Python data Analysis (ii) Pandas missing value processing

ImportPandas as PDImportNumPy as Npdf= PD. DataFrame (Np.random.randn (5, 3), index=['a','C','e','F','h'],columns=[' One',' Both','three']) DF= Df.reindex (['a','b','C','D','e','F','g','h'])Print(DF)Print('############### #缺失值判断 ######################')Print('the missing values of the--------series are judged---------')Print(df[' One'].isnull ())
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The missing values of the--------series are judged---------

A    False

b     truec    falsed     truee    falsef    falseg     trueh    falsename:one, Dtype:bool

" " Print ('---------output series missing values and index--------') Print (df['one'][df['one'].isnull ()])
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---------output series missing values and indexes--------b   NaNd   NaNg   nanname:one, Dtype:float64

'print('--------dataframe missing value---------')print (Df.isnull ())
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--------Dataframe Missing value---------one  Threea  false  false  Falseb   True   True   Truec  false  false  falsed   true   true   truee  false  Falsef  False  false  Falseg   true   true   trueh   false false

"print('--------The missing value and index of the output dataframe---------'= Df[df.isnull ( ). values==True]print(data[~data.index.duplicated ()))
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Missing values and indexes--------output dataframe---------One,  threeb  nan  nan    NaNd  nan  NaN    NaNg  nan  nan     nan

'print('--------output dataframe column with missing values---------')Print  (Df.isnull (). any ())
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--------output dataframe columns with missing values---------one      truetwo      truethree    truedtype:bool

"print('############### #缺失值过滤 ######################')  Print('missing value filter for--------series---------')print(df[')  One'].isnull ())
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############### #缺失值过滤 ######################---------a    falseb     truec    --------Series missing values falsed     truee    falsef    falseg     trueh    falsename:one, Dtype:bool

'print('--------Delete missing data using the Dropna method, return a deleted series--------') Print (df['one'].dropna ())
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--------Use the Dropna method to delete missing data and return a deleted series--------a   -0.211055c   -0.870090e   -0.203259 F    0.490568h    1.437819name:one, Dtype:float64     

'print('--------Dataframe missing values filter---------')print (Df.dropna ())
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--------Dataframe Missing Value filter---------One,     threea-0.211055-2.869212  0.022179C- 0.870090-0.878423  1.071588e-0.203259  0.315897  0.495306F  0.490568-0.968058- 0.999899H  1.437819-0.370934-0.482307    

"print('-------is deleted when the line is all Nan, the parameter how default is any, the missing value is deleted--------')  Print(Df.dropna (how="all"))
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-------when the line is all Nan, delete, the parameter how default is any, with the missing value to delete--------one of the     threea-0.211055-2.869212  0.022179c-0.870090-0.878423  1.071588e-0.203259  0.315897  0.495306F  0.490568- 0.968058-0.999899H  1.437819-0.370934-0.482307    

  
 " print  ( '    '  print  ( '  ------Specify a special value to fill the missing value-------  " Span style= "color: #000000;" >)  print   (Df.fillna (0)) 
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 ############### #缺失值填充 ######################------Specify special values to fill missing values------- one, threea-0.211055-2 .869212 0.022179  b 0.000000 0.000000 0.000000  c-0.870090-0.878423 1.071588  D 0.000000 0.000000 0. 000000  e-0.203259 0.315897 0.495306  f 0.490568-0.968058-0.999899  g 0.000000 0.000000 0.000000  H 1 .437819-0.370934-0.482307         

  
 " print  ( '    

------Different columns are populated with different values------One,     threea-0.211055-2.869212  0.022179b  1.000000  2.000000  3.000000c-0.870090-0.878423  1.071588d  1.000000  2.000000  3.000000 e-0.203259  0.315897  0.495306f 0.490568-0.968058-0.999899g 1.000000 2.000000 3.000000  H 1.437819-0.370934-0.482307       

Print ('------forward to fill------')print( Df.fillna (method="ffill"))
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------Forward fill------One,     threea-0.211055-2.869212  0.022179b-0.211055-2.869212  0.022179c-0.870090-0.878423  1.071588d-0.870090-0.878423  1.071588e-0.203259  0.315897  0.495306F 0.490568-0.968058-0.999899g 0.490568-0.968058-0.999899H 1.437819-0.370934-0.482307 

Print ('------back to fill------')print( Df.fillna (method="bfill"))
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------Back fill------One,     threea-0.211055-2.869212  0.022179b-0.870090-0.878423  1.071588c-0.870090-0.878423  1.071588d-0.203259  0.315897  0.495306e-0.203259  0.315897  0.495306f 0.490568-0.968058-0.999899g 1.437819-0.370934-0.482307H 1.437819-0.370934- 0.482307       

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Print ('------Average fill------') Print (Df.fillna (Df.mean ()))
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------Average fill------One,     threea-0.211055-2.869212  0.022179b  0.128797-0.954146  0.021373c-0.870090-0.878423  1.071588d  0.128797-0.954146  0.021373e-0.203259  0.315897  0.495306f 0.490568-0.968058-0.999899g 0.128797-0.954146 0.021373H 1.437819- 0.370934-0.482307       

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Python data Analysis (ii) Pandas missing value processing