Python 逻辑，如果你想的话。 #load libraries library(tidyverse) library(lubridate) pymts <- read.table(text = "cust_id agreement_id da

逻辑，如果你想的话。

#load libraries
library(tidyverse)
library(lubridate)

pymts <- read.table(text = "cust_id agreement_id    date    payment
1   A   12/1/20 200
1   A   2/2/21  200
1   A   2/3/21  100
1   A   5/1/21  200
1   B   1/2/21  50
1   B   1/9/21  20
1   B   3/1/21  80
1   B   4/23/21 90
2   C   1/21/21 600
3   D   3/4/21  150
3   D   5/3/21  150", header = T)

agmt <- read.table(text = "agreement_id activation  term_months total_fee
A   12/1/20 24  4800
B   1/21/21 6   600
C   1/21/21 6   600
D   3/4/21  6   300", header = T)

#final code

final<- pymts %>% mutate(date = as.Date(date, "%m/%d/%y")) %>%
  left_join(agmt %>% mutate(activation = as.Date(activation, "%m/%d/%y")), by = "agreement_id") %>%
  group_by(cust_id, agreement_id) %>%
  mutate(d = n(),
         date = floor_date(date, "month")) %>%
  complete(date = seq.Date(from = min(date), by = "month", length.out = ifelse(sum(payment) == first(total_fee),
                                                                                                    first(term_months), 
                                                                                                   (year(max(date)) -
                                                                                                      year(min(date)))*12 +
                                                                                                      month(max(date)) - 
                                                                                                      month(min(date)) +1))) %>%
  mutate(payment = sum(payment, na.rm = T)) %>%
  filter(!duplicated(date)) %>%
  mutate(payment = payment/n()) %>%
  select(1:4) %>% ungroup()


final
# A tibble: 22 x 4
   cust_id agreement_id date       payment
     <int> <chr>        <date>       <dbl>
 1       1 A            2020-12-01    117.
 2       1 A            2021-01-01    117.
 3       1 A            2021-02-01    117.
 4       1 A            2021-03-01    117.
 5       1 A            2021-04-01    117.
 6       1 A            2021-05-01    117.
 7       1 B            2021-01-01     60 
 8       1 B            2021-02-01     60 
 9       1 B            2021-03-01     60 
10       1 B            2021-04-01     60 
# ... with 12 more rows

from dateutil.relativedelta import relativedelta

# Transofrm column to date
payments['date']= pd.to_datetime(payments['date'])
agreement['activation']= pd.to_datetime(agreement['activation'])

final =pd.merge(payments,agreement,on='agreement_id',how='left')

# set date to beginning of month
final['date'] = pd.to_datetime(final.date).dt.to_period('M').dt.to_timestamp()


def set_date_range(df):
    if df['payment'].sum() == df['total_fee'].iloc[0]:
        return pd.date_range(min(g['date']), periods=df['term_months'].iloc[0], freq='M')
    else:
        return pd.date_range(min(g['date']),
                             max(g['date'])+relativedelta(months=+1), freq='M' )

# Create dataframe with dates
seq_df = pd.DataFrame()
for i,g in final.groupby(['cust_id', 'agreement_id']):
    seq_df = pd.concat([seq_df,
                        pd.DataFrame({'cust_id': i[0], 'agreement_id': i[1],  'date': set_date_range(g)})])

# Set date to beginnig of month
seq_df['date'] = pd.to_datetime(seq_df.date).dt.to_period('M').dt.to_timestamp()

final = (pd.concat([final, seq_df], sort=True)
              .sort_values(['cust_id', 'agreement_id', 'date'])
              .reset_index(drop=True)
              .reindex(columns=final.columns))

final['payment'] = final.groupby(by=['cust_id', 'agreement_id'])["payment"].transform("sum")

final = final.drop_duplicates(['cust_id', 'agreement_id', 'date'])

final['n'] = final.groupby(by=['cust_id', 'agreement_id'])["cust_id"].transform("count")
final['payment_due'] = final['payment']/final['n']
final[['cust_id','agreement_id','date', 'payment_due']]