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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!
Learn More
 Build a Learning Mindset
 Wikipedia  Array Programming
 Khan Academy  Linear Algebra <== Wonderful courses
 NumPy also has a
matrix
datatype. This is for historical reasons. Learn more aboutarray
vs.matrix
My Notes from Manipulation
## Array Manipulation
* The documentation on [Array Manipulation](https://docs.scipy.org/doc/numpy/reference/routines.arraymanipulation.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]

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You've done an excellent job learning all about NumPy arrays.

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You can create, inspect, and modify them.

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You can manipulate them into new shapes, and

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you can filter out the values you don't want.

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And now, I'd like to take some to show off some common use cases for

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these arrays that we've been getting to know.

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But before we dive in here,

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I'd like to take a second to discuss a phenomenon known as imposter syndrome.

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Imposter syndrome happens when you doubt your skills, and

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you feel like you really aren't a pro.

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You feel like a fraud.

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You feel like everyone else knows more than you.

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It's an unfortunate and quite common feeling, and

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I want you to know that you aren't alone in experiencing it.

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And I especially want you to know that you aren't actually a fraud and

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that you can do this.

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Because of the numerous used cases of NumPy arrays, you're bound to

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see lots of math equations and formulas that you don't yet understand.

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You might have learned once and then forgot.

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What happens is you think, man I only know this much stuff,

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look how much everyone else knows.

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But here's the thing, very few people know it all.

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They all know their own little bit, and you can and

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will lean the extra little bits when you actually need them.

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This is important to remember.

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That feeling is bound to happen to you and I just want you to be ready for it.

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Approach it like, hey, I'm not sure how to do that, yet, cuz you will learn it.

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And look at you, you're doing great already.

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I want you to approach things with the growth mindset.

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Check the teacher's notes for more of that.

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We are going to be doing a different type of programming than we've been

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doing up until now on our Python journey together.

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We are going to be doing what is known as Array Programming.

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Instead of our typical way of looping through a collection of data and

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pulling out values one by one to perform operations,

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what we do in array programming is to perform the operation on

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the entire set of values, all at once, in one fell swoop.

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Not only is it faster, it's also expressed much more concisely in code.

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Array programming code often ends up looking a lot like a math equation.

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We've talked about how an array is referred to as a vector and

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a two dimensional array as a matrix.

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Well the reason for

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that is because we use these arrays to represent their mathematical equivalent.

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It's probably a little too abstract.

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Let me show you what I mean by that statement.

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Well, I've got an idea, let's do some linear algebra.

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Why the long face?

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That doesn't sound fun?

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Did you forget how to do it too?

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Don't worry.

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All we have to do is set up the equation properly using the correct layout, and

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NumPy is going to do all of the work for us.

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I had a moment recently where I had to take back one of those famous quips that

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we all make at one point in our lives, regarding stuff we're learning.

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You know the one.

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When am I ever going to need this?

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Well, probably not very surprising to you by now, but this example involves tacos.

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There's a new taco food cart that just opened by our office, and

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I like to share my love of tacos with my coworkers.

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So I often offer to do pickups for the office.

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And if you've ever been in charge of a taco run, you know that one of the main

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problems is that you're never quite sure of the price of the items.

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So I'm usually just like, just pay me later, I'll put on my credit card.

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And sometimes determining that price from the total is easy.

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Like in the case where you have two tacos and the total is $3.

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You can do that kind of math in your head, right?

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So you might not even know how you did it, but

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you know that a single taco costs $1.50.

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That's pretty simple if that was the only item in the order,

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but that's never the case, right?

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So I know one of the larger orders I bought.

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It was four tacos, one burrito, two horchatas and two Mexican cokes,

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and the total was $20.50.

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Nothing is better than a MexiCoke in a bottle, real sugar.

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With that information though,

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I have no idea how to figure out the price breakdown.

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It's definitely not as that two tacos equation that we did in our head.

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And this is because there are so many other variables now.

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But I seem to remember from math, that if you have enough equations,

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you should be able to solve them.

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So let's see, do I have more data?

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[LAUGH] Of course I do.

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I went once, and I got a burrito and a MexiCoke, and that was 10 bucks.

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And another office pick, if I get six tacos, one horchata,

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and two MexiCokes, and that was $14.25.

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Now, one thing I remember from math is we're going to need to make sure that

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everything is lined up.

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That means I need to make sure that I enter a 0 for

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all the items I didn't purchase, and we get some nice looking equations here.

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Now, if that doesn't look like a math equation to you,

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why don't we change those variables to their typical looking form.

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Don't let those freak you out though.

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Remember, they're just representing tacos and burritos and whatnot.

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So, really this left side here kind of looks like rows and

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columns almost, doesn't it?

