Transform a Counter object into a Pandas DataFrame

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 Counterd = 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()dfOut[40]:                          index   00                         login   11   rt_transaction_confirmation   12                  fb_view_cart  223                    fb_connect   14               rt_view_product  235                     fb_search  296                          sale   17               fb_view_listing  768                   add_to_cart   29                  rt_view_cart  1210                fb_homescreen  6311              fb_view_product  3712            rt_home_start_app  4613             fb_view_wishlist  3914              create_campaign   115                    rt_search  1216                   guest_sale   117             remove_from_cart   118              rt_view_listing  50

You can rename the columns to something more meaningful:

In [43]:df = df.rename(columns={'index':'event', 0:'count'})dfOut[43]:                          event  count0                         login      11   rt_transaction_confirmation      12                  fb_view_cart     223                    fb_connect      14               rt_view_product     235                     fb_search     296                          sale      17               fb_view_listing     768                   add_to_cart      29                  rt_view_cart     1210                fb_homescreen     6311              fb_view_product     3712            rt_home_start_app     4613             fb_view_wishlist     3914              create_campaign      115                    rt_search     1216                   guest_sale      117             remove_from_cart      118              rt_view_listing     50

Another option is to use DataFrame.from_records method

import pandas as pdfrom collections import Counterc = 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_records(list(dict(c).items()), columns=['page','count'])

It's a one-liner and speed seems to be the same.

Or use this variant to have them sorted by most used. Again the performance is about the same.

df = pd.DataFrame.from_records(c.most_common(), columns=['page','count'])

If you want two columns, set the keyword argument orient='index' when creating a DataFrame from a dictionary using from_dict:

final_df = pd.DataFrame.from_dict(final, orient='index')

See the documentation on DataFrame.from_dict