Is it possible to add several columns at once to a pandas DataFrame?

Solution 1:

Pandas has assign method since 0.16.0. You could use it on dataframes like

In [1506]: df1.assign(**df2)
Out[1506]:
   col_1  col_2  col_3  col_4
0      0      4      8     12
1      1      5      9     13
2      2      6     10     14
3      3      7     11     15

or, you could directly use the dictionary like

In [1507]: df1.assign(**additional_data)
Out[1507]:
   col_1  col_2  col_3  col_4
0      0      4      8     12
1      1      5      9     13
2      2      6     10     14
3      3      7     11     15

Solution 2:

What you need is the join function:

df1.join(df2, how='outer')
#or
df1.join(df2) # this works also

Example:

data = {'col_1': [0, 1, 2, 3],
    'col_2': [4, 5, 6, 7]}
df1 = pd.DataFrame(data)

additional_data = {'col_3': [8, 9, 10, 11],
               'col_4': [12, 13, 14, 15]}
df2 = pd.DataFrame(additional_data)

df1.join(df2, how='outer')

output:

   col_1  col_2  col_3  col_4
0      0      4      8     12
1      1      5      9     13
2      2      6     10     14
3      3      7     11     15

Solution 3:

If you don't want to create new DataFrame from additional_data, you can use something like this:

>>> additional_data = [[8, 9, 10, 11], [12, 13, 14, 15]]
>>> df['col3'], df['col4'] = additional_data
>>> df
   col_1  col_2  col3  col4
0      0      4     8    12
1      1      5     9    13
2      2      6    10    14
3      3      7    11    15

It's also possible to do something like this, but it would be new DataFrame, not inplace modification of existing DataFrame:

>>> additional_header = ['col_3', 'col_4']
>>> additional_data = [[8, 9, 10, 11], [12, 13, 14, 15]]
>>> df = pd.DataFrame(data=np.concatenate((df.values.T, additional_data)).T, columns=np.concatenate((df.columns, additional_header)))
>>> df
   col_1  col_2  col_3  col_4
0      0      4      8     12
1      1      5      9     13
2      2      6     10     14
3      3      7     11     15