How can I find out which operands are supported before looping through pandas dataframe?

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 a2 to 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)