How to use the rank function in Tableau

This recipe helps you use the rank function in Tableau

Recipe Objective - How to use the Rank function in Tableau?

Step 1:-

Create any dummy data set with one-two column one is the dimension, and another is measure having 7-8 rows.

Step 2:-

Connect the dataset.

Step 3:-

Drop the dimension onto the "row shelf".

Learn About the Application of ARCH and GARCH models in Real-World

Step 4:-

Drop the measure onto the "detail" in the "marks" card.

Step 5:-

Create a "calculation field" and type “Rank(Sum([Your Measure]))”. Set the name of the field.

Step 6:-

Drag this file and drop it on the "row shelf" and click on the drop-down and select "discrete".

And Now the rank of each row will show up in the different column

Different types of Rank Functions.

1. Rank() - This function sets the rank, but it will skip the highest rank whenever we have the same value.

2. Rank_Dense() - This Function will assign an identical rank to the identical value. This function will not skip any value.

3. Rank_Unique() - This function will not assign an identical rank to the identical value.

What Users are saying..

profile image

Abhinav Agarwal

Graduate Student at Northwestern University
linkedin profile url

I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

Relevant Projects

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Time Series Analysis with Facebook Prophet Python and Cesium
Time Series Analysis Project - Use the Facebook Prophet and Cesium Open Source Library for Time Series Forecasting in Python

Build an AI Quiz Generator from Video with OpenAI API
In this LLM project, you will build a model to automate the transcription of video content and generate interactive quizzes using OpenAI’s Whisper and GPT-4o.

NLP Project on LDA Topic Modelling Python using RACE Dataset
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.

Build Customer Propensity to Purchase Model in Python
In this machine learning project, you will learn to build a machine learning model to estimate customer propensity to purchase.

Machine Learning Project to Forecast Rossmann Store Sales
In this machine learning project you will work on creating a robust prediction model of Rossmann's daily sales using store, promotion, and competitor data.

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Build an Image Segmentation Model using Amazon SageMaker
In this Machine Learning Project, you will learn to implement the UNet Architecture and build an Image Segmentation Model using Amazon SageMaker

Langchain Project for Customer Support App in Python
In this LLM Project, you will learn how to enhance customer support interactions through Large Language Models (LLMs), enabling intelligent, context-aware responses. This Langchain project aims to seamlessly integrate LLM technology with databases, PDF knowledge bases, and audio processing agents to create a comprehensive customer support application.

Build Regression Models in Python for House Price Prediction
In this Machine Learning Regression project, you will build and evaluate various regression models in Python for house price prediction.