Multiple columns with the same name in Pandas

I had a similar issue, not due to reading from csv, but I had multiple df columns with the same name (in my case 'id'). I solved it by taking df.columns and resetting the column names using a list.

In : df.columns
Out: 
Index(['success', 'created', 'id', 'errors', 'id'], dtype='object')

In : df.columns = ['success', 'created', 'id1', 'errors', 'id2']

In : df.columns
Out: 
Index(['success', 'created', 'id1', 'errors', 'id2'], dtype='object')

From here, I was able to call 'id1' or 'id2' to get just the column I wanted.

the relevant parameter is mangle_dupe_cols

from the docs

mangle_dupe_cols : boolean, default True
    Duplicate columns will be specified as 'X.0'...'X.N', rather than 'X'...'X'

by default, all of your 'a' columns get named 'a.0'...'a.N' as specified above.

if you used mangle_dupe_cols=False, importing this csv would produce an error.

you can get all of your columns with

df.filter(like='a')

---

demonstration

from StringIO import StringIO
import pandas as pd

txt = """a, a, a, b, c, d
1, 2, 3, 4, 5, 6
7, 8, 9, 10, 11, 12"""

df = pd.read_csv(StringIO(txt), skipinitialspace=True)
df

df.filter(like='a')