finance_data_process/house/house-zu-ru-he-tong/house_zu_ru_he_tong.py

"""不动产租入合同数据处理
"""

import re  # 导入正则表达式模块，用于字符串处理
import decimal  # 导入decimal模块，用于高精度的数值计算
import subprocess
from datetime import datetime  # 导入datetime模块，用于日期和时间操作
from dateutil.relativedelta import relativedelta  # 导入relativedelta模块，用于日期之间的相对差异计算
from loguru import logger  # 导入loguru模块，用于日志记录
import pandas as pd  # 导入pandas模块，用于数据处理和分析
import psycopg  # 导入psycopg模块，用于连接PostgreSQL数据库
import paramiko

# 配置日志记录器，将日志写入文件a.log
logger.add(sink='a.log')
ssh_hostname = '172.16.107.4'  # 定义远程主机地址
ssh_port = 22  # 定义SSH服务的端口号
ssh_username = 'app'  # 定义登录远程主机的用户名
ssh_password = '(l4w0ST_'  # 定义登录远程主机的密码
# 服务器文件夹路径
remote_dir_path = '/data/history/house/zu-ru-he-tong/'
# 数据库连接信息
db_host = "172.16.107.5"  # 数据库主机地址
db_port = 5432         # 数据库端口号
db_username = "finance"  # 数据库用户名
db_password = "Finance@unicom23"  # 数据库密码
dbname = "financialdb"       # 数据库名称
conn_info = f"host='{db_host}' port={db_port} user='{db_username}' password='{db_password}' dbname='{dbname}'"
# 获取当前日期，并计算上个月的第一天
today = datetime.today()
start_date = today - relativedelta(months=1, day=1)
year_month = start_date.strftime('%Y%m')
# 数据文件路径
input_path = 'data.xlsx'
# 输出文件路径
output_path = 'output.csv'


def data_process():
    org_map = {}  # 存储所有组织机构的ID与详细信息的映射
    third_org_list_map = {}  # 存储二级组织机构与其下属三级组织机构的映射
    area_map = {}  # 存储所有区域的ID与详细信息的映射
    districts_list_map = {}  # 存储一级区域与其下属子区域的映射
    # 连接到PostgreSQL数据库，并使用字典格式返回查询结果
    with psycopg.connect(
            conninfo=conn_info,
            row_factory=psycopg.rows.dict_row  # 使用字典格式返回查询结果
    ) as conn:
        with conn.cursor() as curs:
            # 查询grade为1的组织机构（二级组织机构）
            sql = """
        select * from common.organization where grade = 1
        """
            logger.info(f"sql: {sql}")  # 记录SQL语句到日志
            curs.execute(sql)
            second_orgs = curs.fetchall()
            for x in second_orgs:
                third_org_list_map[x['id']] = []  # 初始化每个二级组织机构的三级组织机构列表

            # 查询所有组织机构
            sql = """
        select * from common.organization
        """
            logger.info(f"sql: {sql}")  # 记录SQL语句到日志
            curs.execute(sql)
            orgs = curs.fetchall()
            for x in orgs:
                if x['parent_id'] in third_org_list_map:
                    third_org_list_map[x['parent_id']].append(x)  # 将三级组织机构添加到对应二级组织机构的列表中
                org_map[x['id']] = x  # 将组织机构ID与详细信息存入org_map

            # 查询area_grade为1的区域（一级区域）
            sql = """
        select * from common.area where area_grade = 1 order by area_id
        """
            logger.info(f"sql: {sql}")  # 记录SQL语句到日志
            curs.execute(sql)
            cities = curs.fetchall()
            for x in cities:
                districts_list_map[x['area_id']] = []  # 初始化每个一级区域的子区域列表

