Anaconda Python

Anaconda Python is an open-source distribution of the Python and R programming languages ​​for Windows, Linux, and macOS. It includes a set of free libraries, a package management system, and other components. It is used for scientific and engineering calculations, data processing, predictive analytics, and machine learning. It is developed and supported by Anaconda Inc., founded by Peter Wang and Travis Oliphant in 2012. The distribution is designed to simplify package deployment and management. 

What is Anaconda?

The Anaconda program is a complete system for working with Python libraries, including an installer program and a set of the following files.

• Libraries. Each includes a specific set of functions and objects designed to solve specific problems—for example, working with data arrays, astronomical calculations, image processing, building and training neural networks, various engineering calculations, statistical analysis, etc. In total, the Anaconda package includes approximately 1,500 libraries covering scientific and engineering topics. 
• The Conda package manager is a cross-platform software designed for installing software packages and libraries, quickly switching between versions, and downloading and installing updates. Conda is written in Python but is language-agnostic, meaning it can work with projects written in any programming language. It can also be used to install Python itself—a feature not offered by all Python package managers. Because it has proven so useful for tasks unrelated to Python, it was released as a separate open-source distribution.
• Virtual Environment Manager. This program allows you to create, edit, delete, and monitor the status of virtual machines. Isolated virtual environments are managed from a single control console. Virtualization is used when working on multiple projects simultaneously, eliminating their impact on each other, thereby ensuring high calculation accuracy.
• Anaconda Navigator. GUI (graphic user interface) is a software shell that allows users to work with libraries using visual elements such as blocks, modules, graphs, and other elements rather than text commands. Anaconda Navigator significantly simplifies interaction with libraries, making work less monotonous and tedious, and enhancing the clarity of the workflow. 

Anaconda isn’t just a collection of individual libraries, but a fully-fledged integrated development environment. This means that all its packages and tools are configured to work together. Its main difference from other package managers, such as pip, is that library installation is based on existing versions and their specific features. When pip installs a package, it automatically installs all Python dependencies without checking for compatibility with anything already installed on the computer. 

For example, a user with a working build of, say, Google Tensorflow, uses pip to install a package that requires a different version of a dependent Numpy library than the one currently used by Tensorflow. This could cause the build to either stop working altogether or produce incorrect results during calculations. 

At the same time, the Conda package manager first checks the current environment, including all currently installed packages and their version restrictions. Based on this analysis, the program either selects a compatible set of dependencies or notifies the user that this is not possible.  

What is Anaconda Navigator?

Anaconda Navigator is a component of Anaconda, 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 runs on Windows, Linux, and macOS. Anaconda Navigator includes the following tools:

• JupyterLab is a development environment that allows you to work with notebooks, code, and data.
• Jupyter Notebook is a browser-based tool for creating beautiful and informative analytics and for sharing code, images, notes, diagrams, formulas, and more.
• Spyder is a Python scientific computing development environment that lets you write, modify, and test code. Using Spyder’s GUI, you can view and edit variables, analyze your program while it’s running, detect errors in real time, and more. Anaconda also supports working with other Python development environments, including PyCharm and Atom.
• VS Code is a code editor with features like debugging, running tasks, and version control.
• Glueviz – This tool allows you to visualize multidimensional data arrays in files. It analyzes interactions both within and between each data set;
• Orange 3 is a component-based framework for deep data analysis and visualization. Orange 3’s functionality is highly interactive and offers a wide range of analytical tools.
• RStudio is a combined tool that includes many features (such as a tutorial and a notebook) to make working with the R programming language more convenient.

Anaconda Navigator is controlled primarily with the mouse. Available tools and libraries appear as icons in the workspace, accompanied by short descriptions. All downloading, installation, and launching operations are performed using virtual buttons. This control method is extremely intuitive, even for novice users. 

Advantages of Anaconda

Versatility. Anaconda is designed for calculations in both Python and R. This makes it an ideal choice for data scientists proficient in both languages, as well as for those just starting and unsure which to master first.

Open source. Anaconda is distributed as free software, meaning it can be installed and used free of charge. Its open-source nature makes the software accessible for review and modification by users, who can adapt the system to suit their specific needs. 

Over 1,500 libraries available. The number of pre-installed packages is one of Anaconda’s key advantages. It eliminates the need for users to search for and install the necessary libraries separately. Furthermore, all Anaconda packages are selected to cover the broadest possible range of tasks in data science, scientific computing, and engineering. 

Extensive documentation. Since Anaconda is an open-source product, there are numerous guides, instructions, and tutorials on installation, configuration, and use, created by both the official developers and third-party specialists and amateur programmers. This extensive documentation, combined with a large expert community, allows users to find answers to any Anaconda problem. Another feature of Anaconda is its excellent documentation.

