How to add values on data points in a chart by using QlikView

This recipe helps you add values on data points in a chart by using QlikView

Recipe Objective: How to add values on data points in a chart by using QlikView?

Step 1:

Open QlikView 12 software. By default, the start page will open. To avoid the start page while launching QlikView, untick the check box at the bottom of the window.

Step 2:

On the start page, we can see the Examples, Recent, and Favorites tab. The saved files will appear under the Recent tab.

Step 3:

When the QlikView 12 Software gets Open, a blank window appears. Go to menu bar-> File menu-> New-> The Main sheet appears. Again go to menu bar-> File menu-> Edit script, or we can also type Ctrl+E-> Table Files->Load the data source. Here, an excel file named "Coffee Chain Sales" is loaded. Click on Reload button from the menu bar and save the file, So that data will also get loaded in the sheet.

Get Access to Plant Species Identification Project using Machine Learning

Step 4:

Now from Main Sheet->Right-click->New sheet object->Chart->select Bar chart->click on Next->Select Dimension as "B-Product" and also select "Coffee Chain Sales" table->under Expression tab->Select "Sum" Aggregation->Select Field as "F-Coffee Sales"->Click on Paste->Ok.

Step 5:

Then click on Next->Next->Finish. The Bar Chart will then be available in the Main sheet/QlikView Document. Right-click on bar chart->Properties->Expressions tab->From the display options->Select Values on data points. Then the values on data points will be available on the bar chart.

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

MLOps AWS Project on Topic Modeling using Gunicorn Flask
In this project we will see the end-to-end machine learning development process to design, build and manage reproducible, testable, and evolvable machine learning models by using AWS

Build Portfolio Optimization Machine Learning Models in R
Machine Learning Project for Financial Risk Modelling and Portfolio Optimization with R- Build a machine learning model in R to develop a strategy for building a portfolio for maximized returns.

Recommender System Machine Learning Project for Beginners-4
Collaborative Filtering Recommender System Project - Comparison of different model based and memory based methods to build recommendation system using collaborative filtering.

AWS MLOps Project for ARCH and GARCH Time Series Models
Build and deploy ARCH and GARCH time series forecasting models in Python on AWS .

Build Piecewise and Spline Regression Models in Python
In this Regression Project, you will learn how to build a piecewise and spline regression model from scratch in Python to predict the points scored by a sports team.

PyTorch Project to Build a GAN Model on MNIST Dataset
In this deep learning project, you will learn how to build a GAN Model on MNIST Dataset for generating new images of handwritten digits.

Learn to Build an End-to-End Machine Learning Pipeline - Part 1
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, addressing a major challenge in the logistics industry.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

Deep Learning Project- Real-Time Fruit Detection using YOLOv4
In this deep learning project, you will learn to build an accurate, fast, and reliable real-time fruit detection system using the YOLOv4 object detection model for robotic harvesting platforms.

Recommender System Machine Learning Project for Beginners-1
Recommender System Machine Learning Project for Beginners - Learn how to design, implement and train a rule-based recommender system in Python