two-level indexing in dataframes in pandas : count the number of second level index for each first level index

Question

I have a dataframe pasted below with two-level indexing (first level is statename and second level is county name): the image contains the statename and the counties in it in the second column

I need to count the number of counties in each state. (In the image pasted i have just information for a single state but the entire dataframe consists of several such similar entries for states and corresponding county names.) for ex. in the above pic it should return 5 for Alabama (i know there are 60 other counties within alabama but i have just pasted a small part of the bigger dataframe). Which pandas function can be used to return the counties count for each state? I am a novice to pandas and just in the learning stage.

thanks for the help in advance.

Answer 1

I think you need groupby and aggregate by size :

df1 = df.groupby(level=0).size()

Or:

df1 = df.groupby('STNAME').size()

Sample:

df = pd.DataFrame({'STNAME':['AL'] * 3 + ['MI'] * 4, 
                   'CTYNAME':list('abcdefg'),
                   'COL': range(7) }).set_index(['STNAME','CTYNAME'])
print (df)
                COL
STNAME CTYNAME     
AL     a          0
       b          1
       c          2
MI     d          3
       e          4
       f          5
       g          6

df1 = df.groupby(level=0).size().reset_index(name='count')
print (df1)
  STNAME  count
0     AL      3
1     MI      4

df1 = df.groupby('STNAME').size().reset_index(name='count')
print (df1)
  STNAME  count
0     AL      3
1     MI      4

Answer 2

it will give count for each st_name . i experimented with small list

df = pd.DataFrame({'st_name': 'alabama', 'cityname': [['alabama'], ['autuguva','county'],['county']]})

df.groupby('st_name').count()