Anaconda (Python distribution): Difference between revisions
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'''Anaconda distribution''' comes with more than 1,500 packages as well as the [[Conda_(package_manager)|Conda]] package and virtual environment manager. It also includes a GUI, '''Anaconda Navigator'''<ref>{{cite web |title=Anaconda Navigator |url=https://docs.anaconda.com/anaconda/navigator/ |website=docs.anaconda.com}}</ref>, as a graphical alternative to the command line interface (CLI). |
'''Anaconda distribution''' comes with more than 1,500 packages as well as the [[Conda_(package_manager)|Conda]] package and virtual environment manager. It also includes a GUI, '''Anaconda Navigator'''<ref>{{cite web |title=Anaconda Navigator |url=https://docs.anaconda.com/anaconda/navigator/ |website=docs.anaconda.com}}</ref>, as a graphical alternative to the command line interface (CLI). |
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The big difference between Conda and the pip package manager is in how package dependencies are managed, which is a significant challenge for Python data science and the reason Conda exists. Pip installs all Python package dependencies required, whether or not those conflict with other packages you installed previously. So your working installation of, for example, Google Tensorflow, can suddenly stop working when you pip install a different package that needs a different version of the numpy library. More insidiously, everything might still appear to work but |
The big difference between Conda and the pip package manager is in how package dependencies are managed, which is a significant challenge for Python data science and the reason Conda exists. Pip installs all Python package dependencies required, whether or not those conflict with other packages you installed previously. So your working installation of, for example, Google Tensorflow, can suddenly stop working when you pip install a different package that needs a different version of the numpy library. More insidiously, everything might still appear to work, but with different results being rendered from your source data, or you are unable to reproduce consistent results because you didn't pip install in the same order. |
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Conda analyzes your current environment, everything you have installed, any version limitations you specify (e.g. you only want tensorflow >= 2.0) and figures out how to install compatible dependencies. Or it will tell you that what you want can't be done. Pip, by contrast, will just install the thing you wanted and any dependencies, even if that breaks other things. |
Conda analyzes your current environment, everything you have installed, any version limitations you specify (e.g. you only want tensorflow >= 2.0) and figures out how to install compatible dependencies. Or it will tell you that what you want can't be done. Pip, by contrast, will just install the thing you wanted and any dependencies, even if that breaks other things. |
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Revision as of 17:57, 13 September 2019
 | |
| Developer(s) | Anaconda, Inc. (previously Continuum Analytics)[1] |
|---|---|
| Initial release | 0.8.0[2]/17 July 2012 |
| Stable release | 2019.07
/ 25 July 2019[3] |
| Written in | Python |
| Operating system | Cross-platform |
| Type | Programming language, machine learning, data science |
| License | New BSD License[4] |
| Website | www.anaconda.com |
Anaconda is a free and open-source[5] distribution of the Python and R programming languages for scientific computing (data science, machine learning applications, large-scale data processing, predictive analytics, etc.), that aims to simplify package management and deployment. Package versions are managed by the package management system conda.[6] The Anaconda distribution is used by over 15 million users and includes more than 1500 popular data-science packages suitable for Windows, Linux, and MacOS.[citation needed]
Overview
Anaconda distribution comes with more than 1,500 packages as well as the Conda package and virtual environment manager. It also includes a GUI, Anaconda Navigator[7], as a graphical alternative to the command line interface (CLI).
The big difference between Conda and the pip package manager is in how package dependencies are managed, which is a significant challenge for Python data science and the reason Conda exists. Pip installs all Python package dependencies required, whether or not those conflict with other packages you installed previously. So your working installation of, for example, Google Tensorflow, can suddenly stop working when you pip install a different package that needs a different version of the numpy library. More insidiously, everything might still appear to work, but with different results being rendered from your source data, or you are unable to reproduce consistent results because you didn't pip install in the same order.
Conda analyzes your current environment, everything you have installed, any version limitations you specify (e.g. you only want tensorflow >= 2.0) and figures out how to install compatible dependencies. Or it will tell you that what you want can't be done. Pip, by contrast, will just install the thing you wanted and any dependencies, even if that breaks other things.
