The key to operate in columns (Series) of strings en mass is the Series.str accessor.

I can think of two .str methods to do what you want.

str.cat()

The first is str.cat. You have to start from a series, but you can pass a list of series (unfortunately you can't pass a dataframe) to concatenate with an optional separator. Using your example:

column_names = df.columns[1:]  # skipping the first, numeric, columnseries_list = [df[c] for c in column_names[1:]]# concatenate:df['result'] = series_list[0].str.cat(series_list[1:], sep=' ')

Or, in one line:

df['result'] = df[df.columns[1]].str.cat([df[c] for c in df.columns[2:]], sep=' ')

str.join()

The second is the .str.join() method, which works like the standard Python method string.join(), but for which you need to have a column (Series) of iterables, for example, a column of tuples, which we can get by applying tuples row-wise to a sub-dataframe of the columns you're interested in:

tuple_series = df[column_names].apply(tuple, axis=1)df['result'] = tuple_series.str.join(' ')

Or, in one line:

df['result'] = df[df.columns[1:]].apply(tuple, axis=1).str.join(' ')

BTW, don't try the above with list instead of tuple. As of pandas-0.20.1, if the function passed into the Dataframe.apply() method returns a list and the returned list has the same number entries as the columns of the original (sub)dataframe, Dataframe.apply() returns a Dataframe instead of a Series.

Here is a slightly alternative solution:

In [57]: df['result'] = df.filter(regex=r'^t').apply(lambda x: x.add(' ')).sum(axis=1).str.strip()In [58]: dfOut[58]:     n t0 t1 t2 t3   result0   92  a  d  g  i  a d g i1  916  b  e  h  j  b e h j2  363  c  f  i  k  c f i k