python - Pandas, groupby and finding maximum in groups, returning value and count -



python - Pandas, groupby and finding maximum in groups, returning value and count -

i have pandas dataframe log data:

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

and want find service on every host gives errors:

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

the solution found grouping host , service, , iterating on level 0 of index.

can suggest better, shorter version? without 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 h,data in df.groupby(level=0): = 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

given df, next step grouping host value lone and aggregate idxmax. gives index corresponds the greatest service value. can utilize df.loc[...] select rows in df correspond greatest service values:

import numpy np import pandas pd df_logfile = pd.dataframe({ 'host' : ['this.com', 'this.com', 'this.com', 'that.com', 'other.net', 'other.net', 'other.net'], 'service' : ['mail', 'mail', 'web', 'mail', 'mail', 'web', 'web' ] }) df = df_logfile.groupby(['host','service'])['service'].agg({'no':'count'}) mask = df.groupby(level=0).agg('idxmax') df_count = df.loc[mask['no']] df_count = df_count.reset_index() print("\noutput\n{}".format(df_count))

yields dataframe

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

python numpy pandas

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