How to merge/combine columns in pandas?

Question

I have a (example-) dataframe with 4 columns:

data = {\'A\': [\'a\', \'b\', \'c\', \'d\', \'e\', \'f\'],
    \'B\': [42, 52, np.nan, np.nan, np.nan, np.nan],         


        

Answer 1

Use difference for columns names without A and then get sum or max:

cols = df.columns.difference(['A'])
df['E'] = df[cols].sum(axis=1).astype(int)
# df['E'] = df[cols].max(axis=1).astype(int)
df = df.drop(cols, axis=1)
print (df)
   A   E
0  a  42
1  b  52
2  c  31
3  d   2
4  e  62
5  f  70

If multiple values per rows:

data = {'A': ['a', 'b', 'c', 'd', 'e', 'f'],
    'B': [42, 52, np.nan, np.nan, np.nan, np.nan],  
    'C': [np.nan, np.nan, 31, 2, np.nan, np.nan],
    'D': [10, np.nan, np.nan, np.nan, 62, 70]}
df = pd.DataFrame(data, columns = ['A', 'B', 'C', 'D'])

print (df)
   A     B     C     D
0  a  42.0   NaN  10.0
1  b  52.0   NaN   NaN
2  c   NaN  31.0   NaN
3  d   NaN   2.0   NaN
4  e   NaN   NaN  62.0
5  f   NaN   NaN  70.0

cols = df.columns.difference(['A'])
df['E'] = df[cols].apply(lambda x: ', '.join(x.dropna().astype(int).astype(str)), 1)
df = df.drop(cols, axis=1)
print (df)
   A       E
0  a  42, 10
1  b      52
2  c      31
3  d       2
4  e      62
5  f      70