            # 查询所有区域
            sql = """
        select * from common.area
        """
            logger.info(f"sql: {sql}")  # 记录SQL语句到日志
            curs.execute(sql)
            areas = curs.fetchall()
            for x in areas:
                if x['parent_id'] in districts_list_map:
                    districts_list_map[x['parent_id']].append(x)  # 将子区域添加到对应一级区域的列表中
                area_map[x['area_id']] = x  # 将区域ID与详细信息存入area_map
    # 读取Excel文件中的数据，并跳过第一行
    df = pd.read_excel(io=input_path, skiprows=1)
    # 删除指定列中的空白字符
    columns_to_clean = list(filter(lambda x: x not in ('签订时间'), df.columns))  # 排除“签订时间”列
    df[columns_to_clean] = df[columns_to_clean].map(lambda x: re.sub(r'\s+', '', x) if type(x) is str else x)

    def get_area_no(x):
        """根据使用单位隶属的地市级公司名称获取二级组织机构编码"""
        second_unit = x['使用单位隶属的地市级公司']
        if '长途通信传输局' == second_unit:
            return '-11'
        for second_org in second_orgs:
            area_name = second_org['name']
            area_no = second_org['id']
            if area_name in second_unit:
                return area_no
        return '-12'

    df['二级组织机构编码'] = df.apply(get_area_no, axis=1)

    def get_area_name(x):
        """根据二级组织机构编码获取二级组织机构名称"""
        area_no = x['二级组织机构编码']
        second_org = org_map[area_no]
        area_name = second_org['name']
        return area_name

    df['二级组织机构名称'] = df.apply(get_area_name, axis=1)

    def get_city_no(x):
        """根据使用单位隶属的区县级公司名称获取三级组织机构编码"""
        third_unit = x['使用单位隶属的区县级公司']
        area_name = x['二级组织机构名称']
        area_no = x['二级组织机构编码']
        if area_name == '石家庄':
            if '矿区' in third_unit:
                return 'D0130185'
            if '井陉' in third_unit:
                return 'D0130121'
        if area_name == '秦皇岛':
            if '北戴河新区' in third_unit:
                return 'D0130185'
            if '北戴河' in third_unit:
                return 'D0130304'
        if area_name == '唐山':
            if '滦县' in third_unit:
                return 'D0130223'
            if '高新技术开发区' in third_unit:
                return 'D0130205'
        if area_name == '邢台':
            if '内丘' in third_unit:
                return 'D0130523'
            if '任泽' in third_unit:
                return 'D0130526'
        if area_name == '邯郸':
            if '峰峰' in third_unit:
                return 'D0130406'
        if area_name == '省机动局':
            if '沧州' in third_unit:
                return 'HECS180'
            if '唐山' in third_unit:
                return 'HECS181'
            if '秦皇岛' in third_unit:
                return 'HECS182'
            if '廊坊' in third_unit:
                return 'HECS183'
            if '张家口' in third_unit:
                return 'HECS184'
            if '邢台' in third_unit:
                return 'HECS185'
            if '邯郸' in third_unit:
                return 'HECS186'
            if '保定' in third_unit:
                return 'HECS187'
            if '石家庄' in third_unit:
                return 'HECS188'
            if '承德' in third_unit:
                return 'HECS189'
            if '衡水' in third_unit:
                return 'HECS720'
            if '雄安' in third_unit:
                return 'HECS728'
            return 'HECS018'
        if '雄安' == area_name:
            third_unit = third_unit.replace('雄安新区', '')
        third_org_list = third_org_list_map[area_no]
        for third_org in third_org_list:
            city_name = third_org['name']
            if city_name in third_unit:
                return third_org['id']
        if '沧州' == area_name:
            return 'D0130911'
        if '唐山' == area_name:
            return 'D0130202'
        if '秦皇岛' == area_name:
            return 'D0130302'
        if '廊坊' == area_name:
            return 'D0131000'
        if '张家口' == area_name:
            return 'D0130701'
        if '邢台' == area_name:
            return 'D0130502'
        if '邯郸' == area_name:
            return 'D0130402'
        if '保定' == area_name:
            return 'D0130601'
        if '石家庄' == area_name:
            return 'D0130186'
        if '承德' == area_name:
            return 'D0130801'
        if '衡水' == area_name:
            return 'D0133001'
        if '雄安' == area_name:
            return 'D0130830'
        return 'HE001'

    df['三级组织机构编码'] = df.apply(get_city_no, axis=1)

