How to update a csv from another csv considering a combination of columns as the primary key?

I am having a block in my assignment to build a python project to do the following task. I'm kindly seeking help from you to make it complete!
I have 2 CSVs with same headers, which are:

[ID,Date,time,type,status,member,account,property,credit quantity, debitquantity, Net quantity, credit value, debit value, Net value, currency]

One csv is input.csv which contains input values for a run and other csv is system.csv which act like a database containing all the summed up values after each run. system.csv must be updated based on the input.csv.

When updating the systems.csv the following combination of fields is considered as the primary key: 'Date', 'member', 'account', 'property'

if the values of the primary key is found in the systems.csv each values under credit quantity, debit quantity, Net quantity, credit value, debit value, Net value must be added to the existing values as follows:

input.csv

ID,Date,time,type,status,member,account,property,credit quantity, debit quantity, Net quantity, credit value, debit value, Net value, currency
id01,2023.03.16,21:00:00,visa,active,xyz,acc001,cc,100, 0, 1000, 100, 0, 1000, usd
id02,2023.03.16,22:00:00,visa,active,abc,acc002,cc,0,200, 2000, 0, 200, 2000, usd

system.csv

id101,2023.03.16,08:00:00,visa,active,xyz,acc001,cc,500, 0, 5000, 400, 0, 4000, usd
id102,2023.03.16,09:00:00,visa,active,abc,acc002,cc,0,600, 6000, 0, 200, 2000, usd

system.csv after run

id101,2023.03.16,21:00:00,visa,active,xyz,acc001,cc,600, 0, 6000, 500, 0, 5000, usd
id102,2023.03.16,22:00:00,visa,active,abc,acc002,cc,0,800, 8000, 0, 400, 4000, usd

Currently I've taken two csvs as dataframes and trying to do the process. but since my lack of knowledge please help me to complete this. Thanks in advance!!

IIUC, you can use :

df_in = pd.read_csv("input.csv")
df_sys = pd.read_csv("system.csv") #with optional `header=None, names=df_in.columns`


pkeys = ["Date", "member", "account", "property"]
scols = df_in.select_dtypes("object").columns.difference(pkeys)
ncols = df_in.columns.difference(scols.union(pkeys))

df_run = (
    pd.concat([df_in, df_sys])
        .groupby(pkeys,  as_index=False, sort=False).agg(
            {**{col: "sum" for col in ncols},
             **{col: "first" for col in scols}})
        [df_in.columns]
)

# df_run.to_csv("system.csv", index=False) #uncomment to overwrite the old `.csv` 

Output (system.csv in a tabular format) :