2022-02-17 16:47:06 +08:00
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import operator
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def social_turnover_salary(param):
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"""
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离职率/人均薪酬水平
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Parameters:
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param dict 数据库数据
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Returns:
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result dict 清洗好的数据
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"""
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# Params
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three_year_data = sorted(param['社会问卷']['近三年公司数据'], key=operator.itemgetter('年份'), reverse=False)
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# Returns
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result = dict()
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def date_range():
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"""
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评价周期年度区间
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Parameters:
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param dict 环境问卷
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Returns:
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result str 区间数据
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"""
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year_begin = three_year_data[0]['年份']
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year_end = three_year_data[2]['年份']
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res = year_begin + '-' + year_end
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return res
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def turnover_ratio(year_param):
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"""
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每年离职比例
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Parameters:
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param dict 年度社会问卷数据
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Returns:
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result type 该年度离职比例
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"""
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# 离职率
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2022-03-08 17:05:26 +08:00
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total = year_param['员工总数（年底）']
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2022-02-17 16:47:06 +08:00
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turnover = year_param['当年离职人数']
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rate = round(turnover / total * 100, 2)
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return str(rate) + '%'
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def calculate_three_year_data():
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"""
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计算三年数据并转为字符串
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Parameters:
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'-'
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Returns:
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result str 三年数据用顿号隔开
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"""
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res = dict()
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year_turnover = list()
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yart_salary_level = list()
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for item in three_year_data:
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turnover_res = turnover_ratio(item)
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year_turnover.append(turnover_res)
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yart_salary_level.append(item['人均薪酬水平（元/月）'])
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# 列表中元素类型float转str
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yart_salary_level_new = [str(x) for x in yart_salary_level]
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# Returns-part1
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res['part_01'] = dict()
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res['part_01']['date_range'] = date_range()
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res['part_01']['turnover_ratio'] = '、'.join(year_turnover)
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res['part_01']['year_03'] = three_year_data[2]['年份']
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res['part_01']['industry_indicators'] = None
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res['part_01']['industry_indicators_average'] = None
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res['part_01']['industry_indicators_median'] = None
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res['part_01']['median_comparison'] = None
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# part2
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res['part_02'] = dict()
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res['part_02']['date_range'] = date_range()
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res['part_02']['salary_level'] = '、'.join(yart_salary_level_new)
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res['part_02']['year_02'] = three_year_data[1]['年份']
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res['part_02']['year_03'] = three_year_data[2]['年份']
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res['part_02']['year01_on_year02'] = round((yart_salary_level[1] / yart_salary_level[0] - 1) * 100, 2)
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res['part_02']['year02_on_year03'] = round((yart_salary_level[2] / yart_salary_level[1] - 1) * 100, 2)
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res['part_02']['industry_indicators'] = None
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res['part_02']['industry_indicators_average'] = None
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res['part_02']['industry_indicators_median'] = None
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res['part_02']['median_comparison'] = None
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return res
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result['part_01'] = calculate_three_year_data()['part_01']
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result['part_02'] = calculate_three_year_data()['part_02']
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return result
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def social_labor_disputes(param):
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"""
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劳动纠纷
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Parameters:
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param dict 数据库数据
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Returns:
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result dict 清洗好的数据
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"""
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data = param['社会问卷']
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# Returns
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result = dict()
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for item in data['近三年公司数据']:
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if item['劳资纠纷次数'] > 0:
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result['describe'] = '劳动纠纷——公司在近三年出现了劳动纠纷，这影响了公司的社会得分。'
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break
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else:
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result['describe'] = False
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return result
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def social_safety_accident(param):
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"""
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安全事故
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Parameters:
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param dict 数据库数据
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Returns:
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result dict 清洗好的数据
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"""
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data = param['社会问卷']
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# Returns
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result = dict()
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for item in data['近三年公司数据']:
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if item['安全事故发生次数'] > 0:
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result['describe'] = '安全事故——公司在近三年出现了安全事故，这影响了公司的社会得分。。'
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break
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else:
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result['describe'] = False
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return result
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def social_training_return(param):
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"""
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提供培训/产品返修、退还、投诉
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Parameters:
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param dict 数据库数据
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Returns:
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result dict 清洗好的数据
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"""
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# Params
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three_year_data = sorted(param['社会问卷']['近三年公司数据'], key=operator.itemgetter('年份'), reverse=False)
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# Returns
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result = dict()
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def date_range():
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"""
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评价周期年度区间
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Parameters:
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param dict 环境问卷
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Returns:
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result str 区间数据
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"""
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year_begin = three_year_data[0]['年份']
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year_end = three_year_data[2]['年份']
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res = year_begin + '-' + year_end
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return res
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def calculate_three_year_data():
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"""
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计算三年数据并转为字符串
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Parameters:
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'-'
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Returns:
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result str 三年数据用顿号隔开
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"""
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res = dict()
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year_training = list()
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year_return = list()
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for item in three_year_data:
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year_training.append(str(item['组织培训次数']))
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year_return.append(str(item['返修、退回、投诉产品对应销售额占全部销售比例（%）']))
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# Returns-part1
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res['part_01'] = dict()
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res['part_01']['date_range'] = date_range()
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res['part_01']['training_number'] = '、'.join(year_training)
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res['part_01']['year_03'] = three_year_data[2]['年份']
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res['part_01']['industry_indicators'] = None
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# part2
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res['part_02'] = dict()
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res['part_02']['date_range'] = date_range()
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res['part_02']['salary_level'] = '、'.join(year_return)
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res['part_02']['year_03'] = three_year_data[2]['年份']
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res['part_02']['industry_indicators'] = None
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return res
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result['part_01'] = calculate_three_year_data()['part_01']
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result['part_02'] = calculate_three_year_data()['part_02']
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return result
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def socail_poverty_alleviation(param):
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"""
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扶贫和捐赠
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Parameters:
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param dict 数据库数据
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Returns:
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result dict 清洗好的数据
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"""
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data = param['社会问卷']
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# Returns
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result = dict()
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for item in data['近三年公司数据']:
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if item['扶贫+捐赠规模（万元）'] > 0:
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result['describe'] = '扶贫和捐赠——公司在近三年有过扶贫、捐赠行为，这提高了公司社会得分。'
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break
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else:
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result['describe'] = False
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return result
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def socail_legal_proceedings(param):
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"""
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司法风险
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Parameters:
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param dict 数据库数据
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Returns:
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result dict 清洗好的数据
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"""
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# Parameter
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data = param['风险数据']
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# Returns
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result = dict()
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for value in data.values():
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if value > 0:
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result['describe'] = '法律诉讼（作为被告）、被执行人、失信人、行政处罚、税收违法、严重违法、经营异常——经查，公司存在1项或多项的法律诉讼（作为被告）、被执行人、失信人、行政处罚、税收违法、严重违法、经营异常情况，这影响了公司社会分数。'
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break
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else:
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result['describe'] = False
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return result
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