I'm trying to write a script to scrub information from csvs. I have a pandas df created from a csv like below:
CUSTOMER ORDERS hashed_customer firstname lastname email order_id status timestamp 0 eater 1_uuid 1_firstname 1_lastname 1_email 12345 OPTED_IN 2020-05-14 20:45:15 1 eater 2_uuid 2_firstname 2_lastname 2_email 23456 OPTED_IN 2020-05-14 20:29:22 2 eater 3_uuid 3_firstname 3_lastname 3_email 34567 OPTED_IN 2020-05-14 19:31:55 3 eater 4_uuid 4_firstname 4_lastname 4_email 45678 OPTED_IN 2020-05-14 17:49:27 4 eater 5_uuid 5_firstname 5_lastname 5_email 56789 OPTED_IN 2020-05-14 16:22:33 I have another csv with the hashed_customers that I need to scrub from this file. So if the hashed_customer in this file is in CUSTOMER ORDERS, I need to remove the firstname, lastname, and email from the row while keeping the rest, to look something like this:
CUSTOMER ORDERS hashed_customer firstname lastname email order_id status timestamp 0 eater 1_uuid NULL NULL NULL 12345 OPTED_IN 2020-05-14 20:45:15 1 eater 2_uuid 2_firstname 2_lastname 2_email 23456 OPTED_IN 2020-05-14 20:29:22 2 eater 3_uuid 3_firstname 3_lastname 3_email 34567 OPTED_IN 2020-05-14 19:31:55 3 eater 4_uuid NULL NULL NULL 45678 OPTED_IN 2020-05-14 17:49:27 4 eater 5_uuid 5_firstname 5_lastname 5_email 56789 OPTED_IN 2020-05-14 16:22:33 My current script looks like this:
print('FIND ORDERS FROM OPT-OUT CUSTOMERS') cust_opt_out_order = [] for index, row in df_in.iterrows(): if row.hashed_eater_uuid in cust_opt_out_id: cust_opt_out_order.append(row.order_id) print('REMOVE OPT-OUT FROM OPT-IN FILE') df_cust_out = df_in[~df_in['hashed_eater_uuid'].isin(cust_opt_out_id)] But this is removing the entire row, and now I need to keep the row and only remove the name and email elements from the row. How can I drop elements from a row using pandas?
