Orange Blog

By: AJDA, Aug 14, 2015

Classifying instances with Orange in Python

Last week we showed you how to create your own data table in Python shell. Now we’re going to take you a step further and show you how to easily classify data with Orange. First we’re going to create a new data table with 10 fruits as our instances. import Orange from Orange.data import * color = DiscreteVariable("color", values=["orange", "green", "yellow"])calories = ContinuousVariable("calories") fiber = ContinuousVariable("fiber") fruit = DiscreteVariable("fruit", values=["orange", "apple", "peach"]) domain = Domain([color, calories, fiber], class_vars=fruit) data=Table(domain, [</span> ["green", 4, 1.


By: AJDA, Aug 7, 2015

Creating a new data table in Orange through Python

IMPORT DATA One of the first tasks in Orange data analysis is of course loading your data. If you are using Orange through Python, this is as easy as riding a bike: import Orange data = Orange.data.Table(“iris”) print (data) This will return a neat data table of the famous Iris data set in the console. CREATE YOUR OWN DATA TABLE What if you want to create your own data table from scratch?

Categories: data examples python

By: AJDA, Jul 31, 2015

Datasets in Orange Bioinformatics Add-On

As you might know, Orange comes with several basic widget sets pre-installed. These allow you to upload and explore the data, visualize them, learn from them and make predictions. However, there are also some exciting add-ons available for installation. One of these is a bioinformatics add-on, which is our specialty. Bioinformatics widget set allows you to pursue complex analysis of gene expression by providing access to several external libraries. There are four widgets intended specifically for this - dictyExpress, GEO Data Sets, PIPAx and GenExpress.


By: AJDA, Jul 10, 2015

Learn with Paint Data

Paint Data widget might initially look like a kids’ game, but in combination with other Orange widgets it becomes a very simple and useful tool for conveying statistical concepts, such as k-means, hierarchical clustering and prediction models (like SVM, logistical regression, etc.). The widget enables you to draw your data on a 2-D plane. You can name the x and y axes, select the number of classes (which are represented by different colors) and then position the points on a graph.


By: AJDA, May 29, 2015

Excel files in Orange 3.0

Orange 3.0 version comes with an exciting feature that will simplify reading your data. If the old Orange required conversion from Excel into either tab-delimited or comma-separated files, the new version allows you to open plain .xlsx format data sets in the program. Naturally, the .txt and .csv files are still readable in Orange, so feel free to use data sets in any of the above-mentioned formats. Since Orange 3.0 is still in the development mode, you will find a smaller selection of widgets available at the moment, but give it a go and see how it works for Excel type data and whether the existing widgets are sufficient for your data analysis.

Categories: data dataloading orange3

By: BLAZ, Jan 18, 2015

Loading your data

By a popular demand, we have just published a tutorial on how to load the data table into Orange. Besides its own .tab format, Orange can load any tab or comma delimited data set. The details are though in writing header rows that tell Orange about the type and domain of each attribute. The tutorial is a step-by-step description on how to do this and how to transfer the data from popular spreadsheet programs like Excel.

Categories: data dataloading orange3

By: BIOLAB, Dec 20, 2013

Paint Your Data

One of the widgets I enjoy very much when teaching introductory course in data mining is the Paint Data widget. When painting in this widget I would intentionally include some clusters, or intentionally obscure them. Or draw them in any strange shape. Then I would discuss with students if these clusters are identified by k-means clustering or by hierarchical clustering. We would also discuss automatic scoring of the quality of clusters, come up with the idea of a silhouette (ok, already invented, but helps if you get this idea on your own as well).

Categories: data visualization