I have a table of codes and dates that is continuously growing from another source, e.g. Code Name Date RM1377 a 30/07/2022 RM1377 b 30/07/2022 RM1377 c 30/07/2022 RM1377 d 30/07/2022 RM1378 e 07/08/2022 RM1378 f 07/08/2022 RM1378 g 07/08/2022 RM1378 h 07/08/2022 And I want to be able to enter a code into an input, and then retrieve the date associated with the code, so that I can query on it. How can I define DM1377 as always being 30/07/2022, and account for DM1389 potentially being in the table with another date? I.e. instead of cruise_date = datetime.date(2022, 7, 30) I imagine it would be something like cruise_date = datetime.date(Voyage_date) from traceback import print_tb import pandas as pd from dateutil.relativedelta import relativedelta import datetime #voyage_id = input('Voyage ID:') # Retrieve the data and assign headers df = pd.read_csv("dcl_manifest_20220729.csv", usecols=[0,2,5,6,7,8], names=['Cruise', 'Booking reference', 'First name', 'Last name', 'DoB', 'Cancellation']) df[['Cruise', 'Booking reference', 'First name', 'Last name', 'DoB', 'Cancellation']] # Convert DoB column to dates df["DoB"] = pd.to_datetime(df["DoB"]) # Define the cruise date cruise_date = datetime.date(2022, 7, 30) # Calculate 5 years ago to identify under 5s years_ago = cruise_date - relativedelta(years=5) # Query under 5s and print the output u5 = df.query('DoB > @years_ago & Cruise == "DM1377" & Cancellation == "No"') print("Number of under 5s: " + str(len(u5.index))) Continue reading...