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In fact, let's make the type of food the heading here.

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And what we have left here is the number of items, or the coefficient.

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In mathematics, to denote a matrix, you will see hard brackets like this.

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And for our totals, we can consider this as a single dimensional ray,

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or as it's called, a vector.

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A row base vector to be precise.

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And with our systems of equations as a matrix, and our totals as a vector,

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we should be able to solve for it.

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There are definitely rules and

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ways to go about solving that system of linear equations.

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But what do you say we let NumPy do the work for us?

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In the teacher notes, attached to this video,

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I've added the equations to create our matrix.

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So I'm gonna copy and paste it here in this cell.

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So here is our orders array, and that's our matrix, right,

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that's what it looks like.

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It's an array with a rank of two, it's got two dimensions, and

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each row here represents an order, right?

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And this is the number of tacos, this first column here is tacos, right.

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And then the next one is, next one being burritos, and

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then horchata, and coke, right.

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And now we need to get our constant prices.

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So that's also in the teacher's notes.

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Let me just paste that here.

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So these are the totals of what came across.

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Scroll this up a little bit.

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Here we go, so we get our $3, 20.50 for the second order, 10, and 14.25.

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So really what we've done here is to build the math equation, right?

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Well, now for the awesome part.

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We don't actually need to know how to solve this, we just need to know that it's

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a system of linear equations that we're trying to solve for.

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And we also need to know that there is a module in

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the NumPy library for linear algebra.

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It's shortened to linalg.

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And here is the beautiful method.

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Solve, and we pass [LAUGH] in our coefficient matrix,

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which was our orders, and we also pass it our totals.

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So we say totals like so.

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And then let's go ahead and take a look at what prices is.

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[LAUGH] Look at that, $1.5 tacos, we knew that,

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8 bucks burritos, $1.25 horchatas, and a Mexican Coke for 2 bucks.

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[LAUGH] Please note here that coke is more expensive than a taco already.

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But well worth it.

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Wait second, if we have this solution,

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I think we should be able to verify it, right.

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I mean, if we just plugged in these prices to the equations,

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it should produce a total, right?

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Shouldn't it?

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So let's see.

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Naturally in Python, I guess we would just loop through the matrix and then loop over

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the prices and multiply them together, and then we'd add them together, right?

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And that should produce the price.

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Now, we could definitely do that, but let's remember that

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one of the benefits of array programming is that we don't need to rely on loops.

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Also, if there's a mathematical way to do something,

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most likely there's a way to do it in NumPy.

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Now what we are talking about here, multiplying matrices with vectors, and

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then summing them together is actually a common math practice.

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It is referred to as the inner product, and it's usually represented with a dot.

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So you would say, A.B, that's exactly what we would've done in a loop but

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expressed in mathematical terms, the notation, right?

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So in Python though, that dot that I just created, that doesn't have any meaning.

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So instead, we can use the at sign, like so.

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So if we say orders @ prices,

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let's see what happens.

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So check that out.

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That is what our totals are.

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Look at that, it adds up.

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So it took these and

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ran them through the equations and ended up with the same totals.

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So we know that it works.

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And so really this is shorthand for

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orders.dot (prices), see it's the same thing.

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Pretty cool how we can use these shortcuts to build and solve equations, right?

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It's super powerful, and

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I'm glad that you have all those array manipulation powers in your bag of tricks.

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As I know you're aware,

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linear algebra has many more applications than just taco price calculation.

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It's great for times, like we just saw, when you're trying to solve for

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missing variables in a bunch of equations.

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And remember, this is just one formula.

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There are so many more.

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And many of those formulas that are available for

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you to use will most likely not be familiar.

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Remember, that's okay.

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No one knows them all.

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You'll learn them as you need them.

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Don't let the options overwhelm you.

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Let them make you feel empowered.

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You don't need to know how to solve them yourself, and that should feel great.

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That linalg.solve function is a bit of an abstraction.

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It really is doing all that work that you would have had to do by hand,

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like you might have done in math class.

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It's very similar to how we could've written that dot loop.

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There's actually a function that does all the heavy lifting for us.

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What do you say we take a quick look at some more common vector based operations

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that you'll encounter as you progress.

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Let me show you off NumPy's universal functions or ufuncs.

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First though, let's take a quick break.

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And why don't you jot down some thoughts and notes about what we just accomplished,

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all without using the loops.

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Yeah, quick reminder, make sure to check out the teacher's notes on this video.

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I've dropped a lot of information about where to learn more about linear algebra,

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if that got you reinvigorated.

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And like I said, it's no big deal if it didn't.

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There are many more use cases for NumPy.

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See you soon and we'll compare notes.
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