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Let's take a look at a detailed example that uses the new array programming paradigm. We'll have NumPy do some linear algebra for us!
Code
orders = np.array([
[2, 0, 0, 0],
[4, 1, 2, 2],
[0, 1, 0, 1],
[6, 0, 1, 2]
])
totals = np.array([3, 20.50, 10, 14.25])
Learn More
- Build a Learning Mindset
- Wikipedia - Array Programming
- Khan Academy - Linear Algebra <== Wonderful courses
- NumPy also has a
matrixdatatype. This is for historical reasons. Learn more about array vs. matrix
My Notes from Manipulation
## Array Manipulation
* The documentation on [Array Manipulation](https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html) is a good one to keep bookmarked.
* `ndarray.reshape` creates a view with a new shape
* You can use `-1` as a value to infer the missing dimension
* `ndarray.ravel` returns a single dimensional view of the array.
* `ndarray.flatten` can be used to make a single dimensional copy.
* `np.lookfor` is great for searching docstrings from within a notebook.
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[MUSIC]
0:00
You've done an excellent job
learning all about NumPy arrays.
0:04
You can create, inspect, and modify them.
0:08
You can manipulate them into new shapes,
and
0:10
you can filter out
the values you don't want.
0:12
And now, I'd like to take some to
show off some common use cases for
0:14
these arrays that we've
been getting to know.
0:17
But before we dive in here,
0:20
I'd like to take a second to discuss
a phenomenon known as imposter syndrome.
0:21
Imposter syndrome happens when
you doubt your skills, and
0:28
you feel like you really aren't a pro.
0:30
You feel like a fraud.
0:32
You feel like everyone
else knows more than you.
0:33
It's an unfortunate and
quite common feeling, and
0:36
I want you to know that you
aren't alone in experiencing it.
0:38
And I especially want you to know
that you aren't actually a fraud and
0:41
that you can do this.
0:45
Because of the numerous used cases
of NumPy arrays, you're bound to
0:46
see lots of math equations and
formulas that you don't yet understand.
0:49
You might have learned once and
then forgot.
0:54
What happens is you think,
man I only know this much stuff,
0:56
look how much everyone else knows.
0:59
But here's the thing,
very few people know it all.
1:01
They all know their own little bit,
and you can and
1:04
will learn the extra little bits
when you actually need them.
1:08
This is important to remember.
1:10
That feeling is bound to happen to you and
I just want you to be ready for it.
1:12
Approach it like, hey, I'm not sure how
to do that, yet, cuz you will learn it.
1:16
And look at you,
you're doing great already.
1:20
I want you to approach things
with the growth mindset.
1:23
Check the teacher's notes for
more of that.
1:25
We are going to be doing a different
type of programming than we've been
1:27
doing up until now on our
Python journey together.
1:30
We are going to be doing what
is known as Array Programming.
1:33
Instead of our typical way of looping
through a collection of data and
1:36
pulling out values one by
one to perform operations,
1:40
what we do in array programming
is to perform the operation on
1:42
the entire set of values,
all at once, in one fell swoop.
1:45
Not only is it faster, it's also
expressed much more concisely in code.
1:49
Array programming code often ends up
looking a lot like a math equation.
1:54
We've talked about how an array
is referred to as a vector and
1:57
a two dimensional array as a matrix.
2:01
Well the reason for
2:03
that is because we use these arrays to
represent their mathematical equivalent.
2:04
It's probably a little too abstract.
2:09
Let me show you what I
mean by that statement.
2:11
Well, I've got an idea,
let's do some linear algebra.
2:13
Why the long face?
2:17
That doesn't sound fun?
2:18
Did you forget how to do it too?
2:20
Don't worry.
2:21
All we have to do is set up the equation
properly using the correct layout, and
2:22
NumPy is going to do all of the work for
us.
2:27
I had a moment recently where I had to
take back one of those famous quips that
2:29
we all make at one point in our lives,
regarding stuff we're learning.
