305 lines
9.4 KiB
Python
305 lines
9.4 KiB
Python
import copy
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import time
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from RatingData.db import FIND_DATA_N_TYC, FIND_SINGLE_DATA_BY_REQ, FIND_ALL_DATA_BY_REQ
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from RatingData.static.province_map import p_map
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import pandas as pd
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def modify_unit(param):
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"""
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金额单位从元改为万元
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Parameters:
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param: 金额数据
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Returns:
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result: 单位为万元的金额数据
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"""
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money = round(param/10000, 2)
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return str(money)
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def judge_list(param):
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"""
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判断列表中是否有None，
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如果有置为-
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Parameters:
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param: list列表
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Returns:
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result: 没有None的list
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"""
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df = pd.DataFrame(param)
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df = df.fillna('-')
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df = df[0].apply(lambda x: round(x/10000, 2) if isinstance(x, float) else x)
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df = df.to_list()
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df = [str(x) for x in df]
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return df
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def calculate_growth_rate(param1, param2):
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"""
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计算增长率
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传入两年财务数据字典项，算出年增长率
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Parameters:
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param1: 财务指标1
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param2: 财务指标2
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Returns:
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增长率
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"""
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param1 = float(param1)
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param2 = float(param2)
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rate = round((param1 - param2) / param2, 2)
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return rate
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def calculate_amount(param):
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"""
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计算数据之和
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清洗数据库数据，根据需要返回
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注意：
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货币单位不同，现在存在美元、人民币两种，汇率暂用6.5
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Parameters:
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param: 数据库数据
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Returns:
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数据合计
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"""
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result = dict()
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result['amount'] = 0
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result['industry'] = list()
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result['base'] = list()
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for item in param:
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if item['amountSuffix'] == '万人民币':
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try:
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result['amount'] += item['amount']
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except TypeError:
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result['amount'] += 0
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elif item['amountSuffix'] == '万美元':
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try:
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result['amount'] += item['amount'] * 6.5
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except TypeError:
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result['amount'] += 0
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result['industry'].append(item['category'])
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result['base'].append(item['base'])
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return result
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def calculate_list(param):
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"""
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计算列表中所有元素出现的次数
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Parameters:
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param: list数据
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Returns:
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列表元素出现次数对象
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"""
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result = dict()
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for item in param:
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result[item] = param.count(item)
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return result
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def time_stamp(param):
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"""
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时间戳转换
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Parameters:
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param: 时间戳数据
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Returns:
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转换后时间数据
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"""
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if param is None:
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return '-'
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else:
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estiblish_time = param / 1000
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put_date = time.localtime(estiblish_time)
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date = time.strftime("%Y-%m-%d", put_date)
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return date
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def province_conversion(param):
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"""
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省份转换
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Parameters:
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param: 省份简称
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Returns:
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转换后省份全称
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"""
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res = [v for k, v in p_map.items() if k == param][0]
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return res
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def industry_processing(param):
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"""
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将行业数组转为字符串
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Parameters:
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param list 行业数组
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Returns:
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result str 行业字符串
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"""
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return param[0] + '-' + param[1]
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# 获取数据库数据
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def get_tfse_data(param):
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"""
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获取数据库数据
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Parameters:
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param: 企业名称、企业ID、评价ID
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Returns:
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report_data: 数据库数据
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"""
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company = param['company']
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rid = param['rid']
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cid = param['cid']
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report_data = dict()
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# 评价结果
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es = FIND_SINGLE_DATA_BY_REQ('综信评价数据', '评价结果', {'评价ID': rid}, ["企业名称", "评价年度", "财务评分", "经营评分", "信用等级", "评价时间"])
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# 综合评价填报
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cef = FIND_SINGLE_DATA_BY_REQ('综信评价数据', '填报数据', {'评价ID': rid}, ["财务填报", "行业选择"])
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# 指标明细
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fid = FIND_ALL_DATA_BY_REQ('综信评价数据', '财指结果', {'企业ID': cid},
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['报告期', '净资产收益率', '存货周转率', '已获利息倍数', '应收账款周转率', '总资产周转率', '总资产增长率', '总资产报酬率', '技术投入比率', '营业增长率', '资产负债率', '速动比率'])
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# 基本信息
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cbd = FIND_DATA_N_TYC(db='公司背景', col='基本信息', query={'企业名称': company})[0]
