What are the types of calculations we can do in tableau Part 2

This recipe explains what are the types of calculations we can do in tableau Part 2

Recipe Objective:-What are the types of calculations we can do in tableau? Part-2

Step 1:-

Import any data set in the data source. For example, here, the "Sample-sales-data-excel" data set excel file is imported.

Step 2:-

Drag and drop the orders sheet in the schema pane.

Step 3:-

Go to sheet1 or name it "Mathematical func.", Here different dimensions and measures are available. Drag and drop the "Profit" (sum of profit) measure in the row shelf, again drag and drop the "Sales" (sum of sales) measure in the row shelf. Then drag the "Category" dimension and drop it on the column shelf; a bar chart will be displayed on the canvas. Go to the show me tab and select the text table; a text table will now be seen in the canvas.

Access House Price Prediction Project using Machine Learning with Source Code

Step 4:-

Now go to the Dimensions and click on the drop-down available at the top right side, click on create a calculated field, A calculation or expression window will appear named as "Calculation1", Now type the expression "Sum (Profit) + Sum(Sales)", click on apply and then ok, A new field named as "Calculation1" will appear at the measures. Now double click on Calculation1 measure so that it will be seen in the table with added values.

Step 5:-

Now follow the same procedure from step 4, only change the expression to "Sum (Profit) - Sum(Sales)", click on apply, and then ok, A new field named "Calculation1" will appear at the measures. Now double click on Calculation1 measure so that it will be seen in the table with subtracted values.

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

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.

Expedia Hotel Recommendations Data Science Project
In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.

Learn to Build a Polynomial Regression Model from Scratch
In this Machine Learning Regression project, you will learn to build a polynomial regression model to predict points scored by the sports team.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

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 (Linear,Ridge,Lasso) Models in NumPy Python
In this machine learning regression project, you will learn to build NumPy Regression Models (Linear Regression, Ridge Regression, Lasso Regression) from Scratch.

Llama2 Project for MetaData Generation using FAISS and RAGs
In this LLM Llama2 Project, you will automate metadata generation using Llama2, RAGs, and AWS to reduce manual efforts.

Build a Collaborative Filtering Recommender System in Python
Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

Build a Music Recommendation Algorithm using KKBox's Dataset
Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

Isolation Forest Model and LOF for Anomaly Detection in Python
Credit Card Fraud Detection Project - Build an Isolation Forest Model and Local Outlier Factor (LOF) in Python to identify fraudulent credit card transactions.