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Whether you’re a big, small or medium enterprise, Anaconda will support your organization. As a free and open-source distribution of Python and R programming language, it’s aim is to easily scale a single user on one laptop to thousands of machines. If you’re looking for a hassle-free data science platform, this is the one for you.
Anaconda Navigator is a desktop graphical user interface included in Anaconda that allows you to launch applications and easily manage conda packages, environments and channels without the need to use command line commands. By data scientists, for data scientists.
- Installing Anaconda. Anaconda is available for Windows, Mac OS X, and Linux, you can find the installation file in the anaconda official site. I suggest you choose the Python version 3.7 64-bit installer if you have a 64-bit machine, otherwise choose the 32-bit installer, instead. Download the graphical macOS installer for your version of Python.
- Psd-tools.py command-line interface is changed, ‘debug’ command is added; pretty-printing of internal structures; pymaging support is fixed; allow ‘MeSa’ to be a signature for image resource blocks (thanks Alexey Buzanov); psdtools.debug.debugview utility function is fixed; Photoshop CC constants are added.
- Download Prey 1.9.12 for Mac for free, without any viruses, from Uptodown. Try the latest version of Prey for Mac.
Anaconda is leading the way for innovative data science platforms for enterprises of all sizes.
Anaconda provides you with more than 1,500 packages in its distribution. In it you will find the Anaconda navigator (a graphical alternative to command line interface), Conda package, virtual environment manager, and GUI. What makes Conda different from other PIP package managers is how package dependencies are managed. PIP installs Python package dependencies, even if they’re in conflict with other packages you’ve already installed. So, for example, a program can suddenly stop working when you’re installing a different package with a different version of the NumPy library. Everything will appear to work but, you data will produce different results because you didn’t install PIP in the same order. This is where Conda comes in. It analyzes your current environment and installations. This includes version limitations, dependencies, and incompatibility. As an open source package, it can be individually installed from the Anaconda repository, Anaconda Cloud or even the conda install command.
You can even create and share custom packages using the conda build command. The developers will then compile and build all the packages in the Anaconda repository, providing binaries for Windows, Linux and MacOS. Basically, you won’t worry about installing anything because Conda knows everything that’s been installed in your computer.
Extend your reach with Anaconda Navigator
The built in graphical user interface or GUI allows you to launch applications while managing Conda packages, environments and channels. This means the GUI will complete the process of installing packages without asking for a command-line command. It even includes these applications by default: JupyterLab & Jupyter Notebook / QtConsole / Spyder / Glueviz / Orange / RStudio / Visual Studio Code.
Where can you run this program?
Anaconda 2019.07 has these system requirements:
- Operating system: Windows 7 or newer, 64-bit macOS 10.10+, or Linux, including Ubuntu, RedHat, CentOS 6+.
- System architecture: Windows- 64-bit x86, 32-bit x86; MacOS- 64-bit x86; Linux- 64-bit x86, 64-bit Power8/Power9.
- 5 GB disk space or more.
Anaconda developers recommends you to install Anaconda for the local user so you won’t need administrator permissions. Or, you can opt to install Anaconda system wide, which does require administrator permissions.
Is there a better alternative?
If you’re looking for simple Python-dedicated environment, then you need PyCharm. Targeted specifically for Python programmers, this integrated development environment is filled with programming tools that can impress both new and experienced developers. It provides all the tools in a centralized system so you can increase your efficiency and effectiveness. Features like code analysis, graphical debugger, and unit tester helps you integrate Python programs with version control systems. In fact, every single output you make will be capable of web development from different web frameworks like Django, web2py, and Flask. It offers automated tools like code refactorings, PEP8 checks, and testing assistance to create your code, but what stands out the most is Smart Assistance. It fixes any of your errors or complete portions of your code. With PyCharm, you can expect a neat and maintainable code.
Anaconda’s host of innovative options makes it the best data science platform for all enterprises. By offering superior collaboration tools, scalability, and security, you never have to worry about gathering big data again.
Should you download it?
If you have experience with other package management and deployment programs, then make the big switch by downloading Anaconda.
- Extensive data science tools
- Functions can be scaled
- Flexible nodes
- Reliable cloud storage
- Complex for beginners
- Hard to maximize by small organizations
- Minimal automated features
Below is a question that gets asked so often that I decided it would be helpful to publish an answer explaining the various ways in which Anaconda can be kept up to date. The question was originally asked on StackOverflow.
I have Anaconda installed on my computer and I'd like to update it. In Navigator I can see that there are several individual packages that can be updated, but also an anaconda package that sometimes has a version number and sometimes says custom. How do I proceed?
What 95% of People Actually Want
In most cases what you want to do when you say that you want to update Anaconda is to execute the command:
This will update all packages in the current environment to the latest version—with the small print being that it may use an older version of some packages in order to satisfy dependency constraints (often this won't be necessary and when it is necessary the package plan solver will do its best to minimize the impact).
This needs to be executed from the command line, and the best way to get there is from Anaconda Navigator, then the 'Environments' tab, then click on the triangle beside the
This operation will only update the one selected environment (in this case, the
Update Individual Packages
If you are only interested in updating an individual package then simply click on the blue arrow or blue version number in Navigator, e.g. for
Anaconda Prompt Mac
Or from the command line:
Updating Just the Packages in the Standard Anaconda Distribution
If you don't care about package versions and just want 'the latest set of all packages in the standard Anaconda Distribution, so long as they work together,' then you should take a look at this gist.
Why Updating the Anaconda Package is Almost Always a Bad Idea
In most cases, updating the Anaconda package in the package list will have a surprising result—you may actually downgrade many packages (in fact, this is likely if it indicates the version as
Leverage conda Environments
The conda documentation has more details and examples.
pip, PyPI, and setuptools?
None of this is going to help with updating packages that have been installed from PyPI via
Commercial Use of Anaconda or Anaconda Enterprise
It's pretty much exactly the same story, with the exception that you may not be able to update the
Where to Go Next
If you have more questions about Anaconda then you can refer to our online documentation, or make use of our commercial (paid) or community (free) support channels. If you are using Anaconda in an enterprise setting, then I think you'll be interested in learning about how Anaconda Enterprise can help you and your colleagues with collaboration, security, governance, and provenance around your data science workflows.