Merge multiple column values into one column in python pandas

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)dfOut[153]:  Column1  Column2  Column3  Column4  Column5  ColumnA0       a        1        2        3        4  1,2,3,41       a        3        4        5      NaN    3,4,52       b        6        7        8      NaN    6,7,83       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.

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

If you have lot of columns say - 1000 columns in dataframe and you want to merge few columns based on particular column name e.g. -Column2 in question and arbitrary no. of columns after that column (e.g. here 3 columns after 'Column2 inclusive of Column2 as OP asked).

We can get position of column using .get_loc() - as answered here

source_col_loc = df.columns.get_loc('Column2') # column position starts from 0df['ColumnA'] = df.iloc[:,source_col_loc+1:source_col_loc+4].apply(    lambda x: ",".join(x.astype(str)), axis=1)dfColumn1  Column2  Column3  Column4  Column5  ColumnA0       a        1        2        3        4  1,2,3,41       a        3        4        5      NaN    3,4,52       b        6        7        8      NaN    6,7,83       c        7        7      NaN      NaN      7,7

To remove NaN, use .dropna() or .fillna()

Hope it helps!