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Hello again, and welcome to Chapter Nine
of Python, Dictionaries.

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As always, this lecture is copyright
Creative Commons Attribution.

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That means the audio, the video, the
slides, and even my scribbles.

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You can use them any way you like, as long
as you attribute them.

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Okay, so this is the second chapter

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where we're talking about collections, and
the collections

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are like a piece of luggage in that you
can put multiple things in them.

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Variables that we've talked about sort of
starting in

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Chapter Two and Chapter Three were simple
variables, scalar.

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They're just kind of one thing, and as
soon as you,

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like, put another thing in there, it
overwrites the first thing.

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And so if you look at the code, you know,
x = 2 and x = 4,

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the question is, you know, where did
the 2 go?

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Right? The 2 was there, x was there,

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there was a 2 in there, and then we cross
it out and put 4 in there.

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This is sort of the basic operation, the
assignment statement, it's a replacement.

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But a dictionary allows us to put more
than one thing.

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Not using this syntax, but it allows us to

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have a variable that's really an aggregate
of many values.

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And the difference between a list and a
dictionary

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is how the values are structured within
that single variable.

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The list is a linear collection,

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indexed by integers 0, 1, 2, 3.

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If there's five of them, it's 0 through 4,
very much like a

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Pringle's can here, where they're just
stacked nicely on top of each other.

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Everything's kind of organized. We talked
about it in the last, in the last lecture.

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This lecture we're talking about dictionaries.

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A dictionary's very powerful.

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It's, and its power comes from a different
way of organizing itself internally.

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It's a bag of values,

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like a just sort of, just stuff's
in it, it's not in any order.

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Big stuff, little stuff.

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Things have labels.

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You can also think of it like a purse with

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just things in it that's like, it's not
like stacked.

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It's just, stuff moves around as you're going

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and that's, that's a very good model for
dictionaries.

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And so dictionaries

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have to have a label because the stuff is
not in order.

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There's no such thing as the third thing.

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There is the thing with the label perfume.

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There's the thing with the label candy.

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There's the thing with the label money.

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And so there's the value, the thing, the money.

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And then there's always also the label.

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We also call these key/value.

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The key is the label and the value is
whatever.

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And so these pink things are all labels for

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various things you could put in your purse.

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So you could say to your purse, "hey purse,
give me my tissues."

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"Hey purse, give me my money."

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And it, it's in there somewhere and the
purse sort of

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gives you back the tissues or the money.

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And it's, Python's most powerful data
collection is the dictionaries.

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And it's when

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you get used to wielding them you'll say,
like,

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whoa, I can do so much with these things.

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And at the beginning you just sort of

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learning sort of how to use them without
hurting yourself.

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But they're very powerful.

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It's like a database.

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It's, it allows you to store very arbitrary
data organized in however you feel like

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organizing it, in a way that advances the
cause of the program that you're writing.

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And we're still kind of at the very
beginning, but as you learn more,

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these will become a very powerful
tool for you.

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They, dictionaries have different names in
different languages.

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PERL or PHP would call them associative
arrays.

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Java would call them a PropertyMap or a
HashMap.

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And C# might call them a property bag or
an attribute bag.

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And so they're, they're just the same
concept.

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It's keys and values is the concept that's
across all these languages.

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Just are very powerful.

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And if you look at the Wikipedia entry

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that I have here

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you can see that it's just, it's a concept

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that we give different names in different
languages. Same concept, different names.

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So like I said, the difference between a
list and a dictionary, they both can store

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multiple values. The question is how we
label them,

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how we store them, and how we retrieve
them.

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So here's an example use of a dictionary.
I'm going to make a thing called purse.

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And I'm going to store in purse, this is
an assignment statement,

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purse sub money.
So this isn't like sub zero.

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This is sub money.

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So I'm actually using a string as the
place.

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And, so I'm going to say stick 12
in my purse

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and stick a Post-it note that says
that's my money.

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Candy is 3. Tissues is 75.

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And if I look at that, it's not just the
numbers 12, 3, and 75 as it

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would be in a list. It is the connection
between money and 12,

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tissues is 75, candy is 3.
And in the key/value, that's the

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key and that's the value.
So candy is the key and 3 is the value.

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Now I can look things up by their name,
print purse sub candy.

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Well it goes and finds it, asking hey purse,
give me back candy, and it

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goes and finds the value, which is 3, and
so out comes a 3.

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We can also put it

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on the right-hand side of an
assignment statement,

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so purse sub candy says give me
the old version of candy,

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and then add 2 to it, which

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gives me 5, and then store it back
in that purse

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under the label candy.

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So we see candy changing to 5.

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And so, this is a place, and you could

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do this with a list except these would be
numbers.

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You could say purse sub two is equal to
purse sub two plus two, or whatever.

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But in dictionaries, there are labels.

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Now, they're not strings.

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Strings is a very common label in
dictionaries, but

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it's not always strings, you can use other
things.

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In this chapter we'll pretty much focus on
strings.

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You can even use numbers and then you
would get a little confused.

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But you can.

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So here's sort of a picture of how this
works.

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So, if we take a look at this line purse
sub money equals 12,

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it's like we were putting a key/value
connection, money is the label for 12.

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And then we sort of move that in.

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And it's up to the purse to decide
where things live.

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If we look at the next line, we're going to
put the value in with a

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3 in with the label candy, and we're
going to put

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the value 75 in with the label of tissues.

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And when we say hey purse, print yourself
out, it just

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goes and pulls these things back out and
hands them to us.

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And what it's really, it's giving us both the
label and the value and it's necessary

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to do that cause they're just like 12,

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75, and 3. What exactly is that?

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And so this syntax with the curly braces

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is what happens when you print a
dictionary out.

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The same thing happens when we're sort of
printing purse sub candy, right?

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Purse sub candy,

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it's like dear purse, go and find the candy
thing.

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Look at that one, look at that one.

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Oh, yep, yep, this is candy.

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But what we're looking for is the value,

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and so that's why 3 is coming out here.

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So go look up under candy, and tell me
what's stored under candy.

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These can be actually more complex things,

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I'm just keeping it simple for this
lecture.

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And then, when we say purse sub candy
equals purse sub candy plus 2, well it

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pulls the 3 out, looking at the label
candy, then adds 3 plus 2 and makes 5,

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and then it assigns it back in, and then
that says, oh, go, go place this number 5

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in the purse with the label of candy,
which then replaces the 3 with a 5.

