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 can I get the first non-null value from each row? E.g. for the above, I'd like to get: [1, 3, 4, None] (or equivalent Series).

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