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machine learning, data science, engineering


Installing Python + Rodeo on Windows, Mac or Linux

by Yhat |


Introduction

Python is a widely used high-level, general-purpose, interpreted, dynamic programming language (Thanks, Wikipedia). More importantly, its a great language for manipulating data, making data visualizations and building statistical models. Rodeo is an app built by Yhat expressly for doing data analysis and data science in Python.

In this post, we'll walk you through how to install Python + Rodeo on a Mac, Linux or Windows computer. We designed Rodeo so that if you don't already have all the necessary dependencies on your computer, Rodeo will prompt you to download them through a convenient packaged version of Python called Anaconda.

Installing Rodeo + Python

  1. Download Rodeo here
  2. Launch Rodeo by double clicking the Rodeo icon
  3. Click Install Jupyter button
  4. Click Install Anaconda button
  5. Follow link to Download Anaconda to the Continuum website
  6. Download Anaconda 4.1.1 either via the Graphical Installer or Command-Line Installer
  7. Go through the Anaconda installation process (takes a few minutes)
  8. Click the Restart Rodeo button

Why do I need Anaconda?

Rodeo has a few dependencies, like jupyter and pip, which conveniently are already bundled up into Anaconda, a packaged version of Python. If you already have Anaconda installed on your computer, Rodeo will detect that and will launch as soon as you download it.

If you haven't already installed Anaconda, Rodeo will guide you through the installation, as shown in the video and steps above. The steps are the same whether you're running Rodeo on a Mac, Windows, or Linux computer.

More keyboard shortcuts

On a completely separate note, we've also added a bunch of keyboard shortcuts to help speed up your workflow. Below are a couple frequently requested ones.

The complete list of keyboard shortcuts for Rodeo is available in the docs

Delete a line ( + d)
Split into lines (ctrl + opt + l)
Select next occurance (ctrl + opt + )



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