2:32
You know the one.
2:36
When am I ever going to need this?
2:36
Well, probably not very surprising to you
by now, but this example involves tacos.
2:39
There's a new taco food cart that
just opened by our office, and
2:43
I like to share my love of
tacos with my coworkers.
2:46
So I often offer to do pickups for
the office.
2:49
And if you've ever been in charge of
a taco run, you know that one of the main
2:52
problems is that you're never quite
sure of the price of the items.
2:55
So I'm usually just like, just pay me
later, I'll put on my credit card.
2:58
And sometimes determining that
price from the total is easy.
3:02
Like in the case where you have
two tacos and the total is $3.
3:05
You can do that kind of math in your head,
right?
3:08
So you might not even know how you did it,
but
3:11
you know that a single taco costs $1.50.
3:13
That's pretty simple if that
was the only item in the order,
3:16
but that's never the case, right?
3:19
So I know one of the larger
orders I bought.
3:21
It was four tacos, one burrito,
two horchatas and two Mexican cokes,
3:23
and the total was $20.50.
3:26
Nothing is better than a Mexi-Coke
in a bottle, real sugar.
3:28
With that information though,
3:34
I have no idea how to figure
out the price breakdown.
3:35
It's definitely not as that two tacos
equation that we did in our head.
3:38
And this is because there are so
many other variables now.
3:41
But I seem to remember from math,
that if you have enough equations,
3:44
you should be able to solve them.
3:48
So let's see, do I have more data?
3:50
[LAUGH] Of course I do.
3:51
I went once, and I got a burrito and
a Mexi-Coke, and that was 10 bucks.
3:53
And another office pick,
if I get six tacos, one horchata,
3:57
and two Mexi-Cokes, and that was $14.25.
4:00
Now, one thing I remember from math is
we're going to need to make sure that
4:03
everything is lined up.
4:07
That means I need to make
sure that I enter a 0 for
4:08
all the items I didn't purchase, and
we get some nice looking equations here.
4:11
Now, if that doesn't look
like a math equation to you,
4:15
why don't we change those variables
to their typical looking form.
4:18
Don't let those freak you out though.
4:21
Remember, they're just representing
tacos and burritos and whatnot.
4:23
So, really this left side here
kind of looks like rows and
4:26
columns almost, doesn't it?
4:28
In fact, let's make the type
of food the heading here.
4:30
And what we have left here is the number
of items, or the coefficient.
4:33
In mathematics, to denote a matrix,
you will see hard brackets like this.
4:37
And for our totals, we can consider
this as a single dimensional array,
4:41
or as it's called, a vector.
4:45
A row base vector to be precise.
4:47
And with our systems of equations as
a matrix, and our totals as a vector,
4:50
we should be able to solve for it.
4:53
There are definitely rules and
4:56
ways to go about solving that
system of linear equations.
4:57
But what do you say we let
NumPy do the work for us?
5:00
In the teacher notes,
attached to this video,
5:03
I've added the equations
to create our matrix.
5:05
So I'm gonna copy and
paste it here in this cell.
5:07
So here is our orders array,
and that's our matrix, right,
5:10
that's what it looks like.
5:13
It's an array with a rank of two,
it's got two dimensions, and
5:14
each row here represents an order, right?
5:17
And this is the number of tacos,
this first column here is tacos, right.
5:21
And then the next one is,
next one being burritos, and
5:27
then horchata, and coke, right.
5:32
And now we need to get
our constant prices.
5:35
So that's also in the teacher's notes.
5:38
Let me just paste that here.
5:39
So these are the totals
of what came across.
5:40
Scroll this up a little bit.
5:47
Here we go, so we get our $3, 20.50 for
the second order, 10, and 14.25.
5:49
So really what we've done here is
to build the math equation, right?
5:54
Well, now for the awesome part.
6:00
We don't actually need to know how to
solve this, we just need to know that it's
6:01
a system of linear equations
that we're trying to solve for.