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Okay?

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And if we print it out, we see that the

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new variable, or the new candy entry,
is now 5.

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Okay?

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So if we just sort of put these things
side by side, we create

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them sort of both the same way and we make
an empty list, and an empty

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dictionary, we call the append method
because

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we're sort of just putting these things in

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order. You gotta put the first one in
first. So it's not telling you where.

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You kind of know that this

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will be the first one, cause we're
starting with an empty one,

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and this will be the second one.

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We put in the values 21 and 183, and then
we print it out, and it's like okay, you gave

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me the values 21 and 183, I will maintain
the order for you,

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there's no keys other than their position.

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The position is the key, as it were, so if
I want to to change the first one to 23,

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well, I say list sub zero, which is this,
and then change that to 23.

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So this is sort of used as a lookup to

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find something. It can be used on either the
right-hand side or the

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left-hand side of an assignment statement.

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Comparing that to dictionaries, I want to
put a 21 in there

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and I want to put it with the label age.

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I'm going to put 182, put that in with the
label course.

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So we don't have to like, make an entry.

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The fact that the entry doesn't exist,

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it creates the age entry and sticks 21 into it,

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creates the course entry, sticks 182 into it.

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We print it out and it says, oh, course
is 182 and age is 21.

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This emphasizes that order is not
preserved in dictionaries.

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I won't go into like great detail as to
why that is.

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It turns out that that's a compromise that

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makes them fast using a technique called
hashing.

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It's how it actually works internally,
go Wikipedia hashing and

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take a look.

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But, the thing that matters to us as
programmers primarily

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is that lists maintain order and
dictionaries do not maintain order.

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They, dictionaries give us power
that we don't have in lists.

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I mean they're very complimentary.

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Now there's not this one that's better
than the other.

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They've very complimentary.

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Different kinds of data is either better
represented as a list

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or as a dictionary, depending on the

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problem you're trying to solve.

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And in a moment we'll, we'll be writing
programs that are using both.

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So if we come down here and I say,

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okay, stick 23 into, assignment statement,
into ddd sub age,

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well that will change this 21 to 23,
so when we print it out.

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So you can, this part, where you look
something up and

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change the value, you can do either way.

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It's just how you do it here

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is a little bit different, okay?

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So let's look through this code again.

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And so I like, I like to use the word key
and value.

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Key is the way we look the thing up,
and in lists

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keys are numbers starting at
zero and with no gaps.

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In dictionaries keys are whatever we want
them to be,

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in this case I'm using strings.

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And then the value is the number we're
storing in it.

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So we create this kind of a list with that
kind, those

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kinds of statements.

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This statement creates this kind of a thing.

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Now, if we, if we think of this assignment
statement as moving data

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into a new, into a place, a new item of
data into a place.

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It's looking at this thing right here.

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Right? It's like, that's where I want to
move it.

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And so it hunts, and says, look the key up.

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And that's the one that I'm going to change.

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And then once it knows which it's going to
change,

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then it's going to take the 23, and it's
going to put the 23 into that location.

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And so that's how this changes from that
to that.

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Similarly when we get down to here, we're
going to stick 23 somewhere and

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this is, this expression, this lookup
expression, the index

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expression ddd sub age, is where we're
going to put it.

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So, we're looking here, where is that thing?

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Well, that thing is this entry

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in the dictionary. And so now when we're
going to store the 23,

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we know where the 23 is going to go.

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It's going to overwrite the 21 and so the
21 is

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going to change to 23, okay? So they're
kind of similar.

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There are things that work similar in them

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and then there are things that work
differently in them.

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We can make literals, constants, with

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curly braces. And they look just like the print.

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That's one nice thing about Python.

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When you print something out it's showing
you how you can make a literal, and

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basically you just open with a curly brace
and say chuck colon 1, fred 42, jan 100.

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And we're making connections.

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key/value pair, key/value pair.
We print it out and

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No order. They don't maintain order.

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Now they might come out in the same order,
but that's just lucky.

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Right?

232
00:12:09,180 --> 00:12:10,550
All the ones I've shown you so far don't

233
00:12:10,550 --> 00:12:12,650
come out in the same order, which is good
to demonstrate it.

234
00:12:12,650 --> 00:12:16,000
If it one time came out in the same order
that wouldn't be broken.

235
00:12:16,000 --> 00:12:18,500
It's not like it doesn't want to come out
in the same order.

236
00:12:18,500 --> 00:12:22,090
It's just, you don't, it's not internally
stored, and you

237
00:12:22,090 --> 00:12:23,859
add an element and it may reorder them.

238
00:12:25,110 --> 00:12:28,030
You can do an empty dictionary with just a
curly brace, curly brace.

239
00:12:33,330 --> 00:12:37,400
So, I'm going give you another example.

240
00:12:37,400 --> 00:12:40,120
And I'm going to show you a series of
names.

241
00:12:40,120 --> 00:12:45,810
And I want you to figure out what the most
common name is

242
00:12:45,810 --> 00:12:48,240
and how many times each name appears.

243
00:12:48,240 --> 00:12:51,726
Now these are real people.
They actually work on the Sakai project.

244
00:12:51,726 --> 00:12:58,540
Steven, Zhen, and Chen, and me.
So these are people that are actually

245
00:12:58,540 --> 00:13:00,710
in the data that we use in this course.

246
00:13:00,710 --> 00:13:04,450
Okay? And so I think I'll show you about
fifteen names

247
00:13:04,450 --> 00:13:06,925
and you're to come up with a way, I'm
going to

248
00:13:06,925 --> 00:13:11,270
show them to you one at a time, you need to
come up with a way to keep track of these.

249
00:13:11,270 --> 00:13:12,390
Okay?

250
00:13:12,390 --> 00:13:15,611
So I'll just, with no further ado I will show
you the names.

251
00:13:15,611 --> 00:13:25,611
[BLANK_AUDIO]

252
00:13:53,752 --> 00:13:57,510
Okay, so that's all the names.
Did you get it?

253
00:13:57,510 --> 00:14:00,160
You might have to go back and do it again.

254
00:14:01,000 --> 00:14:03,520
How did you solve the problem?

255
00:14:03,520 --> 00:14:08,300
What kind of a data structure did you
build to solve the problem?

