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

Question

If I want to create a new DataFrame with several columns, I can add all the columns at once -- for example, as follows:

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

But now suppose farther down the road I want to add a set of additional columns to this DataFrame. Is there a way to add them all simultaneously, as in

additional_data = {'col_3': [8, 9, 10, 11],                    'col_4': [12, 13, 14, 15]} #Below is a made-up function of the kind I desire. df.add_data(additional_data) 

I'm aware I could do this:

for key, value in additional_data.iteritems():     df[key] = value 

Or this:

df2 = pd.DataFrame(additional_data, index=df.index) df = pd.merge(df, df2, on=df.index) 

I was just hoping for something cleaner. If I'm stuck with these two options, which is preferred?

Answer 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 

Answer 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