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So let's demystify the Python
runtime environment a bit.

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Up until now, you've
been learning

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how the code itself works.

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That is, how the
Python interpreter,

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which is a computational process

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running on your computer,

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considers the commands you
give it, which are loops,

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control structures,
variables, functions,

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and how it executes

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the underlying code on hardware
such as the processor,

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video card, and memory
of your computer,

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to create some experience
for the user.

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In much of this course,

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you've been using a web-based
simulation environment,

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Runestone, to create
these experiences.

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With Jupyter though,
we'll be using

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an environment that
is more traditional.

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A key aspect of
this traditional environment

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are the installation files
for Python itself.

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Let's take a look. I'm going
to open up a new terminal.

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Don't worry if this
seems unfamiliar to you,

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we won't be using the terminal
much in this course.

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I just want to help you explore
the Jupyter system a bit.

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You'll notice that
there are a bunch of

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characters when we
open up the terminal.

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The first set of
characters are username.

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You could ignore this.

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It should be the same Jovyan
for all Coursera users.

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Then there's an at sign,

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and the next set of characters,
are the machine name.

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Again, the machine name is just

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auto-generated by
the Coursera system,

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and isn't really relevant for
our discussion right now.

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Finally, the rest
of the string is

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the current path or the location
that we're working in.

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This is useful, and actually
if we type the characters

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l and s in there for
list and hit Enter,

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we'll see a list of all files

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and sub folders in
this directory.

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But I actually want
to show you where

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Python lives on this machine.

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So we're going to change

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the directory with
the command cd.

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On the Coursera system,

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we're using a
specific installation

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for Python called Anaconda.

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Don't worry too much about that.

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Let's just change to
the site-packages directory.

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This is the real heart of
the Python library ecosystem.

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So I'll go cd/opt/Conda/lib/
Python3.7/site-packages.

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When we do this,

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then do a directory listing,

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we see a whole bunch of things.

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First, there's a lot of

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interesting files and
directories in here.

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These are the third party
packages for Python,

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which are installed
on the system.

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We're going to be dealing
with a lot of new packages,

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but I want you to feel
empowered to explore a bit.

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These packages are
just Python files,

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or sometimes other
languages as well,

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which have been
configured to work with

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the current Python environment.

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Let's take a look at one that I'm

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familiar with called pillow.

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Pillow is an imaging
library for Python.

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We can see that
it's installed here

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because there's
a pillow egg file.

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We can actually look
at the source code of

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the Pillow library by going
into the pill directory.

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So let's cd PIL.

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You'll see that when we do an ls,

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most of these files
are just dot py files.

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Python code itself.

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We can even take a look at
the Python source code behind

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this library using the
more command here.

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Let's look at
the main python file in

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this library called image.py.

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So more image.py.

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We can see that there's
a bunch of comments at the top

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reaching all the
way back to 1995.

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We see a few import statements,

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then some top-level
variables like a logger.

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We won't talk much about loggers,

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but they're handy
when debugging code.

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Then we see that there
are a few classes

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which are created,

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then a math expression,

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then a try and except
block, and so forth.

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You can feel free to explore

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this library more by
hitting the space bar,

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or you can exit with
more command by hitting q.

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So that's a very
brief overview of

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where Python libraries
exist on your system.

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Now, that's not exactly
a user friendly way to

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interact with the library
by reading the source code,

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but it's a great way to
learn how libraries work,

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and how programmers create
complex Python solution.

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But let's go back into
the Jupyter notebook and

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explore how to actually
use this library.