- Reading Files 7:00
- Basics 4 questions
- The Basics 5 objectives
- Escape Hatches 5:23
- Escapes 4 questions
- Escapes 2 objectives
- Counts 6:05
- Phone Numbers 1 objective
- Word Length 1 objective
- Sets 5:42
- Email 1 objective
- Negation 8:20
- Negated Numbers 1 objective
- Groups 9:45
- Name Groups 1 objective
- Email Groups 2 objectives
- Compiling and Loops 6:51
- Players Dictionary and Class 2 objectives
- Review: Regular Expressions in Python 8 questions
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Groups
9:45Now that we can search for just about anything, let's organize our results a bit better. Regular expressions give us indexed and named groups, both of which are super-handy.
When we create our patterns, they just
match the entire thing.
0:00
We've seen it already where our match
objects only have one group in them.
0:02
It's often really handy to have multiple
groups defined inside of your pattern, so
0:06
that you can later access just parts of
the text that you care about.
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Like for our case, making a group for the
email address, and a group for
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the phone number, and a group for
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the name would make it a lot simpler later
to pull those out and use them.
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So, at this point,
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we've gotten to where we can catch pretty
much anything in our text file.
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So I think what we should do now is we
should kind of just use all these
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things at once.
0:30
This might get a little confusing, so
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what we're gonna do is we're actually
gonna break these up into groups.
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So let's let's start this out with our
normal print and re.findall.
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And then let's do a large verbose one and
we're gonna need re.X.
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So, all right.
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Now we can write our pattern.
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I'm gonna add in some extra lines her just
so
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I can make this a little bit more
readable.
0:57
All right, so we define groups with
parentheses.
1:00
So, our first group here, we wanna capture
the last name and the first name.
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So for the last name, we need that.
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So hyphens, word characters, and spaces.
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Any number of those from zero on up.
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And then we need a comma, and we need an
actual space, and
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then we need hyphen w space again, and
that's our group.
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So that's last name, comma space first
name.
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And then there's gonna be a tab.
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All right.
So let's make a little note of
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that last and first names.
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Okay.
So now for
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our email address, which was our next
thing in our line.
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those, oops, those should cover our items.
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So hyphens, word characters, numbers,
periods, and plus signs.
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So we've got one or more of those.
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We have an at symbol, and then we again
have hyphens,
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word characters, digits, and periods.
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One or more of those, and then there's a
tab.
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And this is for our email.
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All right, so what comes next?
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Well, next is the phone number.
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So, remember we have to escape these
parentheses and
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we wanna mark them as optional.
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So, then there's three digits.
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And there is closing parentheses that is
optional.
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There is a hyphen that is optional, and a
space that is optional.
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And then there are three numbers, a
hyphen, and the four numbers.
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That's our group, and then there's a tab.
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So we'll say that's phone.
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yep, all right.
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Then we have the job and the company that
they work for.
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So, this is a whole lot like our one that
captures the names, but
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we don't have a lot of stuff in here.
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So, it's pretty much just word characters
and spaces.
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So there can be one or more of those,
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a comma, some sort of white space, and
then again words and spaces.
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And then of course there's a tab.
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So job and company.
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And then the last thing that we put in
there on some of the lines at
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least is a Twitter account.
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So, let's grab that, Twitter is actually
really easy to grab.
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It's just /w/d, because,
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I guess, no underscores are being included
in slash w.
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You can't have hyphens, you can't really
have special characters,
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you can just have numbers and letters.
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So, that's that for Twitter, and let's
mark that Twitter.
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All right.
So, that's our pattern.
3:47
Now it's a really long reg X, and there's
actually a couple of problems with this,
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things that it won't catch.
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But let's run it and, and see what we get.
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So, we'll come down here, python
address_book.
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And we can see like, you notice that
there's opening parenthesis, there's a,
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a tuple.
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Yeah, you see the tuple?
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And the tuple shows all of our little
groups that we caught.
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Each item in the tuple is one of our
groups.
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So that's pretty awesome.
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We're gonna come back to that.
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Do you notice there's anything missing?
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Dave's not here.
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And King Arthur isn't in here.
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And the reason is because they don't have
some of the items that we're looking for.
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So since they don't match exactly, they
don't get included.
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So what we should do is we should go back
and
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mark a couple of things as being optional.
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We're also gonna do a couple of other
tricks here.
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So, let's see.
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The first thing we're gonna do is we're
actually gonna add a symbol right here.
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We're gonna add the carrot, and that means
the beginning of the string.
