  12 10                fb_homescreen  63 11              fb_view_product  37 12            rt_home_start_app  46 13             fb_view_wishlist  39 14              create_campaign   1 15                    rt_search  12 16                   guest_sale   1 17             remove_from_cart   1 18              rt_view_listing  50 

You can rename the columns to something more meaningful:

In [43]: df = df.rename(columns={'index':'event', 0:'count'}) df  Out[43]:                           event  count 0                         login      1 1   rt_transaction_confirmation      1 2                  fb_view_cart     22 3                    fb_connect      1 4               rt_view_product     23 5                     fb_search     29 6                          sale      1 7               fb_view_listing     76 8                   add_to_cart      2 9                  rt_view_cart     12 10                fb_homescreen     63 11              fb_view_product     37 12            rt_home_start_app     46 13             fb_view_wishlist     39 14              create_campaign      1 15                    rt_search     12 16                   guest_sale      1 17             remove_from_cart      1 18              rt_view_listing     50