hi @lungben
I don’t like giving up on things, especially when I think my adventures could help others. That said I would like to investigate your concept of wrapping the code below in a function and calling the whole thing from PyCall ( awesome package by the way). Are there any gotcha’s that you can see in this approach?
The “problem” with julia ( like linux or python) is that there are so many possible approaches and history dictates I will take ALL of the poor ones before stumbling on the right one. My usual approach is to just jump in, make mistakes and then spend too much time duct taping when I should just take time to start off on the right foot. Any tips you might have would be appreciated.
import pandas as pd
import numpy as np
import uuid
env_dir = '/home/dave/j_sandbox/'
dev_env_csv = env_dir + '/csv/' # where to get the expert symbols from
expert_symbols_csv = "tontine_symbols.csv" # the symbols table with SPX index added AND the category stuff added 4 9 20
def get_stuff(csv_file):
df_symbol_to_process_list = pd.read_csv( csv_file )
return df_symbol_to_process_list
if __name__ == '__main__' :
mac_address = hex(uuid.getnode())
df_expert_symbols = get_stuff (dev_env_csv + expert_symbols_csv)