![]() The Anaconda open-source ecosystem is mainly used for data science, machine learning, and large-scale data analysis. Anaconda is a package manager and virtual environment manager, and it includes a set of pre-installed software packages. Then I followed the directions in the documentation, which instructed me to issue the following Bash command whether I was in the Bash shell or not: $ bash Anaconda3-5.1.0-Linux-x86_64. What Is the Anaconda Distribution Anaconda is a trusted suite that bundles Python and R distributions. To install Anaconda on my Linux laptop (an I3 with 4GB of RAM), I downloaded the Anaconda 5.1 Linux installer and ran md5sum to verify the file: $ md5sum Anaconda3-5.1.0-Linux-x86_64.sh The documentation is incredibly detailed and there is an excellent community of users for additional support. The default environment is Python 3.6, but you can also easily install Python 3.5, Python 2.7, or R. I recommend using Anaconda Navigator, a desktop graphical user interface (GUI) system that includes links to all the applications included with the distribution including RStudio, iPython, Jupyter Notebook, JupyterLab, Spyder, Glue, and Orange. As Anaconda's website says, "The Python and R conda packages in the Anaconda Repository are curated and compiled in our secure environment so you get optimized binaries that 'just work' on your system." The distribution comes with more than 1,000 data packages as well as the Conda package and virtual environment manager, so it eliminates the need to learn to install each library independently. I appreciate that Anaconda eases the frustration of getting started for new users. It is easy to download and install, and it is supported on Linux, MacOS, and Windows. As I was trying to work through the challenges of installing data science packages like NumPy and Matplotlib and solving the various dependencies, I learned about the Anaconda Python distribution.Īnaconda is a complete, open source data science package with a community of over 6 million users. When I took Udemy courses on the R and Python programming languages, I downloaded and installed the applications independently. Jupyter Notebook is an increasingly popular system that combines your code, descriptive text, output, images, and interactive interfaces into a single notebook file that is edited, viewed, and used in a web browser.Like many others, I've been trying to get involved in the rapidly expanding field of data science. You can also use Jupyter Notebook the same way. From the Navigator Home page, click the Spyder tile, and use the Spyder interface that opens to write and execute your code. What applications can I access using Navigator?įor information on what applications are available by default in Navigator, see Home page.Īdvanced conda users can also build their own Navigator applications. You can use it to find the packages you want, install them in an environment, run the packages, and update them – all inside Navigator. ![]() So, Why Anaconda Anaconda is a distribution of packages built for data science. Navigator is a graphical interface that enables you work with packages and environments without needing to type conda commands in a terminal window. In this Article we will be installing Anaconda, managing python packages. This helps data scientists ensure that each version of each package has all the dependencies it requires and works correctly. To download the installer for Anaconda, go to and click the button to download the Python 3.6 version. The CLI program conda is both a package manager and an environment manager. ![]() Data scientists often use multiple versions of many packages and use multiple environments to separate these different versions. In order to run, many scientific packages depend on specific versions of other packages.
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