Imputation of missing values for categories in pandas

Question

The question is how to fill NaNs with most frequent levels for category column in pandas dataframe?

In R randomForest package there is
na.roughfix option : A completed data matrix or data frame. For numeric variables, NAs are replaced with column medians. For factor variables, NAs are replaced with the most frequent levels (breaking ties at random). If object contains no NAs, it is returned unaltered.

in Pandas for numeric variables I can fill NaN values with :

df = df.fillna(df.median())

Asked By: Igor Barinov

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Source

Answer 1

You can use df = df.fillna(df['Label'].value_counts().index[0]) to fill NaNs with the most frequent value from one column.

If you want to fill every column with its own most frequent value you can use

df = df.apply(lambda x:x.fillna(x.value_counts().index[0]))

UPDATE 2018-25-10 ⬇

Starting from 0.13.1 pandas includes mode method for Series and Dataframes.
You can use it to fill missing values for each column (using its own most frequent value) like this

df = df.fillna(df.mode().iloc[0])

Answered By: hellpanderr

Answer 2

def fillna(col):
    col.fillna(col.value_counts().index[0], inplace=True)
    return col
df=df.apply(lambda col:fillna(col))

Answered By: Pratik Gohil

Answer 3

In more recent versions of scikit-learn up you can use SimpleImputer to impute both numerics and categoricals:

import pandas as pd
from sklearn.impute import SimpleImputer
arr = [[1., 'x'], [np.nan, 'y'], [7., 'z'], [7., 'y'], [4., np.nan]]
df1 = pd.DataFrame({'x1': [x[0] for x in arr],
                    'x2': [x[1] for x in arr]},
                  index=[l for l in 'abcde'])
imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
print(pd.DataFrame(imp.fit_transform(df1),
                   columns=df1.columns,
                   index=df1.index))
#   x1 x2
# a  1  x
# b  7  y
# c  7  z
# d  7  y
# e  4  y

Answered By: kevins_1

Answer 4

Most of the time, you wouldn’t want the same imputing strategy for all the columns. For example, you may want column mode for categorical variables and column mean or median for numeric columns.

For example:

df = pd.DataFrame({'num': [1.,2.,4.,np.nan],'cate1':['a','a','b',np.nan],'cate2':['a','b','b',np.nan]})

# numeric columns
>>> df.fillna(df.select_dtypes(include='number').mean().iloc[0], inplace=True)

# categorical columns
>>> df.fillna(df.select_dtypes(include='object').mode().iloc[0], inplace=True)

>>> print(df)

     num cate1 cate2
 0 1.000     a     a
 1 2.000     a     b
 2 4.000     b     b
 3 2.333     a     b

Answered By: Sarah

Answer 5

Imputing strategy for all the columns based on the dtype. For example, you may want column mode for categorical variables and column mean for numeric columns.

For example:

df = pd.DataFrame({'num': [1.,2.,4.,np.nan],'cate1':['a','a','b',np.nan],'cate2':['a','b','b',np.nan]})
# numeric columns
for col in df.select_dtypes(include=['number']):
    df[col].fillna(df[col].mean(), inplace=True)

# categorical columns
for col in df.select_dtypes(include=['object']):
    df[col].fillna(df[col].mode()[0], inplace=True)

print(df)

output:
        num cate1 cate2
0  1.000000     a     a
1  2.000000     a     b
2  4.000000     b     b
3  2.333333     a     b

Answered By: Anand Vatkar

Answer 6

If you want to fill a column:

from sklearn.impute import SimpleImputer

# create SimpleImputer object with the most frequent strategy
imputer = SimpleImputer(strategy='most_frequent')

# select the column to impute
column_to_impute = 'customer type'

# impute missing values in the selected column
imputed_column = imputer.fit_transform(df[[column_to_impute]])

# replace the original column with the imputed column
df[column_to_impute] = imputed_column

Answered By: Diego Gustavo Hurtado Olivares

Imputation of missing values for categories in pandas

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