Thread-safe way to use preloaded data in a for-loop

Suppose we apply a set of (inplace) operations within a for-loop on mostly the same fudamental data (mutable). What is a memory efficient (and thread safe) way to do so?

Note the fundamental data should not be altered within the for-loop from iteration to iteration.

Example Code:

Assume we have some Excel files containing fundamental data in a data directory. Further we have some addtional data in the some_more_data directory. I want to apply operations on the data retrieved from the data directory using the files from the some_more_data directory. Afterwards I want to print the results to a new pickle file.

import copy
import pickle
import pandas as pd

# Excel import function to obtain dictionary of pandas DataFrames.
def read_data(info_dict):

   data_dict = dict()
   for dname, dpath in info_dict.items():
       data_dict[dname] = pd.read_excel(dpath, index_col=0)

   return data_dict

# list of data files
data_list = {'price': 'data/price.xlsx', 'eps': 'data/eps.xlsx'}
raw_data = read_data(data_list)

# list of files used for operation (they, for example, have different indices)
some_more_data= {
    'some_data_a': 'some_more_data/some_data_a.xlsx',
    'some_data_b': 'some_more_data/some_data_b.xlsx'
    }
some_more_data = read_data(some_more_data)

# Apply operation to data (explicitly use a for-loop)
for smd_k, smd_v in some_more_data.items():
   rdata = copy.deepcopy(raw_data)

   rdata['price'] = rdata['price'].reindex(smd_v.index)
   rdata['eps'] = rdata['eps'].reindex(columns=smd_v.columns)

   with open(f'data/changed_{smd_k}.pkl', 'wb') as handle:
        pickle.dump(rdata, handle, protocol=pickle.HIGHEST_PROTOCOL)

Is the deepcopy operation in my example above thread safe (assuming I want to use Multithreading)? Or should I repeatedly load the data from Excel within the for loop (very slow)? Or is there an even better way?

Thank you for your help.

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Code to Generate Sample DataFrames and Save Data in Excel Files

Note that the directories data and some_more_data must be created manually first

import pandas as pd
import numpy as np


price = pd.DataFrame([[-1.332298,  0.396217,  0.574269, -0.679972, -0.470584,  0.234379],
                      [-0.222567,  0.281202, -0.505856, -1.392477,  0.941539,  0.974867],
                      [-1.139867, -0.458111, -0.999498,  1.920840,  0.478174, -0.315904],
                      [-0.189720, -0.542432, -0.471642,  1.506206, -1.506439,  0.301714]],
                     columns=['IBM', 'MSFT', 'APPL', 'ORCL','FB','TWTR'], 
                     index=pd.date_range('2000', freq='D', periods=4))

eps = pd.DataFrame([[-1.91,  1.63,  0.51, -.32, -0.84,  0.37],
                      [-0.56,  0.02, 0.56, 1.77,  0.99,  0.97],
                      [-1.67, -0.41, -0.98,  1.20,  0.74, -0.04],
                      [-0.80, -0.43, -0.12,  1.06, 1.59,  0.34]],
                     columns=['IBM', 'MSFT', 'APPL', 'ORCL','FB','TWTR'], 
                     index=pd.date_range('2000', freq='D', periods=4))

some_data_a = pd.DataFrame(np.random.randint(0,100,size=(4, 6)), columns=['IBM', 'MSFT', 'APPL', 'ORCL','FB','TWTR'], index=pd.date_range('2001', freq='D', periods=4))
some_data_b = pd.DataFrame(np.random.randint(0,100,size=(20, 6)), columns=['GM', 'TSLA', 'IBM', 'MSFT', 'APPL', 'ORCL'], index=pd.date_range('2000', freq='D', periods=20))

price.to_excel('data/price.xlsx')
eps.to_excel('data/eps.xlsx')
some_data_a.to_excel('some_more_data/some_data_a.xlsx')
some_data_b.to_excel('some_more_data/some_data_b.xlsx')

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