2026信息素养-数字守艺人-初中组python-4

iftemp<140:heat_up()time.sleep(0.5)C.defmonitor():temp=get_temp()iftemp>160:cool_down()eliftemp<140:heat_up()time.sleep(0.5)whileTrue:monitor()D.temp_list=[]whileTrue:temp=get_temp()temp_list.append(temp)iftemp_list[-1]>160:cool_down()eliftemp_list[-1]<140:heat_up()time.sleep(0.5)制时长":45,"敷彩时长":22,"残次率":0.02},...]，现在需要筛选出残次率低于0.03且刻制时长小于50的匠人数据，下列Python代码正确的是()A.filtered_data=[dfordindataifd["残次率"]<0.03ord["刻制时长"]<50]B.filtered_data=filter(lambdax:x["残次率"]<0.03andx["刻制时长"]<50,data)C.filtered_data=[dfordindataifd.get("残次率",1)<0.03andd.get("刻制时长",100)<50]D.filtered_data=list(filter(lambdax:x["残次率"]<0.03orx["刻制时长"]<50,data))度排序，下列Python代码实现正确的是()A.sorted_data=sorted(artisan_data,key=lambdax:(x["平均工时"],-x["制作数量"],x["残次率"]))B.sorted_data=sorted(artisan_data,key=lambdax:(x["平均工时"],x["制作数量"],x["残次率"]),reverse=True)C.sorted_data=sorted(artisan_data,key=lambdax:(-x["平均工时"],x["制作数量"],-x["残次率"]))D.sorted_data=sorted(artisan_data,key=lambdax:(x["平均工时"],x["制作数量"],x["残次率"]))5.某非遗数据库需要将皮影刻制数据转换为标准化CSV格式，已知原始数据为字典列表，下列PythonA.importcsvwithopen('shadow_puppet.csv','w',newline='')asf:writer=csv.DictWriter(f,fieldnames=["匠人ID","纹样类型","刻制时长","敷彩时长"])writer.writeheader()forrowindata:writer.writerow({k:row.get(k,"")forkinfieldnames})B.importcsvwithopen('shadow_puppet.csv','w',newline='')asf:writer=csv.writer(f)forrowindata:C.importcsvwithopen('shadow_puppet.csv','w')asf:writer=csv.DictWriter(f,fieldnames=["匠人ID","纹样类型","刻制时长","敷彩时长"])writer.writeheader()writer.writerows(data)D.importcsvwithopen('shadow_puppet.csv','w',newline='')asf:writer=csv.DictWriter(f,fieldnames=["匠人ID","纹样类型","刻制时长","敷彩时长"])writer.writeheader()forrowindata:forkinfieldnames:writer.writerow(row)6.在模拟蜡染布面染色时长监控时，需要统计连续3次检测中温度超过80℃的次数，当次数达到2次时触发补色操作。下列Python代码中能正确实现该逻辑的是()A.count=0temp_history=[]whileTrue:temp=get_temp()temp_history.append(temp)iflen(temp_history)>3:temp_history.pop(0)2,...,工时n]要求排除单个工时超过120分钟的异常数据后计算平均值，下列Python函数实现正A.defcalculate_avg_time(artisan_data):avg_dict={}foraid,timesinartisan_data.items():valid_times=[tfortintimesift<=120]avg_dict[aid]=sum(valid_times)/len(valid_times)ifvalid_timeselse0returnavg_dictB.defcalculate_avg_time(artisan_data):avg_dict={}foraid,timesinartisan_data.items():valid_times=filter(lambdax:x<=120,times)avg_dict[aid]=sum(valid_times)/len(times)returnavg_dictC.defcalculate_avg_time(artisan_data):avg_dict={}foraid,timesinartisan_data.items():total=0count=0fortintimes:total+=tcount+=1avg_dict[aid]=total/countreturnavg_dictD.defcalculate_avg_time(artisan_data):avg_dict={}foraid,timesinartisan_data.items():valid_times=[tfortintimesift<120]avg_dict[aid]=sum(valid_times)/len(valid_times)returnavg_dict支持按“纹样类型”进行分组查询。下列Python代码能高效实现分组查询功能的是()A.defgroup_by_pattern(artisan_dict):pattern_groups={}foraid,infoinartisan_dict.items():