Impute categorical missing values in scikit-learn

Question

I've got pandas data with some columns of text type. There are some NaN values along with these text columns. What I'm trying to do is to impute those NaN's by sklearn.preprocessing.Imputer (replacing NaN by the most frequent value). The problem is in implementation. Suppose there is a Pandas dataframe df with 30 columns, 10 of which are of categorical nature. Once I run:

from sklearn.preprocessing import Imputer imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0) imp.fit(df)  

Python generates an error: 'could not convert string to float: 'run1'', where 'run1' is an ordinary (non-missing) value from the first column with categorical data.

Any help would be very welcome

Answer 1

To use mean values for numeric columns and the most frequent value for non-numeric columns you could do something like this. You could further distinguish between integers and floats. I guess it might make sense to use the median for integer columns instead.

import pandas as pd import numpy as np  from sklearn.base import TransformerMixin  class DataFrameImputer(TransformerMixin):      def __init__(self):         """Impute missing values.          Columns of dtype object are imputed with the most frequent value          in column.          Columns of other types are imputed with mean of column.          """     def fit(self, X, y=None):          self.fill = pd.Series([X[c].value_counts().index[0]             if X[c].dtype == np.dtype('O') else X[c].mean() for c in X],             index=X.columns)          return self      def transform(self, X, y=None):         return X.fillna(self.fill)  data = [     ['a', 1, 2],     ['b', 1, 1],     ['b', 2, 2],     [np.nan, np.nan, np.nan] ]  X = pd.DataFrame(data) xt = DataFrameImputer().fit_transform(X)  print('before...') print(X) print('after...') print(xt) 

which prints,

before...      0   1   2 0    a   1   2 1    b   1   1 2    b   2   2 3  NaN NaN NaN after...    0         1         2 0  a  1.000000  2.000000 1  b  1.000000  1.000000 2  b  2.000000  2.000000 3  b  1.333333  1.666667