11/30/2023 0 Comments Anaconda distribution pythonThe conda package manager is part of both the Anaconda python distribution and the smaller miniconda system. To create an environment named "myenv" and install a few packages, you would do something like the following: The pip package manager is used to install packages, which are available through the Python Package Index (PyPI) at. One or more virtual environments can be created, with packages installed into them, and any one of those environments can be activated within a shell at any time. Otherwise, creating python virtual environments is the way to go, using the pip package manager to install packages once you have created a new environment.įor python3, the venv module - part of the Python Standard Library - is used to create virtual environments. If you are using the Anaconda python distribution, or have installed the miniconda system, you will want to create conda environments, and use the conda package manager to coordinate things. The best way to address this sort of situation is to construct and manage separate environments, such as those supported through python virtual environments or conda environments. While multiple Python installations can happily coexist on a single system, that happiness can begin to dissolve if there are other dependencies that need to be kept separate, or if you need to maintain separate installations for separate projects. On managed computational clusters, for example, different Python installations might be installed and managed through an environment module system, such as the Lmod utilities on the clusters supported by the Texas Advanced Computing Center (TACC) and the San Diego Supercomputing Center (SDSC). If you use a shared system, check to see if a suitable Python ecosystem has already been constructed for your use. So if you want to get up and running quickly, you might want to start with one of these distributions. In addition to providing convenient support for installation of the Python scientific computing ecoystem, many of these distributions link to highly-optimized numerical libraries, such as the Intel Math Kernel Library ( MKL). These include Python distributions produced by Anaconda, Enthought,Īctive State, and Intel. Better still, a number of freely available Python distributions are available which bundle together most if not all of the packages one might need to carry out some scientific computation and analysis of interest. Fortunately, a number of tools and utilities have been developed to assist with that sort of package management. The downside of this are potential headaches associated with installing additional packages and managing dependencies among them. The number and functionality of packages available for data science in Python continues to grow at an impressive rate. Most specific functionality tailored toward numerical computing and data science is encoded in third-party libraries of the sort described in Key Packages. Below, we describe various approaches to such configuration. With an alternate installation of Python, you can configure the system with additional libraries needed to support your research. Fortunately, multiple Python installations can happily coexist on a single system, since each installation can keep track of which libraries are installed for use with that version. Therefore, it is generally advisable to leave the default Python installation alone, especially since that default version might not be updated as frequently as users might want. Python is a general-purpose programming language that is widely used for many tasks, is installed by default in many OS distributions, and is used for many systems administration activities.
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