256
00:14:08,300 --> 00:14:10,630
Or did you just say wow that's painful, I

257
00:14:10,630 --> 00:14:14,890
think I will learn Python instead, in
solving that problem.

258
00:14:14,890 --> 00:14:15,524
Okay?

259
00:14:15,524 --> 00:14:19,880
So pause the, pause the video if you want and

260
00:14:19,880 --> 00:14:23,250
write down or go back, write down what you
think the

261
00:14:23,250 --> 00:14:28,070
number of the most common name is and how
many times.

262
00:14:30,200 --> 00:14:32,080
Okay. Now I'll show you.

263
00:14:32,080 --> 00:14:35,180
So here is the whole list.
It's all of them.

264
00:14:35,180 --> 00:14:38,730
And now that we see all of them, we
use our amazing human

265
00:14:38,730 --> 00:14:42,720
mind and we scan around, and look at
purpleness and, and all that stuff.

266
00:14:42,720 --> 00:14:44,320
And then we go like, oh, this is a so

267
00:14:44,320 --> 00:14:46,190
much easier problem when I'm looking
at the whole thing.

268
00:14:47,990 --> 00:14:51,590
And I think that the most common person is
Zhen, and

269
00:14:54,310 --> 00:14:58,770
I think we see Zhen, I think we see Zhen
five times.

270
00:15:00,760 --> 00:15:06,550
And I think csev is one, two, three and
Chen Wen is one, two.

271
00:15:06,550 --> 00:15:08,980
And Steve Marquard is one, two, three.

272
00:15:08,980 --> 00:15:12,530
So the question is, what is an effective
data structure if you going to see

273
00:15:12,530 --> 00:15:15,510
a million of these, what kind of data
structure would you have to produce?

274
00:15:15,510 --> 00:15:16,720
Because you can't keep it in you head

275
00:15:16,720 --> 00:15:19,510
even, even this number of people, you can't

276
00:15:19,510 --> 00:15:22,400
even this amount of data, no way you can
keep it in your head. You have to come

277
00:15:22,400 --> 00:15:24,970
up with some kind of a variable, as it were,

278
00:15:24,970 --> 00:15:28,230
just like largest so far was the variable.

279
00:15:28,230 --> 00:15:29,800
Some kind of variable that gets you to

280
00:15:29,800 --> 00:15:31,450
the point where you understand what's
going on.

281
00:15:31,450 --> 00:15:35,080
And so this is the most common technique
to solve this

282
00:15:35,080 --> 00:15:39,040
problem where you keep a running total of
each of the names.

283
00:15:39,040 --> 00:15:42,500
And if you see a new name, you add them to
the list.

284
00:15:42,500 --> 00:15:45,090
So csev and then you give him a one,

285
00:15:45,090 --> 00:15:47,410
and then you see Zhen and you give her a
one,

286
00:15:47,410 --> 00:15:49,620
and then you see Chen and you give her a
one.

287
00:15:49,620 --> 00:15:51,670
And then you see csev again and you give
him a two.

288
00:15:51,670 --> 00:15:54,825
And you see a two, and a two, and a one
right?

289
00:15:54,825 --> 00:15:57,050
[COUGH]

290
00:15:57,050 --> 00:16:02,760
And so then when you're all done you have
the mapping, right, of these things

291
00:16:02,760 --> 00:16:06,100
and you go oh, okay, let me look through
here and find the largest one.

292
00:16:06,100 --> 00:16:09,960
That's the largest one and so that must be
the person who is the most.

293
00:16:09,960 --> 00:16:12,170
So you need a scratch area,

294
00:16:12,170 --> 00:16:14,710
a data structure or a piece of paper as
it were,

295
00:16:14,710 --> 00:16:19,030
and so that's what, exactly what
dictionaries are really good at.

296
00:16:19,030 --> 00:16:23,910
You could think of this as like a
histogram. You know, it's,

297
00:16:23,910 --> 00:16:27,840
it's a bunch of counters, but counters
that are indexed by a string.

298
00:16:27,840 --> 00:16:29,450
So we use a lot of this.

299
00:16:29,450 --> 00:16:34,130
And so this is a pattern of many counters
with a dictionary, simultaneous counters.

300
00:16:34,130 --> 00:16:35,390
We're counting a bunch of, we're looking

301
00:16:35,390 --> 00:16:39,430
at a series of things, and we're going to
simultaneously keep track

302
00:16:39,430 --> 00:16:42,530
of a large number of counters, rather than
just one counter.

303
00:16:42,530 --> 00:16:46,950
How many names did you see total? Whatever,
12. But how many of each name

304
00:16:46,950 --> 00:16:50,480
did you see is a bunch of counters, so
it's a bunch of simultaneous counters.

305
00:16:51,850 --> 00:16:56,890
So a dictionary is, is great for this,
a dictionary is great for this.

306
00:16:56,890 --> 00:16:58,520
We, when we see somebody for the first

307
00:16:58,520 --> 00:17:00,440
time, we can add an entry to the
dictionary,

308
00:17:00,440 --> 00:17:03,940
which is kind of like going oh,
csev one,

309
00:17:03,940 --> 00:17:07,970
and then Chen Wen one. Now these don't
exist yet.

310
00:17:07,970 --> 00:17:10,480
Right? So we've got csev one and Chen
Wen one, so

311
00:17:10,480 --> 00:17:13,359
that creates an entry and sticks a one in
it and the

312
00:17:13,359 --> 00:17:17,119
mapping between the key csev and the value
one, the key Chen Wen

313
00:17:17,119 --> 00:17:19,740
and the value one and then we say, hey
what's in there?

314
00:17:19,740 --> 00:17:22,740
Oh, we've got a csev is one and
Chen Wen is one.

315
00:17:22,740 --> 00:17:25,550
And then we see Chen Wen a second time,

316
00:17:25,550 --> 00:17:27,450
so we'd add another number right there.

317
00:17:27,450 --> 00:17:30,690
So this old number is one, we add one to
it and we get

318
00:17:30,690 --> 00:17:35,370
two and then we stick that back in and
then we do the calculations.

319
00:17:35,370 --> 00:17:39,100
We do a dump and say oh there's two in
Chen Wen and one in csev.

320
00:17:40,130 --> 00:17:40,630
Okay?

321
00:17:41,630 --> 00:17:46,300
So this is a great data structure for the
simutaneous counters like what's

322
00:17:46,300 --> 00:17:49,940
the most common word, who had the most
commits, da, da, da, da, da.

