112 lines
2.7 KiB
Python
112 lines
2.7 KiB
Python
import json
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import pandas as pd
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from company.company_db import FIND_DATA_IN_COMPANY, FIND_DATA_IN_MACRO_INDUSTRY_ANALYSIS
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from company.company_utils import sub_dict, judge_industry
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def find_company_data_scripts(table, cid):
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return FIND_DATA_IN_COMPANY(table, {"企业ID": cid})
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def get_head_info_impl(cid):
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data = FIND_DATA_IN_COMPANY('综合评价分析', {"企业ID": cid})
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if data:
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return sub_dict(data[0], ['企业名称', '综合信用等级'])
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else:
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company = FIND_DATA_IN_COMPANY('公司基本信息', {"企业ID": cid})[0]
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result = {
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"企业名称": company['企业名称'],
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"综合信用等级": {
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"评价时间": "N/A",
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"信用等级": "N/A",
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"信用评分": "N/A"
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},
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}
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return result
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def find_financial_index_detail_scripts(table, cid):
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index_detail = FIND_DATA_IN_COMPANY(table, {"企业ID": cid})
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if not index_detail:
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return False
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df = pd.DataFrame(index_detail).sort_values('年报期', ascending=False)
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cols = [
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'年报期',
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'净资产收益率',
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'总资产报酬率',
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'总资产周转率',
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'应收账款周转率',
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'存货周转率',
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'资产负债率',
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'已获利息倍数',
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'速动比率',
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'营业增长率',
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'总资产增长率',
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'技术投入比率'
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]
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unit_cols = [
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'净资产收益率',
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'总资产报酬率',
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'总资产周转率',
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'存货周转率',
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'资产负债率',
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'速动比率',
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'营业增长率',
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'总资产增长率',
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'技术投入比率'
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]
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def percent_unit(param):
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return param.map(lambda x: '{}%'.format(x) if (param.name in unit_cols and not pd.isna(x)) else x)
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df_result = df[cols].apply(lambda x: percent_unit(x))
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return list(json.loads(df_result.T.to_json()).values())
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def industry_analysis_impl(cid):
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"""
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行业宏观分析数据
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Parameters:
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cid: str 企业ID
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Returns:
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Case Success:
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data: dict 行业宏观分析数据
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Case Fail:
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data: dict 空字典
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"""
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industry = judge_industry(cid)
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if industry is False:
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return {}
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data = FIND_DATA_IN_MACRO_INDUSTRY_ANALYSIS(industry)
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if data is False:
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return {}
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return data
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def esg_analysis_impl(cid):
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"""
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ESG要素分析数据
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Parameters:
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cid: str 企业ID
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Returns:
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Case Success:
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data: dict ESG分析数据
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Case Fail:
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data: dict 空字典
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"""
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data = FIND_DATA_IN_COMPANY('ESG要素分析',{"企业ID": cid})
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if data is False:
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return {}
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return data |