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
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