323
00:17:51,090 --> 00:17:54,220
Now, everything we do we have to figure
out

324
00:17:54,220 --> 00:17:55,990
like, when you're going to get in trouble
with Python.

325
00:17:55,990 --> 00:18:00,250
When Python's going to give you the old
thumbs down and say oh, you went too far.

326
00:18:00,250 --> 00:18:06,360
So one thing Python does not like is if
you reference a key before it exists.

327
00:18:06,360 --> 00:18:09,900
We'll, we'll talk in a second how to
work around this. But if you simply

328
00:18:09,900 --> 00:18:11,600
create a dictionary and say, oh, print out

329
00:18:11,600 --> 00:18:15,090
what's under csev, it gives you a
traceback.

330
00:18:15,090 --> 00:18:15,710
It's like,

331
00:18:15,710 --> 00:18:17,940
I'm going to inform you that that's not
there.

332
00:18:17,940 --> 00:18:20,490
And it says key error, csev.

333
00:18:20,490 --> 00:18:24,810
Now, the thing that allows us to solve
this is the in operator.

334
00:18:24,810 --> 00:18:28,140
We've used the in operator to see if a
substring was in a string.

335
00:18:28,140 --> 00:18:30,120
Or if a number was in a list.

336
00:18:30,120 --> 00:18:37,090
So, so this in operator says, in operator
says, hey, ask a question.

337
00:18:37,090 --> 00:18:42,140
Is the string csev a current key in the
dictionary ccc?

338
00:18:43,210 --> 00:18:46,460
Is the string csev a current key in the
dictionary ccc?

339
00:18:46,460 --> 00:18:47,750
And it says, False.

340
00:18:49,090 --> 00:18:52,240
So now we have something that doesn't give
a traceback

341
00:18:52,240 --> 00:18:55,290
that can tell us whether or not the key is
there.

342
00:18:55,290 --> 00:18:57,480
So if you remember the algorithm, the
first time you see it, you

343
00:18:57,480 --> 00:19:01,270
set them to one, and every other time, you
add one to them.

344
00:19:02,520 --> 00:19:04,030
So this is how we do that in Python.

345
00:19:05,150 --> 00:19:08,220
So here's how we implement that program
that I just gave you

346
00:19:08,220 --> 00:19:12,080
in Python. So, here's our names.

347
00:19:12,080 --> 00:19:14,760
It's shorter so my slide works better.

348
00:19:14,760 --> 00:19:17,470
Here's a variable, our iteration variable,
it's going to, you know,

349
00:19:17,470 --> 00:19:20,570
go through all five of these one at a time.

350
00:19:20,570 --> 00:19:24,553
And within the body of the
loop we have an if statement.

351
00:19:24,553 --> 00:19:26,793
If the name is not currently in the

352
00:19:26,793 --> 00:19:30,929
counts dictionary, counts is the name of
my dictionary.

353
00:19:30,929 --> 00:19:33,617
If the name is not currently in the
counts dictionary,

354
00:19:33,617 --> 00:19:35,210
I say counts sub name equals one.

355
00:19:36,440 --> 00:19:39,680
else, that must mean it's already there
which means

356
00:19:39,680 --> 00:19:42,886
it's okay to retrieve it, counts sub name
plus 1.

357
00:19:42,886 --> 00:19:46,590
We're going to add a 1 to it and stick it
back in, okay?

358
00:19:46,590 --> 00:19:49,350
And so when this finishes it's going to
add

359
00:19:49,350 --> 00:19:52,730
entries and then add one to entries that
already exist.

360
00:19:52,730 --> 00:19:57,370
And not traceback at all. And when we
print it out we're going to see the counts.

361
00:19:57,370 --> 00:19:58,720
And literally this could have gone

362
00:19:58,720 --> 00:20:02,400
a million times and it would just be fine
and it would just keep expanding.

363
00:20:02,400 --> 00:20:02,900
Okay?

364
00:20:05,260 --> 00:20:07,270
So this pattern of checking to see if a key

365
00:20:07,270 --> 00:20:10,690
is in a dictionary, setting it to some
number, or

366
00:20:11,750 --> 00:20:14,770
adding one to it is a really, really common
pattern.

367
00:20:16,030 --> 00:20:19,550
It's so common, as a matter of fact, that
there is a

368
00:20:19,550 --> 00:20:24,580
a special thing built into dictionaries
that does this for us, okay?

369
00:20:24,580 --> 00:20:26,700
And there is this method called get.

370
00:20:27,960 --> 00:20:30,490
And so, counts is the name of the
dictionary,

371
00:20:30,490 --> 00:20:34,120
get is a built-in capability of
dictionaries.

372
00:20:34,120 --> 00:20:35,630
And it takes two parameters.

373
00:20:35,630 --> 00:20:43,110
The first parameter is a key name, like a
string, like csev or chen wen or marquard.

374
00:20:43,110 --> 00:20:50,880
And then the second parameter is a value
to give back if this doesn't exist.

375
00:20:50,880 --> 00:20:54,300
It's a default value if the key does not
exist.

376
00:20:54,300 --> 00:20:55,850
And there's no traceback.

377
00:20:55,850 --> 00:21:00,710
So this way you can encapsulate, in effect,
an if-then-else.

378
00:21:00,710 --> 00:21:06,160
If the name parameter is in the counts,
print the thing out, otherwise print zero.

379
00:21:06,160 --> 00:21:11,490
So this expression will either get the
number

380
00:21:11,490 --> 00:21:16,810
if it exists or it will give me back a
zero if it doesn't exist.

381
00:21:16,810 --> 00:21:18,770
So this is really valuable.

382
00:21:18,770 --> 00:21:21,080
Right? This is really valuable.

383
00:21:21,080 --> 00:21:22,630
That's a really bad smiley face.

384
00:21:22,630 --> 00:21:28,590
So this is really valuable because it,
once, once we understand the idiom,

385
00:21:28,590 --> 00:21:32,520
it really takes four lines of code and
turns it into one line of code.

386
00:21:32,520 --> 00:21:34,620
Because we're going to be doing this
if-then-else all the time.

387
00:21:35,800 --> 00:21:39,060
Now, and so we can reconstruct that loop

388
00:21:39,060 --> 00:21:44,010
a lot easier and a lot more cleanly using this
idiom, right?

