Say, for example, client A is using Pandas 0.19, client B Pandas 0.25, and client C Pandas 1.0.1. If I have multiple clients over the years, they might use different versions of libraries. The purpose of an environment is to allow per project library dependencies.įor example, I consult with different clients. Create an EnvironmentĪfter you have installed the conda utility, you will need to make an "environment." An environment is a sandboxed installation of Python libraries and utilities. If it does not do that, then you did not install Anaconda successfully. Type into the terminal or command prompt:Īnd it should print out the version, something like conda 4.7.5. On Windows, go to the start menu and search for "Conda Prompt". On UNIX platforms (Mac and Linux) open a terminal (if you are on Mac, you can hit Command-space and then type "Terminal" and hit enter). (Many of my clients work inside a firewall and should use the Anaconda distribution that is available locally inside of their firewall.)Īfter you have installed Anaconda or Miniconda, you should have an executable called conda available. This is a smaller download that does not include various libraries but rather lets you download and install them on demand. The first is to install the whole kitchen sink, which is referred to as the "Anaconda Distribution" a 600MB+ download that includes many common libraries for machine learning, data analysis, and visualization. You have two options for installing Anaconda (the product). The steps you need to take if you want to use it are: It uses its own package system to make it simple to create working Python environments on most platforms. The Anaconda distribution includes "Python" it just has some extras. Make sure you use the Python 3.x version. There are versions for both Python 3.x and Python 2.7. This page will demo the free option, but I'm assuming that the paid product is similar after install. The company supports Python data tooling and makes the distribution available as a free product, but also as a supported enterprise paid for product. Using Anaconda TLDR: Use these to create an environment using AnacondaĪnaconda is both a product and a company. If you are new to Python, only doing "data science" work, or using Windows, I recommend using Anaconda. You can use the Python executable that you can get from the Python website, or you can use the Anaconda distribution. You have two main options for installing a Python environment. These examples will use Python 3 (which I recommend as Python 2 is no longer being developed). Here are instructions for getting a Python stack installed on Windows, Mac, and Linux.
You can set up your environment using either method with three commands
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