Which datasets-merging operation would do this in Pandas? - python-2.7

Question

Lets say I have two pandas DataFrames, X and Y:
X =
+---+----------+---------+
| | Value1 | Value2 |
+---+----------+---------+
| A | 1 | NaN |
| B | 0 | 0 |
+---+----------+---------+
Y =
+---+----------+---------+
| | Value1 | Value2 |
+---+----------+---------+
| A | 2 | NaN |
| C | 30 | NaN |
+---+----------+---------+
I want to merge / join them based on the index (row name) resulting in this:
+---+----------+---------+
| | Value1 | Value2 |
+---+----------+---------+
| A | 1 | 2 |
| B | 0 | 0 |
| C | 30 | NaN |
+---+----------+---------+
Using merge and 'outer', the resulting table has columns per table, instead of just concatenating. I need something that appends new rows to the end, but also appends new columns for a matching index.
This is the result of an 'outer' merge:
+---+----------+---------+----------+---------+
| | Value1_X | Value2_X| Value1_Y | Value2_Y|
+---+----------+---------+----------+---------+
| A | 1 | NaN | 2 | NaN |
| B | 0 | 0 | NaN | NaN |
| C | NaN | NaN | 30 | NaN |
+---+----------+---------+----------+---------+
Which is almost what I want, but ignoring the original column labels...

Answer 1

On the result of the 'outer' merge:
X =
+---+----------+---------+----------+---------+
| | Value1_X | Value2_X| Value1_Y | Value2_Y|
+---+----------+---------+----------+---------+
| A | 1 | NaN | 2 | NaN |
| B | 0 | 0 | NaN | NaN |
| C | NaN | NaN | 30 | NaN |
+---+----------+---------+----------+---------+
do, X = X.apply(lambda x: pd.Series(x.dropna().values), axis = 1)
which will give
0 1
A 1.0 2.0
B 0.0 0.0
C 30.0 NaN