6:05
And we also need to know
that there is a module in
6:08
the NumPy library for linear algebra.
6:12
It's shortened to linalg.
6:16
And here is the beautiful method.
6:17
Solve, and
we pass [LAUGH] in our coefficient matrix,
6:22
which was our orders, and
we also pass it our totals.
6:24
So we say totals like so.
6:29
And then let's go ahead and
take a look at what prices is.
6:31
[LAUGH] Look at that,
$1.5 tacos, we knew that,
6:34
8 bucks burritos, $1.25 horchatas,
and a Mexican Coke for 2 bucks.
6:39
[LAUGH] Please note here that coke is
more expensive than a taco already.
6:44
But well worth it.
6:49
Wait second, if we have this solution,
6:50
I think we should be able to verify it,
right.
6:53
I mean, if we just plugged in
these prices to the equations,
6:55
it should produce a total, right?
6:59
Shouldn't it?
7:02
So let's see.
7:03
Naturally in Python, I guess we would just
loop through the matrix and then loop over
7:04
the prices and multiply them together,
and then we'd add them together, right?
7:09
And that should produce the price.
7:15
Now, we could definitely do that,
but let's remember that
7:17
one of the benefits of array programming
is that we don't need to rely on loops.
7:21
Also, if there's a mathematical
way to do something,
7:25
most likely there's
a way to do it in NumPy.
7:27
Now what we are talking about here,
multiplying matrices with vectors, and
7:29
then summing them together is
actually a common math practice.
7:33
It is referred to as the inner product,
and it's usually represented with a dot.
7:36
So you would say, A.B, that's exactly
what we would've done in a loop but
7:40
expressed in mathematical terms,
the notation, right?
7:46
So in Python though, that dot that I just
created, that doesn't have any meaning.
7:50
So instead,
we can use the at sign, like so.
7:53
So if we say orders @ prices,
7:58
let's see what happens.
8:03
So check that out.
8:07
That is what our totals are.
8:08
Look at that, it adds up.
8:09
So it took these and
8:10
ran them through the equations and
ended up with the same totals.
8:11
So we know that it works.
8:15
And so really this is shorthand for
8:17
orders.dot (prices),
see it's the same thing.
8:20
Pretty cool how we can use these shortcuts
to build and solve equations, right?
8:25
It's super powerful, and
8:30
I'm glad that you have all those array
manipulation powers in your bag of tricks.
8:31
As I know you're aware,
8:36
linear algebra has many more applications
than just taco price calculation.
8:37
It's great for times, like we just saw,
when you're trying to solve for
8:41
missing variables in a bunch of equations.
8:45
And remember, this is just one formula.
8:47
There are so many more.
8:50
And many of those formulas
that are available for
8:52
you to use will most
likely not be familiar.
8:54
Remember, that's okay.
8:57
No one knows them all.
8:59
You'll learn them as you need them.
9:00
Don't let the options overwhelm you.
9:02
Let them make you feel empowered.
9:03
You don't need to know how to solve them
yourself, and that should feel great.
9:05
That linalg.solve function
is a bit of an abstraction.
9:10
It really is doing all that work that
you would have had to do by hand,
9:13
like you might have done in math class.
9:16
It's very similar to how we
could've written that dot loop.
9:18
There's actually a function that
does all the heavy lifting for us.
9:22
What do you say we take a quick look at
some more common vector based operations
9:25
that you'll encounter as you progress.
9:29
Let me show you off NumPy's
universal functions or ufuncs.
9:31
First though, let's take a quick break.
9:36
And why don't you jot down some thoughts
and notes about what we just accomplished,
9:37
all without using the loops.
9:41
Yeah, quick reminder, make sure to check
out the teacher's notes on this video.
9:42
I've dropped a lot of information about
where to learn more about linear algebra,
9:46
if that got you re-invigorated.
9:49
And like I said,
it's no big deal if it didn't.
9:51
There are many more use cases for NumPy.
9:54
See you soon and we'll compare notes.
9:57
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