我有一个如下所示的 Dataframe ：

import pandas as pd

student_dict = {
"ID":[101,102,103,104,105],
"Student":["AAA","BBB","CCC","DDD","EEE"],
"Mark":[50,100,99,60,80],
"Address":["St.AAA","St.BBB","St.CCC","St.DDD","St.EEE"],
"PhoneNo":[1111111111,2222222222,3333333333,4444444444,5555555555]
}

df = pd.DataFrame(student_dict)

ID  Student Mark    Address PhoneNo
101 AAA       50    St.AAA  1111111111
102 BBB      100    St.BBB  2222222222
103 CCC       99    St.CCC  3333333333
104 DDD       60    St.DDD  4444444444
105 EEE       80    St.EEE  5555555555

MySql数据表（StudentTable）：

CREATE TABLE StudentTable(
    ID int,
    Student varchar(255),
    Mark int,
    Address varchar(255),
    PhoneNo varchar(20),
    primary key (ID)
);

为了将 Dataframe df插入到MySQL表中，我使用了：

import pymysql
pymysql.install_as_MySQLdb()
from sqlalchemy import create_engine

# Create engine

engine = create_engine('mysql://user:pass@127.0.0.1/dbname')

# Create the connection and close it(whether successed of failed)

with engine.begin() as connection:
    df.to_sql(name='StudentTable', con=connection, if_exists='append', index=False)

并且 Dataframe 成功插入

，如果我尝试插入另一个df，如下所示：

student_dict2 = {
"ID":[122,102,123],
"Student":["XXX","YYY","ZZZ"],
"Mark":[60,80,95],
"Address":["St.XXX","St.YYY","St.ZZZ"],
"PhoneNo":[1111111110,2222222222,3333333315]
}

df = pd.DataFrame(student_dict2)

ID  Student Mark    Address PhoneNo
122 XXX       60    St.XXX  1111111110
102 YYY       80    St.YYY  2222222222
123 ZZZ       95    St.ZZZ  3333333315

它将抛出如下错误

IntegrityError: (pymysql.err.IntegrityError) (1062, "Duplicate entry '102' for key 'StudentTable.PRIMARY'")

它不会将不重复的其他行插入到StudentTable中。我需要的是，*它需要将不重复的行插入到表中，并且 * 仅对重复的行抛出错误 。是否有任何方法可以在StudentTable中创建一个Timestamp列，以自动捕获数据插入时间