I have a single dataframe like the following: 

```
               ip              domain
0  46.101.214.145  cnwkabrnyld1c0[.]com
1  46.101.214.145        anfj63m[.]com
2  46.101.214.145       anf3xnem[.]com
3   69.195.129.70  cnwkabrnyld1c0[.]com
4   69.195.129.70        anfj63ms[.]com

```

I am trying to groupby the "ip" column such that it will aggregate the duplicates onto the same line.

Desired output:

```
               ip              domain
0  46.101.214.145  cnwkabrnyld1c0[.]com | anfj63m[.]com | anf3xnem[.]com
3   69.195.129.70  cnwkabrnyld1c0[.]com
4   69.195.129.70        anfj63ms[.]com

```

This seems pretty straight forward based on the countless SOF and Panda documentation I've been reading.

Here's my code:

```
pDNSPanda = pd.read_csv('/tmp/pDNSCSV.csv', names=['ip', 'domain'])
g = pDNSPanda.groupby("ip").agg('|'.join)

```

Here's what I actually get:

```
                 domain
ip
0.0.0.0       ip|domain
1.1.1.200     ip|domain
1.148.56.163  ip|domain
1.167.81.129  ip|domain
1.193.28.230  ip|domain

```

Edit: 

It was suggested that I try my own code against the above data set.

```
test.csv
46.101.214.145  cnwkabrnyld1c0[.]com
46.101.214.145        anfj63m[.]com
46.101.214.145       anf3xnem[.]com
69.195.129.70  cnwkabrnyld1c0[.]com
69.195.129.70        anfj63ms[.]com

>>> import pandas as pd
>>> p = pd.read_csv('/tmp/test.csv', names=['ip', 'domain'])
>>> g = p.groupby("ip").agg("|".join)
>>> print g
                                         domain
ip
46.101.214.145        anfj63m[.]com   ip|domain
46.101.214.145       anf3xnem[.]com   ip|domain
46.101.214.145  cnwkabrnyld1c0[.]com  ip|domain
69.195.129.70        anfj63ms[.]comp|domain
69.195.129.70  cnwkabrnyld1c0[.]com   ip|domain

```