389
00:21:44,010 --> 00:21:46,160
It's something that looks kind of complex
but you'll

390
00:21:46,160 --> 00:21:49,140
get used to it really fast, okay?

391
00:21:49,140 --> 00:21:51,530
So we have, everything here is the same,

392
00:21:51,530 --> 00:21:53,780
we create an empty dictionary, we have five
names to

393
00:21:53,780 --> 00:21:55,760
go through, we're going to write a
for loop

394
00:21:55,760 --> 00:21:58,320
and it's going to go through each of
those.

395
00:21:58,320 --> 00:22:04,550
And then we're going to say counts sub name
equals counts dot get the value stored

396
00:22:04,550 --> 00:22:08,120
at name, and if you don't find it, give me
back a zero.

397
00:22:08,120 --> 00:22:11,550
And then whatever comes back, either the
old value or

398
00:22:11,550 --> 00:22:16,760
the zero, add 1 to that and then take that
sum and stick it in counts name.

399
00:22:17,870 --> 00:22:19,530
Okay? So this is either

400
00:22:21,650 --> 00:22:22,790
going to create,

401
00:22:26,170 --> 00:22:29,740
or it's going to update.

402
00:22:30,070 --> 00:22:32,990
If there is no entry, it's going to create
it and set it to one.

403
00:22:32,990 --> 00:22:36,520
If there is an entry it's going to add one to
the current entry.

404
00:22:37,530 --> 00:22:39,240
Okay? So this is,

405
00:22:42,770 --> 00:22:44,660
this line is kind of an idiom.

406
00:22:46,510 --> 00:22:48,420
Read about it in the book, figure it out,

407
00:22:48,420 --> 00:22:50,340
get used to the notion of what this is doing.

408
00:22:50,340 --> 00:22:53,370
Understand what that is doing, okay?

409
00:22:54,430 --> 00:22:57,320
Because I'm going to start using it as if
you understand it.

410
00:22:58,490 --> 00:23:05,300
So, the next problem is a problem of
finding the most common word.

411
00:23:05,300 --> 00:23:07,910
So, finding the most common, the top

412
00:23:07,910 --> 00:23:12,330
five, is often a, a trigger that says, use

413
00:23:12,330 --> 00:23:14,390
dictionaries because if you're going to
have to count things up,

414
00:23:14,390 --> 00:23:15,990
you're going to, you know, you don't

415
00:23:15,990 --> 00:23:18,000
know what the most common thing is at the
beginning.

416
00:23:18,000 --> 00:23:22,220
First you have to count everything up, and
dictionaries are a great way to count.

417
00:23:22,220 --> 00:23:25,220
So here's a little problem and I would
like you to read

418
00:23:25,220 --> 00:23:29,490
this text and find me the most common word
in the text.

419
00:23:29,490 --> 00:23:32,960
And tell me what the most common word is
and how many times

420
00:23:34,550 --> 00:23:36,520
it occurs. Ready?

421
00:23:36,520 --> 00:23:39,800
I'm going to give you a thousandth of a
second, just like I would give a computer.

422
00:23:39,800 --> 00:23:41,975
I would expect it'd be able to do this in
a thousandth of a second.

423
00:23:41,975 --> 00:23:43,149
[SOUND] There you go.

424
00:23:43,149 --> 00:23:45,978
[BLANK_AUDIO]

425
00:23:45,978 --> 00:23:48,040
Okay, I gave you five seconds.
Time's up.

426
00:23:48,040 --> 00:23:48,580
Did you get it?

427
00:23:49,580 --> 00:23:52,620
Or did you say to yourself, you know what,
I hate

428
00:23:52,620 --> 00:23:55,840
that, it's no good, I think I'll write a
Python program instead.

429
00:23:55,840 --> 00:23:59,200
And he'll probably show me a Python
program if I wait long enough.

430
00:23:59,200 --> 00:24:02,800
So here's a slightly easier problem from
the first lecture.

431
00:24:02,800 --> 00:24:04,030
Ready?

432
00:24:04,030 --> 00:24:04,936
It's the same problem.

433
00:24:04,936 --> 00:24:07,915
Find the most common word and how many
times the word occurs.

434
00:24:07,915 --> 00:24:12,171
[BLANK AUDIO]

435
00:24:12,171 --> 00:24:34,171
[MUSIC]

436
00:24:35,437 --> 00:24:40,190
Did you get it?
I believe the answer is, and I could look

437
00:24:40,190 --> 00:24:45,900
really dumb here, oops, the answer is the,
and I think it's seven times.

438
00:24:45,900 --> 00:24:48,310
So, that's the right answer. Okay?

439
00:24:48,310 --> 00:24:50,160
Again, things humans are not so good at.

440
00:24:51,430 --> 00:24:54,760
So, here's a piece of code that's starting
to combine some

441
00:24:54,760 --> 00:24:57,690
of the things we've been doing in the past
few chapters all together.

442
00:24:57,690 --> 00:25:01,110
We are going to read a line of text,

443
00:25:01,110 --> 00:25:05,940
split it into words, count the occurrence, 
how many times

444
00:25:05,940 --> 00:25:10,070
each word occurs, and then print out a map.

445
00:25:10,070 --> 00:25:14,580
So, so here's what we're going to do,
we're going to say okay, start

446
00:25:14,580 --> 00:25:18,998
a dictionary, an empty dictionary, read
the line of input.

447
00:25:20,460 --> 00:25:27,160
Then split it, remember, the split takes a
string and produces a list.

448
00:25:27,160 --> 00:25:31,900
So words is a list, line is a string, and
then we'll print that out.

449
00:25:31,900 --> 00:25:34,260
Then we're going to write a for loop
that's going to go

450
00:25:34,260 --> 00:25:37,520
through each of the words, and
then create, use this idiom

451
00:25:37,520 --> 00:25:42,180
counts sub word equals counts.get word, 0 + 1.

452
00:25:42,180 --> 00:25:45,270
So this is going to do exactly what we talked
about in the previous

453
00:25:45,270 --> 00:25:51,210
couple slides back, either create the
entries or add to those entries, okay?

454
00:25:51,210 --> 00:25:52,383
And then we're going to print

455
00:25:52,383 --> 00:25:52,860
them out.

456
00:25:52,860 --> 00:25:55,620
So here's what that program does when it
prints out.

457
00:25:56,630 --> 00:25:58,860
Now this is actually one long line I'm

458
00:25:58,860 --> 00:26:00,820
just cutting it so you can see it.

