Anaconda Vs. Miniconda


Answer :

Per the original docs:

Choose Anaconda if you:

  • Are new to conda or Python
  • Like the convenience of having Python and over 1500 scientific packages automatically installed at once
  • Have the time and disk space (a few minutes and 3 GB), and/or
  • Don’t want to install each of the packages you want to use individually.

Choose Miniconda if you:

  • Do not mind installing each of the packages you want to use individually.
  • Do not have time or disk space to install over 1500 packages at once, and/or
  • Just want fast access to Python and the conda commands, and wish to sort out the other programs later.

I use Miniconda myself. Anaconda is bloated. Many of the packages are never used and could still be easily installed if and when needed.

Note that Conda is the package manager (e.g. conda list displays all installed packages in the environment), whereas Anaconda and Miniconda are distributions. A software distribution is a collection of packages, pre-built and pre-configured, that can be installed and used on a system. A package manager is a tool that automates the process of installing, updating, and removing packages.

Anaconda is a full distribution of the central software in the PyData ecosystem, and includes Python itself along with the binaries for several hundred third-party open-source projects. Miniconda is essentially an installer for an empty conda environment, containing only Conda, its dependencies, and Python. Source.

Once Conda is installed, you can then install whatever package you need from scratch along with any desired version of Python.

2-4.4.0.1 is the version number for your Anaconda installation package. Strangely, it is not listed in their Old Package Lists.

In April 2016, the Anaconda versioning jumped from 2.5 to 4.0 in order to avoid confusion with Python versions 2 & 3. Version 4.0 included the Anaconda Navigator.

Release notes for subsequent versions can be found here.


The difference is that miniconda is just shipping the repository management system. So when you install it there is just the management system without packages. Whereas with Anaconda, it is like a distribution with some built in packages.

Like with any Linux distribution, there are some releases which bundles lots of updates for the included packages. That is why there is a difference in version numbering. If you only decide to upgrade Anaconda, you are updating a whole system.


Brief

conda is both a command line tool, and a python package.

Miniconda installer = Python + conda

Anaconda installer = Python + conda + meta package anaconda

meta Python pkg anaconda = about 160 Python pkgs for daily use in data science

Anaconda installer = Miniconda installer + conda install anaconda

Detail

  1. conda is a python manager and an environment manager, which makes it possible to

    • install package with conda install flake8
    • create an environment with any version of Python with conda create -n myenv python=3.6

  2. Miniconda installer = Python + conda

    conda, the package manager and environment manager, is a Python package. So Python is installed. Cause conda distribute Python interpreter with its own libraries/dependencies but not the existing ones on your operating system, other minimal dependencies like openssl, ncurses, sqlite, etc are installed as well.

    Basically, Miniconda is just conda and its minimal dependencies. And the environment where conda is installed is the "base" environment, which is previously called "root" environment.

  3. Anaconda installer = Python + conda + meta package anaconda

  4. meta Python package anaconda = about 160 Python pkgs for daily use in data science

    Meta packages, are packages that do NOT contain actual softwares and simply depend on other packages to be installed.

    Download an anaconda meta package from Anaconda Cloud and extract the content from it. The actual 160+ packages to be installed are listed in info/recipe/meta.yaml.

