python-performance | Performance benchmarks of Python , Numpy , etc | GPU library

This question is strongly related to my question earlier: here
Sorry that I have to ask again!

The code below is running and delivering the correct results but its again somehow slow (4 mins for 80K rows). I have problems to use the Series class from pandas for concrete values. Can someone recommend how I can instead classify those columns?

Could not find relevant information in the documentary:
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html

I have a financial dataset with ~2 million rows. I would like to import it as a pandas dataframe and add additional columns by applying rowwise functions utilizing some of the existing column values. For this purpose I would like to not use any techniques like parallelization, hadoop for python, etc, and so I'm faced with the following:

I am already doing this similar to the example below and performance is poor, ~24 minutes to just get through ~20K rows. Note: this is not the actual function, it is completely made up. For the additional columns I am calculating various financial option metrics. I suspect the slow speed is primarily due to iterating over all the rows, not really the functions themselves as they are fairly simple (e.g. calculating price of an option). I know I can speed up little things in the functions themselves, such as using erf instead of the normal distribution, but for this purpose I want to focus on the holistic problem itself.