Python Environment


To avoid errors later, it's best to update all the packages in the default environment. Open the Anaconda Prompt application. In the prompt, run the following commands:

conda upgrade conda
conda upgrade --all

If you are seeing the following "conda command not found" and are using ZShell, you have to do the following:

export PATH="/Users/username/anaconda/bin:$PATH"

or update above command line to your .zsh_config file.

Once you have Anaconda installed, managing packages is fairly straightforward. To install a package, type

conda install package_name 

in your terminal. For example, to install numpy, type

conda install numpy.

You can install multiple packages at the same time. Something like conda install numpy scipy pandas will install all those packages simultaneously. It's also possible to specify which version of a package you want by adding the version number such as

conda install numpy=1.10.

Conda also automatically installs dependencies for you. For example scipy depends on numpy, it uses and requires numpy. If you install just scipy (conda install scipy), Conda will also install numpy if it isn't already installed.

Most of the commands are pretty intuitive. To uninstall, use

conda remove package_name

To update a package

conda update package_name

If you want to update all packages in an environment, which is often useful, use

conda update --all

And finally, to list installed packages, it's

conda list 

If you don't know the exact name of the package you're looking for, you can try searching with

conda search search_term

For example, I know I want to install Beautiful Soup, but I'm not sure of the exact package name. So, I try

conda search beautifulsoup


Conda can be used to create environments to isolate your projects. To create an environment, use

conda create -n env_name list of packages in your terminal

Here -n env_name sets the name of your environment (-n for name) and list of packages is the list of packages you want installed in the environment. For example, to create an environment named my_env and install numpy in it, type

conda create -n my_env numpy

When creating an environment, you can specify which version of Python to install in the environment. This is useful when you're working with code in both Python 2.x and Python 3.x. To create an environment with a specific Python version, do something like

conda create -n py3 python=3 or conda create -n py2 python=2

I actually have both of these environments on my personal computer. I use them as general environments not tied to any specific project, but rather for general work with each Python version easily accessible. These commands will install the most recent version of Python 3 and 2, respectively. To install a specific version, use

conda create -n py python=3.3

for Python 3.3.

Once you have an environment created, use

source activate my_env 

to enter it on OSX/Linux. On Windows, use

activate my_env

When you're in the environment, you'll see the environment name in the terminal prompt. Something like (my_env) ~ $. The environment has only a few packages installed by default, plus the ones you installed when creating it. You can check this out with conda list. Installing packages in the environment is the same as before:

conda install package_name

Only this time, the specific packages you install will only be available when you're in the environment. To leave the environment, type

source deactivate (on OSX/Linux)

On Windows, use


Saving and loading environments

A really useful feature is sharing environments so others can install all the packages used in your code, with the correct versions. You can save the packages to a YAML file with

conda env export > environment.yaml

The first part

conda env export 

writes out all the packages in the environment, including the Python version. Above you can see the name of the environment and all the dependencies (along with versions) are listed. The second part of the export command,

> environment.yaml 

writes the exported text to a YAML file environment.yaml. This file can now be shared and others will be able to create the same environment you used for the project.

To create an environment from an environment file use

conda env create -f environment.yaml

This will create a new environment with the same name listed in environment.yaml.

Listing environments

If you forget what your environments are named (happens to me sometimes), use

conda env list 

to list out all the environments you've created. You should see a list of environments, there will be an asterisk next to the environment you're currently in. The default environment, the environment used when you aren't in one, is called root.

Removing environments

If there are environments you don't use anymore,

conda env remove -n env_name 

will remove the specified environment (here, named env_name).

Using environments

One thing that’s helped me tremendously is having separate environments for Python 2 and Python 3. I used

conda create -n py2 python=2


conda create -n py3 python=3 

to create two separate environments, py2 and py3. Now I have a general use environment for each Python version. In each of those environments, I've installed most of the standard data science packages (numpy, scipy, pandas, etc.)

I’ve also found it useful to create environments for each project I’m working on. It works great for non-data related projects too like web apps with Flask. For example, I have an environment for my personal blog using Pelican.

Sharing environments

When sharing your code on GitHub, it's good practice to make an environment file and include it in the repository. This will make it easier for people to install all the dependencies for your code. I also usually include a pip requirements.txt file using pip freeze (learn more here) for people not using conda.

More to learn

To learn more about conda and how it fits in the Python ecosystem, check out this article by Jake Vanderplas: Conda myths and misconceptions. And here's the conda documentation you can reference later.