- #Python conda install package code
- #Python conda install package download
- #Python conda install package free
Let’s take a look at some common scenarios! Downloading and Installing Packages With the release of conda 1.3, we’d like to show off some of conda’s more important features and provide a helpful “Getting Started” guide as a blog post as well. The conda documentation is available online, and contains a complete command reference as well as a number of examples. By using the ‘conda’ command line tool, users can easily switch between environments, create environments, and install different versions of libraries and modules into them. Under the hood, we have created a concept of environments which are conceptually similar to virtualenvs, but which use filesystem-level hard links to create entirely self-contained Python runtime layouts.
#Python conda install package code
The main problem is that they are focused around Python, neglecting non-Python library dependencies, such as HDF5, MKL, LLVM, etc., which do not have a setup.py in their source code and also do not install files into Python’s site-packages directory. Having been involved in the python world for so long, we are all aware of pip, easy_install, and virtualenv, but these tools did not meet all of our specific requirements. Because we found conda to be so useful for managing packages in Anaconda and Wakari, we have open-sourced it, so that others might benefit from it as well. Think of it as git branching for site-packages, combined with yum for Python packages. It’s also possible for conda users to host their own channels, so that they may pull their own packages easily into conda environments.
#Python conda install package free
Continuum hosts a number of repository channels that provide many free open source packages (as well commercial channels for distributing commercial packages).
#Python conda install package download
It allows users to install multiple versions of binary packages (and any required libraries) appropriate for their platform and easily switch between them, as well as easily download updates from an upstream repository.
We created the conda package and environment management system to solve these problems. Moreover, they must be able to easily share live, runnable versions of their work, including all supporting packages, to their colleagues or the general public.
Our users need to work with different versions of Python, NumPy, SciPy, and a variety of other packages. When we began building Wakari, a cloud-based platform for shareable, reproducible analytics, we also experienced this pain. One day, tools like PyPy and Numba may rescue us from this state of affairs, but data scientists working today need solutions-today. This pain can be especially acute with scientific and analytical libraries, which are often a mix of Python with compiled, platform-dependent C, C++, or Fortran code. For all the many strengths of Python, people often express frustration in finding, building, and installing third party packages.
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