Anaconda

Anaconda is a full-fledged system for working with Python libraries, which includes an installer program and a set of the following files

Anaconda was founded in 2012 by Peter Wang and Travis Oliphant because of the need to introduce Python to business data analytics. Since then, the use of Python has exploded to become the most popular programming language in use today.

Anaconda Python is an open-source distribution of the Python and R programming languages for Windows, Linux, and MacOS, which includes a set of free libraries, a package management system, and other components. It is used to perform scientific and engineering calculations, solve problems in data processing, predictive analytics, and machine learning.

Pip is a package management system used to install and manage application packages written in Python. The system has its limitations. It can only be used for Python packages.

The Pip package manager works with Python and ignores dependencies from non-Python libraries (HDF5, MKL, LLVM) that do not have an installer file in their source code. Simply put, pip is a package manager that makes it easy to install, update, and remove Python packages. It works with Python virtual environments.

Conda is a package manager for any software (install, update, uninstall). It works with virtual system environments. Conda is a package management tool and installer with much more functionality than pip. Conda can handle library dependencies outside of Python packages as well as Python packages. In addition, Conda creates a virtual environment.

Conda comes in two versions:

• Anaconda – over 150 pre-installed packages (approx. 3 GB) + over 250 packages ready to install with the command conda install package_name
• Miniconda – over 400 packages ready to install with the command conda install package_name

Anaconda and Miniconda include:

• Conda
• Python interpreter
• pip

Many tasks in the field of scientific and engineering computing and data processing do not require the extensive functionality offered by Anaconda. Besides, the more libraries you have, the more space they take up on your computer’s hard disk. To save space, you can use a minimal distribution called Miniconda.

It includes:

• The Python package – if the user already has Python installed on their PC, there is no need to uninstall it. Miniconda simply installs its version of the library in parallel with the existing one;
• Conda package manager – its functionality is not inferior to the version supplied with the Anaconda distribution;
• Python and Conda dependency packages required for their normal operation;
• A small set of additional libraries for working with Python, such as pip, zlib, etc.

The Miniconda download file weighs 73.6 MB, while the Anaconda installer weighs 1 GB, which is more than 10 times the size of the two. The installed Miniconda package takes up no more than 1 GB on the hard disk, while the unpacked Anaconda package takes up more than 4 GB.

In addition to the smaller size of the installer file and, consequently, faster download and installation, Miniconda has several other advantages over the standard Anaconda distribution.

It includes fewer components, which makes it easier to learn – this is especially true for users who have not previously worked with Python development and management systems.

Users can choose the libraries they need, while Anaconda installs all of its content at once.

At the same time, the absence of components such as the Anaconda Navigator graphical user interface makes Miniconda less convenient for working with libraries. However, the user always has the option to download the missing tools from the repositories.

Miniconda is the best option for experienced users who are well versed in various Python tools and libraries and know which ones they need for their work. For those who are just starting to learn this language, it is recommended to install the full version, as it eliminates the need to download the necessary components on their own. It’s enough to install Anaconda on your PC, and you can start working right away.

Conda is written in pure Python, which makes it easy to use in Python virtual environments. In addition, Conda is suitable for libraries, R packages, Java packages, etc. It installs binary systems. The conda build tool builds packages from source code, and conda install performs installation from Conda build packages.

• Conda is the package manager for Anaconda, a Python distribution provided by Continuum Analytics.
• Anaconda is a set of binary systems that includes Scipy, Numpy, Pandas, and their dependencies.
• Scipy is a statistical analysis package.
• Numpy is a numerical computing package.
• Pandas is a data abstraction layer for combining and transforming data.

Anaconda is useful because it combines all of these into a single system. The Anaconda binary system is an installer that assembles all packages with dependencies inside your system.

Anaconda Navigator is an Anaconda component that provides a graphical user interface on the desktop. It is designed to easily launch and manage applications, environments, packages, and channels without using the command line. It can also be used to search for and download updates from the Anaconda Cloud or a local repository. Like the entire package management system, it works on Windows, Linux, and MacOS.

