CODEWITHSUNDEEP
pythonscikit-learndata-scienceimputation
Rahul
Rahul

Reputation: 47126

How to impute Null values in python for categorical data?

I have seen in R, imputation of categorical data is done straight forward by packages like DMwR, Caret and also I do have algorithm options like KNN or CentralImputation. But I do not see any libraries in python doing the same. FancyImpute performs well on numeric data.

Is there a way to do imputation of Null values in python for categorical data?

Edit: Added the top few rows of the data set. 

    >>> data_set.head()
       1stFlrSF  2ndFlrSF  3SsnPorch Alley  BedroomAbvGr BldgType BsmtCond  \
    0       856       854          0   NaN             3     1Fam       TA   
    1      1262         0          0   NaN             3     1Fam       TA   
    2       920       866          0   NaN             3     1Fam       TA   
    3       961       756          0   NaN             3     1Fam       Gd   
    4      1145      1053          0   NaN             4     1Fam       TA   

      BsmtExposure  BsmtFinSF1  BsmtFinSF2  ...   SaleType ScreenPorch  Street  \
    0           No       706.0         0.0  ...         WD           0    Pave   
    1           Gd       978.0         0.0  ...         WD           0    Pave   
    2           Mn       486.0         0.0  ...         WD           0    Pave   
    3           No       216.0         0.0  ...         WD           0    Pave   
    4           Av       655.0         0.0  ...         WD           0    Pave   

       TotRmsAbvGrd TotalBsmtSF  Utilities WoodDeckSF YearBuilt YearRemodAdd  \
    0             8       856.0     AllPub          0      2003         2003   
    1             6      1262.0     AllPub        298      1976         1976   
    2             6       920.0     AllPub          0      2001         2002   
    3             7       756.0     AllPub          0      1915         1970   
    4             9      1145.0     AllPub        192      2000         2000   

      YrSold  
    0   2008  
    1   2007  
    2   2008  
    3   2006  
    4   2008  

    [5 rows x 81 columns]

Upvotes: 0

Views: 4556

Answers (1)

demirbilek
demirbilek

Reputation: 351

There are few ways to deal with missing values. As I understand you want to fill NaN according to specific rule. Pandas fillna can be used. Below code is example of how to fill categoric NaN with most frequent value.

df['Alley'].fillna(value=df['MSZoning'].value_counts().index[0],inplace =True)

Also this might be helpful sklearn.preprocessing.Imputer

For more information about pandas fillna pandas.DataFrame.fillna

Hope this will work

Upvotes: 3

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