pandas left join and update existing column

You can use merge() between left and right with how='left' on 'a' column.

In [74]: final = left.merge(right, on='a', how='left')

In [75]: final
Out[75]:
   a  b  c_x  c_y   d
0  1  4    9    7  13
1  2  5   10    8  14
2  3  6   11    9  15
3  4  7   12  NaN NaN

Replace NaN value from c_y with c_x value

In [76]: final['c'] = final['c_y'].fillna(final['c_x'])

In [77]: final
Out[77]:
   a  b  c_x  c_y   d   c
0  1  4    9    7  13   7
1  2  5   10    8  14   8
2  3  6   11    9  15   9
3  4  7   12  NaN NaN  12

Drop unwanted columns, and you have the result

In [79]: final.drop(['c_x', 'c_y'], axis=1)
Out[79]:
   a  b   d   c
0  1  4  13   7
1  2  5  14   8
2  3  6  15   9
3  4  7 NaN  12

One way to do this is to set the a column as the index and update:

In [11]: left_a = left.set_index('a')

In [12]: right_a = right.set_index('a')

Note: update only does a left join (not merges), so as well as set_index you also need to include the additional columns not present in left_a.

In [13]: res = left_a.reindex(columns=left_a.columns.union(right_a.columns))

In [14]: res.update(right_a)

In [15]: res.reset_index(inplace=True)

In [16]: res
Out[16]:
   a   b   c   d
0  1   4   7  13
1  2   5   8  14
2  3   6   9  15
3  4   7  12 NaN