459
00:26:00,820 --> 00:26:05,390
Here's this line we enter, and the words
the, there's seven of them.

460
00:26:05,390 --> 00:26:08,390
Then it takes this line and splits it into a

461
00:26:08,390 --> 00:26:11,240
list, and there is the beginning and end
of the list.

462
00:26:11,240 --> 00:26:13,680
The list maintains the order, so the

463
00:26:13,680 --> 00:26:17,690
list simply breaks all these words into
separate

464
00:26:17,690 --> 00:26:21,620
words in a list of strings.
From one string

465
00:26:22,770 --> 00:26:29,120
to many strings. This is many strings.
And so the, and the spaces are gone.

466
00:26:29,120 --> 00:26:31,040
And so now here's this list.

467
00:26:31,040 --> 00:26:33,820
And then what we're going to do is we're
going to run through the list.

468
00:26:35,470 --> 00:26:39,030
And we're going to keep running totals of
each of the words in the list.

469
00:26:39,030 --> 00:26:40,180
And then when we're done with the list,

470
00:26:40,180 --> 00:26:43,890
we're going to print out the contents of
that dictionary.

471
00:26:43,890 --> 00:26:45,050
And we can inspect it and

472
00:26:45,050 --> 00:26:47,480
go like, let's look for the biggest one,
na, na, na, na, na.

473
00:26:47,480 --> 00:26:47,990
It's kind of like

474
00:26:47,990 --> 00:26:50,510
looking for the largest, like, oh,
seven.

475
00:26:50,510 --> 00:26:54,010
That's the largest and the largest word is
the.

476
00:26:54,010 --> 00:26:54,730
Okay?

477
00:26:54,730 --> 00:26:59,210
So that's how the program runs, it
reads a line,

478
00:26:59,210 --> 00:27:01,640
splits it into a list of words, and then

479
00:27:01,640 --> 00:27:05,090
accumulates a running total for each word,
and then we

480
00:27:05,090 --> 00:27:08,930
hand inspect to see what the most common
word is.

481
00:27:08,930 --> 00:27:09,430
Okay?

482
00:27:10,870 --> 00:27:13,220
Oh no, no, I don't want that song again.

483
00:27:13,220 --> 00:27:14,190
There we go.

484
00:27:14,190 --> 00:27:18,280
And so and so here we have the, in it's
kind of a smaller fashion.

485
00:27:19,350 --> 00:27:23,660
We make a dictionary.
This entering a line of text is here.

486
00:27:23,660 --> 00:27:25,150
It's all one line.

487
00:27:25,150 --> 00:27:27,170
We do the split and then we print the
words out.

488
00:27:29,160 --> 00:27:32,500
And so that split creates a list of
strings from a single

489
00:27:32,500 --> 00:27:37,100
string based on where the blanks are at,
chop, chop, chop, chop.

490
00:27:37,100 --> 00:27:38,450
And then here

491
00:27:38,450 --> 00:27:39,150
at counting,

492
00:27:41,180 --> 00:27:45,510
we're going to loop through each of the
words one at a time and use this idiom,

493
00:27:45,510 --> 00:27:52,710
counts sub word equals counts.get word, 0 + 1,
which is going to create and/or update.

494
00:27:52,710 --> 00:27:54,960
And then we print the counts out and that
comes out there.

495
00:27:56,110 --> 00:27:56,610
Okay?

496
00:27:57,710 --> 00:27:59,610
So, again, this is the new thing that
we've done.

497
00:27:59,610 --> 00:28:01,710
Everything else we've kind of seen before.

498
00:28:04,750 --> 00:28:08,429
Now we can also loop through dictionaries
with for loops.

499
00:28:12,550 --> 00:28:15,320
The for loop, we've been, put all kinds of
things over here.

500
00:28:15,320 --> 00:28:18,890
We've put strings over here, we've put
lists of numbers over here.

501
00:28:18,890 --> 00:28:21,110
We've put files over here.

502
00:28:21,110 --> 00:28:23,470
And basically what it really says is you

503
00:28:23,470 --> 00:28:26,360
know, if this is a collection of things,

504
00:28:26,360 --> 00:28:28,340
run this little indent code once for
each item in

505
00:28:28,340 --> 00:28:32,850
the collection, and key then becomes our
iteration variable.

506
00:28:32,850 --> 00:28:35,150
And key is very mnemonic here.

507
00:28:35,150 --> 00:28:37,200
It doesn't know that they are keys.

508
00:28:37,200 --> 00:28:39,480
And so, keys.

509
00:28:39,480 --> 00:28:44,680
The key here is that, there's a bit, the
important

510
00:28:44,680 --> 00:28:50,180
concept here is that dictionaries are 
key/value pairs and so this is

511
00:28:50,180 --> 00:28:52,900
only one variable and so it actually
decides that, they've decided that

512
00:28:52,900 --> 00:28:56,140
it goes through the keys, which is
actually quite useful.

513
00:28:56,140 --> 00:29:00,700
So key is going to take on the successive
values of the labels.

514
00:29:00,700 --> 00:29:02,400
Not the successive values of

515
00:29:02,400 --> 00:29:04,060
the values stored at the labels.

516
00:29:04,060 --> 00:29:10,250
But it's really easy for us to retrieve
the contents at that label counts sub key.

517
00:29:10,250 --> 00:29:15,080
So we're going to use the key 'chuck',
'fred', 'jan', to look up the 1, 42, 100.

518
00:29:15,080 --> 00:29:17,900
And so it prints out the key,

519
00:29:17,900 --> 00:29:22,180
and then the value at it, the key, and the
value at it, and the key, and the value.

520
00:29:22,180 --> 00:29:25,050
And so we're able to sort of go through

521
00:29:25,050 --> 00:29:27,330
the dictionary and look at all the
key/value pairs,

522
00:29:27,330 --> 00:29:29,900
which is the common thing that you really
want to do.

523
00:29:31,000 --> 00:29:31,500
Okay?

524
00:29:35,240 --> 00:29:38,400
Now there's some methods inside of
dictionaries that allow

525
00:29:38,400 --> 00:29:42,620
us to convert dictionaries into lists
of things.

