![]() ![]() It can be used to manage both packages and virtual environments. It's installed with Python 3.9+ by default (unless you are on a Debian-based OS install python3-pip in that case).Īllows you to manage separate package installations for different projects and is installed with Python 3 by default (unless you are on a Debian-based OS install python3-venv in that case) The Python package manager that installs and updates packages. The following table lists the various tools involved with Python environments: Tool Conda environmentsĪ conda environment is a Python environment that's managed using the conda package manager (see Getting started with conda).Choosing between conda and virtual environments depends on your packaging needs, team standards, etc. Note: While it's possible to open a virtual environment folder as a workspace, doing so is not recommended and might cause issues with using the Python extension. When you install packages into a virtual environment it will end up in this new folder, and thus isolated from other packages used by other workspaces. A virtual environment creates a folder that contains a copy (or symlink) to a specific interpreter. Virtual environmentsĪ virtual environment is a built-in way to create an environment. These environments allow you to install packages without affecting other environments, isolating your workspace's package installations. There are two types of environments that you can create for your workspace: virtual and conda. Tip: In Python, it is best practice to create a workspace-specific environment, for example, by using a local environment. Any packages that you install or uninstall affect the global environment and all programs that you run within it. For example, if you just run python, python3, or py at a new terminal (depending on how you installed Python), you're running in that interpreter's global environment. Types of Python environments Global environmentsīy default, any Python interpreter installed runs in its own global environment. Note: If you'd like to become more familiar with the Python programming language, review More Python resources. Configure IntelliSense for cross-compilingĪn "environment" in Python is the context in which a Python program runs that consists of an interpreter and any number of installed packages.A virtual environment is a self-contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages. Setting Up a Python Virtual Environmentīefore we dive into changing the backend, let’s set up a Python virtual environment. On the other hand, if you’re working in a Jupyter notebook, you might want to use an interactive backend for inline plots. For instance, if you’re working in a headless environment (like a server without a display), you’ll need a non-interactive backend. Why Change the Backend?ĭifferent backends offer different functionalities and compatibility. Matplotlib supports a variety of backends, including interactive backends (e.g., TkAgg, GTKAgg, Qt5Agg, WxAgg) and non-interactive backends for generating image files (e.g., Agg, SVG, PDF, PS). The backend provides the concrete implementation of the abstract interface to the graphics system. Matplotlib, a versatile Python library for data visualization, operates on a system of backends. This blog post will guide you through the process of changing the matplotlib backend in a Python virtual environment. However, you may have encountered issues related to the backend used by matplotlib. | Miscellaneous Change Matplotlib Backend in Python Virtualenv: A GuideĪs data scientists, we often find ourselves working with Python’s matplotlib library for data visualization.
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