Python/Pandas: Conditional summing of columns

Question

In python I have a pandas data frame similar to the one below:

           | AUG12               | UNDERLYING | VOL |
           |---------------------|            |     |
           | 45  | 49  | 50 | 55 |            |     |
====================================================|
2012-11-14 | 1   | 1   | 2  | 3  | 49         | ?   |
...          ...   ...   ...   ...

The task is: For each row, find column names which are greater than UNDERLYING (49), sum the values (2+3) and put the result in to VOL (5). How can I accomplish this in python? Many thanks in advance!

shanmuga · Accepted Answer · 2016-05-07 16:42:45Z

1

You could use DataFrame.apply function

def conditional_sum(row):
    underlying = row['UNDERLYING'][0]  # extra '[0]' is required due to multi leve index in column names
    return row.loc['AUG12'].apply(lambda x: 0 if x < underlying else x).sum()

df.apply(conditional_sum, axis=1)

answered May 7, 2016 at 16:42

shanmuga

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1 Comment

sascha Over a year ago

That is exactly what I was looking for. I had to replace AUG12 with the range of my columns. But your snippet did the trick!

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