bestloha.blogg.se - Miniconda vs anaconda

#MINICONDA VS ANACONDA INSTALL#
#MINICONDA VS ANACONDA CODE#
#MINICONDA VS ANACONDA FREE#

All conda environments that have Python installed should also include pip by default. Not all packages are available on conda, so pip is still useful even if you’re primarily using conda.

#MINICONDA VS ANACONDA CODE#

It works particularly well for pure Python packages, but things can get complicated when compiled code and external (non-Python) dependencies are involved.

#MINICONDA VS ANACONDA INSTALL#

Pip is a more basic package manager than conda that allows you to install software from PyPI (Python Package Index) as well as from GitHub. The “conda” program is available whether you choose to install Anaconda or Miniconda. “conda” is simply the package and environment manager program that allows new software to be installed. Miniconda is a lightweight implementation of the Anaconda distribution that provides the “conda” package manager, but does not include the large collection of scientific Python packages installed by default like Anaconda does. Anaconda includes a wide selection of Python packages that are installed by default, with the ability to install more packages using the “conda” package manager program.

#MINICONDA VS ANACONDA FREE#

The environment construction method with Miniconda is summarized below.Package Management with Conda and Pip Anaconda vs Miniconda vs “conda”Īnaconda is a free and open-source distribution of the Python programming language for scientific computing. I first built the environment with Anaconda, but I couldn't grasp the contents, so I uninstalled it and rebuilt it with Miniconda.Īlthough Anaconda is standard and rich in tools, you end up having to look into the package when you write your own programs.I think it's important that you know what's in it.

People who don't like installing unnecessary packages.

People who want to know which package they are using.

Those who want to start machine learning as soon as possible.

People who don't care if there are unnecessary packages.

People who do not want to have a hard time building an environment.

Which one should build the environment Suitable for Anaconda Installation of python is easy, but necessary packages and execution environment are built individually using conda. The smallest configuration version of Anaconda.

Graphical User Interface (GUI): Anaconda Navigator.

Integrated Development Environment (IDE): Jupyter, JupyterLab, Spyder, RStudio.

Package: numpy, pandas, Matplotlib, Scikit-learn, Tensorflow.

If you install Anaconda, you will be able to use packages for scientific calculation and data science together with Python.It also includes "R", a programming language for data science alongside Python, and their comprehensive development environment.Roughly speaking, the following applications are installed. "Python + R language + conda + 1000 or more related packages + execution environment + etc. It's true that Anaconda makes it easy to build an environment, but it also has its disadvantages.Therefore, I compared the characteristics of Anaconda and Miniconda. When it comes to building a machine learning environment with python, many books and sites say that you should use Anaconda for the time being.