[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fFtzLHAGSQ-hV58VkEGo2JgIO13ZOM9KqyUE5VZwzcto":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":16,"related":17,"source":27,"type":28},[],"2023-12-19 11:34:04",116562795,[8,9,10,11],"s.iloc['缺失数据',nan']","s.replace (np.nan,'缺失数据')","s.replace ('缺失数据',inplace=nan)","s.replace('nan',replace='缺失数据')",{"courseId":13,"courseImg":14,"courseName":15},"602ee4445410ff26f8245f1efa50b906","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fffd8452377c96c561822fe1732615d0b.jpg","数据处理和特征工程","{ 一个含有NaN数据的序列Series: s = pd.Series([12,33,45,23,np.nan,np.nan,66,54,np.nan,99]) ()把序列s中的缺失值替换成'缺失数据' }",[18,29,38,47,56,65,74,83,92,101],{"answer":19,"createTime":5,"id":20,"options":21,"question":26,"source":27,"type":28},[],116562764,[22,23,24,25],"s.dropna(inplace = True)","drop(inplace = S)","s.dropna(inplace = False)","df.dropna(axis=0,inplace=s)","{ 一个含有NaN数据的序列Series: s = pd.Series([12,33,45,23,np.nan,np.nan,66,54,np.nan,99]) ()删除第一题中s的缺失值,替换原有s? }","v1",0,{"answer":30,"createTime":5,"id":31,"options":32,"question":37,"source":27,"type":28},[],116562768,[33,34,35,36],"df.dropna(s)","df.dropna()","s.dropna(replace=S)","df.dropna(axis=1,subset=['valuel'])","{ 一个含有NaN数据的数据帧DataFrame: df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190], 'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']}) ( )删除df的缺失值. }",{"answer":39,"createTime":5,"id":40,"options":41,"question":46,"source":27,"type":28},[],116562772,[42,43,44,45],"df.dropna(axis=0,subset=3)","dropna(inplace = 3,subset=[3])","dropna(axis=1,subset=['valuel'])","df.dropna(axis=1,subset=[3])","{ 一个含有NaN数据的数据帧DataFrame: df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190], 'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']}) ()使用subset删除第二题中df中索引为3中的缺失值. }",{"answer":48,"createTime":5,"id":49,"options":50,"question":55,"source":27,"type":28},[],116562774,[51,52,53,54],"dropna(axis=1,subset=['valuel1'])","dropna(subset=['valuel'])","dropna(Subset=['value1'])","df.dropna(axis=0,subset=['value1'])","{ 一个含有NaN数据的数据帧DataFrame: df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190], 'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']}) ()使用subset删除第二题中df中索引为'value1'中的缺失值. }",{"answer":57,"createTime":5,"id":58,"options":59,"question":64,"source":27,"type":28},[],116562778,[60,61,62,63],"dropna(axis=0,how='any')","df.dropna(axis=0,how='left')","df.dropna(axis=0,how='any')","df.dropna(drop='nan',how='any')","{ 一个含有NaN数据的数据帧DataFrame: df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190], 'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']}) ()使用any参数删除df中有部分nan的值. }",{"answer":66,"createTime":5,"id":67,"options":68,"question":73,"source":27,"type":28},[],116562780,[69,70,71,72],"s.fillna(0,inplace = True)","s.fillna(axis=0,inplace = 0)","s.fillna(['0'])","s.fillna(0,inplace = False)","{ 一个含有NaN数据的序列Series: s = pd.Series([12,33,45,23,np.nan,np.nan,66,54,np.nan,99]) ()给序列s填充0的代码? }",{"answer":75,"createTime":5,"id":76,"options":77,"question":82,"source":27,"type":28},[],116562785,[78,79,80,81],"df = df.fillna(0,inplace = False)","df = df.fillna(0,inplace=0)","df =df.fillna(fillna(0))","df = df.fillna(axis=0,subset=[0])","{ 一个含有NaN数据的数据帧DataFrame: df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190], 'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']}) ()给df的缺失值填充0. }",{"answer":84,"createTime":5,"id":85,"options":86,"question":91,"source":27,"type":28},[],116562790,[87,88,89,90],"df = df.fillna(df,inplace = False)","df = df.fillna(inplace='True'how='ffill')","df = df(method='ffill',how='fillna')","df = df.fillna(method='ffill',inplace = False)","{ 一个含有NaN数据的数据帧DataFrame: df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190], 'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']}) ()给df填充前面的值. }",{"answer":93,"createTime":5,"id":94,"options":95,"question":100,"source":27,"type":28},[],116562793,[96,97,98,99],"df= df.fillna(method='ffill',inplace = False)","df = fillna(method='ffill',inplace = True)","df = df.fillna(method='bfill',inplace = False)","df = df.fillna(axis=1,how='bfill')","{ 一个含有NaN数据的数据帧DataFrame: df = pd.DataFrame({'value1':[12,33,45,23,np.nan,np.nan,66,54,np.nan,99,190], 'value2':['a','b','c','d','e',np.nan,np.nan,'f','g',np.nan,'g']}) ()给df填充后面的值. }",{"answer":102,"createTime":5,"id":6,"options":103,"question":16,"source":27,"type":28},[],[8,9,10,11]]