Difference between revisions of "Anaconda"

Latest revision as of 21:35, 14 September 2024

THIS ARTICLE IS STILL IN EDITING MODE.

1. What is a Python Distribution?

A Python distribution is a pre-packaged set of tools for using Python. It includes the core Python language, along with additional libraries and programs that make it easier to write and run Python code almost immediately. There are severall python distributions such as PyPy, ActivePython and Anaconda, being the latter the most used for data science work.

2. What is Anaconda?

Source: wikimedia.org

Anaconda Distribution is a popular, free, and open-source distribution of data science programming languages. It's designed to simplify package management and deployment for scientific computing, data science, machine learning, and large-scale data processing. Anaconda comes with over 1,500 pre-installed packages, including popular libraries like Pandas, Numpy and built-in integrated development environments (IDE) such as Jupyter Lab, Rstudio, PyCharm, Visual Studio Code, among others.

3. Key Features

User-Friendly Interface: Anaconda Navigator provides a graphical interface to manage packages and environments, making it beginner-friendly.
Package Management: Anaconda includes conda, a package manager that makes it easy to install, update, and remove packages and dependencies.
Environment Management: You can create isolated virtual environments to manage different projects with different dependencies without conflicts.
Cross-Platform: Available for Windows, macOS, and Linux.

Data science and machine-learning packages: As of June 2024, Anaconda comes with over 8000 popular open-source data science packages, including NumPy, pandas, matplotlib, and Jupyter Notebook, saving you the hassle of installing them individually

4. Disadvantages of using Anaconda

Large Size: Anaconda requires 4.4 GB of disk space for its installation. This can be a concern for devices with limited storage. For a minimalistic installation without the user interface from conda, miniconda or miniforge are excellent options.
Slow start: Launching Anaconda is a bit slow (>30 seconds, depending on the device) as it has to load the whole user interface.
Redundancy: If you are an experienced user, you might find that Anaconda includes more tools and libraries than you need.
Learning Curve: Although Anaconda simplifies many tasks, there’s still a learning curve, especially for complete beginners.

5. Installing Anaconda

Anaconda Distribution can be installed free of charge for private use for Windows, macOS and Linux. The newest version currently features Python 3.12. Its installation process is straight forward:

1. Download the installer. If you are a beginner and/or don't feel comfortable using the your computer's terminal, use the graphical installer depending on your operative system (if the page asks for registration, you can skip it).

2. Double-click the installer in ‘Downloads’ folder. For beginners, it is not recommended to launch installer from the Favorites folder. Make sure the installer is not saved in any cloud service you may have.

3. For the installation, follow the steps as indicated in the official documentation in https://docs.anaconda.com/anaconda/install/. Use this link for Windows devices and this link for macOS users. We assume that Linux users are competent enough to handle the installation themselves.

6. Using Anaconda Navigator

The great advantage of using Anaconda is its Anaconda Navigator, which is a graphical user interface (GUI) to manage environments and packages via a graphical user interface rather than the conda command line interface. In the left column of the Navigator you can switch between the program's four main components. On the Learning page and Community page you can access documentation and training related to the Navigator and find out about support forums and social networking activities in the field.

Home page

Source: https://docs.anaconda.com/navigator/overview/

On the Home page, you can launch and install applications in the active environment by clicking the respective button on one of the application tiles. Additionally, you can use the gear icon in the top-right corner of the tiles to update, remove, or install a specific version of an application. Note that these actions will only apply to the currently-active environment, which you can select with the Applications on drop-down located on top of the tiles menu. Two important built-in applications are Visual Studio Code for writing and executing code and Jupyter Notebook for generating notbooke files. The Anaconda documentation includes a comprehensive list of all applications that are included by default and instructions on how to build your own Navigator applications.

References

(1) https://www.anaconda.com/

(2) https://www.anaconda.com/products/distribution

(3) https://docs.anaconda.com/navigator/overview/

(4) https://www.dominodatalab.com/data-science-dictionary/anaconda/

Authors: Carlo Krügemeier, Joldon, Gustavo Rodriguez | Back to page: Setting up Python Work Environment page

