Slow to run a pivot table of a DataFrame with over 1,000,000 rows by using pandas on Anaconda. Any suggestions to improve efficiency?
I don't have the issue by following the steps:

convert data type from 'str' to 'float' for the column I meant to sum through pivot_table; this is the calculation that I had problem with.
convert DataFrame to numpy array
calculate sum of the sliced numpy array (column). It tool less than 5 sec to return the total.

All good.
I have a DataFrame with 1M+ rows. I'm trying to run pivot table using pivot_table method from pandas on Anaconda. However, it has been running over 15+ min without result.
I've tried to install modin, and vaex on Anaconda with no luck as both libraries are not accepted by Anaconda.
Not sure if this matters, I'm using vs code to run Anaconda.
Anyone has suggestions? Thank you,