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Python

Python is an extremely versatile scripting language with extensive community support. Some of the advantages of using Python are:

  • Standard Libraries
  • Third-Party Libraries (pip)
  • Open Source
  • User-Friendly Data Structures
  • Easy to learn syntax
  • Productivity
  • Speed

Jupyter

Jupyter is a cross-platform and cross-language project for interactive computing.

An example of R running in jupyter:

PIP

The most famous package management system for python software distribution. Can be used in conjunction with Anaconda and VirtualEnv to create virtual environments for ease of package installation, collision prevention, and distribution.

If we wanted to setup the libraries required in the course's examples we can run (on the terminal):

pip install numpy
pip install plotly
pip install spyder

VirtualEnv

An easy way to deal with dependencies and libraries clashes is to generate a self-contained environment for each of our projects. These environments will hold the libraries (in their current versions) so that we do not break other projects, or require changes every time a library is updated.

Anaconda

Installing packages in the Anaconda cloud is easy:

conda install PACKAGENAME

For example, if we want to install tensorflow:

conda install -c conda-forge tensorflow

For instructions on how to install the required conda environment from anaconda, please follow the instructions on the introduction.

Setting Up the Course's Environment in Anaconda (the user-friendly way)

Download and install Anaconda:

Open the Anaconda Navigator:

Once the Navigator is Open:

click on the Environments section:

and then import:

Locate the ./conda/dataViz.yml file in the project's repository, name it dataViz and click import. The environment should now be ready for use in our computers.

Setting up the Course's Environment in Anaconda + Atom (the not-so-user-friendly way)

To setup the anaconda environment directly in the atom text editor follow the instructions detailed in the dataViz conda environment readme file.

Checking that everything is running ok

To verify that everything is in shape please run the examples in the hello python demos.