- Chances are,
if you're on a Mac or a Windows laptop,
you already have Python installed on your computer.
There's also a really good chance
that this is an outdated version of Python.
So one of the first things you'll need to do
is head over to the Python.org website
and make a decision
on how you want to run your Python programs.
There are many ways to run Python programs,
and it can get a little bit confusing as a beginner
to understand the differences between each of these.
Looking at the Python.org website,
as of writing this course,
in addition to the reference implementation,
there were five additional implementations of Python,
as well as eight repackages of the reference implementation
that provide additional libraries
that are meant for a specific application.
If all of this is making you dizzy,
don't worry.
You can always start
by simply going to the Python.org website,
downloading the reference implementation of Python,
which is linked on the homepage.
And after you download that,
you're ready to start writing your first programs.
But what exactly are all those other things
that are listed on the website?
Python is really just an agreed upon way
in which a user can write instructions to the computer.
Those instructions need to be interpreted
and turned into machine language.
The reference implementation of Python
uses the C language to do this.
So you'll often see Python also referred to as C Python.
There are other implementations though,
that do this process using other languages,
such as Jython,
which runs Python on the Java virtual machine,
and IronPython,
which runs Python on .NET.
As a beginner, however,
using the reference implementation should be just fine.
Another popular approach for running Python
that's worth mentioning is Anaconda.
Anaconda is a distribution of Python
that includes popular, open source libraries
for data science,
including NumPy and pandas,
and also the statistical programming language R.
In other words,
Anaconda just packages up a bunch of tools
that are especially popular in data science
to make it faster and easier for data scientists
to do their work.
As you can see,
it's easy to get started with Python.
So pick your distribution and get coding today.