[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fCTfZ4-l0imVGNZo8nTgQgFmlBwWX97B2-Ze6ZdhLK_8":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":31,"type":32},[],"2024-12-13 13:04:53",170861002,[8,9,10,11],"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),:]",{"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","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",[21,33,42,51,60,69,78,81,90,99],{"answer":22,"createTime":23,"id":24,"options":25,"question":30,"source":31,"type":32},[],"2024-12-13 13:04:52",170860989,[26,27,28,29],"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":34,"createTime":23,"id":35,"options":36,"question":41,"source":31,"type":32},[],170860991,[37,38,39,40],"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":43,"createTime":23,"id":44,"options":45,"question":50,"source":31,"type":32},[],170860993,[46,47,48,49],"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":52,"createTime":23,"id":53,"options":54,"question":59,"source":31,"type":32},[],170860996,[55,56,57,58],"append","join","concat","merge","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",{"answer":61,"createTime":5,"id":62,"options":63,"question":68,"source":31,"type":32},[],170860998,[64,65,66,67],"apply->split->combine","split ->apply->combine","combine >split ->apply","split -> combine ->apply","分组的基本原理是",{"answer":70,"createTime":5,"id":71,"options":72,"question":77,"source":31,"type":32},[],170861000,[73,74,75,76],"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":79,"createTime":5,"id":6,"options":80,"question":19,"source":31,"type":32},[],[8,9,10,11],{"answer":82,"createTime":5,"id":83,"options":84,"question":89,"source":31,"type":32},[],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":5,"id":92,"options":93,"question":98,"source":31,"type":32},[],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":5,"id":101,"options":102,"question":107,"source":31,"type":32},[],170861009,[103,104,105,106],"merge()","concat()","join()","append()","在pandas中,用于合并两个DataFrame的方法是"]