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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

What is Conditional Formatting in Excel?

Pankaj Singh
Pankaj Singh 12 Aug, 2024
6 min read

Introduction

Conditional Formatting in Excel is more than just a feature—it’s a powerful tool transforming how you interact with data. Whether you’re managing a simple budget or analyzing complex datasets, Conditional Formatting helps you highlight important trends, patterns, and outliers with just a few clicks. In this article, we’ll explore how you can utilize this feature to make your data more visually appealing and informative.

conditional formatting

Overview

  1. Learn how Conditional Formatting in Excel turns raw data into visual insights, making trends and outliers stand out.
  2. Discover how to use Excel’s Conditional Formatting rules to automatically highlight critical data points with colors, icons, and data bars.
  3. Explore applying customized Conditional Formatting using Excel formulas for more nuanced data analysis.
  4. Use Conditional Formatting to spot top/bottom performers quickly and duplicate entries in large datasets.
  5. Gain practical tips to maximize the impact of Conditional Formatting without overwhelming your spreadsheets.
  6. Master Conditional Formatting to create visually appealing and informative spreadsheets that communicate data stories effectively.

Table of contents

Conditional Formatting: An Overview

At its core, Conditional Formatting allows you to apply specific formatting—such as colors, icons, or data bars—to cells based on the values they contain. This automatic formatting is triggered by rules you define, making it easy to spot critical data points or trends without manually sifting through numbers.

Also read: Microsoft Excel for Data Analysis

Highlighting Cells Based on Value

One of the simplest yet most effective uses of Conditional Formatting is highlighting cells based on their value. For instance, if you want to quickly identify which sales figures exceed a certain threshold, conditional formatting can help you. Here’s how:

Here’s how you can highlight cells based on values:

  1. Select the Range

    Choose the cells you want to format.conditional formatting

  2. Navigate to Conditional Formatting

    Find the Conditional Formatting option on the Home tab under the Styles group.Conditional Formatting

  3. Choose Your Rule

    Select “Highlight Cells Rules” and then “Greater Than.” Then, Enter the threshold value and choose your desired formatting style.Conditional formatting

  4. Apply the Formatting

    Click OK, and Excel will highlight the cells that meet your criteria.conditional formatting

Using Formulas for Custom Formatting

Excel’s flexibility shines when you use formulas to apply Conditional Formatting. This allows for more nuanced and customized rules. For example, you might want to highlight cells based on a condition that isn’t simply a comparison to a static value, like flagging sales higher than the previous month’s average.

  1. Select Your Range: Highlight the cells you want to format.
Using Formulas for Custom Formatting
  1. Create a New Rule: Under the Conditional Formatting menu, select “New Rule” and choose “Use a formula to determine which cells to format.”
Using Formulas for Custom Formatting
  1. Input the Formula: Enter your formula, which should return a TRUE or FALSE outcome.
Using Formulas for Custom Formatting
  1. Select a Format: Choose how you want the cells to appear if the condition is met.
  1. Apply: Hit OK to see the changes in your spreadsheet.
Using Formulas for Custom Formatting

Also read: A Comprehensive Guide on Advanced Microsoft Excel for Data Analysis.

Highlighting Extremes: Top/Bottom Rules

Conditional Formatting can also help you focus on the highest and lowest values in a dataset, such as identifying the top 10 performers or the bottom 5 sales figures.

  • Select Your Data Range: Highlight the relevant cells.
Top/Bottom Rules
  • Access Conditional Formatting: Navigate to Conditional Formatting > Top/Bottom Rules.
  • Set the Parameters: Select whether to highlight the top or bottom values and specify the number of items.
  • Finalize the Formatting: Select the desired formatting style and apply the rule.
Top/Bottom Rules

Similarly, identifying duplicate entries is crucial in large datasets. Conditional Formatting can quickly highlight duplicates, helping you maintain data accuracy.

  • Select the Range: Choose the cells you want to check for duplicates.
  • Choose Duplicate Values Rule: Under the Conditional Formatting menu, select “Highlight Cells Rules” and “Duplicate Values.”
  • Apply Formatting: Pick the formatting style and click OK.

Tips for Effective Use of Conditional Formatting in Excel

Here are the tips:

  1. Start Simple: Begin with basic rules like highlighting values greater than or less than a specific number. This will help you get comfortable with how Conditional Formatting works.
  2. Use Cell References for Flexibility: Instead of using static values in your Conditional Formatting rules, refer to another cell. This allows you to change the criteria by simply updating the reference cell.
  3. Leverage Formulas for Custom Rules: Excel’s formula-based Conditional Formatting lets you create highly customized rules. Ensure that your formula returns a TRUE or FALSE value to apply the formatting correctly.
  4. Combine Multiple Rules: Don’t hesitate to apply multiple Conditional Formatting rules to the same range of cells. For example, you can use both Color Scales and Icon Sets to provide different layers of data insight.
  5. Review and Manage Rules Regularly: As you add more Conditional Formatting, check and manage your rules to avoid conflicts or overly complex setups. Use the “Manage Rules” option to edit or delete rules as needed.
  6. Prioritize Rules: When applying multiple rules, remember that Excel prioritizes them in the order they are listed. You can change the priority by moving rules up or down in the “Manage Rules” dialogue.
  7. Use Preview to Your Advantage: Before applying conditional formatting, use the preview function to see how your data will look. This helps you tweak the settings for the best visual result.
  8. Avoid Overloading with Formatting: While Conditional Formatting is powerful, too much can overwhelm your spreadsheet and make it difficult to read. Use formatting sparingly to ensure clarity.
  9. Experiment with Custom Formats: Excel allows you to create custom formatting styles (e.g., specific colors or font styles) in Conditional Formatting. This is particularly useful for maintaining a consistent look across your spreadsheets.
  10. Clear Formatting When Needed: If your data changes significantly, clear the old formatting rules to start fresh. This prevents outdated rules from applying incorrectly to new data.

Conclusion

Conditional Formatting in Excel is not just a handy feature—it’s a game-changer for anyone who works with data. By leveraging this tool, you can turn a simple spreadsheet into a dynamic, visually appealing report highlighting key insights and trends at a glance. Whether you’re a data novice or an experienced analyst, mastering Conditional Formatting will enhance your ability to communicate data-driven stories effectively. So, the next time you’re in Excel, don’t just look at the numbers—let Conditional Formatting help you see the bigger picture.

Frequently Asked Questions

Q1. What is Conditional Formatting?

Ans. Conditional Formatting in Excel lets you apply specific formatting to cells based on their values, such as highlighting, color scales, or icon sets.

Q2. How do I apply Conditional Formatting?

Ans. Select the cells, go to the “Home” tab, click “Conditional Formatting,” choose a rule, and configure it as needed.

Q3. Can I use formulas with Conditional Formatting?

Ans. Yes, you can use formulas to create custom Conditional Formatting rules by selecting “Use a formula to determine which cells to format.”

Q4. How do I remove Conditional Formatting?

Ans. Select the cells, go to “Conditional Formatting” in the “Home” tab, and choose “Clear Rules” to remove the formatting.

conditional formattingdata formattingdatasetExcelexcel formattingMicrosoftMicrosoft Excel
Pankaj Singh
Pankaj Singh 12 Aug, 2024

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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