How can I find out which operands are supported before looping through pandas dataframe? - python-3.x

Question

I am attempting to iterate over rows in a Series within a Pandas DataFrame. I would like to take the value in each row of the column csv_df['Strike'] and plug it into variable K, which gets called in function a.
Then, I want the output a1 and a2 to be put into their own columns within the DataFrame.
I am receiving the error: TypeError: unsupported operand type(s) for *: 'int' and 'zip', and I figure that if I can find out which operands are supported, I could convert a1 and a2to that.
Am I thinking about this correctly?
Note: S is just a static number as the df is just one row, while K has many rows.
Code is below:
from scipy.stats import norm
from math import sqrt, exp, log, pi
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
import fix_yahoo_finance as yf
yf.pdr_override()
import numpy as np
import datetime
from pandas_datareader import data, wb
import matplotlib.pyplot as plt
#To get data:
start = datetime.datetime.today()
end = datetime.datetime.today()
df = data.get_data_yahoo('AAPL', start, end) #puts data into a pandas dataframe
csv_df = pd.read_csv('./AAPL_TEST.csv')
for row in csv_df.itertuples():
def a(S, K):
a1 = 100 * K
a2 = S
return a1
S = df['Adj Close'].items()
K = csv_df['strike'].items()
a1, a2 = a(S, K)
df['new'] = a1
df['new2'] = a2

Answer 1

It seems an alternate way of doing what you want would be to apply your method to each data frame separately, as in:
df = data.get_data_yahoo('AAPL', start, end)
csv_df = pd.read_csv('./AAPL_TEST.csv')
df['new'] = csv_df['strike'].apply(lambda x: 100 * x)
df['new2'] = df['Adj Close']
Perhaps, applying the calculation directly to the Pandas Series (a column of your data frame) is a way to avoid defining a method that is used only once.
Plus, I wouldn't define a method within a loop as you have.
Cheers
ps. I believe you have forgotten to return both values in your method.
def a(S, K):
a1 = 100 * K
a2 = S
return (a1, a2)