Open source packages can be individually installed from the Anaconda repository[8], Anaconda Cloud (anaconda.org), or your own private repository or mirror, using the conda install command. Anaconda Inc compiles and builds all the packages in the Anaconda repository itself, and provides binaries for Windows 32/64 bit, Linux 64 bit and MacOS 64-bit. You can also install anything on PyPI into a Conda environment using pip, and Conda knows what it has installed and what pip has installed.
Custom packages can be made using the conda build command, and can be shared with others by uploading them to Anaconda Cloud,[9] PyPI or other repositories.
The default installation of Anaconda2 includes Python 2.7 and Anaconda3 includes Python 3.7. However, you can create new environments that include any version of Python packaged with conda[10].
Anaconda Navigator is a desktop graphical user interface (GUI) included in Anaconda distribution that allows users to launch applications and manage conda packages, environments and channels without using command-line commands. Navigator can search for packages on Anaconda Cloud or in a local Anaconda Repository, install them in an environment, run the packages and update them. It is available for Windows, macOS and Linux.
The following applications are available by default in Navigator[11]:
Conda
Conda is an open source,[12] cross-platform,[13] language-agnostic[14] package manager and environment management system[15][16][17] that installs, runs, and updates packages and their dependencies.[12] It was created for Python programs, but it can package and distribute software for any language (e.g., R), including multi-language projects.[14] The Conda package and environment manager is included in all versions of Anaconda, Miniconda,[18] and Anaconda Repository.[8]
Anaconda Cloud
Anaconda Cloud is a package management service by Anaconda where you can find, access, store and share public and private notebooks, environments, and conda and PyPI packages. Cloud hosts useful Python packages, notebooks and environments for a wide variety of applications. You do not need to log in or to have a Cloud account, to search for public packages, download and install them.
You can build new packages using the Anaconda Client command line interface (CLI), then manually or automatically upload the packages to Cloud.
See also
- Pip (package management system)
- pipenv
- Setuptools
- Package manager
- List of software package management systems
References
- ^ "What is Anaconda, Inc.?". docs.anaconda.com.
Anaconda is a software development and consulting company of passionate open source advocates based in Austin, Texas, USA. We are committed to the open source community. We created the Anaconda Python distribution and contribute to many other open source-based data analytics tools.
- ^ "Archived copy". Archived from the original on 2018-10-12. Retrieved 2017-10-26.
{{cite web}}: Unknown parameter|dead-url=ignored (|url-status=suggested) (help)CS1 maint: archived copy as title (link) - ^ https://docs.anaconda.com/anaconda/install/hashes/Anaconda3-2019.07-MacOSX-x86_64.sh-hash/
- ^ "Anaconda End User License Agreement". continuum.io. Continuum Analytics. Retrieved May 30, 2016.
- ^ "Open Source Community". Anaconda. Retrieved 2019-02-27.
- ^ "Conda – Conda documentation". Retrieved February 25, 2016.
- ^ "Anaconda Navigator". docs.anaconda.com.
- ^ a b "Anaconda repository". anaconda.org.
- ^ "Anaconda Cloud". anaconda.org.
- ^ "Managing Python with conda". conda.io.
- ^ "What application can I access using navigator?". docs.anaconda.com.
- ^ a b "Conda". pydata.org. Retrieved 9 April 2015.
- ^ "Building Conda Packages for Multiple Operating Systems". Pydannt. 29 January 2015. Retrieved 9 April 2015.
- ^ a b Doig, Christine (21 May 2015). "Conda for Data Science". Archived from the original on 16 June 2015. Retrieved 16 Jun 2015.
Conda works with Linux, OSX, and Windows, and is language agnostic, which allows us to use it with any programming language or even multi-language projects.
- ^
Gorelick (Author), Micha; Ozsvald, Ian (September 2014). High Performance Python: Practical Performant Programming for Humans (1st ed.). O'Reilly Media. p. 370. ISBN 1449361595.
{{cite book}}:|last=has generic name (help) - ^ Jackson, Joab (Feb 5, 2013). "Python gets a big data boost from DARPA". networkworld. Retrieved October 30, 2014.
- ^ Lorica, Ben (March 24, 2013). "Python data tools just keep getting better". O'Reilly Radar. Retrieved October 30, 2014.
- ^ "Miniconda". conda.io.
