Does Conda replace the need for virtualenv? Does Conda replace the need for virtualenv? python python

Does Conda replace the need for virtualenv?

  1. Conda replaces virtualenv. In my opinion it is better. It is not limited to Python but can be used for other languages too. In my experience it provides a much smoother experience, especially for scientific packages. The first time I got MayaVi properly installed on Mac was with conda.

  2. You can still use pip. In fact, conda installs pip in each new environment. It knows about pip-installed packages.

For example:

conda list

lists all installed packages in your current environment.Conda-installed packages show up like this:

sphinx_rtd_theme          0.1.7                    py35_0    defaults

and the ones installed via pip have the <pip> marker:

wxpython-common                    <pip>

Short answer is, you only need conda.

  1. Conda effectively combines the functionality of pip and virtualenv in a single package, so you do not need virtualenv if you are using conda.

  2. You would be surprised how many packages conda supports. If it is not enough, you can use pip under conda.

Here is a link to the conda page comparing conda, pip and virtualenv:

Virtual Environments and pip

I will add that creating and removing conda environments is simple with Anaconda.

> conda create --name <envname> python=<version> <optional dependencies>> conda remove --name <envname> --all 

In an activated environment, install packages via conda or pip:

(envname)> conda install <package>(envname)> pip install <package>

These environments are strongly tied to conda's pip-like package management, so it is simple to create environments and install both Python and non-Python packages.


In addition, installing ipykernel in an environment adds a new listing in the Kernels dropdown menu of Jupyter notebooks, extending reproducible environments to notebooks. As of Anaconda 4.1, nbextensions were added, adding extensions to notebooks more easily.


In my experience, conda is faster and more reliable at installing large libraries such as numpy and pandas. Moreover, if you wish to transfer your the preserved state of an environment, you can do so by sharing or cloning an env.