Merging values of two columns of dataframe with different dtypes

Question

I have the following pandas dataframe :

import pandas as pd

df = pd.DataFrame({"pos": [1, 2, 3], "chain": ["A", "B", "C"]})

Giving:

  chain  pos
0     A    1
1     B    2
2     C    3

and df.types :

chain    object
pos       int64
dtype: object

I'm looking for a way to merge Series df["chain"] and df["pos"] to have the following:

   chain+pos
0     A1
1     B2
2     C3

and df.dtypes :

chain+pos    object
dtype: object

Is there an easy way to do it?

Answer 1

df.astype(str).sum(1)
Out[489]: 
0    A1
1    B2
2    C3
dtype: object

Answer 2

In [34]: df['chain'] += df.pop('pos').astype(str)

In [35]: df
Out[35]:
  chain
0    A1
1    B2
2    C3

renaming column:

In [37]: df = df.rename(columns={'chain':'chain+pos'})

In [38]: df
Out[38]:
  chain+pos
0        A1
1        B2
2        C3

Answer 3

The solution by MaxU works very well. Otherwise you can use the following also

df["chain+pos"] = df['chain'] + df['pos'].map(str)

After this, you have to drop df['chain'] and df['pos'] to attain the desired result.

----------------- Edit

As @MaxU pointed out in his comment below, here is a concise way of achieving the desired result -

df['chain+pos'] = df.pop('chain') + df.pop('pos').map(str)