Why does newly-formed dict only consist of last row of each year?

[Mark17]

What are you trying to do, count baby names by year? You could group your dataframe by (groupby) year and baby name and count the number of babies in each group.

This is a good exercise... I'll work on a .groupby solution.

I'm trying to get a frequency count.

.value_counts() is a start:

values = baby_names['NAME'].value_counts()
print(values)

This gets me a list (actually a series) of name frequencies. I see I can also tack on .to_dict() and get a dictionary... but the names are keys and the frequencies are values. What I really want are the frequencies as keys and lists of names as values (since multiple names often occur with the same frequency)--and then I'd want to see that for each year.

I next tried a .groupby() solution...

print(baby_names.groupby(['BRITH_YEAR', 'NAME']))

...but I just get a groupby object:

Output:
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000018E277041C0>

If I tack on .sum(), then I get this:

Output:
                    COUNT  RANK
BRITH_YEAR NAME                
2011       AALIYAH    528   140
           AARAV       60   204
           AARON     1092   516
           ABBY        40   312
           ABDIEL      48   368
...                   ...   ...
2014       Zion        40    33
           Zissy       25    71
           Zoe        240    86
           Zoey       116   164
           Zuri        21    30

[4882 rows x 2 columns]

This is a bit confusing. 'COUNT' and 'RANK' are the last two column names. The actual number of times 'ABBY' appears is five (four in 2011 and one in 2012), so .sum() isn't counting occurrences of the names:

baby_names[baby_names['NAME'] == 'ABBY']

Output:
      BRITH_YEAR  GENDER        ETHNICTY  NAME  COUNT  RANK
1295        2011  FEMALE        HISPANIC      ABBY     10        78
2767        2011  FEMALE        HISPANIC      ABBY     10        78
4267        2011  FEMALE        HISPANIC      ABBY     10        78
6230        2011  FEMALE        HISPANIC      ABBY     10        78
7852        2012  FEMALE  ASIAN AND PACI  ABBY     11        44

Lots of stuff here... I appreciate any light you can shine on these concepts to help me understand them!