Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Handling Missing Data with SimpleImputer

Parth Shukla
Parth Shukla Last Updated : 09 Nov, 2022
4 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Missing data in machine learning is a type of data that contains “None” or “NaN” type of values. One should take care of the missing data while dealing with machine learning algorithms and training. Missing data can be filled using basic python programming, pandas library, and a sci-kit learn library named SimpleImputer. Handling missing values using the sci-kit learns library SimpleImputer is the easiest and most convenient method of all the other missing data handling methods.

data science

In simple words, SimpleImputer is a sci-kit library used to fill in the missing values in the datasets. As the name suggests, the class performs simple imputations on the dataset, and it replaces the missing data with another value based on a given strategy.

The basic syntax or structure of a SimpleImputer initialization is:

SimpleImputer(*, missing_values=nan, strategy='mean', fill_value=None, verbose=0, copy=True, add_indicator=False)

Parameters of the SimpleImputer

missing_values: indicates the missing values in the dataset. By default np.nan is set in the missing_values, which means all the values containing np.nan will be considered as missing values.

strategy: it is the method by using which we want to fill in the missing values.the value of the strategy could be “mean”, “median”, “most_frequent”, or “constant”.

fill_value: It is a parameter only used when the strategy is set to be constant. When the strategy is constant, the Nan values will be replaced by the value passes in fill_value.

verbose: It is the parameter that is used to control the verbosity of the SimpleImputer. By default, the value of verbose is set to 0.

copy: If this parameter is set to be True, then the copy of the dataset will be created, else imputation will be done without copying.

add_indicator: If this parameter is set to be True, the MissingIndicator transform will stack onto the output of the imputer’s transform. 

Handling Missing Data With SimpleImputer

To start with the SimpleImputer library, first, we must install and import the library from the sci-kit learn.

To install the library from sci-kit learn, use the code below:

pip install scikit-learn

Once the library is installed in the machine, it should be imported to the Python IDE you are using. Use the code below to import the library:

# importing sklearn
import sklearn
# importing simpleimputer
from sklearn.impute import SimpleImputer

Performing “Mean” Imputation

Using the strategy “Mean” in SimpleImputer allows us to impute the missing value by the mean of the particular dataset. This strategy can only be used on a numerical dataset.

 

Let’s suppose we have a numerical column named “Age” in our data set in which some of the values are missing. Then using the Mean strategy will allow us to fill in the missing values in the column by the mean of all age values.

Code:

imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer.fit(df['age'])
df['age']= imputer.fit_transform(df['age'])

Example:

imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
imp_mean.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])
age = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]
print(imp_mean.transform(age))

The Output of the particular code would be:

[[ 7. 2. 3. ] 
 [ 4. 3.5 6. ] 
 [10. 3.5 9. ]]

While working with mean strategy imputation, the scenario of an outlier should be considered as the mean strategy counts the mean of the values and fill the missing values by the counted mean values, but in the case of an outlier, it is possible that due to the outlier the mean can be shifted to one side and it is biased, which results in in-accurate mean value imputation.

Performing “Median” Imputation

Using the strategy “Median” in the SimpleImputer allows us to impute the missing value by the median value of the particular dataset. This strategy can only be used on the numerical dataset.

Let’s suppose we have a column age in our dataset in which we have a missing value. Using the strategy median will allow us to fill the missing values by the median of the values from the column age.

Code:

imputer = SimpleImputer(missing_values=np.nan, strategy='median')
imputer.fit(df['age'])
df['age']= imputer.fit_transform(df['age'])

Example:

imp_median = SimpleImputer(missing_values=np.nan, strategy='median')
imp_median.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])
age = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]
print(imp_median.transform(age))

The Output of the particular code would be:

[[ 7. 2. 3. ]
[ 4. 3.5 6. ]
[10. 3.5 9. ]]

Performing “Most_frequent” Imputation

Most frequent imputation is a technique that is used for handling categorical missing data. This technique is used when we have missing values in a categorical column.

Using a most frequent imputation technique on the particular categorical column will allow us to fill the missing values bu the most frequent value from the column occurring in the dataset.

Code:

imputer = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
imputer.fit(df['category'])
df['category']= imputer.fit_transform(df['category'])

Example:

imp_mf = SimpleImputer(missing_values=np.nan, strategy='median')
imp_mf.fit([['one', 'two', 'three'], ['four', np.nan, 'six'], ['two', 'five', 'two']])
category = [[np.nan, 'two', 'two'], ['four', np.nan, 'six'], ['ten', np.nan, 'nine']]
print(imp_mf.transform(category))

The Output of the particular code would be:

[[ 'two' 'two' 'two' ]
[ 'four' 'two' 'six' ]
[ 'ten' 'two' 'nine' ]]

Performing “Constant” Imputation

Constant imputation is a technique in simple imputer using which we can fill the missing value by any desired value we want. This can be used on strings and numerical datasets.

Passing the desired value to the fill_value parameter, we can fill all the missing values present in the dataset by the value passed in the fill_value parameter.

Code:

imputer = SimpleImputer(missing_values=np.nan, strategy='constant',fill_value=20)
imputer.fit(df['age'])
df['age']= imputer.fit_transform(df['age'])

Example:

imp_constant = SimpleImputer(missing_values=np.nan, strategy='constant',fill_value=20)
imp_constant.fit([[7, 2, 3], [4, np.nan, 6], [10, 5, 9]])
age = [[np.nan, 2, 3], [4, np.nan, 6], [10, np.nan, 9]]
print(imp_constant.transform(age))

The Output of the particular code would be:

[[20. 2. 3.]
 [ 4. 20. 6.]
 [10. 20. 9.]]

Conclusion

In this article, the handling of missing data with the class SimpleImputer is discussed in detail. A total of 4 strategies, mean median, most_frequent, and constant, can be used to fill in the missing value and are discussed in the code example above.

Some Key Takeaways From this article are:

1. We should consider an outlier scenario while working with a meaningful strategy, as outliers can impact the data imputed and may result in a less accurate model with unexpected behavior. (avoid using mean strategy in case of outliers).

2. Mean and Median is a strategy that only can be used on numerical data, and the most frequent strategy can be used only on categorical data. They are one of the easiest and lower computational methods.

3. Constant strategy can be used when we have a better understanding of the dataset, and we already know the impact of imputing the missing values by our desired number or string. It can be used on strings and numerical data.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

blogathonhandling missing dataMissing Datasimpleimputer
Parth Shukla
Parth Shukla

UG (PE) @PDEU | 50+ Published Articles on Data Science | Technical Writer (AI/ML/DL) | Data Science Freelancer | Amazon ML Summer School '22 | Reach Out @[email protected], @portfolio.parthshukla.live

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag
Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
5 Techniques to Handle Imbalanced Data For a Classification Problem
5 Techniques to Handle Imbalanced Data For a Classification Problem
Dealing with Missing Values for Data Science Beginners
Dealing with Missing Values for Data Science Beginners

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details