Count by unique pair of columns in pandas

Question

I’m trying to figure out how to count by number of rows per unique pair of columns (ip, useragent), e.g.

d = pd.DataFrame({'ip': ['192.168.0.1', '192.168.0.1', '192.168.0.1', '192.168.0.2'], 'useragent': ['a', 'a', 'b', 'b']})

     ip              useragent
0    192.168.0.1     a
1    192.168.0.1     a
2    192.168.0.1     b
3    192.168.0.2     b

To produce:

ip           useragent  
192.168.0.1  a           2
192.168.0.1  b           1
192.168.0.2  b           1

Ideas?

Asked By: barnybug

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Source

Answer 1

If you use groupby, you will get what you want.

d.groupby(['ip', 'useragent']).size()

produces:

ip          useragent               
192.168.0.1 a           2
            b           1
192.168.0.2 b           1

Answered By: Matti John

Answer 2

print(d.groupby(['ip', 'useragent']).size().reset_index().rename(columns={0:''}))

gives:

            ip useragent   
0  192.168.0.1         a  2
1  192.168.0.1         b  1
2  192.168.0.2         b  1

Another nice option might be pandas.crosstab:

print(pd.crosstab(d.ip, d.useragent) )
print('nsome cosmetics:')
print(pd.crosstab(d.ip, d.useragent).reset_index().rename_axis('',axis='columns') )

gives:

useragent    a  b
ip               
192.168.0.1  2  1
192.168.0.2  0  1

some cosmetics:
            ip  a  b
0  192.168.0.1  2  1
1  192.168.0.2  0  1

Answered By: Markus Dutschke

Count by unique pair of columns in pandas

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