Project Jupyter is an open-source project that creates tools to help people develop scientific computation and data science in an iterative way and multiple languages.

You can have as many virtualenv as you like inside your operational system. Since you can create multiple virtualenvs, each project can (and normally should) have its own “box”, making easier to manage packages and libraries for each project, always keeping your operational system intact. Since you created a “little box”, what’s in it is completely isolated from your general environment, and it can be easily deleted if you stop working on that project.Įxample on how a virtualenv is a “little box” inside the “big box” that is your operational system Your operational system is like a box, with a specific version of Python and the Python libraries it needs, which specific versions it needs.Ĭreating a virtualenv is like you created a “little box”, and inside it you can install whatever you eant: any version of Python and any version of the libraries you need. Imagine that your computer (and operational system) is a huge box that has a lot of packages: Change it can cause severe complications on your whole system. Imagine that your system uses a specific library with a specific version. It is specially good if you consider that some operational systems use Python as a default tool. It is really handy when you need to isolate projects that use different versions of Python and packages. Virtualenv is a Python tool developed to isolate development environments. This is not a text about how to open a spreadsheet and process data, but where data science tools live and which has the right things for you. Although sometimes the tools is attached necessarily, the idea is to explain more about the tools then what each language can do. In this text, I brought some “simple” explanation for some of the most common tools used on data science development. Usually, most tutorials and texts focus on languages to be studied but this time I decided to bring something different. There are thousands of concepts and meanings, most of them thrown at you at the same time.

When you start entering the data science world, things can become really messy.

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