Impute categorical missing values in scikit-learn

This code fills in a series with the most frequent category:

import pandas as pd
import numpy as np

# create fake data 
m = pd.Series(list('abca'))
m.iloc[1] = np.nan #artificially introduce nan

print('m = ')
print(m)

#make dummy variables, count and sort descending:
most_common = pd.get_dummies(m).sum().sort_values(ascending=False).index[0] 

def replace_most_common(x):
    if pd.isnull(x):
        return most_common
    else:
        return x

new_m = m.map(replace_most_common) #apply function to original data

print('new_m = ')
print(new_m)

Outputs:

m =
0      a
1    NaN
2      c
3      a
dtype: object

new_m =
0    a
1    a
2    c
3    a
dtype: object

Copying and modifying sveitser’s answer, I made an imputer for a pandas.Series object

import numpy
import pandas 

from sklearn.base import TransformerMixin

class SeriesImputer(TransformerMixin):

    def __init__(self):
        """Impute missing values.

        If the Series is of dtype Object, then impute with the most frequent object.
        If the Series is not of dtype Object, then impute with the mean.  

        """
    def fit(self, X, y=None):
        if   X.dtype == numpy.dtype('O'): self.fill = X.value_counts().index[0]
        else                            : self.fill = X.mean()
        return self

    def transform(self, X, y=None):
       return X.fillna(self.fill)

To use it you would do:

# Make a series
s1 = pandas.Series(['k', 'i', 't', 't', 'e', numpy.NaN])


a  = SeriesImputer()   # Initialize the imputer
a.fit(s1)              # Fit the imputer
s2 = a.transform(s1)   # Get a new series

Similar. Modify Imputer for strategy='most_frequent':

class GeneralImputer(Imputer):
    def __init__(self, **kwargs):
        Imputer.__init__(self, **kwargs)

    def fit(self, X, y=None):
        if self.strategy == 'most_frequent':
            self.fills = pd.DataFrame(X).mode(axis=0).squeeze()
            self.statistics_ = self.fills.values
            return self
        else:
            return Imputer.fit(self, X, y=y)

    def transform(self, X):
        if hasattr(self, 'fills'):
            return pd.DataFrame(X).fillna(self.fills).values.astype(str)
        else:
            return Imputer.transform(self, X)

where pandas.DataFrame.mode() finds the most frequent value for each column and then pandas.DataFrame.fillna() fills missing values with these. Other strategy values are still handled the same way by Imputer.

Inspired by the answers here and for the want of a goto Imputer for all use-cases I ended up writing this. It supports four strategies for imputation mean, mode, median, fill works on both pd.DataFrame and Pd.Series.

mean and median works only for numeric data, mode and fill works for both numeric and categorical data.

class CustomImputer(BaseEstimator, TransformerMixin):
    def __init__(self, strategy='mean',filler='NA'):
       self.strategy = strategy
       self.fill = filler

    def fit(self, X, y=None):
       if self.strategy in ['mean','median']:
           if not all(X.dtypes == np.number):
               raise ValueError('dtypes mismatch np.number dtype is 
                                 required for '+ self.strategy)
       if self.strategy == 'mean':
           self.fill = X.mean()
       elif self.strategy == 'median':
           self.fill = X.median()
       elif self.strategy == 'mode':
           self.fill = X.mode().iloc[0]
       elif self.strategy == 'fill':
           if type(self.fill) is list and type(X) is pd.DataFrame:
               self.fill = dict([(cname, v) for cname,v in zip(X.columns, self.fill)])
       return self

   def transform(self, X, y=None):
       return X.fillna(self.fill)

usage

>> df   
    MasVnrArea  FireplaceQu
Id  
1   196.0   NaN
974 196.0   NaN
21  380.0   Gd
5   350.0   TA
651 NaN     Gd


>> CustomImputer(strategy='mode').fit_transform(df)
MasVnrArea  FireplaceQu
Id      
1   196.0   Gd
974 196.0   Gd
21  380.0   Gd
5   350.0   TA
651 196.0   Gd

>> CustomImputer(strategy='fill', filler=[0, 'NA']).fit_transform(df)
MasVnrArea  FireplaceQu
Id      
1   196.0   NA
974 196.0   NA
21  380.0   Gd
5   350.0   TA
651 0.0     Gd 

You can use sklearn_pandas.CategoricalImputer for the categorical columns. Details:

First, (from the book Hands-On Machine Learning with Scikit-Learn and TensorFlow) you can have subpipelines for numerical and string/categorical features, where each subpipeline’s first transformer is a selector that takes a list of column names (and the full_pipeline.fit_transform() takes a pandas DataFrame):

class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

You can then combine these sub pipelines with sklearn.pipeline.FeatureUnion, for example:

full_pipeline = FeatureUnion(transformer_list=[
    ("num_pipeline", num_pipeline),
    ("cat_pipeline", cat_pipeline)
])

Now, in the num_pipeline you can simply use sklearn.preprocessing.Imputer(), but in the cat_pipline, you can use CategoricalImputer() from the sklearn_pandas package.

note: sklearn-pandas package can be installed with pip install sklearn-pandas, but it is imported as import sklearn_pandas

