150 lines
5.6 KiB
Python
150 lines
5.6 KiB
Python
import pandas as pd
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import sqlalchemy
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from sqlalchemy import create_engine
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import configparser
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import os
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from urllib.parse import quote_plus
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def get_db_connection_string(config, section):
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"""
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从配置文件中构建数据库连接字符串
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:param config: 配置解析器对象
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:param section: 配置节名(source_db或target_db)
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:return: 数据库连接字符串
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"""
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db_type = 'mysql' # 假设使用MySQL数据库
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driver = 'pymysql' # MySQL驱动
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return f"{db_type}+{driver}://{config[section]['user']}:{quote_plus(config[section]['password'])}@" \
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f"{config[section]['host']}:{config[section]['port']}/{config[section]['database']}"
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def transform_and_load_bm_project(config_file_path):
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"""
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从源数据库提取bm_project数据，转换后加载到目标数据库
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:param config_file_path: 配置文件路径
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"""
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# 读取配置文件
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if not os.path.exists(config_file_path):
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raise FileNotFoundError(f"配置文件不存在: {config_file_path}")
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config = configparser.ConfigParser()
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config.read(config_file_path)
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# 定义替换映射
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imp_unit_mapping = {
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1: 327, # 送电一分公司 → 327
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2: 102, # 送电二分公司 → 102
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3: 309, # 宏源变电工程处 → 309
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5: 338, # 土建分公司 → 338
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8: 309, # 宏源送电工程处 → 309
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9: 100, # 变电分公司 → 100
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10: 101, # 机具（物流）分公司 → 101
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11: 345, # 外部往来单位 → 345
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12: 344, # 机械化分公司 → 344
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13: 346, # 运检分公司 → 346
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15: 340, # 安徽顺全电力工程有限公司 → 340
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16: 337, # 检修试验分公司 → 337
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17: 339, # 安徽顺安电网建设有限公司 → 339
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18: 342, # 公司机关 → 342
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21: 341 # 班组管理中心 → 341
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}
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pro_type_mapping = {
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1: 0, # 线路工程 → 0
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2: 1, # 变电工程 → 1
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3: 2, # 业务工程 → 2
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4: 3 # 其他工程 → 3
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}
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try:
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# 获取数据库连接字符串
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source_conn_str = get_db_connection_string(config, 'source_db')
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target_conn_str = get_db_connection_string(config, 'target_db')
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# 创建数据库引擎
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source_engine = create_engine(source_conn_str)
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target_engine = create_engine(target_conn_str)
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# 从源数据库读取数据
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print("正在从源数据库读取bm_project表数据...")
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source_query = """
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SELECT ID, NAME, NUM, PRO_ID, HTZT, time, COMPANY_ID, TYPE_ID, COMPANY, IS_ACTIVE
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FROM bm_project
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WHERE COMPANY = 1 AND IS_ACTIVE = 1 \
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"""
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source_df = pd.read_sql(source_query, source_engine)
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if source_df.empty:
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print("没有符合条件的数据需要转换")
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return
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print(f"读取到{len(source_df)}条待转换数据")
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# 数据转换
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print("正在进行数据转换...")
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target_df = pd.DataFrame()
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target_df['pro_id'] = source_df['ID'] # 复制ID → pro_id
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target_df['pro_name'] = source_df['NAME'] # 复制NAME → pro_name
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target_df['pro_code'] = source_df['NUM'] # 复制NUM → pro_code
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target_df['external_id'] = source_df['PRO_ID'] # 复制PRO_ID → external_id
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target_df['contract_part'] = source_df['HTZT'] # 复制HTZT → contract_part
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target_df['create_time'] = source_df['time'] # 复制time → create_time
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# 替换COMPANY_ID → imp_unit
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target_df['imp_unit'] = source_df['COMPANY_ID'].map(imp_unit_mapping)
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# 替换TYPE_ID → pro_type_id
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target_df['pro_type_id'] = source_df['TYPE_ID'].map(pro_type_mapping)
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# 检查是否有未映射的值
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if target_df['imp_unit'].isna().any():
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unmapped_units = source_df[target_df['imp_unit'].isna()]['COMPANY_ID'].unique()
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print(f"警告: 发现未映射的COMPANY_ID值: {unmapped_units}")
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if target_df['pro_type_id'].isna().any():
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unmapped_types = source_df[target_df['pro_type_id'].isna()]['TYPE_ID'].unique()
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print(f"警告: 发现未映射的TYPE_ID值: {unmapped_types}")
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# 写入目标数据库
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print("正在将数据写入目标数据库...")
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target_df.to_sql(
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'bm_project',
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target_engine,
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if_exists='append',
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index=False,
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dtype={
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'pro_id': sqlalchemy.types.INTEGER(),
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'pro_name': sqlalchemy.types.VARCHAR(length=255),
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'pro_code': sqlalchemy.types.VARCHAR(length=50),
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'external_id': sqlalchemy.types.VARCHAR(length=50),
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'contract_part': sqlalchemy.types.VARCHAR(length=50),
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'create_time': sqlalchemy.types.DateTime(),
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'imp_unit': sqlalchemy.types.INTEGER(),
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'pro_type_id': sqlalchemy.types.INTEGER()
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}
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)
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print(f"成功写入{len(target_df)}条数据到目标数据库")
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except Exception as e:
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print(f"处理过程中发生错误: {str(e)}")
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raise
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finally:
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# 关闭数据库连接
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if 'source_engine' in locals():
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source_engine.dispose()
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if 'target_engine' in locals():
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target_engine.dispose()
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# 使用示例
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if __name__ == "__main__":
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# 配置文件路径
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config_file = "config.ini" # 假设配置文件在当前目录下
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try:
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# 执行转换
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transform_and_load_bm_project(config_file)
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except Exception as e:
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print(f"程序执行失败: {str(e)}") |