["python","pandas","numpy","dataframe"]
Coalesce values from 2 columns into a single column in a pandas dataframe

I'm looking for a method that behaves similarly to coalesce in T-SQL. I have 2 columns (column A and B) that are sparsely populated in a pandas dataframe. I'd like to create a new column using the following rules: If the value in column A is not null, use that value for the new column C If the value in column A is null, use the value in column B for the new column C Like I mentioned, this can be accomplished in MS SQL Server via the coalesce function. I haven't found a good pythonic method for this; does one exist?
use combine_first(): In [16]: df = pd.DataFrame(np.random.randint(0, 10, size=(10, 2)), columns=list('ab')) In [17]: df.loc[::2, 'a'] = np.nan In [18]: df Out[18]: a b 0 NaN 0 1 5.0 5 2 NaN 8 3 2.0 8 4 NaN 3 5 9.0 4 6 NaN 7 7 2.0 0 8 NaN 6 9 2.0 5 In [19]: df['c'] = df.a.combine_first(df.b) In [20]: df Out[20]: a b c 0 NaN 0 0.0 1 5.0 5 5.0 2 NaN 8 8.0 3 2.0 8 2.0 4 NaN 3 3.0 5 9.0 4 9.0 6 NaN 7 7.0 7 2.0 0 2.0 8 NaN 6 6.0 9 2.0 5 2.0