Cross-platform. Anaconda can be installed on Windows, Linux, and MacOS. This allows multiple developers across different platforms to work on the same project. Furthermore, products created with Anaconda can be ported across platforms without the risk of breaking. 

Simplicity and convenience. Anaconda offers users a wide range of options for installing and managing software packages. Those just beginning to learn the basics of Python can use a graphical interface that allows them to manage components and libraries with just a few clicks. For more experienced users, there’s the Conda command console, which allows for text-based commands. Each user can choose the method of interacting with components that best suits their needs. 

Why use multiple Python environments?

When working with Python, you install a specific version of the language with the latest versions of the packages needed for working calculations and experiments. 

Once you’ve mastered the basics of Python and downloaded Python distributions from GitHub, Kaggle, or other repositories, you may need different library versions than those you’re currently using. In this situation, you’ll need to configure different environments to ensure that old and new versions don’t conflict with each other and break your applications. 

An environment is an isolated environment (virtual machine) that simulates the operation of a full-fledged physical PC. Anaconda’s built-in virtual environment manager is responsible for its creation and operation, allocating some of the PC’s physical resources (RAM, disk space, CPU power, etc.) for this purpose. This tool allows you to simultaneously create multiple working environments. 

You may also need to use multiple Python environments in the following situations:

• When an application malfunctions. Let’s say you have software installed on your computer that initially worked perfectly, but suddenly stops working. This can happen, for example, due to a critical incompatibility between a specific version of the package and other program components. One solution is to set up a separate environment for the application, containing a newer version of Python and fully compatible libraries.
• When working together. When developing applications in a team, it’s often necessary to test the program’s performance on different computers. This doesn’t necessarily require transferring the application from one physical PC to another—it’s enough to set up an isolated Python environment with operating system emulation on one and install the program being tested there.
• To test the program on the client’s PC. Commercial applications are required to be tested for functionality by end users. While testing them immediately after delivery to customers is possible, this isn’t very convenient—if the product doesn’t work properly, it will have to be recalled for fixes. This incurs additional costs. Furthermore, not every client will wait for the developer to fix the bugs. By creating virtual environments with different operating systems on their workstation, the developer can test the application in advance and send users the final version for their operating system. 
• When developing and using programs with different versions. If a developer is creating multiple products simultaneously, they may have different requirements for Python or library versions. Using isolated virtual environments, you can work on several applications simultaneously by installing different Python distributions with separate packages for different tasks. 

How channels work in Anaconda

When installing or updating packages, Conda searches for them in special repositories called channels. It uses channels according to their hierarchy. Conda checks the highest-priority channels first when searching for the required package. Users can change the priority of existing channels and add new ones.

One problem with this hierarchical system is that Conda, when downloading a package from a low-priority channel, may overwrite a library downloaded from a higher-priority repository. Consequently, this can lead to incompatibility between the overwritten package and other Anaconda components.  

To prevent this, add new channels so that they are positioned at the end of the hierarchical list and have the lowest priority. This way, the user can add “special” packages that aren’t included in those already installed by default. This ensures that they receive all the default packages, reducing the likelihood of them being modified on a lower-priority channel.

What is Miniconda?

Many scientific and engineering computing and data processing tasks don’t require the extensive functionality offered by Anaconda. Furthermore, the more libraries you have, the more space they take up on your computer’s hard drive. To save space, you can use a minimal distribution called Miniconda. It includes:

• Python package – if the user already has Python installed on their PC, there’s no need to uninstall it. Miniconda simply installs its own version of the library in parallel with the existing one.
• Conda package manager – its functionality is not inferior to the version supplied in the Anaconda distribution.
• Python and Conda dependency packages are required for their proper 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 is 1 GB—a more than 10x difference. The installed Miniconda package takes up no more than 1 GB of hard drive space, while the unpacked Anaconda package takes up over 4 GB. 

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

• It includes fewer components, making it easier to learn—especially useful for users who haven’t worked with Python development and management systems before.
• The user can select the libraries he needs, while Anaconda immediately installs all its contents.

At the same time, the lack of components such as the Anaconda Navigator graphical user interface makes Miniconda less convenient for working with libraries. However, users can always download missing tools from 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 just starting with the language, we recommend installing the full version, as it eliminates the need to manually download the necessary components. Simply install Anaconda on your PC, and you’re ready to start working. 

How to launch Anaconda

Installing Anaconda depends on the operating system you are using and the type of distribution (standard or minimal).

Anaconda provides users with the broadest Python capabilities for scientific calculations, data analysis and processing, and more. This software package works seamlessly, eliminating the possibility of conflicts between different library versions. For a more focused set of tasks, a stripped-down version of this distribution is available, allowing you to add the necessary components from a local repository or cloud storage. Anaconda is currently one of the most comprehensive Python distributions, enjoying extensive support from the official developers and a large user community.