    def get_city_name(x):
        """根据三级组织机构编码获取三级组织机构名称"""
        city_no = x['三级组织机构编码']
        third_org = org_map[city_no]
        city_name = third_org['name']
        return city_name

    df['三级组织机构名称'] = df.apply(get_city_name, axis=1)

    def get_rent_months(x):
        """根据租入开始时间和终止时间计算租期月数"""
        rent_start_date = x['租入开始时间(合同生效时间)']
        rent_end_date = x['租入终止时间(合同终止时间)']
        if pd.isna(rent_start_date) or pd.isna(rent_end_date):
            return ''
        rent_start_date = pd.to_datetime(rent_start_date)
        rent_end_date = pd.to_datetime(rent_end_date)
        delta = relativedelta(rent_end_date, rent_start_date)
        rent_months = delta.years * 12 + delta.months + (1 if delta.days > 0 else 0)
        return rent_months

    df['租期月数'] = df.apply(get_rent_months, axis=1)

    def get_gross_amount_month(x):
        """根据合同总金额和租期月数计算月含税合同额"""
        gross_amount = x['合同总金额(含税)(元)']
        rent_months = x['租期月数']
        if pd.notna(gross_amount) and pd.notna(rent_months) and rent_months and rent_months > 0:
            return (decimal.Decimal(gross_amount) / decimal.Decimal(rent_months)).quantize(decimal.Decimal('0.00'))
        return None

    df['月含税合同额'] = df.apply(get_gross_amount_month, axis=1)

    def get_unit_price(x):
        """根据租入建筑面积和月含税合同额计算每平米单价"""
        building_area = x['租入建筑面积（平米）']
        gross_amount_month = x['月含税合同额']
        if pd.notna(building_area) and pd.notna(gross_amount_month) and building_area > 0 and gross_amount_month > 0:
            return (decimal.Decimal(gross_amount_month) / decimal.Decimal(building_area)).quantize(
                decimal.Decimal('0.00'))
        return None

    df['每平米单价'] = df.apply(get_unit_price, axis=1)

    def get_rent_years(x):
        """根据租期月数计算租期年数"""
        rent_months = x['租期月数']
        if pd.isna(rent_months) or not rent_months:
            return None
        return (decimal.Decimal(rent_months) / decimal.Decimal('12')).quantize(decimal.Decimal('0.00'))

    df['rent_years'] = df.apply(get_rent_years, axis=1)

    def get_unit_price2(x):
        """根据合同总金额、租入建筑面积和租期年数计算另一种每平米单价"""
        gross_amount = x['合同总金额(含税)(元)']
        building_area = x['租入建筑面积（平米）']
        rent_years = x['rent_years']
        if pd.notna(building_area) and pd.notna(gross_amount) and pd.notna(
                rent_years) and building_area > 0 and gross_amount > 0 and rent_years > 0:
            return (decimal.Decimal(gross_amount) / decimal.Decimal(building_area) / decimal.Decimal(
                rent_years) / decimal.Decimal(12)).quantize(decimal.Decimal('0.00'))
        return None

    df['unit_price2'] = df.apply(get_unit_price2, axis=1)


    def remove_extra_dots(s):
        if pd.isna(s) or not s:
            return None
        match = re.search(r'\.', s)
        if match:
            first_dot_index = match.start()
            return s[:first_dot_index + 1] + s[first_dot_index + 1:].replace('.', '')
        else:
            return s


    df['地址经度坐标'] = df['地址经度坐标'].map(remove_extra_dots)
    df['地址纬度坐标'] = df['地址纬度坐标'].map(remove_extra_dots)
    df.insert(0, '年月', year_month)  # 在数据框的第一列插入年月字段
    # 打印数据框的基本信息
    print(df.info())
    # 将处理后的数据保存到CSV文件中
    df.to_csv(path_or_buf=output_path,
              index=False,
              header=['year_month', 'serial_no', 'data_num', 'house_name', 'owner_type', 'rent_type', 'first_address',
                      'second_address', 'third_address', 'fourth_address', 'city_region', 'area_sector', 'lng', 'lat',
                      'building_area', 'usable_area', 'investor', 'unit_level', 'first_unit', 'second_unit',
                      'third_unit', 'field', 'use_type', 'use_description', 'building_area_self_use',
                      'building_area_sublet', 'first_rent_date', 'contract_no', 'contract_name', 'contract_type',
                      'sign_date', 'lessee', 'lessor', 'gross_amount', 'vat', 'rent_start_date', 'rent_end_date',
                      'undertaking_department', 'undertaker', 'phone', 'amount_accrued', 'amount_reimbursement',
                      'contract_nature', 'contract_status', 'area_no', 'area_name', 'city_no', 'city_name',
                      'rent_months', 'gross_amount_month', 'unit_price', 'rent_years', 'unit_price2'],
              encoding='utf-8-sig')


