Conveniently manage Python virtual environments: use conda

Use conda to easily manage Python virtual environments

With the popularity of Python and the continuous expansion of its application fields, developers often need to use different Pythons on the same machine. versions and libraries. At this time, using a virtual environment becomes very important. Virtual environments can help us easily manage multiple independent Python environments on the same machine and avoid various version and dependency conflicts. In Python's virtual environment management, conda is a widely used tool.

conda is an open source package management and environment management tool for Python. It can help us create, manage and switch different Python virtual environments. Using conda to manage virtual environments makes it easier to install, update, and delete Python dependent libraries, while also ensuring the consistency of Python versions and dependent libraries. Next, this article will introduce how to use conda to easily manage Python virtual environments and provide specific code examples.

First, we need to install conda. conda can be installed through Anaconda or Miniconda. Anaconda is a Python distribution in the field of scientific computing. It contains many commonly used libraries for scientific computing, data analysis and machine learning. Miniconda is a more streamlined distribution that only contains conda and some basic Python libraries. Here we take Anaconda as an example to install.

1. Download the Anaconda installation package. You can find the Anaconda installation package on the https://www.anaconda.com/products/individual page, and select the version suitable for your operating system to download.
2. Run the installation package to install. Double-click the downloaded installation package and follow the installation wizard's prompts to install. After the installation is completed, the system environment variables will be automatically configured.

After the installation is complete, we can use the following command to check whether conda is installed correctly:

conda --version

Next, we can use conda to create a new Python virtual environment. When creating a virtual environment, we need to specify the Python version, the name of the virtual environment, and the required dependent libraries. The following is an example of creating a virtual environment named "myenv" and specifying the Python version as 3.7:

conda create -n myenv python=3.7

After the creation is completed, we can use the following command to activate the virtual environment:

conda activate myenv

After activating a virtual environment, the name of the virtual environment is displayed in front of the command line. At this time, running Python commands on the command line or installing new dependent libraries will be done in this virtual environment.

Next, we can use the following command to install the required dependent libraries:

conda install numpy

In this way, conda will automatically resolve the dependencies and install numpy and all the dependent libraries it requires.

If we wish to use a different version of Python, we can use the following command to create a new virtual environment:

conda create -n myenv2 python=3.8

Similarly, we can use the following command to activate the virtual environment and install it in it Required dependent libraries:

conda activate myenv2
conda install tensorflow

At this point, we can switch between different virtual environments by using the conda activate command. After using the virtual environment, you can use the following command to exit the virtual environment:

conda deactivate

In addition, we can also use the following command to list all created virtual environments:

conda info --envs

That’s it Basic steps and common commands to easily manage Python virtual environments using conda. Through conda, we can easily create, switch and manage multiple independent Python virtual environments, making Python development more flexible. It can not only improve development efficiency, but also ensure the consistency of Python versions and dependent libraries. I hope this article will be helpful to students who use conda to manage Python virtual environments.

References:

1. https://docs.conda.io/en/latest/
2. https://www.anaconda.com/

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