Anaconda Navigator includes the following tools:

• JupyterLab – a development environment that allows you to work with notebooks, code, and data;
• Jupyter Notebok – a browser-based tool for designing beautiful and informative analytics, sharing code, pictures, notes, diagrams, formulas, etc;
• Spyder is a Python development environment for scientific computing that allows you to write, modify, and test code. With Spyder’s GUI, you can view and edit variables, analyze the program directly at runtime, detect errors in real time, and more. You can also work with Anaconda with other Python development environments, including PyCharm and Atom;
• VS Code – a code editor with the ability to perform operations such as debugging, running tasks, and version control;
• Glueviz – this tool allows you to visualize multidimensional data sets in files. It analyzes interactions both within each data set and between them;
• Orange 3 is a component-based framework for deep data analysis and visualization. Orange 3 functionality is highly interactive and has a wide range of different analytical tools;
• RStudio is a combined tool that includes many functions (for example, a training course and a notebook) for more convenient work with the R programming language

Advantages of Anaconda

• Versatility. Anaconda is designed for calculations in Python and R. This makes it the best choice for data analysts and data processors who are proficient in both languages, as well as for those who are just taking the first steps in their study and do not yet know which one to master first.
• Open source. Anaconda is distributed as free software, meaning that it can be installed and used for free. And the openness of the source code makes the software available for review and modification by the user, who can adapt the system to perform a specific task.
• Availability of more than 1500 libraries. This number of pre-installed packages is one of the main advantages of Anaconda. This eliminates the need for the user to search for and install the necessary libraries separately. In addition, all packages in Anaconda are selected taking into account the widest possible range of tasks performed within the framework of data science, scientific and engineering computing.
• Rich documentation. Since Anaconda is an open source product, there are many manuals, instructions, and tutorials on how to install, configure, and use it, created by both official developers and third-party specialists and amateur programmers. Rich documentation combined with a large expert community allows the user to find an answer to any problem that arises in working with Anaconda. Another feature of Anaconda is its excellent documentation.
• Cross-platform compatibility. Anaconda can be installed on Windows, Linux, and MacOS. This, on the one hand, allows several developers using different platforms to work on the same project with the system. On the other hand, products created with Anaconda can be transferred between platforms without the risk that they will not work.
• Simplicity and convenience. Anaconda offers users a wide range of ways to install and manage software packages. For those who are just starting to learn the basics of Python, a graphical interface is available that allows you to manage components and libraries with a few mouse clicks. For more experienced users, there is the Conda command console, which is operated using text commands. Each user has the opportunity to choose the way of interacting with components that is closer to them.

Anaconda offers two versions: free (Anaconda Individual) and paid (Anaconda Enterprise). Here are the main differences between them:

Free version (Anaconda Individual):

Suitable for:

• Individual users
• Non-profit projects
• Education and research

Features:

• More than 170 Python packages
• Jupyter Notebook
• Spyder IDE
• Qt Designer
• Anaconda Navigator
• And much more

Restrictions:

• Not for commercial use
• No support
• Restrictions on access to some packages
• No access to Anaconda Repository

Paid version (Anaconda Enterprise):

Suitable for:

Commercial organizations
Enterprises
Teams.

Features:

• All features of Anaconda Individual
• 24/7 support
• Access to the Anaconda Repository
• Package and environment management
• Integration with CI/CD
• Security and compliance
• And much more

Cost: Depends on the number of users and features required (Compare further)

The version of Anaconda you choose depends on your needs. If you are:

An individual user:

• Using Anaconda for non-commercial projects
• Do not need support
• You don’t need access to the Anaconda Repository
• Then the free version of Anaconda Individual is right for you.

Commercial organization:

• Using Anaconda for commercial projects
• You need support
• You need access to the Anaconda Repository
• Then we recommend the paid version of Anaconda Enterprise.