• strategy = ‘most_frequent’ can be used only with quantitative feature, not with qualitative. This custom impuer can be used for both qualitative and quantitative. Also with scikit learn imputer either we can use it for whole data frame(if all features are quantitative) or we can use ‘for loop’ with list of similar type of features/columns(see the below example). But custom imputer can be used with any combinations.

      from sklearn.preprocessing import Imputer
      impute = Imputer(strategy='mean')
      for cols in ['quantitative_column', 'quant']:  # here both are quantitative features.
            xx[cols] = impute.fit_transform(xx[[cols]])
  

• Custom Imputer :

     from sklearn.preprocessing import Imputer
     from sklearn.base import TransformerMixin
  
     class CustomImputer(TransformerMixin):
           def __init__(self, cols=None, strategy='mean'):
                 self.cols = cols
                 self.strategy = strategy
  
           def transform(self, df):
                 X = df.copy()
                 impute = Imputer(strategy=self.strategy)
                 if self.cols == None:
                        self.cols = list(X.columns)
                 for col in self.cols:
                        if X[col].dtype == np.dtype('O') : 
                               X[col].fillna(X[col].value_counts().index[0], inplace=True)
                        else : X[col] = impute.fit_transform(X[[col]])
  
                 return X
  
           def fit(self, *_):
                 return self
  

• Dataframe:

        X = pd.DataFrame({'city':['tokyo', np.NaN, 'london', 'seattle', 'san 
                                   francisco', 'tokyo'], 
            'boolean':['yes', 'no', np.NaN, 'no', 'no', 'yes'], 
            'ordinal_column':['somewhat like', 'like', 'somewhat like', 'like', 
                              'somewhat like', 'dislike'], 
            'quantitative_column':[1, 11, -.5, 10, np.NaN, 20]})
  
  
              city              boolean   ordinal_column  quantitative_column
          0   tokyo             yes       somewhat like   1.0
          1   NaN               no        like            11.0
          2   london            NaN       somewhat like   -0.5
          3   seattle           no        like            10.0
          4   san francisco     no        somewhat like   NaN
          5   tokyo             yes       dislike         20.0
  

• 1) Can be used with list of similar type of features.

   cci = CustomImputer(cols=['city', 'boolean']) # here default strategy = mean
   cci.fit_transform(X)
  

• can be used with strategy = median

   sd = CustomImputer(['quantitative_column'], strategy = 'median')
   sd.fit_transform(X)
  

• 3) Can be used with whole data frame, it will use default mean(or we can also change it with median. for qualitative features it uses strategy = ‘most_frequent’ and for quantitative mean/median.

   call = CustomImputer()
   call.fit_transform(X)   
  

There is a package sklearn-pandas which has option for imputation for categorical variable
https://github.com/scikit-learn-contrib/sklearn-pandas#categoricalimputer

>>> from sklearn_pandas import CategoricalImputer
>>> data = np.array(['a', 'b', 'b', np.nan], dtype=object)
>>> imputer = CategoricalImputer()
>>> imputer.fit_transform(data)
array(['a', 'b', 'b', 'b'], dtype=object)

You could try the following:

replace = df.<yourcolumn>.value_counts().argmax()

df['<yourcolumn>'].fillna(replace, inplace=True) 

sklearn.impute.SimpleImputer instead of Imputer can easily resolve this, which can handle categorical variable.

As per the Sklearn documentation:
If “most_frequent”, then replace missing using the most frequent value along each column. Can be used with strings or numeric data.

https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html

impute_size=SimpleImputer(strategy="most_frequent") 
data['Outlet_Size']=impute_size.transform(data[['Outlet_Size']])

Missforest can be used for the imputation of missing values in categorical variable along with the other categorical features. It works in an iterative way similar to IterativeImputer taking random forest as a base model.

Following is the code to label encode the features along with the target variable, fitting model to impute nan values, and encoding the features back

import sklearn.neighbors._base
from sklearn.preprocessing import LabelEncoder
import sys
sys.modules['sklearn.neighbors.base'] = sklearn.neighbors._base
from missingpy import MissForest

def label_encoding(df, columns):
    """
    Label encodes the set of the features to be used for imputation
    Args:
        df: data frame (processed data)
        columns: list (features to be encoded)
    Returns: dictionary
    """
    encoders = dict()
    for col_name in columns:
        series = df[col_name]
        label_encoder = LabelEncoder()
        df[col_name] = pd.Series(
            label_encoder.fit_transform(series[series.notnull()]),
            index=series[series.notnull()].index
        )
        encoders[col_name] = label_encoder
    return encoders

# adding to be imputed global category along with features
features = ['feature_1', 'feature_2', 'target_variable']
# label encoding features
encoders = label_encoding(data, features)
# categorical imputation using random forest 
# parameters can be tuned accordingly
imp_cat = MissForest(n_estimators=50, max_depth=80)
data[features] = imp_cat.fit_transform(data[features], cat_vars=[0, 1, 2])
# decoding features
for variable in features:
    data[variable] = encoders[variable].inverse_transform(data[variable].astype(int))