@@ Line 1: / Line 1: @@
-== 1. What is Anaconda? ==
+THIS ARTICLE IS STILL IN EDITING MODE.
-[[File:Anaconda Logo.png|thumb|right|Source: wikimedia.org]]
-Anaconda is an open-source Python and R distribution that includes access to different IDEs, such as Spyder and pyCharm, as well as Jupyter Notebook among others. It is aiming at simplifying package management and claims to be the “world's most popular data science plattform” (1).
+== 1. What is a Python Distribution? ==
+A Python distribution is a pre-packaged set of tools for using Python. It includes the core Python language, along with additional libraries and programs that make it easier to write and run Python code almost immediately. There are severall python distributions such as PyPy, ActivePython and Anaconda, being the latter the most used for data science work.
-== 2. What's included in Anaconda Distribution? ==
+== 2. What is Anaconda? ==
+[[File:Anaconda Logo.png|thumb|right|Source: wikimedia.org]]
-Included in the basic Anaconda distribution is the Conda package and environment management system as well as the applications Anaconda Navigator, a Powershell terminal, JupyterLab and Jupyter Notebook, as well as the Spyder IDE. These modules are briefly introduced in the following.
+Anaconda Distribution is a popular, free, and open-source distribution of data science programming languages. It's designed to simplify package management and deployment for scientific computing, data science, machine learning, and large-scale data processing. Anaconda comes with over 1,500 pre-installed packages, including popular libraries like Pandas, Numpy and built-in integrated development environments (IDE) such as Jupyter Lab, Rstudio, PyCharm, Visual Studio Code, among others.
-Conda
+== 3. Key Features ==
-  “Conda is an open-source package and environment management system that runs on Windows, macOS, and Linux. Conda quickly installs, runs, and updates packages and their dependencies. It also easily creates, saves, loads, and switches between environments” (2) on a local computer.
-Anaconda Navigator
-  “Desktop application to easily manage integrated applications, packages, and environments” (2) with a graphical user interface instead of using the command line.
-Jupyter Notebook
-  “Web-based, interactive computing notebook environment to edit and run human-readable documents while describing data analysis.” (3)
-JupyterLab
-  “Extensible Environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture.” (3)
-Spyder IDE
-  A scientific Integrated Python Development Environment “with advanced editing, interactive testing, debugging and runtime introspection features.” (3)
-== 3. Basic features/properties of Anaconda ==
+* User-Friendly Interface: Anaconda Navigator provides a graphical interface to manage packages and environments, making it beginner-friendly.
+* Package Management: Anaconda includes conda, a [[Package Managers in Python|package manager]] that makes it easy to install, update, and remove packages and dependencies.
+* Environment Management: You can create isolated [[Virtual Environments in Python|virtual environments]] to manage different projects with different dependencies without conflicts.
+* Cross-Platform: Available for Windows, macOS, and Linux.
-The three main features Anaconda claims is that the distribution is
+Data science and machine-learning packages: As of June 2024, Anaconda comes with over 8000 popular open-source data science packages, including NumPy, pandas, matplotlib, and Jupyter Notebook, saving you the hassle of installing them individually
-. "open-source", <br>
+== 4. Disadvantages of using Anaconda ==
-. "user-friendly" and <br>
-. "trusted".
-This means that the code is freely accessible and assessable for projects in any field. Moreover, the intuitive platform allows for easy package and environment handling, all while the “securely hosted packages and artifacts are methodically tested and regularly updated” (2) to avoid conflicts, bugs or scams.
+* Large Size: Anaconda requires 4.4 GB of disk space for its installation. This can be a concern for devices with limited storage. For a minimalistic installation without the user interface from conda, [[Package Managers in Python|miniconda or miniforge]] are excellent options.
+* Slow start: Launching Anaconda is a bit slow (>30 seconds, depending on the device) as it has to load the whole user interface.
+* Redundancy: If you are an experienced user, you might find that Anaconda includes more tools and libraries than you need.
+* Learning Curve: Although Anaconda simplifies many tasks, there’s still a learning curve, especially for complete beginners.
-== 4. How to install Anaconda on my machine ==
+== 5. Installing Anaconda ==
-Anaconda Distribution can be installed free of charge for private use for Windows, macOS and Linux. The newest version currently features Python 3.9.
+Anaconda Distribution can be installed free of charge for private use for Windows, macOS and Linux. The newest version currently features Python 3.12. Its installation process is straight forward:
-Its installation process is straight forward: Download the installation file, choose a path and follow the steps. No account is needed. As a student you can choose to sign up for Anaconda Nucleus for “Community membership, unlimited environment backups (and) exclusive data science content” (4) for free.
+. [https://www.anaconda.com/download/success Download the installer]. If you are a beginner and/or don't feel comfortable using the your computer's terminal, use the graphical installer depending on your operative system (if the page asks for registration, you can skip it).
-Click [https://www.anaconda.com/products/distribution#Downloads here] to directly download the installer for your operating system. For Windows and MacOS, graphical installers are provided. For Linux, there are only installation scripts that have to be run from the terminal.
+. Double-click the installer in ‘Downloads’ folder. For beginners, it is not recommended to launch installer from the Favorites folder. Make sure the installer is not saved in any cloud service you may have.
-For an in-depth description and links to download Anaconda for older operating systems, you can make use of their extensive [https://docs.anaconda.com/anaconda/install/ documentation].
