How to merge two DataFrame columns and apply pandas.to_datetime to it?

How to merge two DataFrame columns and apply pandas.to_datetime to it?

I''m learning to use pandas, to use it for some data analysis. The data is
supplied as a csv file, with several columns, of which i only need to use
4 (date, time, o, c). I'll like to create a new DataFrame, which uses as
index a DateTime64 number, this number is creating by merging the first
two columns, applying pd.to_datetime on the merged string.
My loader code works fine:
st = pd.read_csv("C:/Data/stockname.txt",
names=["date","time","o","h","l","c","vol"])
The challenge is converting the loaded DataFrame into a new one, with the
right format. The below works but is very slow. Moreover, it just makes
one column with the new datetime64 format, and doesnt make it the index.
My code
st_new = pd.concat([pd.to_datetime(st.date + " " + st.time), (st.o + st.c)
/ 2, st.vol],
axis = 1, ignore_index=True)
What would be a more pythonic way to merge two columns, and apply a
function into the result? How to make the new column to be the index of
the DataFrame?