526
00:29:42,620 --> 00:29:47,140
And so if you simply take a dictionary, so
here's a little dictionary with

527
00:29:47,140 --> 00:29:51,750
three items in it, and we can say list sub
and then give a dictionary name

528
00:29:51,750 --> 00:29:54,060
right there, and then that converts it
into a

529
00:29:54,060 --> 00:29:56,640
list. But it's just a list of the keys.

530
00:29:57,680 --> 00:30:01,320
We can also say jjj dot keys, kind of do
the same thing.

531
00:30:01,320 --> 00:30:05,170
Say give me a list consisting of the keys.

532
00:30:05,170 --> 00:30:10,150
And then jjj dot values gives you a list
of the values, 1, 42, and 100.

533
00:30:10,150 --> 00:30:12,810
Of course they're not in the same order.

534
00:30:12,810 --> 00:30:16,060
Now interestingly, as long as you don't
modify the dictionary,

535
00:30:16,060 --> 00:30:19,510
the order of these two things corresponds
as long as

536
00:30:19,510 --> 00:30:23,050
in between here you're not changing it.
So the first jan maps to 100,

537
00:30:23,050 --> 00:30:25,420
chuck maps to 1,

538
00:30:25,420 --> 00:30:27,680
and fred maps to 42.

539
00:30:27,680 --> 00:30:30,200
So the order, you can't predict the order
they're

540
00:30:30,200 --> 00:30:32,170
going to come out but these two things
will

541
00:30:32,170 --> 00:30:34,550
come out in the same order, whatever that
order

542
00:30:34,550 --> 00:30:38,110
happens to be. Okay, and so there's one
more thing.

543
00:30:39,220 --> 00:30:44,190
So we've got the keys, we've got the
values, and we've got a thing called items.

544
00:30:44,190 --> 00:30:50,460
items also returns a list, it's a list.
But it's a list of

545
00:30:50,460 --> 00:30:54,920
what Python calls tuples.
That's what the next chapter is about.

546
00:30:54,920 --> 00:30:56,700
We'll talk more about tuples in the next
chapter.

547
00:30:57,910 --> 00:31:01,160
A tuple is a key/value pair.

548
00:31:01,160 --> 00:31:05,970
So this list has three things in it.
One, two, three.

549
00:31:05,970 --> 00:31:10,240
The first one jan maps to 100, the
second is chuck maps to 1, the

550
00:31:10,240 --> 00:31:15,570
third one is fred maps to 42. So,
just kind of bear with me for a second.

551
00:31:15,570 --> 00:31:17,520
We'll hit this a little harder in the next
chapter.

552
00:31:18,920 --> 00:31:20,850
But the place that this, the idiom where

553
00:31:20,850 --> 00:31:23,930
this works very beautifully is on a for
loop.

554
00:31:23,930 --> 00:31:26,720
Now, for those of you who have programmed
in other languages, this will be

555
00:31:26,720 --> 00:31:29,700
kind of weird because other languages have

556
00:31:29,700 --> 00:31:33,680
iterations but they don't have two
iteration variables.

557
00:31:33,680 --> 00:31:35,770
Python has two iteration variables.

558
00:31:35,770 --> 00:31:37,480
It can be used for many things but one of the

559
00:31:37,480 --> 00:31:41,090
things that it's used for that's really
quite nice is

560
00:31:41,090 --> 00:31:46,110
we can have two iteration variables.
This jj items returns pairs of

561
00:31:46,110 --> 00:31:51,200
things and then aaa and bbb are iteration
variables that sort of

562
00:31:51,200 --> 00:31:56,580
move in synchronized, move, are
synchronized as they move through.

563
00:31:56,580 --> 00:32:01,250
So aaa takes on the value of the key.

564
00:32:01,250 --> 00:32:05,670
bbb takes on the value of the, the
value.

565
00:32:05,670 --> 00:32:09,110
And then the loop runs once.

566
00:32:09,110 --> 00:32:13,090
Then aaa is advanced to the next key.

567
00:32:13,090 --> 00:32:17,410
And bbb is advanced to the next value
simultaneously, synchronized.

568
00:32:17,410 --> 00:32:19,910
Then they print that out, then it advances
to the

569
00:32:19,910 --> 00:32:22,700
next one, and the next one, and they print
that out.

570
00:32:22,700 --> 00:32:27,210
So they are moving in a synchronized way.

571
00:32:27,210 --> 00:32:31,050
Now again, the order jan, chuck, fred is not
the same.

572
00:32:31,050 --> 00:32:33,360
But the correspondence between jan 100,

573
00:32:33,360 --> 00:32:37,090
chuck 1, and fred,
that's going to, that's going to work.

574
00:32:37,090 --> 00:32:40,680
And so basically, as these things go, they
work

575
00:32:40,680 --> 00:32:43,960
their way through whatever order they're
stored in the dictionary.

576
00:32:43,960 --> 00:32:45,440
So this is quite nice.

577
00:32:45,440 --> 00:32:48,870
Two iteration variables going through
key/value.

578
00:32:48,870 --> 00:32:53,850
Now if I was making these names mnemonic,
and they made more sense,

579
00:32:53,850 --> 00:32:57,200
I would call this the key variable and
that would be the value variable.

580
00:32:58,440 --> 00:33:00,590
But for now I'm just using kind of silly
names

581
00:33:00,590 --> 00:33:02,910
to show you that key and value are not
special.

582
00:33:02,910 --> 00:33:05,580
They're not Python reserved words in any
way.

583
00:33:05,580 --> 00:33:09,215
They're just a good way to name these
things, key/value pairs.

584
00:33:09,215 --> 00:33:09,715
Okay?

585
00:33:12,020 --> 00:33:13,360
Okay.

586
00:33:13,360 --> 00:33:16,920
Now we're going to circle all the way
back to the beginning.

587
00:33:16,920 --> 00:33:18,500
All the way back to the first lecture.

588
00:33:18,500 --> 00:33:24,050
And I gave you this program, and I said
don't worry about it.

589
00:33:24,050 --> 00:33:27,660
We'll learn about it later.
Well, now later.

590
00:33:27,660 --> 00:33:32,030
At this point you should be able to
understand every line of this program.

591
00:33:33,490 --> 00:33:38,280
This is the program that's going to count
the most common word in a file.

592
00:33:38,280 --> 00:33:39,000
Okay?

593
00:33:39,000 --> 00:33:41,190
So let's walk through what it does and
hopefully

594
00:33:41,190 --> 00:33:44,550
by now this will make a lot of sense.

