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

Here is another way to do it:

In [183]: df.stack().groupby(level=0).first().reindex(df.index)
Out[183]: 
0     1
1     3
2     4
3   NaN
dtype: float64

---

The idea here is to use stack to move the columns into a row index level:

In [184]: df.stack()
Out[184]: 
0  A    1
   C    2
1  B    3
2  B    4
   C    5
dtype: float64

Now, if you group by the first row level -- i.e. the original index -- and take the first value from each group, you essentially get the desired result:

In [185]: df.stack().groupby(level=0).first()
Out[185]: 
0    1
1    3
2    4
dtype: float64

All we need to do is reindex the result (using the original index) so as to include rows that are completely NaN:

df.stack().groupby(level=0).first().reindex(df.index)

This is a really messy way to do this, first use first_valid_index to get the valid columns, convert the returned series to a dataframe so we can call apply row-wise and use this to index back to original df:

In [160]:
def func(x):
    if x.values[0] is None:
        return None
    else:
        return df.loc[x.name, x.values[0]]
pd.DataFrame(df.apply(lambda x: x.first_valid_index(), axis=1)).apply(func,axis=1)
​
Out[160]:
0     1
1     3
2     4
3   NaN
dtype: float64

EDIT

A slightly cleaner way:

In [12]:
def func(x):
    if x.first_valid_index() is None:
        return None
    else:
        return x[x.first_valid_index()]
df.apply(func, axis=1)

Out[12]:
0     1
1     3
2     4
3   NaN
dtype: float64

Fill the nans from the left with fillna, then get the leftmost column:

df.fillna(method='bfill', axis=1).iloc[:, 0]