+. For the installation, follow the steps as indicated in the official documentation in https://docs.anaconda.com/anaconda/install/. Use this [https://docs.anaconda.com/anaconda/install/windows/ link for Windows] devices and [https://docs.anaconda.com/anaconda/install/mac-os/ this link for macOS] users. We assume that Linux users are competent enough to handle the installation themselves.
-If a more minimalistic approach is preferred, Miniconda can be installed. This version just includes conda, Python and necessary dependencies:
-[https://docs.conda.io/en/latest/miniconda.html Install Miniconda]
-In case of issues during the installation process, please refer to [https://docs.anaconda.com/anaconda/user-guide/troubleshooting/ the help page].
-== 5. Using Conda ==
-Conda is a command line tool used in a terminal to interact with Anaconda. It is a package and environment management software. It can be used to install or update packages, create, save and load environments. To start using conda, open a terminal, type ''conda'' and press enter.
-To open a terminal on windows, press CTRL + R, type ''cmd. exe'' (write this without a space, we're sorry, this is due to Wiki formatting) and press enter. On macOS, open launcher and type ''terminal'' into the search box, clicking the icon when it appears. On Linux, the shortcut Super + T should do the job, otherwise it can be found in the applications menu.
 == 6. Using Anaconda Navigator ==
-Anaconda Navigator is a desktop application to manage environments and packages via a graphical user interface rather than the conda command line interface. It is automatically installed with Anaconda Distribution version 4.0.0 or higher. In the left column of the Navigator you can switch between the program's four main components. On the '''Learning page''' and '''Community page''' you can access documentation and training related to the Navigator and find out about support forums and social networking activities in the field. The following section describe in more depth the '''Home page''' and the '''Environments page''' that allow you to manage applications and environments.
+The great advantage of using Anaconda is its Anaconda Navigator, which is a graphical user interface (GUI) to manage environments and packages via a graphical user interface rather than the conda command line interface. In the left column of the Navigator you can switch between the program's four main components. On the '''Learning page''' and '''Community page''' you can access documentation and training related to the Navigator and find out about support forums and social networking activities in the field.
 === Home page ===
@@ Line 58: / Line 43: @@
 On the Home page, you can launch and install applications in the active environment by clicking the respective button on one of the application tiles. Additionally, you can use the gear icon in the top-right corner of the tiles to update, remove, or install a specific version of an application. Note that these actions will only apply to the currently-active environment, which you can select with the '''Applications on''' drop-down located on top of the tiles menu.
-Two important built-in applications are Spyder for writing and executing code and Jupyter Notebook for generating notbooke files. The Anaconda documentation includes a [https://docs.anaconda.com/navigator/overview/#home-page comprehensive list] of all applications that are included by default and instructions on [https://docs.anaconda.com/navigator/tutorials/nav-app/#building-anaconda-navigator-applications how to build your own Navigator applications].
+Two important built-in applications are Visual Studio Code for writing and executing code and Jupyter Notebook for generating notbooke files. The Anaconda documentation includes a [https://docs.anaconda.com/navigator/overview/#home-page comprehensive list] of all applications that are included by default and instructions on [https://docs.anaconda.com/navigator/tutorials/nav-app/#building-anaconda-navigator-applications how to build your own Navigator applications].
-=== Environments page ===
-[[File:Anaconda Navigation.png|thumb|right|Source: https://docs.anaconda.com/navigator/overview/]]
-The Environments page, like Conda, allows you to create and activate environments as well as install, update, and remove packages.
-==== Create an environment ====
-Click on the '''Create''' button at the bottom of the Environments list. A dialog box appears where you can name your environment and select the versions of Python and/or R you want to work with. You can also choose to import an environment from your local drive or Anaconda Nucleus ('''Import''' button) or clone an existing environment ('''Clone''' button).
-==== Activate the environment ====
-In the environments list in the left column, click on the arrow button next to the name to activate the environment you want to use.
-==== Installing, updating and removing packages ====
-By default, the right table displays all packages that are installed in the active environment. You can use the filter on top of the list to view not installed, updatable, selecte, or all packages instead. Note that you can use the '''Channels''' button to adjust the locations where Navigator searches for packages. See [https://docs.anaconda.com/navigator/tutorials/manage-channels/ Managing channels] for further information on channels. To install a new package, click the '''Apply''' button for an uninstalled package. To update a package, remove a package or install a different package version, click on the checkbox infront of the package name, select the respective action, and click '''Apply'''.
-If you run into problems or want to explore the functions of Anaconda Navigator further, you can access the [https://docs.anaconda.com/navigator/ documentation] via the '''Help''' button in the top-left corner of the Navigator.
 == References ==
@@ Line 87: / Line 54: @@
 (3) https://docs.anaconda.com/navigator/overview/<br>
-(4) https://www.anaconda.com/pricing#<br>
+(4) https://www.dominodatalab.com/data-science-dictionary/anaconda/<br>
+Authors: Carlo Krügemeier, Joldon, Gustavo Rodriguez | Back to page: [[Setting up Python Work Environment|Setting up Python Work Environment page]]
-(5) https://www.dominodatalab.com/data-science-dictionary/anaconda/<br>
+[[Category: Python]] [[Category: Python Setup]]