[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f2aaErnHbLz5lLO0YqPnU9aBmIGqNM7Tj81ndNTCJQVM":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":30,"type":31},[],"2024-12-13 13:04:52",170860996,[8,9,10,11],"append","join","concat","merge",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},41,"5fd64a34b7f9409fa1761407cfcf7ebd","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F0d27132e8e4c25614916fe2373258c2e.jpg","数据分析与实践-Python-23","work_39075322","作业pandas 20241121","若已从一个DataFrame对象df中选择了两部分数据(保持数据属性完整)分别存入df1和df2中,代码行如下, 请从如下选项中选出可以正确合并这两部分数据的函数\u002F方法补充完整代码",[21,32,41,50,53,63,72,81,90,99],{"answer":22,"createTime":5,"id":23,"options":24,"question":29,"source":30,"type":31},[],170860989,[25,26,27,28],"names='#'","sep='#'","index_col='#'","engine='#'","pandas模块中的read_csv()函数在日常使用较多,它除了可以读取csv格式的文件并将结果转换成一个DataFrame外,还可以读取其他的格式化文本文件.假设有一个文本文件的每一行均含有相同个数的数值,且数据间都用一个#分隔,形如: 12#34#5.67#1234 12#346#5.67#77 ... 12#3.4#67#67.89 请问在read_csv()函数中需要添加如下哪一个选项中的参数设置","v1",0,{"answer":33,"createTime":5,"id":34,"options":35,"question":40,"source":30,"type":31},[],170860991,[36,37,38,39],"isnull()方法可以用来判断缺失值","drop()方法可以用来删除缺失行","fillna()方法可以用来填充缺失行","fillna()方法可通过method参数指定缺失值用其上或其下的第一个非缺失值填充","对于一个Series或DataFrame对象来说,如下选项中说法错误的是哪一个",{"answer":42,"createTime":5,"id":43,"options":44,"question":49,"source":30,"type":31},[],170860993,[45,46,47,48],"属性规约是对数据集属性的规约,目的是获得有代表性的较少的数据列的规约表示","PCA是重要的属性规约方法","箱型图常用来实现数值规约","抽样是数值规约的常见手段,常见的包括如随机抽样,聚类抽样和分层抽样","请选出以下关于数据规约的两种形式——属性规约和数值规约说法中错误的选项",{"answer":51,"createTime":5,"id":6,"options":52,"question":19,"source":30,"type":31},[],[8,9,10,11],{"answer":54,"createTime":55,"id":56,"options":57,"question":62,"source":30,"type":31},[],"2024-12-13 13:04:53",170860998,[58,59,60,61],"apply->split->combine","split ->apply->combine","combine >split ->apply","split -> combine ->apply","分组的基本原理是",{"answer":64,"createTime":55,"id":65,"options":66,"question":71,"source":30,"type":31},[],170861000,[67,68,69,70],"df_obj.loc[df_obj.iloc[:,"c"]>1,:]","df_obj.loc[df_obj.loc[:,"c"]>1,:]","df_obj.loc[df_obj.loc[:,2]>1,:]","df_obj.loc[:,df_obj.loc[2,:]>1]","有dataframe结构的数据df_obj = pd.DataFrame([[1,2,3],[2,1,1],[4,3,1],[5,1,2]],columns=['a','b','c']) 输出列名为"c"的列中元素大于1的所有列数据",{"answer":73,"createTime":55,"id":74,"options":75,"question":80,"source":30,"type":31},[],170861002,[76,77,78,79],"df_obj.loc[(df_obj.loc[1,:]==1)&(df_obj.loc[2,:]==3),:]","df_obj.loc[:,(df_obj.loc[2,:]==1)&(df_obj.loc[3,:]==3)]","df_obj.loc[:,(df_obj.loc[1,:]==1)&(df_obj.loc[2,:]==3)]","df_obj.loc[(df_obj.loc[2,:]==1)&(df_obj.loc[3,:]==3),:]","有dataframe结构的数据df_obj = pd.DataFrame([[1,2,3],[2,1,1],[4,3,1],[5,1,2]],columns=['a','b','c']) 输出第二行中为1,并且第三行中为3的列数据",{"answer":82,"createTime":55,"id":83,"options":84,"question":89,"source":30,"type":31},[],170861005,[85,86,87,88],"read_csv()","read_excel()","read_json()","read_sql()","在pandas中,用于读取CSV文件的方法是",{"answer":91,"createTime":55,"id":92,"options":93,"question":98,"source":30,"type":31},[],170861007,[94,95,96,97],"drop()","remove()","delete()","pop()","在pandas中,用于删除DataFrame中某一列的方法是",{"answer":100,"createTime":55,"id":101,"options":102,"question":107,"source":30,"type":31},[],170861009,[103,104,105,106],"merge()","concat()","join()","append()","在pandas中,用于合并两个DataFrame的方法是"]