Add multiple empty columns to pandas DataFrame

I'd concat using a DataFrame:

In [23]:
df = pd.DataFrame(columns=['A'])
df

Out[23]:
Empty DataFrame
Columns: [A]
Index: []

In [24]:    
pd.concat([df,pd.DataFrame(columns=list('BCD'))])

Out[24]:
Empty DataFrame
Columns: [A, B, C, D]
Index: []

So by passing a list containing your original df, and a new one with the columns you wish to add, this will return a new df with the additional columns.

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Caveat: See the discussion of performance in the other answers and/or the comment discussions. reindex may be preferable where performance is critical.

You could use df.reindex to add new columns:

In [18]: df = pd.DataFrame(np.random.randint(10, size=(5,1)), columns=['A'])

In [19]: df
Out[19]: 
   A
0  4
1  7
2  0
3  7
4  6

In [20]: df.reindex(columns=list('ABCD'))
Out[20]: 
   A   B   C   D
0  4 NaN NaN NaN
1  7 NaN NaN NaN
2  0 NaN NaN NaN
3  7 NaN NaN NaN
4  6 NaN NaN NaN

reindex will return a new DataFrame, with columns appearing in the order they are listed:

In [31]: df.reindex(columns=list('DCBA'))
Out[31]: 
    D   C   B  A
0 NaN NaN NaN  4
1 NaN NaN NaN  7
2 NaN NaN NaN  0
3 NaN NaN NaN  7
4 NaN NaN NaN  6

The reindex method as a fill_value parameter as well:

In [22]: df.reindex(columns=list('ABCD'), fill_value=0)
Out[22]: 
   A  B  C  D
0  4  0  0  0
1  7  0  0  0
2  0  0  0  0
3  7  0  0  0
4  6  0  0  0