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Okay.
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And to compliment that, right down here
right after that closing parenthesis,
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we're gonna put in a dollar sign, which
marks the end of the string.
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'Kay, we've got another trick we're gonna
do for
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that in just a minute, but remember those.
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So Tim doesn't have a last name.
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So we'll mark those as completely
optional.
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And everybody's got email.
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I don't think there's anything we need to
change on email.
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And some of them.
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Let's see.
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I think they all have phone numbers.
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Some of them, however, don't have jobs
listed.
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So, rather, they have jobs listed.
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They may not have if they don't have a
phone number,
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then we mar, oh, sorry, yeah, we wanna
change this.
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A phone number is optional.
5:41
We wanna make that phone number optional.
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If they don't have a phone number, it
won't be there.
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The tab after job, if they don't have a
Twitter account,
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the tab after job will actually be a new
line.
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So that tab won't be there.
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We wanna mark that tab as being optional.
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And really over here in the company name,
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we should add in a dot as being a possible
character.
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Because we've got that one, that co dot.
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So we want to be able to mark that, or
catch it.
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And then some of them don't have Twitter
accounts, so
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let's make Twitter optional as well.
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The other thing we need to add, because we
marked beginning and end of the string.
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And our string is this entire thing.
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We want our string to be in one line.
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Right?
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So what we need to do is we need to add in
re.MULTILINE.
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And what that says is treat each line a
return me and count our slash in.
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Treat that as the end of the string.
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So, it turns our one big string into a lot
of strings,
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as far as the regular expression engine is
concerned.
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Okay?
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If we want we can do re.M, instead of
re.MULTILINE.
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So either way that's gonna work.
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All right, let's try this one out.
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Look at that.
We've got a whole lot more stuff.
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I do believe we've got everything for
everyone.
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There's the doctor, even with his big
email address.
6:59
[BLANK_AUDIO]
7:02
We got Tim.
7:05
We got everybody in there.
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All of our stuff is there.
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So that's amazing.
7:09
That's awesome.
7:09
So, what we wanna do now though, is we
wanna make this regular expression.
7:11
It's really handy as it is, but
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it's just giving us out a list of tuples
when we do this find all.
7:17
And no matter what we did, we would only
get tuples, and
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we would get like index positions.
7:24
What I wanna do though, is I wanna be able
to turn this into a dictionary, so
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that I can use that dictionary and do
something else with it.
7:29
So let's take our groups and make them
named groups.
7:34
So the way that we do that, we don't have
to change any of our code.
7:38
Our code gets to stay the same.
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We just add on a couple of things.
7:42
We add a question mark and a p, and this
is what makes it a name.
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And then we specify the name inside of
less than and greater than signs.
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So we're gonna name this first group name,
cuz that's what it is.
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The second group we're going to name
email.
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The third group we will name phone.
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The fourth group we'll name job.
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And the last group, we'll name Twitter.
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All right?
I think that's pretty good.
8:16
But let's actually, instead of doing all
of this here and, and
8:18
printing, let's make this a little easier
for ourselves.
8:23
Let's say line equals and let's do a
search.
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[BLANK_AUDIO]
8:28
And then we need to get rid of one of
these.
8:30
All right?
So line is a search.
8:32
For right now it's just gonna be that
first line.
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It's just gonna be me.
8:35
But we can print out what this gets.
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So let's print out line.
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[BLANK_AUDIO]
8:41
And then let's also print out line
line.groupdict.
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And let's see what's these two things do.
8:50
So, okay, let's come down here, address
book.
8:52
So when we print out line, we get this
match objects.
8:55
All right.
8:57
And the match object catches a whole bunch
of stuff.
8:58
But when we print the dictionary, look
what we get.
9:00
We've got the dictionary that has the name
and email address, and the job.
9:02
Yeah, it gets the slash t on the job, but
that's okay.
9:06
And Twitter gets kennethlove and the phone
gets the phone number.
9:10
So we got all this stuff.
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That's so much better than what we've
gotten before when we
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were just getting these tuples.
9:17
So our next video, we've got just two last
big steps and
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we'll have turned this in to something
absolutely amazing.
9:24
Wow, using groups, especially named
groups,
9:26
makes our string almost act like an object
or dictionary.
9:29
We've turned a simple string into really
useful data, good job, us.
9:32
All right, just a bit more to go, and
we'll have this in the bag.
9:37
In our next video, let's look at making
reusable patterns, and
9:39
how we can loop over our addresses in a
more useful manner.
9:43