PrintAnother option for packaging and distributing your Python programs is to use conda (we will discuss Anaconda a bit later in the lesson). Just like pip, it is a package manager. In addition, it is also an environment manager. What that means is that you can use conda to create virtual environments for Python, while specifying the packages you want to have available in that environment. A little more about that in a moment. Conda comes installed with ArcGIS Pro. While conda should be installed if you were able to install spyder in Lesson 1, we can doublecheck that it is by opening the Python Command Prompt and then typing in:
cd Scripts
followed by:
conda –-version
The output should show the conda version.
In order to find out what packages are installed type in:
conda list
Your output should look something like Figure 2.34:
The first column shows the package name, the second the version of the package. The third column provides clues on how the package was installed. You will see that for some of the packages installed, Esri is listed, showing they are related to the Esri installation. The list option of conda is useful, not only to find out if the package you need is already installed but also to confirm that you have the appropriate version.
Conda has the functionality to create different environments. Think of an environment as a sandbox – you can set up the environment with a specific Python version and different packages. That allows you to work in environments with different packages and Python versions without affecting other applications. The default environment used by conda is called base environment. We do not need to create a new environment, but, should you need to, the process is simple – here is an example:
conda create -n gisenv python=3.6 arcpy numpy
the –n flag is followed by the name of the environment (in this case gisenv), then you would choose the Python version which matches the one you already have installed (3.5, 3.6 etc.) and follow that up with a list of packages you want to add to it. If you later find out you need other packages to be added, you could use the install option of conda, for example:
conda install –n gisenv matplotlib
To activate an environment, you would run:
activate gisenv
And to deactivate an environment, simply:
deactivate
There are other options you can use with environments – you can clone them and delete them, for example. A great resource for the different options is Conda's Managing Environments page.