[Python] Pandas, groupby and finding maximum in groups, returning value and count

I have a pandas DataFrame with log data:

host service
0 this.com mail
1 this.com mail
2 this.com web
3 that.com mail
4 other.net mail
5 other.net web
6 other.net web


And I want to find the service on every host that gives the most errors:

host service no
0 this.com mail 2
1 that.com mail 1
2 other.net web 2


The only solution I found was grouping by host and service, and then iterating over the level 0 of the index.

Can anyone suggest a better, shorter version? without the Iteration?

df = df_logfile.groupby(['host','service']).agg({'service':np.size})

df_count = pd.DataFrame()
df_count['host'] = df_logfile['host'].unique()
df_count['service'] = np.nan
df_count['no'] = np.nan

for h,data in df.groupby(level=0):
i = data.idxmax()[0]
service = i[1]
no = data.xs(i)[0]
df_count.loc[df_count['host'] == h, 'service'] = service
df_count.loc[(df_count['host'] == h) & (df_count['service'] == service), 'no'] = no


full code https://gist.github.com/bjelline/d8066de66e305887b714

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