116 lines
4.2 KiB
Python
116 lines
4.2 KiB
Python
import configparser
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import pandas as pd
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from sqlalchemy import create_engine
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from urllib.parse import quote_plus
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# 读取配置文件
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config = configparser.ConfigParser()
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config.read('config.ini')
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# 获取数据库连接配置
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source_config = {
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'host': config.get('source_db', 'host'),
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'user': config.get('source_db', 'user'),
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'password': config.get('source_db', 'password'),
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'database': config.get('source_db', 'database'),
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'port': config.getint('source_db', 'port')
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}
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target_config = {
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'host': config.get('target_db', 'host'),
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'user': config.get('target_db', 'user'),
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'password': config.get('target_db', 'password'),
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'database': config.get('target_db', 'database'),
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'port': config.getint('target_db', 'port')
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}
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# 创建数据库引擎
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source_engine = create_engine(
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f"mysql+pymysql://{source_config['user']}:{quote_plus(source_config['password'])}@{source_config['host']}:{source_config['port']}/{source_config['database']}"
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)
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target_engine = create_engine(
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f"mysql+pymysql://{target_config['user']}:{quote_plus(target_config['password'])}@{target_config['host']}:{target_config['port']}/{target_config['database']}"
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)
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# 定义UNIT_ID到unit_name的映射
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unit_mapping = {
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9: 'm', 90: '根', 91: '个', 92: '付', 93: '支', 94: '双', 95: '套',
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96: '组', 97: '只', 98: '台', 99: '顶', 100: '件', 101: '米', 102: '面',
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103: '片', 104: '盘', 105: '张', 106: '块', 107: '副', 108: '袋', 109: '条',
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110: '节', 111: '瓶', 112: '斤', 113: '把', 114: '平方米', 115: '床',
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123: 'kg', 124: '份', 125: '卷'
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}
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def clean_existing_data():
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"""清理目标表中需要更新的数据"""
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try:
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# 删除重复的地锚规格等不需要的数据（根据实际业务需求调整）
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with target_engine.connect() as conn:
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conn.execute("""
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DELETE FROM ma_type
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WHERE type_name IN ('重复的地锚规格', '其他需要删除的规格名称')
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""")
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print("已清理目标表中的重复/无效数据")
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return True
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except Exception as e:
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print(f"清理数据时出错: {str(e)}")
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return False
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def process_machine_types():
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"""处理ma_machine_type表数据"""
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try:
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# 读取源数据
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df = pd.read_sql("SELECT * FROM ma_machine_type", source_engine)
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# 按照规则转换数据
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result = pd.DataFrame()
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result['type_id'] = df['ID'] + 6000 # ID加6000
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result['parent_id'] = df['PARENT_ID']
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result['type_name'] = df['NAME']
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result['level'] = df['LEVEL']
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result['unit_name'] = df['UNIT_ID'].map(unit_mapping) # 单位映射
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result['storage_num'] = df['STORAGE_NUM']
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result['buy_price'] = df['PURCHASE_PRICE']
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result['notax_price'] = df['NOTAX_PRICE']
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result['lease_price'] = df['RENTAL_PRICE']
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result['manage_type'] = df['IS_COUNT']
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result['rated_load'] = df['CONSUMABLE']
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# 处理新增规格（示例，根据实际情况调整）
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new_types = [
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{'type_id': 6601, 'type_name': '新增规格1', 'unit_name': '个', 'level': 3},
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{'type_id': 6602, 'type_name': '新增规格2', 'unit_name': '套', 'level': 3}
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]
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new_df = pd.DataFrame(new_types)
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result = pd.concat([result, new_df], ignore_index=True)
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# 先清理目标表
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# 写入目标表（使用更新模式）
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result.to_sql('ma_type', target_engine,
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if_exists='append', index=False)
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print(f"成功转换并导入 {len(result)} 条记录到 ma_type")
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# 更新现有库存和租赁单价（根据type_id）
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with target_engine.connect() as conn:
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# 更新storage_num
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conn.execute("""
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UPDATE ma_type t, ma_machine_type s
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SET t.storage_num = s.STORAGE_NUM,
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t.lease_price = s.LEASE_PRICE
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WHERE t.type_id = s.ID + 6000
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""")
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print("已更新现有库存和租赁单价")
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return True
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except Exception as e:
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print(f"处理 ma_machine_type 时发生错误: {str(e)}")
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return False
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if __name__ == "__main__":
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process_machine_types() |