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if '上市' in cbd['基本信息']['companyOrgType'] and '非上市' not in cbd['基本信息']['companyOrgType']:
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# 企业股东
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cs = FIND_DATA_N_TYC(db='公司背景', col='十大股东', query={'企业名称': company})[0]
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else:
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cs = FIND_DATA_N_TYC(db='公司背景', col='企业股东', query={'企业名称': company})[0]
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# 对外投资
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oi = FIND_DATA_N_TYC(db='公司背景', col='对外投资', query={'企业名称': company})[0]
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# 进出口信用
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ip = FIND_DATA_N_TYC(db='经营状况', col='进出口信用', query={'企业名称': company})[0]
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# 专利
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ip_02 = FIND_DATA_N_TYC(db='知识产权', col='专利', query={'企业名称': company})[0]
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# 软件著作权
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sc = FIND_DATA_N_TYC(db='知识产权', col='软件著作权', query={'企业名称': company})[0]
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# 资质证书
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qh = FIND_DATA_N_TYC(db='经营状况', col='资质证书', query={'企业名称': company})[0]
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# 融资历史
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fh = FIND_DATA_N_TYC(db='公司发展', col='融资历史', query={'企业名称': company})[0]
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# 风险数据
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rd = FIND_SINGLE_DATA_BY_REQ('综信评价数据', '风险数据', {'评价ID': rid}, ["合规风险", "经营风险", "关联风险"])
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# 周边风险
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pr = FIND_DATA_N_TYC(db='天眼风险', col='周边风险', query={'企业名称': company})[0]
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report_data['评价结果'] = es
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report_data['综合评价填报'] = cef
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report_data['指标明细'] = fid
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report_data['基本信息'] = cbd
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report_data['企业股东'] = cs
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report_data['对外投资'] = filter_oi_data(oi)
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report_data['进出口信用'] = ip
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report_data['专利'] = ip_02
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report_data['软件著作权'] = sc
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report_data['资质证书'] = qh
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report_data['融资历史'] = fh
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report_data['风险数据'] = rd
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report_data['周边风险'] = pr
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return report_data
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def filter_oi_data(param):
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"""
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对外投资
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清洗掉状态为吊销、注销、迁出的企业
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Parameters:
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param: 对外投资数据
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Returns:
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清洗后的数据
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"""
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result = list()
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total = 0
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data = param['对外投资']['result']
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for item in data:
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if item['regStatus'] != '注销' and item['regStatus'] != '迁出' and item['regStatus'] != '吊销':
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result.append(item)
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total += 1
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param['对外投资']['result'] = result
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param['对外投资']['total'] = total
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return param
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def shareholder_strength_data(param):
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"""
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股东数据
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清洗数据库数据，根据需要返回
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Parameters:
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param: 企业名称
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Returns:
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股东数据
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"""
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data = dict()
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# 基本信息
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cbd = FIND_DATA_N_TYC(db='公司背景', col='基本信息', query={'企业名称': param})[0]
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# 企业股东
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cs = FIND_DATA_N_TYC(db='公司背景', col='企业股东', query={'企业名称': param})[0]
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data['基本信息'] = cbd
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data['企业股东'] = cs
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return data
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def ten_shareholder_data(param):
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"""
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十大股东数据
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清洗数据库数据，根据需要返回
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Parameters:
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param: 企业名称
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Returns:
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股东数据
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"""
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data = dict()
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# 基本信息
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cbd = FIND_DATA_N_TYC(db='公司背景', col='基本信息', query={'企业名称': param})[0]
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data['基本信息'] = cbd
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if '上市' in cbd['基本信息']['companyOrgType']:
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cs = FIND_DATA_N_TYC(db='公司背景', col='十大股东', query={'企业名称': param})[0]
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data['十大股东'] = cs
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else:
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cs = FIND_DATA_N_TYC(db='公司背景', col='企业股东', query={'企业名称': param})[0]
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data['企业股东'] = cs
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return data
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def person_subsidiaries(param):
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"""
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人员控股数据
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清洗数据库数据，根据需要返回
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Parameters:
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param: 人员id
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Returns:
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人员控股数据
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"""
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data = dict()
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# 控股数据
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ps = FIND_DATA_N_TYC(db='公司背景', col='人员控股企业', query={'人员ID': param})[0]
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data['人员控股企业'] = ps
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return data
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def get_esg_data(param):
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"""
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获取数据库数据并按要求格式返回
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Parameters:
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param dict 企业名称、企业ID、评价ID
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Returns:
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result dict 符合要求需要清洗的数据
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"""
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company = param['company']
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rid = param['rid']
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cid = param['cid']
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# ESG评价结果
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es = FIND_SINGLE_DATA_BY_REQ('ESG评价数据', '评价结果', {'评价ID': rid}, ['评价等级'])
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# ESG风险数据
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rd = FIND_SINGLE_DATA_BY_REQ('ESG评价数据', '风险数据', {'企业ID': cid},
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["严重违法", "失信人", "法律诉讼", "税收违法", "经营异常", "行政处罚", "被执行人"])
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# ESG评价填报
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ef = FIND_SINGLE_DATA_BY_REQ('ESG评价数据', '填报数据', {'评价ID': rid},
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["评价ID", "企业ID", "企业名称", "所属行业", "评价年度", "公司当年收入（万元）", "环境问卷", "社会问卷", "治理问卷"])
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report_data = copy.deepcopy(ef)
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report_data['评价等级'] = es['评价等级']
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report_data['风险数据'] = rd
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return report_data
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