[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fcJbv0aSZAaGx0ondFeQ_WVYHeFDcIIgTrof8lCZZmjA":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":30,"type":31},[],"2024-05-23 18:50:48",145605598,[8,9,10,11],"merge","concat","append","join",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},18,"cc4f5409e7be9a4e1042ab9ddb26433b","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F848dfca66560efee64dc471bef348a0f.jpg","Python数据分析","work_34616250","22级指令系统作业(目标2)","pandas中哪个方法可以把2个以上的dataFrame对象合并",[21,32,35,44,53,62,71,80,89,98],{"answer":22,"createTime":5,"id":23,"options":24,"question":29,"source":30,"type":31},[],145605597,[25,26,27,28],"id name age 0 1 2 0 101.0 tom 21.0 NaN NaN NaN 1 102.0 jerry 22.0 NaN NaN NaN 2 106.0 mike 23.0 NaN NaN NaN 0 NaN NaN NaN 112.0 jerry 21.0","id name age 0 101 tom 21 1 102 jerry 22 2 106 mike 23 3 112 jerry 21","id name age 0 1 2 0 101.0 tom 21.0 NaN NaN NaN 1 102.0 jerry 22.0 NaN NaN NaN 2 106.0 mike 23.0 NaN NaN NaN 3 112.0 NaN NaN NaN jerry 21.0","id name age 0 101 tom 21 1 112 jerry 21 2 106 mike 23","id,name,age 101,tom,21 102,jerry,22 106,mike,23 以上为id1.csv内容 df1=pd.read_csv("id1.csv") df1=df1.append([(112,"jerry",21)]) print(df1) 输出结果是()","v1",0,{"answer":33,"createTime":5,"id":6,"options":34,"question":19,"source":30,"type":31},[],[8,9,10,11],{"answer":36,"createTime":5,"id":37,"options":38,"question":43,"source":30,"type":31},[],145605599,[39,40,41,42],"连续变量离散化,等宽划分为5段","连续变量离散化,等宽划分为4段","连续变量离散化,等频划分为5段","连续变量离散化,等频划分为4段","mybins=df2["expenditure"].quantile([0,0.25,0.5,0.75,1]) print(pd.cut(df2["expenditure"],bins=mybins)) 以上代码功能",{"answer":45,"createTime":5,"id":46,"options":47,"question":52,"source":30,"type":31},[],145605600,[48,49,50,51],"1,3,5","1,2,3,4,5","2,3,4","1,3,4,5","缺失值如何处理? 1 删除缺失值所在行 2 删除缺失值所在列 3 固定值填充,例如均值,中位数等 4 邻近值填充 5 插值法填充",{"answer":54,"createTime":5,"id":55,"options":56,"question":61,"source":30,"type":31},[],145605601,[57,58,59,60],"合并df1和df9,df1中的id与df9中的xh进行关联,id,xh列都保留,如果df1,df9中有同名的列,分别加上("_基本信息","_成绩")做为后缀","合并df1和df9,df1中的id与df9中的xh进行关联,id和xh分别加上("_基本信息","_成绩")做为后缀","合并df1和df9,df1中的id与df9中的xh进行关联,仅id保留,如果df1,df9中有同名的列,分别加上("_基本信息","_成绩")做为后缀","合并df1和df9,df1中的id与df9中的xh进行关联,id和xh分别加上("_基本信息","_成绩")做为新列名","print(pd.merge(df1,df9 ,left_on="id",right_on="xh" ,suffixes=("_基本信息","_成绩") ,how="outer")) 代码功能是",{"answer":63,"createTime":5,"id":64,"options":65,"question":70,"source":30,"type":31},[],145605602,[66,67,68,69],"要对数据做升序排序","要对数据做降序排序","不需要排序","必须要进行数据采样","如果对某列数据进行帕累托贡献度分析,那么",{"answer":72,"createTime":5,"id":73,"options":74,"question":79,"source":30,"type":31},[],145605603,[75,76,77,78],"固定值插补","中位数插补","均值插补","随机数插补","在数据预处理里,对缺失值做插补,不属于数据插补方法的是",{"answer":81,"createTime":5,"id":82,"options":83,"question":88,"source":30,"type":31},[],145605604,[84,85,86,87],"通过相关系数矩阵查找","任两列做散点图观察","通过循环比较每个值","任两列做差,每个差均为0","如果数据有很多列,需要找出重复的列,最优方案是____",{"answer":90,"createTime":5,"id":91,"options":92,"question":97,"source":30,"type":31},[],145605605,[93,94,95,96],"一个小于9的数","一个小于11且大于9的数","一个大于11的数","一定是10","from scipy.interpolate import interp1d as xxcz from scipy.interpolate import lagrange as lg x=[1,2,3,5,6,7] y=[3,7,11,9,6,4] print(lg(x,y1)(4)) 输出结果可能是",{"answer":99,"createTime":5,"id":100,"options":101,"question":106,"source":30,"type":31},[],145605606,[102,103,104,105],"有监督学习","无监督学习","数据预处理","Kmeans分类","把大量图片分别标注上猫、狗或马.计算机AI学习后,给一张图,AI给出一个答案猫、狗或马.这是"]