Merge multiple column values into one column in python pandas

Question

I have a pandas data frame like this:

   Column1  Column2  Column3  Column4  Column5  0    a        1        2        3        4  1    a        3        4        5  2    b        6        7        8  3    c        7        7         

What I want to do now is getting a new dataframe containing Column1 and a new columnA. This columnA should contain all values from columns 2 -(to) n (where n is the number of columns from Column2 to the end of the row) like this:

  Column1  ColumnA 0   a      1,2,3,4 1   a      3,4,5 2   b      6,7,8 3   c      7,7 

How could I best approach this issue? Any advice would be helpful. Thanks in advance!

Answer 1

You can call apply pass axis=1 to apply row-wise, then convert the dtype to str and join:

In [153]: df['ColumnA'] = df[df.columns[1:]].apply(     lambda x: ','.join(x.dropna().astype(str)),     axis=1 ) df  Out[153]:   Column1  Column2  Column3  Column4  Column5  ColumnA 0       a        1        2        3        4  1,2,3,4 1       a        3        4        5      NaN    3,4,5 2       b        6        7        8      NaN    6,7,8 3       c        7        7      NaN      NaN      7,7 

Here I call dropna to get rid of the NaN, however we need to cast again to int so we don't end up with floats as str.

Answer 2

I propose to use .assign

df2 = df.assign(ColumnA = df.Column2.astype(str) + ', ' + \   df.Column3.astype(str) + ', ' df.Column4.astype(str) + ', ' \   df.Column4.astype(str) + ', ' df.Column5.astype(str)) 

it's simple, maybe long but it worked for me