The code creates a dataframe, encrypts one of the columns, and then creates two more dataframes with only the encrypted and decrypted columns.

encryption-data-frame-test.py

import pandas as pd
from cryptography.fernet import Fernet


# Create a test dataframe with 3 columns. The first column is an index, the second are random float values and third are one of three types (Delta, Gamma, Vega)
test_df = pd.DataFrame(
    {
        "index": [1, 2, 3],
        "trader_name": ['Luis Arteaga', 'Peter Meier', 'Max Mustermann'],
        "trader_group": ["Delta", "Gamma", "Vega"]
    }
)

# Create a new dataframe called new_df out of test_df with an additional column that encrypt the column random_float

new_df = test_df.copy()
new_df["fernet_key_for_trader_name"] = new_df.apply(lambda x: Fernet.generate_key(), axis=1)
new_df["trader_name_in_bytes"] = new_df["trader_name"].apply(lambda x: bytes(x, encoding='ASCII'))
new_df["type_trader_name_in_bytes"] = new_df["trader_name_in_bytes"].apply(lambda x: type(x))

new_df["encrypted_trader_name"] = new_df.apply(
    lambda x: Fernet(x["fernet_key_for_trader_name"]).encrypt(x["trader_name_in_bytes"]),
    axis=1,
)

# Create a dataframe from new_df with index, trader_group and the decryption of encrypted_trader_name

test_decrypt_df = pd.DataFrame()
test_decrypt_df["decrypted_trader_name"] = new_df.apply(
    lambda x: Fernet(x["fernet_key_for_trader_name"]).decrypt(x["encrypted_trader_name"]),
    axis=1,
    result_type="expand",
    )


# Create a new dataframe from new_df with only the columns index, fernet_key_for_trader_name
output1_df = new_df[["index", "fernet_key_for_trader_name"]]

# Create a new dataframe from new_df with only index, trader_group and encrypted_trader_name
output2_df = new_df[["index", "trader_group", "encrypted_trader_name"]]

print(test_decrypt_df)