First non-null value per row from a list of Pandas columns

Question

If I\'ve got a DataFrame in pandas which looks something like:

    A   B   C
0   1 NaN   2
1 NaN   3 NaN
2 NaN   4   5
3 NaN NaN NaN

How ca

Answer 1

This is nothing new, but it's a combination of the best bits of @yangie's approach with a list comprehension, and @EdChum's df.apply approach that I think is easiest to understand.

First, which columns to we want to pick our values from?

In [95]: pick_cols = df.apply(pd.Series.first_valid_index, axis=1)

In [96]: pick_cols
Out[96]: 
0       A
1       B
2       B
3    None
dtype: object

Now how do we pick the values?

In [100]: [df.loc[k, v] if v is not None else None 
    ....:     for k, v in pick_cols.iteritems()]
Out[100]: [1.0, 3.0, 4.0, None]

This is ok, but we really want the index to match that of the original DataFrame:

In [98]: pd.Series({k:df.loc[k, v] if v is not None else None
   ....:     for k, v in pick_cols.iteritems()})
Out[98]: 
0     1
1     3
2     4
3   NaN
dtype: float64