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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 Freeze Panes in Excel?

JanviKumari01
JanviKumari01 02 Aug, 2024
5 min read

Introduction

Microsoft Excel is among the best programs for organizing and evaluating data. One of its most important features is the capacity to freeze panes. This function allows you to select certain rows or columns to keep visible while browsing the rest of your spreadsheet, making data monitoring and comparison easier. This post will look at using Excel’s Freeze Panes feature and provide some helpful tips and examples.

How to Freeze Panes in Excel?

Overview

  • Freezing panes in Excel keep specific rows or columns visible while scrolling through large datasets, aiding in data monitoring and comparison.
  • Enhances navigation, maintains header visibility, and simplifies data comparison within extensive spreadsheets.
  • Instructions for freezing the top row, first column, or multiple rows/columns using the View tab and Freeze Panes option.
  • Steps to remove frozen panes are through the View tab and the Unfreeze Panes option.
  • Illustrations of using Freeze Panes for data entry, comparative analysis, and improved readability in various spreadsheet scenarios.
  • Plan layout, use the Split tool for more flexibility, and combine Freeze Panes with other Excel features like Filters and Conditional Formatting.

Table of contents

What is Freezing Panes?

Excel has a function called “freezing panes” that allows you to freeze specific rows and columns so they stay visible while you navigate through the remainder of the spreadsheet. This is quite helpful when working with enormous datasets and needing the headers or critical columns to remain visible.

Use of Freeze Panes:

  1. Better Navigation: Move through huge spreadsheets more quickly and accurately without getting lost.
  2. Maintain Visibility of Headers: Keep your column and row headers visible as you scroll through large datasets.
  3. Easier Data Comparison: Remember the most important information when comparing data from various sections of your worksheet.

Also read: Microsoft Excel for Data Analysis

How to Freeze Panes in Excel?

Freezing the Top Row

To keep the top row visible while scrolling down:

  1. Open your Excel spreadsheet.
  2. Go to the View tab on the Ribbon.
  3. Click on Freeze Panes in the Window group.
  4. Select Freeze Top Row from the dropdown menu.

The top row of your spreadsheet is now frozen and will remain visible as you scroll down.

Freezing the First Column

To keep the first column visible while scrolling to the right:

  1. Open your Excel spreadsheet.
  2. Go to the View tab on the Ribbon.
  3. Click on Freeze Panes in the Window group.
  4. Select the Freeze First Column from the dropdown menu.

The first column of your spreadsheet is now frozen and will remain visible as you scroll horizontally.

Freezing Multiple Rows or Columns

To freeze multiple rows or columns, or both:

  1. Select the cell below the rows and to the right of the columns you want to freeze. For example, to freeze the first two rows and the first column, select cell B3.
  2. Go to the View tab on the Ribbon.
  3. Click on Freeze Panes in the Window group.
  4. Select Freeze Panes from the dropdown menu.

The rows above and columns to the left of your selected cell are now frozen.

Unfreezing Excel Panes

If you need to unfreeze the panes:

  1. Go to the View tab on the Ribbon.
  2. Click on Freeze Panes in the Window group.
  3. Select Unfreeze Panes from the dropdown menu.

This will remove all frozen panes in your spreadsheet.

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

Practical Examples of Freezing Panes in Excel

Here are the examples:

Example 1: Freezing the Top Row for Data Entry

Let’s say you have a big dataset with headers (e.g., Name, Age, Department) on the first row, each representing a distinct item. By freezing the top row, you can ensure that the headers stay displayed while you enter data into the following rows.

Example 2: Freezing the First Column for Comparative Analysis

Let’s say you have a financial report that lists several financial metrics in the following columns and months in the first column. Comparing the first column is easier when it is frozen since you can scroll through the metrics and always see the month.

Example 3: Freezing Both Rows and Columns for Enhanced Readability

When you navigate a spreadsheet with both column headings and row labels—for example, a sales report with product names in the first column and sales regions in the top row freezing the first column and the top row makes it easier to maintain track of both dimensions of your data.

Tips for Using Freeze Panes Effectively

Here are tips for using freeze:

  1. Plan Your Layout: Determine which rows and columns are crucial for navigation and comparison before freezing panes.
  2. Use Split for More Flexibility: If you need even more freedom, you can use the Split tool (located under the View tab) to create separate scrollable sections.
  3. Incorporate with Additional Features: Combine Freeze Panes with other Excel capabilities, such as Filters, Tables, and Conditional Formatting, to improve your data analysis workflow.

Conclusion

Excel’s freezing panes are a straightforward but effective technique that improves your ability to explore and analyze big datasets. You can preserve context and increase productivity by making key rows and columns visible. Using Excel’s Freeze Panes tool will improve your productivity, whether you’re entering data, comparing metrics, or reading reports.

Frequently Asked Questions

Q1. Can I freeze panes on multiple sheets at once?

Ans. No, you need to freeze panes individually on each sheet where you want this feature enabled.

Q2. How do freezing panes affect printing?

Ans. Freezing panes does not affect how your worksheet is printed. If you want to repeat header rows or columns on each printed page, use the Page Layout tab and select Print Titles to set rows or columns to repeat.

Q3. Why do my frozen panes disappear when I switch to Page Layout view?

Ans. Frozen panes are not visible in the Page Layout view. To see them again, switch back to Normal view or Page Break Preview.

Q4.  Do freezing panes affect formulas and data calculations?

Ans. Freezing panes does not affect formulas or data calculations. It only changes how you view the worksheet by keeping certain rows or columns visible while scrolling.

Q5. What are some best practices for using freeze panes?

Ans. Here are the best practices:
1. Identify Key Rows/Columns: Determine which rows or columns are most important for navigation and data comparison.
2. Combine with Other Features: For better data analysis, use freeze panes along with filters, tables, and conditional formatting.
3. Regular Updates: Regularly review and update frozen panes as your data and analysis needs change.

ExcelMicrosoftMicrosoft Excel
JanviKumari01
JanviKumari01 02 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

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