Python Projects for Beginners a ten-Week Bootcamp Approach to Python Programming

Activating the Virtual Environment

The second step in using a virtual environment is activating it. Activating an environment allows the computer to execute our scripts from a separate executable. By default, we use the Python executable file stored in our program’s directory. We can see the PATH of the executable by entering the following commands into the terminal: We need to activate the Python shell first:

$ python

Now we can view the PATH by typing in the following lines:

>>> import os
>>> import sys
>>> os.path.dirname(sys.executable)

You’ll notice that the PATH is your default folder where Python was originally installed. Go ahead and exit the Python shell once you’re done. We’ll come back to these same commands at the end of this section to see how the PATH has changed once the environment is activated.
Once you create the environment, you don’t need to create it again; you can simply activate it anytime you need to use it. Before you’re able to download libraries into the environment, you must first activate it. Depending on your operating system, write the following command in terminal: For Windows:

$ activate data_analysis

After activating the environment, you’ll see the name appear within parenthesis on the left side of the terminal. It will be shown like the following:
>>> (data_analysis) C:\Users...
For Mac/Linux:

$ source activate data_analysis

Like Windows, after activating the environment, you’ll see the name to the left of
your directory:
>>> (data_analysis) ~/Desktop...
If you can see the name on the side, you’ve successfully activated the environment. Before we move on, let’s see where our executable is now by running the same commands in the Python shell from the beginning of this section, to view the PATH of the executable:

>>> import os
>>> import sys
>>> os.path.dirname(sys.executable)

After running those same lines, you’ll notice that a different PATH has been output. This is the executable of our Conda environment that will be running our scripts. Now we can begin to install any libraries or packages we may need to work with.