Usually Jupyter Lab runs local server and opens in default browser as a separate tab. It implies all web related parts of the browser, which shrink accessible workspace. One of the ways to make work in Jupyter more comfortable is to run it in app mode.

Short “How To” about emulating alias Linux functionality on Windows machine. My default box at work runs Windows OS. Corporate policy. I am creature of habits. Could not change them so drastically, therefore I decided to temper a bit with Windows to provide me at least some basic functionalities I am used to.

Jenkins is an open source automation server. It can run any task with sophisticated set of rules regarding source control and/or dependencies between jobs. It is used to automate all sorts of tasks such as building, testing, and deploying software. Jenkins can be installed through native system packages, Docker, or even run standalone by any machine with the Java Runtime Environment installed. In this article I will show how to set up automatic CI/CD pipelines for python applications using Jenkins.

Flask application presenting social media accounts analysis in form of dashboard. It transforms various data sources into clear and concise report. Part II describes creation of landing page and dashboard, including blueprints pattern, jinja templates, login/register mechanism and data storage in development and production.

Flask application presenting social media accounts analysis in form of dashboard. It transforms various data sources into clear and concise report. This part describes backbone of the application, which is basic Flask configuration, Travis Continuous Integration (with tests) and Heroku Continuous Deployment to multiple environments with a single button. It is under version control, tested and CI/CD ready.

TravisCI is a very nice service, which allows to easily build your own CI/CD platform using GitHub project and configuration file in YAML (YAML Ain’t Markup Language) format. Easy to read by ordinary people not speaking binary. CI/CD stands for Continuous Integration and Continuous Deployment (or Delivery). Basic concepts in modern software engineering, which are fancy terms for automating entire software production pipeline: from unit test, acceptance tests to deployment on the production server.

Use R, Julia, Scala or Python? The answer is: Yes! How to create versatile environment, in which different languages are available and able to communicate with each other? Without changing program you work in and where data may be passed between specific structures characteristic for the language? This post will show you how to do it with four most powerful languages used in Data Science: Python, R, Scala and Julia.

Data analysis using Jupyter Notebook. Natural sciences more and more rely on skills related to Data Science. Experiments produce more and more data, and skilled researcher has to know how to deal with variety of data and sometime very large datasets. Anaconda Python Distribution offers large set of great tools to manipulate any kind of data “out of the box”. Multitude of community packages allows to read, analyze and report all kinds of data produced by science. It is caused mostly by simple fact, that scientific community is developing it’s tools mostly in Python. How to work with small and large data in python and make Jupyter Notebook your lab-book? Check this article.