Heads up! To view this whole video, sign in with your Courses account or enroll in your free 7-day trial. Sign In Enroll
Well done!
You have completed Introduction to NumPy!
You have completed Introduction to NumPy!
Preview
Let's get started!
Related Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign upRelated Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign up
[MUSIC]
0:00
Hi, I'm Craig and I'm a developer.
0:09
In this course we're going to be taking
a look at Python's wonderful data
0:11
library, NumPy.
0:14
You'll find NumPy in all
sorts of applications, and
0:16
therefore, it's fairly critical that you
have an understanding of its fundamentals.
0:18
It appears in every direction
you may head in Python.
0:23
Should you plan to get into data analysis,
there's scientific computing, or
0:25
even machine learning,
you're going to bump into NumPy and
0:28
that's what this course is all about.
0:31
It's an introduction.
0:33
I want to introduce you
to the library early on.
0:35
I'll walk through hands-on examples
that will give you a great introduction
0:37
to the library, its main concepts and
the surrounding terminology.
0:40
When you've complete the course,
you'll have a great foundation and
0:44
you'll know where to turn when you
need more specific information.
0:46
But, before we get started, let's take
some time to make sure that you're
0:50
familiar with your learning environment.
0:54
First off, there are some prerequisites to
this course, and I'd love for you to make
0:56
sure that we're on the same page about
where you are in your coding journey.
0:59
There's speed control on the video player,
so please feel free to speed me up or slow
1:03
me down, pause me, make me repeat myself,
whatever you like, I won't mind at all.
1:08
You are in complete
control of your learning.
1:12
A quick reminder,
there are notes attached to each video.
1:15
This section is usually filled with
additional information that will
1:18
enhance your knowledge should you want
to dive deeper into related topics.
1:21
Get in the habit of checking this space,
and
1:24
I'll do my best to remind you when I've
put info there that you just have to see.
1:26
One more tip,
1:32
remember that there is a community of
fellow learners also taking this course.
1:33
I encourage you to lean on each other.
1:36
If you have a question,
make sure to ask it.
1:38
Our community is very friendly and
approachable.
1:40
Also, remember,
1:43
nothing helps to cement your learning
better than answering a question.
1:43
Make sure to check out the community
throughout the course, and
1:47
see if you can help out a fellow learner.
1:49
We've established that NumPy is
extremely popular in many fields of
1:52
the Python landscape.
1:55
But what is it exactly?
1:57
NumPy is short for numerical Python.
1:59
It deals with numbers.
2:02
So that makes sense, all those
applications that I mentioned would indeed
2:03
need to use numbers and
math equations in some shape or form.
2:07
But as someone who is actively learning
Python, you might cleverly state,
2:11
wait a second, I can use numbers and
do math just fine in plain old Python.
2:15
What's the big deal?
2:19
Why do we need this?
2:20
That's a wonderful question and
the short answer is that NumPy is much
2:21
faster than the straight Python approach,
no matter how great of a coder you are.
2:25
It leans on a paradigm which we'll
get into here shortly called
2:29
array programming.
2:32
It completely removes the need to loop
over your data which speeds things up
2:33
tremendously.
2:37
NumPy also provides additional
mathematical abilities
2:39
not available in standard Python.
2:42
Many numerical concepts have
been extracted away for you and
2:44
provided as functions.
2:47
Chances are you probably aren't going
to use all of those helper functions.
2:49
However, the applications
that you're building, you know,
2:53
the ones that are relying on the library,
they most likely will.
2:55
NumPy exposes concepts from linear
algebra, matrix multiplication,
2:59
fourier transformations and many more
themes that you might remember from
3:03
your math class if math
is in your area of study.
3:07
Now, just a heads up,
it's totally fine is math isn't your jam.
3:10
It really doesn't need to be.
3:14
That's kind of the beauty
of these abstractions.
3:15
You'll use them when you need them.
3:17
My advice is just to stay focus
on where we're headed and
3:19
don't let the shiny tools and
terms distract you too much.
3:22
I'll point out what I think is important
at this part of your learning journey.
3:25
Now, believe it or
3:28
not, that was the short answer to the why
would you want to use NumPy question.
3:29
The long answer is gonna
take me a couple of videos
3:34
to get you to see
the beauty that is NumPy.
3:37
One of the more challenging tasks
of picking up NumPy is simply just
3:40
remembering to how to use
the object that it provides.
3:43
So I was thinking of facing
that challenge head on.
3:46
Let's do this.
3:49
Let's build a Jupiter Notebook together.
3:49
And then you'd have a reference, and we
can kind of treat it like a cheat sheet.
3:52
You can then quickly glance at it or
3:55
even practice some more with
the datasets that we build up.
3:57
Sound good?
3:59
Speaking of practice,
that gives me a great idea.
4:01
Have you heard of the movement
called 100 days of code?
4:03
It's a wonderful idea that the life-long
learner, Alexander Callaway came up with.
4:06
The way it works is this,
4:11
you publicly commit to coding at
least an hour a day for 100 days.
4:12
You post about it on social media,
usually Twitter, and
4:16
you hold yourself accountable.
4:19
It's wonderful for learning.
4:21
Steady practice will
strengthen your skills.
4:23
It creates a great habit of learning.
4:25
It also seems like a great way to
explore the NumPy array data structure.
4:27
We can use it to track and
analyze our time.
4:32
The only downside that I can see is that
it might create some pretty mega tweets.
4:34
If you are committing to learning NumPy
and creating a log to help track and
4:39
analyze your 100 days of code in NumPy,
4:42
reporting on your learning is going to
create a tongue twister of a tweet.
4:44
You'll figure it out.
4:48
So what are we waiting for?
4:50
What do you say we get things all set up?
4:51
You need to sign up for Treehouse in order to download course files.
Sign upYou need to sign up for Treehouse in order to set up Workspace
Sign up