141 lines
5.2 KiB
Python
141 lines
5.2 KiB
Python
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def conclusion(param):
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"""
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结论
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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: 数据库数据
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Returns:
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结论数据
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"""
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# Params
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data_01 = param['评价结果']
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# Returns
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result = list()
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def para_01():
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part_01 = dict()
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part_01['describe'] = list()
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def describe():
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# 环境部分得分
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ep_s = data_01['经营评分']['环境']
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if ep_s > 10 * 0.5:
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part_01['describe'].append('公司为环境友好型企业')
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else:
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part_01['describe'].append('公司绿色发展理念相对薄弱')
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# 社会责任部分得分
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sr_s = data_01['经营评分']['社会责任']
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if sr_s > 10 * 0.5:
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part_01['describe'].append('积极履行社会责任')
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else:
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part_01['describe'].append('社会责任履行能力有待进一步提升')
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# 公司治理部分得分
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cg_s = data_01['经营评分']['公司治理']
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if cg_s > 10 * 0.5:
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part_01['describe'].append('内部治理水平较高')
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else:
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part_01['describe'].append('治理水平有待进一步提升')
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part_01['describe'] = '，'.join(part_01['describe'])
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describe()
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return part_01
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def para_02():
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part_02 = dict()
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def enterprise_quality():
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# 企业整体素质
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eq_s = data_01['经营评分']['合计']
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if eq_s >= 30:
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part_02['enterprise_quality'] = '好'
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elif eq_s >= 30 * 0.8:
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part_02['enterprise_quality'] = '较好'
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elif eq_s >= 30 * 0.6:
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part_02['enterprise_quality'] = '一般'
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elif eq_s >= 30 * 0.4:
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part_02['enterprise_quality'] = '较差'
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else:
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part_02['enterprise_quality'] = '差'
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def profitability_detail():
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p = float(data_01['财务评分']['盈利能力']['净资产收益率'])
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s = float(data_01['财务评分']['盈利能力']['总资产报酬率'])
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p_s = p + s
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if p_s >= 16:
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part_02['profitability'] = '强'
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elif p_s >= 16 * 0.8:
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part_02['profitability'] = '较强'
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elif p_s >= 16 * 0.6:
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part_02['profitability'] = '尚可'
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elif p_s >= 16 * 0.4:
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part_02['profitability'] = '较弱'
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else:
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part_02['profitability'] = '弱'
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def asset_quality_detail():
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a = float(data_01['财务评分']['资产质量']['总资产周转率'])
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q = float(data_01['财务评分']['资产质量']['应收账款周转率'])
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s = float(data_01['财务评分']['资产质量']['存货周转率'])
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aq_s = a + q + s
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if aq_s >= 18:
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part_02['asset_quality'] = '好'
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elif aq_s >= 18 * 0.8:
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part_02['asset_quality'] = '较好'
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elif aq_s >= 18 * 0.6:
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part_02['asset_quality'] = '一般'
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elif aq_s >= 18 * 0.4:
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part_02['asset_quality'] = '较差'
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else:
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part_02['asset_quality'] = '差'
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def debt_risk_detail():
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# 债务风险得分
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d = float(data_01['财务评分']['债务风险']['资产负债率'])
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r = float(data_01['财务评分']['债务风险']['已获利息倍数'])
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s = float(data_01['财务评分']['债务风险']['速动比率'])
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dr_s = d + r + s
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if dr_s >= 18:
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part_02['debt_risk'] = '高'
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elif dr_s >= 18 * 0.8:
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part_02['debt_risk'] = '较高'
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elif dr_s >= 18 * 0.6:
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part_02['debt_risk'] = '适中'
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elif dr_s >= 18 * 0.4:
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part_02['debt_risk'] = '较低'
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else:
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part_02['debt_risk'] = '低'
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def growth_ability_detail():
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g = float(data_01['财务评分']['经营增长']['营业增长率'])
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s = float(data_01['财务评分']['经营增长']['总资产增长率'])
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i = float(data_01['财务评分']['经营增长']['技术投入比率'])
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# 成长空间得分
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gs_s = g + s + i
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if gs_s >= 18:
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part_02['growth_ability'] = '大'
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elif gs_s >= 18 * 0.8:
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part_02['growth_ability'] = '较大'
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elif gs_s >= 18 * 0.6:
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part_02['growth_ability'] = '不大'
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elif gs_s >= 18 * 0.4:
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part_02['growth_ability'] = '较小'
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else:
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part_02['growth_ability'] = '小'
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enterprise_quality()
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profitability_detail()
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asset_quality_detail()
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debt_risk_detail()
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growth_ability_detail()
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return part_02
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def para_03():
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part_03 = dict()
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part_03['company'] = data_01['企业名称']
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part_03['credit_rank'] = data_01['信用等级']
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return part_03
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result.append(para_01())
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result.append(para_02())
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result.append(para_03())
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return result
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