Combine duplicated columns within a DataFrame

Question

If I have a dataframe that has columns that include the same name, is there a way to combine the columns that have the same name with some sort of function (i.e. sum)?

For instance with:

In [186]:

df["NY-WEB01"].head()
Out[186]:
                NY-WEB01    NY-WEB01
DateTime        
2012-10-18 16:00:00  5.6     2.8
2012-10-18 17:00:00  18.6    12.0
2012-10-18 18:00:00  18.4    12.0
2012-10-18 19:00:00  18.2    12.0
2012-10-18 20:00:00  19.2    12.0

How might I collapse the NY-WEB01 columns (there are a bunch of duplicate columns, not just NY-WEB01) by summing each row where the column name is the same?

Answer 1

I believe this does what you are after:

df.groupby(lambda x:x, axis=1).sum()

Alternatively, between 3% and 15% faster depending on the length of the df:

df.groupby(df.columns, axis=1).sum()

EDIT: To extend this beyond sums, use .agg() (short for .aggregate()):

df.groupby(df.columns, axis=1).agg(numpy.max)