how to get the average of dataframe column values

Question

                    A        B DATE                  2013-05-01        473077    71333 2013-05-02         35131    62441 2013-05-03           727    27381 2013-05-04           481     1206 2013-05-05           226     1733 2013-05-06           NaN     4064 2013-05-07           NaN    41151 2013-05-08           NaN     8144 2013-05-09           NaN       23 2013-05-10           NaN       10 

say i have the dataframe above. what is the easiest way to get a series with the same index which is the average of the columns A and B? the average needs to ignore NaN values. the twist is that this solution needs to be flexible to the addition of new columns to the dataframe.

the closest i have come was

df.sum(axis=1) / len(df.columns) 

however, this does not seem to ignore the NaN values

(note: i am still a bit new to the pandas library, so i'm guessing there's an obvious way to do this that my limited brain is simply not seeing)

Answer 1

Simply using df.mean() will Do The Right Thing(tm) with respect to NaNs:

>>> df                  A      B DATE                      2013-05-01  473077  71333 2013-05-02   35131  62441 2013-05-03     727  27381 2013-05-04     481   1206 2013-05-05     226   1733 2013-05-06     NaN   4064 2013-05-07     NaN  41151 2013-05-08     NaN   8144 2013-05-09     NaN     23 2013-05-10     NaN     10 >>> df.mean(axis=1) DATE 2013-05-01    272205.0 2013-05-02     48786.0 2013-05-03     14054.0 2013-05-04       843.5 2013-05-05       979.5 2013-05-06      4064.0 2013-05-07     41151.0 2013-05-08      8144.0 2013-05-09        23.0 2013-05-10        10.0 dtype: float64 

You can use df[["A", "B"]].mean(axis=1) if there are other columns to ignore.