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