595
00:33:45,610 --> 00:33:47,910
Okay? So we're going to start out, we're
going to ask

596
00:33:47,910 --> 00:33:51,070
for a file name, we're going to open that
file for read.

597
00:33:52,140 --> 00:33:54,710
Then, because we know it's not a very large

598
00:33:54,710 --> 00:33:56,760
file, we're going to read it all in one go.

599
00:33:56,760 --> 00:34:00,480
So handle dot read says read the whole
file, newlines and all,

600
00:34:00,480 --> 00:34:03,580
blanks, newlines, whatever,
and put it in

601
00:34:03,580 --> 00:34:07,530
the variable called text, it's just
mnemonic. Remember I'm, in this one

602
00:34:07,530 --> 00:34:12,650
I'm using the mnemonic variable names.
Then go through that whole

603
00:34:12,650 --> 00:34:16,630
string, which was the whole file, go
through and split it all.

604
00:34:16,630 --> 00:34:19,840
Newlines don't hurt it.
Newlines are treated like blanks.

605
00:34:19,840 --> 00:34:21,540
And it understands all that.

606
00:34:21,540 --> 00:34:23,420
It throws the newlines away and the
blanks away

607
00:34:23,420 --> 00:34:27,040
and splits it into a beautiful list of
just words with no blanks.

608
00:34:28,540 --> 00:34:33,070
And the list of the words in that file
ends up in the variable words.

609
00:34:33,070 --> 00:34:36,009
words is a list, text is a string, words
is a list.

610
00:34:37,090 --> 00:34:41,600
Then what I do is the pattern of
accumulating counters in a dictionary.

611
00:34:41,600 --> 00:34:43,790
I make an empty dictionary.

612
00:34:43,790 --> 00:34:47,500
I have the word variable that goes through
all the words

613
00:34:47,500 --> 00:34:52,830
and then I just say, counts sub word equals
counts dot get(word,0) + 1,

614
00:34:53,920 --> 00:34:56,659
and that, like we just got done saying,
it both creates

615
00:34:56,659 --> 00:35:02,020
and/or increments the value in the
dictionary as needed.

616
00:35:02,020 --> 00:35:03,610
So now at the end of the, at the, at this

617
00:35:03,610 --> 00:35:11,650
point in the program, we have a full
dictionary with the word:count.

618
00:35:11,650 --> 00:35:12,460
Okay?

619
00:35:12,460 --> 00:35:15,040
And there's many of them.
You know, all the words, all the counts.

620
00:35:15,040 --> 00:35:17,280
They're not in any particular order. So now what

621
00:35:17,280 --> 00:35:21,800
we're going to do is we're going to write
a largest loop, find the largest.

622
00:35:21,800 --> 00:35:23,680
Which is another thing that we've done.

623
00:35:23,800 --> 00:35:27,010
So not only do I need to now know what
largest count I've seen so far,

624
00:35:27,010 --> 00:35:29,640
I need to know what word that is.

625
00:35:29,640 --> 00:35:32,870
So I'm going to set the largest count
we've seen so far to None, set

626
00:35:32,870 --> 00:35:36,780
the largest word we've seen so far
to None, and then I'm going to use this

627
00:35:36,780 --> 00:35:38,740
two-iteration variable pattern to say

628
00:35:38,740 --> 00:35:44,230
go through the key/value pairs word and
count in counts.items.

629
00:35:44,230 --> 00:35:44,920
So it's just going to

630
00:35:44,920 --> 00:35:47,120
go through [SOUND] all of them.

631
00:35:47,120 --> 00:35:52,930
And then I'm going to ask if the largest
number I've seen so far is None or

632
00:35:52,930 --> 00:35:56,410
the current count that I'm looking at is
greater then the largest I've seen so far,

633
00:35:59,280 --> 00:36:03,260
keep them. Take the current word, stick it
in biggest word so far,

634
00:36:03,260 --> 00:36:07,180
take the current count, stick it in
the biggest count so far.

635
00:36:07,180 --> 00:36:09,670
So this is going run through all of the

636
00:36:09,670 --> 00:36:14,290
word.count pairs, word.count key/value pairs.

637
00:36:14,290 --> 00:36:16,640
And then when it comes out, it's going to
print out

638
00:36:16,640 --> 00:36:19,430
the word that's the most common and how
many times.

639
00:36:20,680 --> 00:36:24,290
So if we feed in that clown text, it will
run all this stuff, and print out

640
00:36:24,290 --> 00:36:29,170
oh, the is the most common word, and it
appeared seven times.

641
00:36:29,170 --> 00:36:33,540
Or if I print the stuff that was two
slides back, words.txt, from the actual

642
00:36:33,540 --> 00:36:37,790
textbook, then it says the word to is the
most common and it happened 16 times.

643
00:36:37,790 --> 00:36:43,380
So I could easily, you know, throw 10
million, 10 million

644
00:36:43,380 --> 00:36:46,380
words through this thing, and it would
just be totally happy.

645
00:36:46,380 --> 00:36:49,370
Right? And so, this is not that complex

646
00:36:49,370 --> 00:36:52,700
of a problem, but it's using a whole bunch
of idioms that we've been using.

647
00:36:52,700 --> 00:36:57,380
The splitting of words, the accumulation
of multiple counters in a dictionary.

648
00:36:57,380 --> 00:37:02,110
And so, it sort of is the beginning of
doing some kind of data

649
00:37:02,110 --> 00:37:06,040
analysis that's hard for humans to do, and
error-prone for humans to do.

650
00:37:06,040 --> 00:37:08,500
And so this is, we're reviewing collections.

651
00:37:08,500 --> 00:37:10,510
We've introduced dictionaries.

652
00:37:10,510 --> 00:37:13,310
We've done the most common word pattern,
talked about that.

653
00:37:13,310 --> 00:37:14,300
The lack of order, and

654
00:37:14,310 --> 00:37:16,270
I did that a bunch of times.

655
00:37:16,270 --> 00:37:19,750
And we've looked ahead at tuples,
which is the next,

656
00:37:19,750 --> 00:37:22,210
the third kind of collection that we're
going to talk about.

657
00:37:22,210 --> 00:37:25,890
And they're actually in some ways a little
simpler than dictionaries.

658
00:37:25,890 --> 00:37:27,150
And simpler than lists.

659
00:37:27,150 --> 00:37:33,000
So, see you in the next lecture, Chapter
10, tuples.