# Imputing Missing Data Using Sklearn SimpleImputer

# Imputing Missing Data Using Sklearn SimpleImputer

### In this post, learn how to use Python's Sklearn SimpleImputer for imputing/replacing numerical and categorical missing data using different strategies.

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Join For FreeIn this post, you will learn about how to use Python's Sklearn SimpleImputer for imputing/replacing numerical and categorical missing data using different strategies. In one of the related articles posted sometime back, the usage of fillna method of Pandas DataFrame is discussed. Here is the link, Replace missing values with mean, median and mode. Handling missing values is a key part of data preprocessing and hence, it is of utmost importance for data scientists/machine learning engineers to learn different techniques in relation imputing / replacing numerical or categorical missing values with appropriate value based on appropriate strategies.

The following topics will be covered in this post:

- SimpleImputer explained with Python code example
- SimpleImputer for imputing numerical missing data
- SimpleImputer for imputing categorical missing data

## SimpleImputer Explained With Python Code Example

SimpleImputer is a class found in package sklearn.impute. It is used to impute / replace the numerical or categorical missing data related to one or more features with appropriate values such as following:

Each of the above type represents **strategy** when creating an instance of SimpleImputer. Here is the Python code sample representing the usage of SimpleImputor for replacing numerical missing value with the mean.

First and foremost, let's create a sample Pandas Dataframe representing marks, gender and result of students.

`import pandas as pd`

`import numpy as np`

`students = [[85, 'M', 'verygood'],`

` [95, 'F', 'excellent'],`

` [75, None,'good'],`

` [np.NaN, 'M', 'average'],`

` [70, 'M', 'good'],`

` [np.NaN, None, 'verygood'],`

` [92, 'F', 'verygood'],`

` [98, 'M', 'excellent']]`

`dfstd = pd.DataFrame(students)`

`dfstd.columns = ['marks', 'gender', 'result']`

There are two columns / features (one numerical - marks, and another categorical - gender) which are having missing values and need to be imputed. In the code below, an instance of SimpleImputer is created with strategy as "mean". The missing value is represented using NaN. Note some of the following:

**sklearn.impute**package is used for importing**SimpleImputer**class.- SimpleImputer takes two argument such as missing_values and strategy.
- fit_transform method is invoked on the instance of SimpleImputer to impute the missing values.

`xxxxxxxxxx`

`from sklearn.impute import SimpleImputer`

`#`

`# Missing values is represented using NaN and hence specified. If it `

`# is empty field, missing values will be specified as ''`

`#`

`imputer = SimpleImputer(missing_values=np.NaN, strategy='mean')`

`dfstd.marks = imputer.fit_transform(dfstd['marks'].values.reshape(-1,1))[:,0]`

`dfstd`

Here is how the output would look like. Note that missing value of marks is imputed / replaced with the mean value, 85.83333

## SimpleImputer for Imputing Numerical Missing Data

For the numerical missing data, the following strategy can be used.

The code example below represents the instantiation of SimpleImputer with appropriate strategies for imputing numerical missing data

`xxxxxxxxxx`

`#`

`# Imputing with mean value`

`#`

`imputer = SimpleImputer(missing_values=np.NaN, strategy='mean')`

`#`

`# Imputing with median value`

`#`

`imputer = SimpleImputer(missing_values=np.NaN, strategy='median')`

`#`

`# Imputing with most frequent / mode value`

`#`

`imputer = SimpleImputer(missing_values=np.NaN, strategy='most_frequent')`

`#`

`# Imputing with constant value; The command below replaces the missing`

`# value with constant value such as 80`

`#`

`imputer = SimpleImputer(missing_values=np.NaN, strategy='constant', fill_value=80)`

## SimpleImputer for Imputing Categorical Missing Data

For handling categorical missing values, you could use one of the following strategies. However, it is the "most_frequent" strategy which is preferably used.

- Most frequent (strategy='most_frequent')
- Constant (strategy='constant', fill_value='someValue')

Here is how the code would look like when imputing missing value with strategy as **most_frequent**. In the code sample used in this post, gender is having missing values. Note how the missing value under gender column is replaced with 'M' which occurs most frequently.

`xxxxxxxxxx`

`from sklearn.impute import SimpleImputer`

`imputer = SimpleImputer(missing_values=None, strategy='most_frequent')`

`dfstd.gender = imputer.fit_transform(dfstd['gender'].values.reshape(-1,1))[:,0]`

`dfstd`

Here is how the code would look like when imputing missing value with strategy as **constant**. Note how the missing value under gender column is replaced with 'F' which is assigned using fill_value parameter.

`xxxxxxxxxx`

`from sklearn.impute import SimpleImputer`

`imputer = SimpleImputer(missing_values=None, strategy='constant', fill_value='F')`

`dfstd.gender = imputer.fit_transform(dfstd['gender'].values.reshape(-1,1))[:,0]`

`dfstd`

## Conclusions

Here is the summary of what you learned in this post:

- You can use
**Sklearn.impute**class**SimpleImputer**to**impute/replace missing values**for both numerical and categorical features. - For
**numerical missing values**, a strategy such as**mean**,**median**,**most frequent,**and**constant**can be used. - For
**categorical features**, a strategy such as the**most frequent**and**constant**can be used.

Published at DZone with permission of Ajitesh Kumar , DZone MVB. See the original article here.

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