Orange Blog

Author: AJDA, Jun 12, 2018

From Surveys to Orange

Today we have finished a series of workshops for the Ministry of Public Affairs. This was a year-long cooperation and we had many students asking many different questions. There was however one that we talked about a lot. If I have a survey, how do I get it into Orange? Related: Analyzing Surveys We are using EnKlik Anketa service, which is a great Slovenian product offering a wide array of options for the creation of surveys.


Author: AJDA, May 30, 2018

Spectroscopy Workshop at BioSpec and How to Merge Data

Last week Marko and I visited the land of the midnight sun - Norway! We held a two-day workshop on spectroscopy data analysis in Orange at the Norwegian University of Life Sciences. The students from BioSpec lab were yet again incredible and we really dug deep into Orange. Related: Orange with Spectroscopy Add-on A class full of dedicated scientists. One thing we did was see how to join data from two different sources.


Author: AJDA, Feb 16, 2018

How to enable SQL widget in Orange

A lot of you have been interested in enabling SQL widget in Orange, especially regarding the installation of a psycopg backend that makes the widget actually work. This post will be slightly more technical, but I will try to keep it to a minimum. Scroll to the bottom for installation instructions. Related: SQL for Orange Why won’t Orange recognize psycopg? The main issue for some people was that despite having installed the psycopg module in their console, the SQL widget still didn’t work.

Categories: data pypi sql

Author: AJDA, Oct 26, 2017

Analyzing Surveys

Our streak of workshops continues. This time we taught professionals from public administration how they can leverage data analytics and machine learning to retrieve interesting information from surveys. Thanks to the Ministry of Public Administration, this is only the first in a line of workshops on data science we are preparing for public sector employees. For this purpose, we have designed EnKlik Anketa widget, which you can find in Prototypes add-on.


Author: AJDA, Mar 9, 2017

Why Orange?

Why is Orange so great? Because it helps people solve problems quickly and efficiently. Sašo Jakljevič, a former student of the Faculty of Computer and Information Science at University of Ljubljana, created the following motivational videos for his graduation thesis. He used two belowed datasets, iris and zoo, to showcase how to tackle real-life problems with Orange.

Categories: data examples youtube

Author: AJDA, Jan 23, 2017

Preparing Scraped Data

One of the key questions of every data analysis is how to get the data and put it in the right form(at). In this post I’ll show you how to easily get the data from the web and transfer it to a file Orange can read. Related: Creating a new data table in Orange through Python First, we’ll have to do some scripting. We’ll use a couple of Python libraries - urllib.


Author: AJDA, Jan 13, 2017

Data Preparation for Machine Learning

We’ve said it numerous times and we’re going to say it again. Data preparation is crucial for any data analysis. If your data is messy, there’s no way you can make sense of it, let alone a computer. Computers are great at handling large, even enormous data sets, speedy computing and recognizing patterns. But they fail miserably if you give them the wrong input. Also some classification methods work better with binary values, other with continuous, so it is important to know how to treat your data properly.


Author: AJDA, Jun 10, 2016

Scripting with Time Variable

It’s always fun to play around with data. And since Orange can, as of a few months ago, read temporal data, we decided to parse some data we had and put it into Orange. TimeVariable is an extended class of continuous variable and it works with properly formated ISO standard datetime (Y-M-D h:m:s). Oftentimes our original data is not in the right format and needs to be edited first, so Orange can read it.


Author: AJDA, Apr 14, 2016

Univariate GSoC Success

Google Summer of Code application period has come to an end. We’ve received 34 applications, some of which were of truly high quality. Now it’s upon us to select the top performing candidates, but before that we wanted to have an overlook of the candidate pool. We’ve gathered data from our Google Form application and gave it a quick view in Orange. First, we needed to preprocess the data a bit, since it came in a messy form of strings.


Author: AJDA, Jan 29, 2016

Tips and Tricks for Data Preparation

Probably the most crucial step in your data analysis is purging and cleaning your data. Here are a couple of cool tricks that will make your data preparation a bit easier. Use a smart text editor. We can recommend Sublime Text as it an extremely versatile editor that supports a broad variety of programming languages and markups, but there are other great tools out there as well. One of the best things you’ll keep coming back to in your editor is ‘Replace’ function that allows you to replace specified values with different ones.

Categories: data dataloading