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

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
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
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
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 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

How to Remove Duplicates in Excel?

JanviKumari01
JanviKumari01 16 Aug, 2024
4 min read

Introduction

Accurate and clean data is the backbone of effective decision-making. Whether you’re managing a sales record, contact list, or any other dataset, duplicate entries can quickly turn into a source of confusion and errors. Imagine making a critical business decision based on faulty data—it’s a risk you can’t afford. That’s why mastering the skill of removing duplicates in Excel is essential. This simple yet powerful technique ensures your data remains reliable and easy to manage, setting the stage for more confident and accurate analysis. Here’s how you can easily remove duplicates in Excel.

duplicates in Excel

Overview

  1. Removing duplicates ensures your data remains reliable for accurate analysis and decision-making.
  2. Learn how to quickly select and clean up your data range to remove duplicates in Excel.
  3. Utilize Excel’s “Remove Duplicates” tool to eliminate redundant entries from your dataset efficiently.
  4. Choose specific columns for checking duplicates to tailor the process to your data needs.
  5. After removal, verify your data to ensure accuracy and use backup strategies for safety.

Table of contents

Why You Should Remove Duplicates in Excel?

Removing duplicates in Excel is essential for maintaining data accuracy and integrity. Duplicate entries can skew analysis, lead to incorrect conclusions, and create report inconsistencies. Eliminating duplicates ensures that each data point is unique, enhancing your results’ reliability. It also helps optimize storage, reduce file size, and improve Excel operations’ performance. Whether managing a large dataset or preparing a report, removing duplicates streamlines your data, making it cleaner and more manageable for analysis and decision-making.

Step 1: Select the Data Range

First, select the cell range you want to clean up to remove duplicates. Here’s how:

  1. Open your Excel file and go to the worksheet with the data.
Remove Duplicates in Excel
  1. Highlight the cells you want to check for duplicates. You can use the keyboard shortcut Ctrl + Shift + Arrow keys or drag your mouse across the cells.
  2. To select the entire worksheet, press Ctrl + A or click the triangle icon at the top-left corner of the grid.
Remove Duplicates in Excel

Also read: Microsoft Excel for Data Analysis

Step 2: Access the Remove Duplicates Tool

A built-in feature in Excel is designed to help eliminate duplicate values:

  1. Open the Excel ribbon at the top of the screen and select the “Data” tab.
Remove Duplicates in Excel
  1. The “Remove Duplicates” option is in the “Data Tools” group. To get the Remove Duplicates dialog box, click on it.
Remove Duplicates in Excel

Step 3: Choose the Columns for Duplicate Check

You can choose which columns to check for duplicates in the Remove Duplicates dialog box:

  1. By default, all columns in the selected range are checked.
Remove Duplicates in Excel
  1. Rows with the same values in the checked columns are considered duplicates.
  2. If you only want to check specific columns, uncheck the others.
  3. For example, if you’re managing a customer list and want to remove duplicates based on email addresses, uncheck all columns except the one with the email addresses.
  4. After choosing the columns, press “OK.”

Step 4: Review the Results

Excel will process the data once you click “OK,” and it will display a summary:

  1. You’ll be able to see how many unique values are left in the message box and the number of duplicate values identified and eliminated.
  2. Click “OK” to close the message box.
Remove Duplicates in Excel

Step 5: Verify Your Data

It’s wise to confirm that your data is what you expected and that duplicates have been appropriately eliminated:

  1. By scrolling through it, ensure no more duplicates are in your dataset.
  2. If something doesn’t appear right, you can use the “Undo” option (Ctrl + Z) to undo the modifications.

Additional Tips

  • Maintain a Backup: Preserving a backup copy of your original data is a good idea before eliminating duplicates. You can accomplish this by making a copy of the worksheet or renaming the file.
  • Advanced Filtering: If you want even more control, you can use Excel’s “Advanced Filter” option (located in the “Data” tab) to filter and show only unique entries without truly eliminating duplicates.

Conclusion

Excel’s easy-to-use yet effective duplicate removal feature helps preserve the accuracy of your data. Following these steps, you can quickly tidy up your spreadsheets and ensure your data is correct and dependable. The features in Excel make it simple to handle duplicates effectively, regardless of the size of the dataset you’re working with.

Frequently Asked Questions

Q1. How do I remove duplicates in Excel?

Ans. Select your data, go to the “Data” tab, and click on “Remove Duplicates.”

Q2. Can I remove duplicates from specific columns only?

Ans. Yes, in the “Remove Duplicates” dialog box, uncheck the columns you don’t want to include.

Q3. What happens to the first occurrence of a duplicate?

Ans. Excel keeps the first occurrence and removes the subsequent duplicates.

Q4. Can I undo the removal of duplicates?

Ans. Yes, you can undo it immediately by pressing Ctrl + Z.

Q5. How can I remove duplicates without losing any data?

Ans. Use the “Advanced Filter” option to filter and view unique values without deleting data.

duplicates in ExcelExcelMicrosoft
JanviKumari01
JanviKumari01 16 Aug, 2024

Hi I am Janvi Kumari currently a Data Science Intern at Analytics Vidhya, passionate about leveraging data for insights and innovation. Curious, driven, and eager to learn. If you'd like to connect, feel free to reach out to me on LinkedIn

Free Courses

image.name 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.
image.name 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.
image.name 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.
image.name 4.8

Building Your first RAG System using LlamaIndex

Build your first RAG model with LlamaIndex in this free course. Dive into Retrieval-Augmented Generation now!
image.name 4.7

Building Production Ready RAG systems using LlamaIndex

Learn Retrieval-Augmented Generation (RAG): learn how it works, the RAG framework, and use LlamaIndex for advanced systems.

Recommended Articles

Frequently Asked Questions

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

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
Rahul Shah img

Rahul Shah

27
Sion Chakrabarti img

Sion Chakrabarti

16
CHIRAG GOYAL img

CHIRAG GOYAL

87
kaustubh gupta img

kaustubh gupta

24
Barney Darlington img

Barney Darlington

5
Suvojit Hore img

Suvojit Hore

9
Arnab Mondal img

Arnab Mondal

15