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

Answer 2

Option 1
Using assign and drop

In [644]: cols = ['B', 'C', 'D']

In [645]: df.assign(E=df[cols].sum(1)).drop(cols, 1)
Out[645]:
   A     E
0  a  42.0
1  b  52.0
2  c  31.0
3  d   2.0
4  e  62.0
5  f  70.0

---

Option 2
Using assignment and drop

In [648]: df['E'] = df[cols].sum(1)

In [649]: df = df.drop(cols, 1)

In [650]: df
Out[650]:
   A     E
0  a  42.0
1  b  52.0
2  c  31.0
3  d   2.0
4  e  62.0
5  f  70.0

---

Option 3 Lately, I like the 3rd option.
Using groupby

In [660]: df.groupby(np.where(df.columns == 'A', 'A', 'E'), axis=1).first() #or sum max min
Out[660]:
   A     E
0  a  42.0
1  b  52.0
2  c  31.0
3  d   2.0
4  e  62.0
5  f  70.0

In [661]: df.columns == 'A'
Out[661]: array([ True, False, False, False], dtype=bool)

In [662]: np.where(df.columns == 'A', 'A', 'E')
Out[662]:
array(['A', 'E', 'E', 'E'],
      dtype='|S1')

Answer 3

You can also use ffill with iloc:

df['E'] = df.iloc[:, 1:].ffill(1).iloc[:, -1].astype(int)
df = df.iloc[:, [0, -1]]

print(df)

   A   E
0  a  42
1  b  52
2  c  31
3  d   2
4  e  62
5  f  70

Answer 4

The question as written asks for merge/combine as opposed to sum, so posting this to help folks who find this answer looking for help on coalescing with combine_first, which can be a bit tricky.

df2 = pd.concat([df["A"], 
             df["B"].combine_first(df["C"]).combine_first(df["D"])], 
            axis=1)
df2.rename(columns={"B":"E"}, inplace=True)
   A     E
0  a  42.0
1  b  52.0
2  c  31.0
3  d  2.0 
4  e  62.0
5  f  70.0

What's so tricky about that? in this case there's no problem - but let's say you were pulling the B, C and D values from different dataframes, in which the a,b,c,d,e,f labels were present, but not necessarily in the same order. combine_first() aligns on the index, so you'd need to tack a set_index() on to each of your df references.

df2 = pd.concat([df.set_index("A", drop=False)["A"], 
             df.set_index("A")["B"]\
             .combine_first(df.set_index("A")["C"])\
             .combine_first(df.set_index("A")["D"]).astype(int)], 
            axis=1).reset_index(drop=True)
df2.rename(columns={"B":"E"}, inplace=True)

   A   E
0  a  42
1  b  52
2  c  31
3  d  2 
4  e  62
5  f  70