Python pandas - particular merge/replacement

This takes a couple steps, left merge on the columns that match, this will create 'x' and 'y' where there are clashes:

In [25]:

merged = df.merge(subdf, on=['id', 'name'], how='left')
merged
Out[25]:
   id name  val1_x  val2_x  val3  val1_y  val2_y
0   1    a       0       0     0     0.3       4
1   2    a       0       0     0     NaN     NaN
2   1    b       0       0     0     0.4       5
3   2    b       0       0     0     NaN     NaN
4   1    c       0       0     0     NaN     NaN
5   2    c       0       0     0     0.7       4
In [26]:
# take the values that of interest from the clashes
merged['val1'] = np.max(merged[['val1_x', 'val1_y']], axis=1)
merged['val2'] = np.max(merged[['val2_x', 'val2_y']], axis=1)
merged
Out[26]:
   id name  val1_x  val2_x  val3  val1_y  val2_y  val1  val2
0   1    a       0       0     0     0.3       4   0.3     4
1   2    a       0       0     0     NaN     NaN   0.0     0
2   1    b       0       0     0     0.4       5   0.4     5
3   2    b       0       0     0     NaN     NaN   0.0     0
4   1    c       0       0     0     NaN     NaN   0.0     0
5   2    c       0       0     0     0.7       4   0.7     4
In [27]:
# drop the additional columns
merged = merged.drop(labels=['val1_x', 'val1_y','val2_x', 'val2_y'], axis=1)
merged
Out[27]:
   id name  val3  val1  val2
0   1    a     0   0.3     4
1   2    a     0   0.0     0
2   1    b     0   0.4     5
3   2    b     0   0.0     0
4   1    c     0   0.0     0
5   2    c     0   0.7     4

Another method would be to sort both df's on 'id' and 'name' and then call update:

In [30]:

df = df.sort(columns=['id','name'])
subdf = subdf.sort(columns=['id','name'])
df.update(subdf)
df
Out[30]:
   id name  val1  val2  val3
0   1    a   0.3     4     0
2   2    c   0.7     4     0
4   1    c   0.0     0     0
1   1    b   0.4     5     0
3   2    b   0.0     0     0
5   2    c   0.0     0     0