Data Science:- Introduction of Orange Tools

Jay Patel
4 min readSep 23, 2021


This blog is all about an orange tool for data mining. We can do a lot of stuff with the help of the orange tool like visual programming, data visualization, data exploration, data mining, etc… The orange tool is free and open-source and you can install it very easily on any os.

What is orange?

Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative data analysis and interactive data visualization, and can also be used as a Python library.

Classifications: data visualization, machine lea…

Programming languages used: Python

Developer: Open-source software

Download source :-

For windows download click here.

Let started the orange tools,

Here you can see the canvas of orange where you will do all your data exploration. On the left-hand side, you can see there is a total of 5 sections and that all 5 sections contain different-different widgets which we will use in the future for data exploration.


Workflows in Orange resemble actual optical systems; setting up a simulation in this way is intuitive and easy to inspect and modify. Passing data from one widget to another imposes no additional overhead in terms of CPU time.

File and Data Info

  1. Drag the File widget to the canvas.
  2. Drag the Data Info widget to the canvas.
  3. At the right side of the File widget, there is a semi-circular shape. Mouse down on it and drag it to the Data Info widget.
  4. Notice that there is a link between both widget with the word data on top.

Click on data info widget to see information of data set

File and Data Table

  1. Drag a Data Table widget to the canvas.
  2. Connect File widget to the Data Table widget.
  3. Double click on the Data Table widget to see the rows & columns of dataset.


The Distribution widget displays the value distribution of discrete or continuous attributes. If the data contains a class variable, distributions may be conditioned on the class.

  1. Drag a Distribution widget to the canvas.
  2. Connect File widget to the Distribution widget.
  3. Double click on the Distribution widget to see the visualization.
  4. At the top left, select a different variable and check the distribution results

Scatter Plot

The Scatterplot widget provides a 2-dimensional scatterplot visualization for both continuous and discrete-valued attributes. The data is displayed as a collection of points, each having the value of the x-axis attribute determining the position on the horizontal axis and the value of the y-axis attribute determining the position on the vertical axis.

  1. Drag a Scatter Plot widget into the canvas.
  2. Connect the File widget to the Scatter Plot widget. The step is similar to how it was for the Distribution widget.
  3. Double click on the Scatter Plot widget to see the visualization.
  4. You can change the x-axis and y-axis based on the features available.

If you are unsure which features to select, click on the “Find Informative Projections” and you will see the following interface. Click on “Start” and select any item from the list. The scatter plot will modified based on your selection.


We have explored orange tool in detail and visualized the dataset that we have loaded. We tried out Distribution, and Scatter Plot widgets using the Iris dataset.