Pandas to_sql 将列类型从 varchar 更改为 text [英] Pandas to_sql change column type from varchar to text

问题描述

我写了一个小脚本来在 SQL 服务器之间复制一个表.它有效，但其中一列将类型从 varchar 更改为 text ...如何复制具有相同列类型的表?

I wrote a little script to copy a table between SQL servers. It works, but one of the columns changed type from varchar to text... How do I make it to copy a table with the same columns types?

import pymssql
import pandas as pd
from sqlalchemy import create_engine


db_server= 1.2.3.4\\r2
db_database="Test_DB"
db_user="vaf"
db_password="1234"

local_db_server="1.1.1.1\\r2"
local_db_database="Test_DB"
local_db_user="vaf"
local_db_password="1234"

some_query=("""
select * from some_table
""")




def main():
    conn=pymssql.connect(server=local_db_server,user=local_db_user,password=local_db_password,database=local_db_database,charset='UTF-8')
    data=pd.io.sql.read_sql(some_query,conn)

    connection_string='mssql+pymssql://{}:{}@{}/{}'.format(db_user,db_password,db_server,db_database)
    engine=create_engine(connection_string)
    data.to_sql(name="some_table",con=engine,if_exists='replace',index=False)

if __name__ == "__main__":
    main()

谢谢

推荐答案

考虑三种方法:

指定类型 (提前预测)

使用 dtype 参数">pandas.DataFrame.to_sql，传递sqlalchemy 类型 用于命名列.

Using the dtype argument of pandas.DataFrame.to_sql, pass a dictionary of sqlalchemy types for named columns.

data.to_sql(name="some_table", con=engine, if_exists='replace', index=False,
            dtype={'datefld': sqlalchemy.DateTime(), 
                   'intfld':  sqlalchemy.types.INTEGER(),
                   'strfld': sqlalchemy.types.VARCHAR(length=255),
                   'floatfld': sqlalchemy.types.Float(precision=3, asdecimal=True),
                   'booleanfld': sqlalchemy.types.Boolean}

删除数据 (提前预测)

使用 DELETE 操作查询清除表.然后，由于 to_sql replace 参数重新创建表，所以只将数据从 Pandas 迁移到 SQL Server，而不改变表的结构.这种方法假设数据框始终与数据库表一致(没有新列/更改的类型).

Clean out table with DELETE action query. Then, migrate only the data from pandas to SQL Server without structurally changing table since to_sql replace argument re-creates the table. This approach assumes dataframe is always consistent (no new columns / changed types) with database table.

def main():
   connection_string = 'mssql+pymssql://{}:{}@{}/{}'\
                         .format(db_user,db_password,db_server,db_database)
   engine = create_engine(connection_string)

   # IMPORT DATA INTO DATA FRAME
   data = pd.read_sql(some_query, engine)

   # SQL DELETE (CLEAN OUT TABLE) VIA TRANSACTION
   with engine.begin() as conn:     
      conn.execute("DELETE FROM some_table")

   # MIGRATE DATA INTO DATA FRAME (APPEND NOT REPLACE)
   data.to_sql(name='some_table', con=engine, if_exists='append', index=False)

修改列 (响应式，因为它修复了临时)

使用 DDL SQL 语句在迁移后更改列.

Alter the column after migration with a DDL SQL statement.

def main():
   connection_string = 'mssql+pymssql://{}:{}@{}/{}'\
                         .format(db_user,db_password,db_server,db_database)
   engine = create_engine(connection_string)

   # IMPORT DATA INTO DATA FRAME
   data = pd.read_sql(some_query, engine)

   # MIGRATE DATA INTO DATA FRAME 
   data.to_sql(name="some_table", con=engine, if_exists='replace', index=False)

   # ALTER COLUMN TYPE (ASSUMING USER HAS RIGHTS/PRIVILEGES)
   with engine.begin() as conn:     
      conn.execute("ALTER TABLE some_table ALTER COLUMN mytextcolumn VARCHAR(255);")

我推荐第二种方法，因为我认为数据库应该与 python 和 pandas 等应用程序代码无关.因此，表模式的初始构建/重新构建应该是一个有计划的手动过程，任何脚本都不应该在结构上动态更改数据库，只与数据交互.

这篇关于Pandas to_sql 将列类型从 varchar 更改为 text的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！