Conda frequent commands
When working on a Python project, it's considered good practice to create a virtual environment. In this case, every project is running in a separate environment, with their own dependencies. This can save you tons of headache - if you install all your projects' dependencies in one place, things may quickly crash with each other.
I personally learned this lesson the hard way after spending an entire night trying to fix version crashes. Eventually, I had no choice but to delete everything and start from scratch.
You certainly do not want to experience the same frustration as me - and you don't have to. With conda virtual environment, all your projects have their own territory to play and there is zero chance for crashing with each other.
To gain a proper understanding of conda, the following resources will be helpful. Additionally, some commonly used commands will be extracted and listed in this blog post for easy reference.
- Managing conda - Conda
- Managing environments - Conda
- Managing packages - Conda
- Managing Python - Conda
- Managing virtual packages - Conda
Managing conda
| task | command |
|---|---|
| view conda version | conda --version |
| update conda | conda update conda |
Managing environments
| task | command |
|---|---|
| view all environments | conda env list |
| create new environment | conda create -n <env_name> python=<version> |
| remove environment | conda remove -n <env_name> --all |
| activate environment | conda activate <env_name> |
| deactivate environment | conda deactivate |
P.S. Before removing an environment, deactivate the environment in all shells first.
Managing packages
| task | command |
|---|---|
| view all packages | conda list |
| searching for packages | conda search <pkg_name> |
| installing package | conda install (--name <env_name>) <pkg_name>(=<version>) |
Batch installation with requirements.txt
You can list all dependencies of your project in a requirements.txt file. It's very convenient for setting up your project in another machine - for example, you bought a new laptop/desktop, or a friend want to run your scripts in his computer.
Prepare a requirements.txt, like this one:
numpy == <version>
pandas >= <version>
scipy != <version>
matplotlib <= <version>
....
And then, create a new virtual environment for it and batch install dependencies with pip:
P.S. According to conda's official documentation, issues may arise when using pip and conda together. When combining conda and pip, it is best to use an isolated conda environment. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software.
conda create -n <env_name> python=<version>
conda activate <env_name>
pip install -r requirements.txt
Then you are all set👏