当您的数据很大时，是否有一种有效的方法可以使用第二张表来填充正确的不一致数据？

import pandas as pd

df1 = pd.DataFrame(data=[
    {"flight_id":"000000_20180121","ENGIne_number":"000000","aircraft_tail":"G-RHBZ","year":"2018","month":"01"},{"flight_id":"258741_20171021","ENGIne_number":"258741","aircraft_tail":"H-RZBE","year":"2017","month":"10"},{"flight_id":"_20150214","ENGIne_number":"","aircraft_tail":"V-RDER","year":"2015","month":"02"},{"flight_id":"_20110287","ENGIne_number":"NO-numbER","aircraft_tail":"G-EHRK","year":"2011","month":"12"}]
)
df2 = pd.DataFrame(data=[
    {"ENGIne_number":"258741",{"ENGIne_number":"348741","month":"03"},{"ENGIne_number":"589745",{"ENGIne_number":"587981","month":"12"}]
    )

# Validator function
def bad_ENGIne_number_detector(ENGIne_number):

    lst_invalid_ENGIne_number = ["000000","NO-numbER"]

    is_bad_ENGIne_number = false
    if ENGIne_number == "":
        is_bad_ENGIne_number = True
    elif ENGIne_number in lst_invalid_ENGIne_number:
        is_bad_ENGIne_number = True

    return is_bad_ENGIne_number
    
# Identify invalid entries on df1
mask = df1["ENGIne_number"].apply(bad_ENGIne_number_detector)

# Merge both tables (df1 filtered only with bad entries)
df1.loc[mask].merge(df2,on=["aircraft_tail","year","month"],suffixes=["_bad","_good"])