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. Please find the daily build for OSX here.


Orange 3.0 can read Excel files.

Orange Fridays

You might think “casual Fridays” are the best thing since sliced bread. But what if I were to tell you we have “Orange Fridays” at our lab, where lab members focus solely on debugging Orange software and making improvements to existing features. This is because the new developing version of Orange (3.0) still needs certain widgets to be implemented, such as net explorer, radviz, and survey plot.

But there’s more. We are currently hosting an expert on data fusion from the University of Leuven, prof. dr. Yves Moreau, to discuss new venues and niches for the development of Orange. The big debate is how to scale the program to fit large data sets and make it possible to process such sets in a shorter period of time. If you have any ideas and suggestions, please feel free to share them on our community forum.


prof. dr. Yves Moreau – Prioritization of candidate disease genes and drug—target interactions by genomic data fusion


Working with SQL data in Orange 3

Orange 3 is slowly, but steadily, gaining support for working with data stored in a SQL database. The main focus is to allow huge data sets that do not fit into RAM to be analyzed and visualized efficiently. Many widgets already recognize the type of input data and perform the necessary computations intelligently. This means that data is not downloaded from the database and analyzed locally, but is retained on the remote server, with the computation tasks translated into SQL queries and offloaded to the database engine. This approach takes advantage of the state-of-the-art optimizations relational databases have for working with data that does not fit into working memory, as well as minimizes the transfer of required information to the client.

We demonstrate how to explore and visualize data stored in a SQL table on a remote server in the following short video. It shows how to connect to the server and load the data with the SqlTable widget, manipulate the data (Select Columns, Select Rows), obtain the summary statistics (Box plot, Distributions), and visualize the data (Heat map, Mosaic Display).



The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no 318633