def data_import():
    # 定义 PowerShell 脚本的路径
    script_path = r"../../copy.ps1"
    # 目标表和文件信息
    table = "house.rent_in_month"  # 数据库目标表名
    # 表字段列名，用于指定导入数据的列顺序
    columns = "year_month,serial_no,data_num,house_name,owner_type,rent_type,first_address,second_address,third_address,fourth_address,city_region,area_sector,lng,lat,building_area,usable_area,investor,unit_level,first_unit,second_unit,third_unit,field,use_type,use_description,building_area_self_use,building_area_sublet,first_rent_date,contract_no,contract_name,contract_type,sign_date,lessee,lessor,gross_amount,vat,rent_start_date,rent_end_date,undertaking_department,undertaker,phone,amount_accrued,amount_reimbursement,contract_nature,contract_status,area_no,area_name,city_no,city_name,rent_months,gross_amount_month,unit_price,rent_years,unit_price2"
    # 构造执行 PowerShell 脚本的命令
    command = f"powershell -File {script_path} -db_host {db_host} -db_port {db_port} -db_username {db_username} -db_password {db_password} -dbname {dbname} -table {table} -filename {output_path} -columns {columns}"
    # 打印生成的命令，方便调试和日志记录
    logger.info("command: {}", command)
    # 使用 subprocess 模块运行 PowerShell 命令，并捕获输出
    completed_process = subprocess.run(
        command,  # 执行的命令
        check=False,  # 如果命令执行失败，不抛出异常
        text=True,  # 将输出作为字符串处理
        capture_output=True,  # 捕获标准输出和标准错误
    )
    # 打印命令执行的结果，包括返回码、标准输出和标准错误
    logger.info("导入结果：\n{}\n{}\n{}", completed_process.returncode, completed_process.stdout,
                completed_process.stderr)
    # 定义正则表达式，用于匹配标准输出中的 COPY 结果
    p = re.compile(r"^(COPY) (\d+)$")
    count = None  # 初始化计数变量
    matcher = p.match(completed_process.stdout)  # 匹配标准输出中的 COPY 结果
    if matcher:
        count = int(matcher.group(2))  # 提取导入的数据行数
    # 如果没有成功提取到导入数据的行数，抛出运行时异常
    if count is None:
        raise RuntimeError("导入数据失败")


def upload_file():
    remote_path = f'{remote_dir_path}{year_month}.xlsx'  # 定义远程主机的目标文件路径
    # 使用paramiko.SSHClient创建一个SSH客户端对象，并通过with语句管理其上下文
    with paramiko.SSHClient() as ssh:
        # 设置自动添加主机密钥策略，避免因未知主机密钥导致连接失败
        ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
        # 连接到远程主机，传入主机地址、端口、用户名和密码
        ssh.connect(ssh_hostname, port=ssh_port, username=ssh_username, password=ssh_password)
        # 执行远程命令，创建远程目录（如果不存在）
        ssh.exec_command(f'mkdir -p {remote_dir_path}')
        # 打开SFTP会话，用于文件传输，并通过with语句管理其上下文
        with ssh.open_sftp() as sftp:
            # 记录日志，提示即将上传的本地文件和远程目标路径
            logger.info("upload {} to {}", input_path, remote_path)
            # 使用SFTP的put方法将本地文件上传到远程主机
            sftp.put(input_path, remote_path)
            # 记录日志，提示文件已成功上传
            logger.info("uploaded {}", input_path)


data_process()
data_import()
upload_file()