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单选题 { 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? }

A. s.dropna(inplace = True)

B. drop(inplace = S)

C. s.dropna(inplace = False)

D. df.dropna(axis=0,inplace=s)

数据处理和特征工程课程封面

学科：数据处理和特征工程

时间：2023-12-19 03:34:04

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{ 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? }

A. s.dropna(inplace = True)B. drop(inplace = S)C. s.dropna(inplace = False)D. df.dropna(axis=0,inplace=s)

{ 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. }

A. df.dropna(s)B. df.dropna()C. s.dropna(replace=S)D. 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']}) ()使用subset删除第二题中df中索引为3中的缺失值. }

A. df.dropna(axis=0,subset=3)B. dropna(inplace = 3,subset=[3])C. dropna(axis=1,subset=['valuel'])D. 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'value1'中的缺失值. }

A. dropna(axis=1,subset=['valuel1'])B. dropna(subset=['valuel'])C. dropna(Subset=['value1'])D. 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any参数删除df中有部分nan的值. }

A. dropna(axis=0,how='any')B. df.dropna(axis=0,how='left')C. df.dropna(axis=0,how='any')D. df.dropna(drop='nan',how='any')

{ 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? }

A. s.fillna(0,inplace = True)B. s.fillna(axis=0,inplace = 0)C. s.fillna(['0'])D. s.fillna(0,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的缺失值填充0. }

A. df = df.fillna(0,inplace = False)B. df = df.fillna(0,inplace=0)C. df =df.fillna(fillna(0))D. 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填充前面的值. }

A. df = df.fillna(df,inplace = False)B. df = df.fillna(inplace='True'how='ffill')C. df = df(method='ffill',how='fillna')D. 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填充后面的值. }

A. df= df.fillna(method='ffill',inplace = False)B. df = fillna(method='ffill',inplace = True)C. df = df.fillna(method='bfill',inplace = False)D. df = df.fillna(axis=1,how='bfill')

{ 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' }

A. s.iloc['缺失数据',nan']B. s.replace (np.nan,'缺失数据')C. s.replace ('缺失数据',inplace=nan)D. s.replace('nan',replace='缺失数据')

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