MITīackport of new features in Python's weakref module / Python-2.0 Specifications for callback functions passed in to an API / BSD-3-Clauseīackport of new features in Python's os module / Python Software FoundationĪ backport of the get_terminal_size function from Python 3.3's shutil. Utilities to internationalize and localize Python applications / BSD 3-clause Microsoft Azure SDK for Python / Apache License 2.0 GPL 3Ī tool that automatically formats Python code to conform to the PEP 8 style guide / MITĪn Auto-Visualization library for pandas dataframes / BSD 3-clause Tool for automatically generating Makefile.in files compliant with the GNU Coding Standards. Apache 2.0Īttrs is the Python package that will bring back the joy of writing classes by relieving you from the drudgery of implementing object protocols (aka dunder methods). Timeout context manager for asyncio programs / Apache 2.0Ī fast PostgreSQL Database Client Library for Python/asyncio. LGPL-2.1-onlyĬommunity-developed Python Library for Astronomy / BSD-3-Clause Python ASN.1 library with a focus on performance and a pythonic API / MITĪ abstract syntax tree for Python with inference support. Public Domainīash tab completion for argparse / Apache 2.0īetter dates & times for Python / Apache-2.0 Simple package for registering an app with apple Launch Services to handle UTI and URL / MITĭisable App Nap on OS X 10.9 / BSD 2-ClauseĬontrol AppleScriptable applications from Python. GPL-3.0Ī small Python module for determining appropriate platform-specific dirs. Tool for (passively) verifying conda recipes and conda packages / BSDĬonvert text with ANSI color codes to HTML or to LaTeX. Tool for encapsulating, running, and reproducing data science projects / BSD 3-Clause MITĭelete Anaconda configuration files / BSDĪ command line client library / BSD 3-clauseĪnaconda Navigator / proprietary - Continuum Analytics, Inc. BSD 3-ClauseĪ database migration tool for SQLAlchemy. MITĪsync client for aws services using botocore and aiohttp / Apache 2Īsync http client/server framework (asyncio) / Apache 2.0Ĭonfigurable, Python 2+3 compatible Sphinx theme. MITĪgate-excel adds read support for Excel files (xls and xlsx) to agate. COPYINGĪgate-dbf adds read support for dbf files to agate. BSD-3-ClauseĪ data analysis library that is optimized for humans instead of machines. Matrices describing affine transformation of the plane. To create a new Conda environment with a specific Python version (3.Packages included in Anaconda 2020.02 for macOS with Python 3.8 #Ī configuration metapackage for enabling Anaconda-bundled jupyter extensions / BSD On Windows, you can use the Anaconda Prompt, while on macOS and Linux, you can use the standard terminal. Once you have installed Conda, open your terminal. If you haven’t installed Conda yet, you can download it from the official website to download the version compatible with your operating system. Step-by-Step Guide to Creating a Conda Environment with a Specific Python Version Step 1: Install Conda This is particularly vital when working on collaborative projects, where maintaining alignment regarding Python versions and package dependencies is paramount. Opting for a specific Python version ensures the consistent execution of your code across different environments. Different Python versions often come with varying features and compatibility levels for various packages. The choice of Python version can significantly impact the outcome of your data science projects. Conda allows you to create separate environments containing files, packages, and their dependencies that will not interact with other environments. It is widely used in the data science and machine learning fields due to its ability to handle library dependencies effectively. What is Conda?Ĭonda is an open-source package management system and environment management system. By adhering to these best practices, you’ll be better equipped to manage your data science projects effectively. In this comprehensive guide, we will walk you through the step-by-step process of creating a Conda environment with a specific Python version. This practice ensures that your project’s dependencies remain isolated and consistent across diverse environments, ultimately enhancing your workflow. It helps ensure that your project’s dependencies are isolated and consistent across different environments. | Miscellaneous How to Create a Conda Environment with a Specific Python VersionĬreating a Conda environment with a specific Python version is a common requirement for data scientists.
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