実現したいこと

下記に示すRのコードのコメントの行(addtionaltable)をPythonに書き換えたい

前提

Python3を用いています。
Rと変数の置き方が同じです。
コードはレコメンドエンジンのコードです。
for文を回すことだけはしたくないです。

書き換えたいRのコード

library(data.table)

submission = fread("data/sample_submission.csv", col.names=c("KaiinID", "AuctionID"))
submission = unique(submission[, .(KaiinID)], by = NULL)

auction = fread("data/auction.csv", col.names=c('AuctionID','ShouhinShubetsuID','ShouhinID', 'SaishuppinKaisuu','ConditionID', 'BrandID', 'GenreID','GenreGroupID', 'LineID','ColorID', 'DanjobetsuID', 'SankouKakaku', 'CreateDate'))
auction$'CreateDateOrder' = as.double(as.Date(substring(auction$CreateDate, 1, 10))-as.Date("2019-10-01"))

watchlist = fread("data/watchlist.csv", col.names=c('KaiinID', 'AuctionID','TourokuDate','SakujoFlag', ))
watchlist$TourokuDate = as.double(as.Date(substring(watchlist$TourokuDateOrder, 1, 10))-as.Date("2019-10-01"))
watchlist = merge(watchlist, auction, by = "AuctionID")

shudounyuusatsu = fread("data/shudounyuusatsu.csv", col.names=c('AuctionID','KaiinID','ShudouNyuusatsuDate', 'Kingaku', 'Suuryou','SokketsuFlag', 'SakujoFlag'))
shudounyuusatsu$ShudouNyuusatsuDateOrder = as.double(as.Date(substring(shudounyuusatsu$ShudouNyuusatsuDate, 1, 10))-as.Date("2019-10-01"))
shudounyuusatsu = merge(shudounyuusatsu, auction, by = 'AuctionID')

dateorder = -7

predict = rbind(
   auction[, .(KaiinID, AuctionID, Point = 1 / (dateorder - CreateDateOrder) ** 0.5 / (dateorder - ShudouNyuusatsuDateOrder))]
   , watchlist[SakujoFlag == 1, .(KaiinID, AuctionID, Point = 4 / (dateorder - CreateDateOrder) ** 0.5 / (dateorder - TourokuDateOrder))]
)
predict = merge(predict, submission, by = "KaiinID", all.y = T)
predict = predivt[, .(Point = max(Point)), .(KaiinID, AuctionID)]
predict = predict[order(-Point)]

addtionaltable = auction[CreateDateOrder == dateorder - 5, .(AuctionID =  sample(AuctionID, 20))]
predict = predict[, .(Ranking = 1:20, AuctionID = c(AuctionID[!is.na(AuctionID)], addtionaltable$AuctionID)[1:20]), .(KaiinID)] 
#additionaltableの箇所をpythonのpandasを用いて書き換えたい

書き換え後のPythonのコード例

import pandas as pd
from datetime import datetime 
auction = pd.read_csv("data/auction.csv")
shudounyuusatsu = pd.read_csv("data/shudounyuusatsu.csv")
watchlist = pd.read_csv("data/watchlist.csv")

submission = pd.read_csv("data/sample_submission.csv")
submission = submission.KaiinID.unique()
submission = pd.DataFrame([list(submission)],index=["KaiinID"]).T

auction["CreateDateOrder"] = auction.CreateDate.map(lambda x: x[0:10]).map(
    lambda x: datetime.strptime(x, '%Y-%m-%d'))-datetime.strptime('2019-10-01', '%Y-%m-%d')

watchlist["TourokuDateOrder"] = watchlist.TourokuDate.map(lambda x: x[0:10]).map(
    lambda x: datetime.strptime(x, '%Y-%m-%d'))-datetime.strptime('2019-10-01', '%Y-%m-%d')
watchlist = pd.merge(watchlist, auction, on="AuctionID")

shudounyuusatsu["ShudouNyuusatsuDateOrder"] = shudounyuusatsu.ShudouNyuusatsuDate.map(lambda x: x[0:10]).map(
    lambda x: datetime.strptime(x, '%Y-%m-%d'))-datetime.strptime('2019-10-01', '%Y-%m-%d')
shudounyuusatsu = pd.merge(shudounyuusatsu, auction, on='AuctionID')
order = -7

shudounyuusatsu["Point"] = 1/(order-shudounyuusatsu.CreateDateOrder.map(lambda x: x.days))**0.5/(
    order-shudounyuusatsu.ShudouNyuusatsuDateOrder.map(lambda x: x.days))
watchlist["Point"] = 4/(order-watchlist.CreateDateOrder.map(lambda x: x.days)
                        )**0.5/(order-watchlist.TourokuDateOrder.map(lambda x: x.days))

predict = pd.concat([shudounyuusatsu[["KaiinID", "AuctionID", "Point"]],
                     watchlist[watchlist.SakujoFlag == 1][["KaiinID", "AuctionID", "Point"]]])

predict = predict.groupby(["KaiinID", "AuctionID"]).max()
predict = predict.reset_index()
predict = pd.merge(submission, predict, how="left")
predict = predict.sort_values(
    ["Point", "KaiinID", "AuctionID"], ascending=[False, True, True])

addtionaltable = auction[auction.CreateDateOrder.map(
    lambda x:x.days) == order-5].sample(n=20, random_state=0)
addtionaltable = kari["AuctionID"]

#ここからの記述方法がわからない