    package:     name: anaconda     version: '2019.07' build:     ignore_run_exports:         - '*'     number: '0'     pin_depends: strict     string: py36_0 requirements:     build:         - python 3.6.8 haf84260_0     is_meta_pkg:         - true     run:         - alabaster 0.7.12 py36_0         - anaconda-client 1.7.2 py36_0         - anaconda-project 0.8.3 py_0         # ...         - beautifulsoup4 4.7.1 py36_1         # ...         - curl 7.65.2 ha441bb4_0         # ...         - hdf5 1.10.4 hfa1e0ec_0         # ...         - ipykernel 5.1.1 py36h39e3cac_0         - ipython 7.6.1 py36h39e3cac_0         - ipython_genutils 0.2.0 py36h241746c_0         - ipywidgets 7.5.0 py_0         # ...         - jupyter 1.0.0 py36_7         - jupyter_client 5.3.1 py_0         - jupyter_console 6.0.0 py36_0         - jupyter_core 4.5.0 py_0         - jupyterlab 1.0.2 py36hf63ae98_0         - jupyterlab_server 1.0.0 py_0         # ...         - matplotlib 3.1.0 py36h54f8f79_0         # ...         - mkl 2019.4 233         - mkl-service 2.0.2 py36h1de35cc_0         - mkl_fft 1.0.12 py36h5e564d8_0         - mkl_random 1.0.2 py36h27c97d8_0         # ...         - nltk 3.4.4 py36_0         # ...         - numpy 1.16.4 py36hacdab7b_0         - numpy-base 1.16.4 py36h6575580_0         - numpydoc 0.9.1 py_0         # ...         - pandas 0.24.2 py36h0a44026_0         - pandoc 2.2.3.2 0         # ...         - pillow 6.1.0 py36hb68e598_0         # ...         - pyqt 5.9.2 py36h655552a_2         # ...         - qt 5.9.7 h468cd18_1         - qtawesome 0.5.7 py36_1         - qtconsole 4.5.1 py_0         - qtpy 1.8.0 py_0         # ...         - requests 2.22.0 py36_0         # ...         - sphinx 2.1.2 py_0         - sphinxcontrib 1.0 py36_1         - sphinxcontrib-applehelp 1.0.1 py_0         - sphinxcontrib-devhelp 1.0.1 py_0         - sphinxcontrib-htmlhelp 1.0.2 py_0         - sphinxcontrib-jsmath 1.0.1 py_0         - sphinxcontrib-qthelp 1.0.2 py_0         - sphinxcontrib-serializinghtml 1.1.3 py_0         - sphinxcontrib-websupport 1.1.2 py_0         - spyder 3.3.6 py36_0         - spyder-kernels 0.5.1 py36_0         # ...

    The pre-installed packages from meta pkg anaconda are mainly for web scraping and data science. Like requests, beautifulsoup, numpy, nltk, etc.

    If you have a Miniconda installed, conda install anaconda will make it same as an Anaconda installation, except that the installation folder names are different.

  5. Miniconda2 v.s. Miniconda. Anaconda2 v.s. Anaconda.

    2 means the bundled Python interpreter for conda in the "base" environment is Python 2, but not Python 3.


Comments

Post a Comment

Popular posts from this blog

Converting A String To Int In Groovy

Answer : Use the toInteger() method to convert a String to an Integer , e.g. int value = "99".toInteger() An alternative, which avoids using a deprecated method (see below) is int value = "66" as Integer If you need to check whether the String can be converted before performing the conversion, use String number = "66" if (number.isInteger()) { int value = number as Integer } Deprecation Update In recent versions of Groovy one of the toInteger() methods has been deprecated. The following is taken from org.codehaus.groovy.runtime.StringGroovyMethods in Groovy 2.4.4 /** * Parse a CharSequence into an Integer * * @param self a CharSequence * @return an Integer * @since 1.8.2 */ public static Integer toInteger(CharSequence self) { return Integer.valueOf(self.toString().trim()); } /** * @deprecated Use the CharSequence version * @see #toInteger(CharSequence) */ @Deprecated public static Integer toInteger(String self) { return toInteg
Read more

"Cannot Create Cache Directory /home//.composer/cache/repo/https---packagist.org/, Or Directory Is Not Writable. Proceeding Without Cache"

Answer : if anyone pass through here, this is shorter solution: sudo chown -R $USER $HOME/.composer it seems to me the group information is missing in your command sudo chown -R <user> /home/<user>/.composer/cache/repo/https---packagist.org Shoud be sudo chown -R <user>:<group> /home/<user>/.composer/cache/repo/https---packagist.org But to avoid other permission issues, I would rather advise: sudo chown -R <user>:<group> /home/<user>/.composer/cache (you'll need access to other folders in there) and sudo chown <user>:<group> /home/<user>/.composer To make sure your user has permissions enough on the global composer folder. Mind the missing recursion so the user don't own keys created by root. If you need to find out the group: groups <user>
Read more

Android How Can I Convert A String To A Editable

Answer : As you probably found out, Editable is an interface so you cannot call new Editable() . However an Editable is nothing more than a String with Spannables, so use SpannableStringBuilder: Editable editable = new SpannableStringBuilder("Pass a string here"); If you only want to change the Spannables, never the text itself, you can use a basic SpannableString. Use Editable.Factory.getInstance().newEditable(str) From the android documentation: Returns a new SpannedStringBuilder from the specified CharSequence. You can override this to provide a different kind of Spanned.
Read more