import pandas as pd
df = pd.DataFrame([[1,'192.168.1.10', '45.7.12.34', 'abc'],[4, '10.10.1.11', '90.90.67.33', 'def'], [77, '52.1.7.90', '67.5.3.5', 'ghi' ], [90, '19.19.90.7', '77.88.99.44', 'xyz']], columns=['A', 'sip', 'dip', 'location'])
# add new columns
df = df.assign(FromHost='from.noname', ToHost='to.noname')
addrs = {'192.168.1.10': 'myhost.net', '45.7.12.34': 'myhost-goo.net', '10.10.1.11': 'myhost-foo.net' }`enter code here`
So it produces the following table:
>>> df
A sip dip location FromHost ToHost
0 1 192.168.1.10 45.7.12.34 abc from.noname to.noname
1 4 10.10.1.11 90.90.67.33 def from.noname to.noname
2 77 52.1.7.90 67.5.3.5 ghi from.noname to.noname
3 90 19.19.90.7 77.88.99.44 xyz from.noname to.noname
Now, I need to replace values in 'FromHost' and 'ToHost' columns with addrs dictionary, where key matches 'sip'/'dip' columns respectively. Example:
A sip dip location FromHost ToHost
0 1 192.168.1.10 45.7.12.34 abc myhost.net myhost-goo.net
I tried e.g. df.loc but it returns DataFrame, I think I need Series.
How can I achieve this?
