Compiling and Loops

Eventually, you're going to be in a situation where you need to 0:00 use a pattern on 100 different items in a big loop. 0:02 Or maybe it'll be 500, or 1000. 0:05 You don't wanna have to build and match the pattern every single time. 0:08 So the regular expression library has given us a way to compile a pattern into 0:11 an object that we can match against. 0:15 Let's look at how to do that. 0:17 We've already seen that we can make patterns as strings. 0:18 We stored them as a variable back, way, way back in the first video. 0:21 But, that's not really the most useful thing in the world. 0:25 I mean like, if we have, say this string, yeah, it's handy having this as 0:27 a variable, but it doesn't save us a lot of time or trouble. 0:32 It'd be better if we could say that pattern in a state where it 0:36 was ready to go. 0:40 It's ready to be used as a regular expression. 0:41 And we can do that. 0:44 That's what the Compile method let's us do. 0:46 So that's actually change line here. 0:49 Instead of Search, we'll wanna say Compile. 0:52 And what that does is this is gonna take the regular expression and compile it, 0:55 get it ready for use. 0:59 Now the one other thing we have to change, is we have to take out where it says data. 1:01 Because when we compile a regular expression, 1:06 we don't compile it with the data it's gonna be run against. 1:08 It's like making the regular expression a bit more generic. 1:11 We can now run it against a lot of different things. 1:13 Not just the one thing that we did the search against, or the match or whatever. 1:16 So, I'm gonna take out this line one and let's look at this one. 1:21 So we've got line.groupdict and 1:24 that no longer makes sense because line isn't a match. 1:26 So let's go ahead and do a match. 1:29 We'll do re.search, line, data, and then groupdict. 1:31 So what we did here, is we created a regular expression search, 1:39 just like before, and we said, okay, your pattern is this compiled one, it's line. 1:43 And the string you're gonna match it against is data. 1:49 And now notice, we didn't specify any flags. 1:51 You specify the flags when you do the compile, not when you do the search. 1:54 So let's save that and let's Run. 1:59 And there we go, we got that same thing. 2:03 So, that's great. 2:06 But what's really, really, really cool, 2:07 at least I think so, is we don't have to do this re.search stuff. 2:10 What we can do, instead, is we can just use line directly. 2:14 [BLANK_AUDIO] 2:19 So we can just say, okay, take line. 2:23 It's a pattern. We know it's a pattern. 2:25 Do a search with it against data. 2:27 So let's take that out. 2:31 Run this again, and we should get the exact same content, and we do. 2:33 So, that's pretty cool. 2:37 But, we've only got one thing. 2:39 We're only, we're only getting my line. 2:41 How do we get the rest of the lines? 2:42 Well, this is the last part of the r-a library that I wanna go over with you. 2:44 But there's still more in there to explore. 2:49 Go check the docs. 2:51 They're awesome. 2:52 It's a method that's named finditer. 2:53 And it gives us back an iterable of each non-overlapping match. 2:55 It's kind of like giving us back a list, but it's not exactly a list. 2:59 It's also kinda like using Find All, but instead of getting back topples, 3:02 we get back a match object, like when we use re.match or re.search. 3:07 So let's try this out. 3:11 Let's come back up here and let's say for 3:13 match in line.search, oops sorry. 3:18 Not line.search. 3:22 Line.finditer against data. 3:24 We want to print match.group. 3:29 Name. 3:32 So, this .group method, 3:33 when you have a match object says, show me whatever is inside of the group. 3:36 Now, I can say, group with a, with nothing, and 3:41 it'll show me the whole thing that it captures. 3:44 I can do group with a number, and it'll show me the group at that index. 3:47 So, 0, 1, 2, so on. 3:50 Or I can give it a name and it'll show me the group at that name. 3:52 All right, so let's try that out. 3:56 So there we go. 4:00 There's everybody's names. 4:01 You can see Ryan Carson, The Doctor, Exampleson, Example, so on. 4:03 All right, that's all of our names. 4:06 We can of course ask for any group that we want or do all sorts of other stuff. 4:09 For example, let's make a fully qualified email to headers. 4:14 You, you've seen this before where it's like a name and then less than, 4:17 their email address and greater than, right? 4:21 Okay, so before we do that, 4:23 I want to actually come back and edit our pattern, and add two things. 4:25 So what's cool is we can have subpatterns, or subgroups, sorry. 4:30 So we can do here last, and that's the last name. 4:34 And we can do here first, and that of course is the first name. 4:42 So there's our first name, there's our last name, as these little sub patterns. 4:49 'Kay? 4:52 So now in our. 4:53 For loop, we need to print out a new thing. 4:55 And let's print out first, last, email. 5:00 'Kay. 5:09 And we wanna format this. 5:10 With the match.groupdict. 5:12 Right? It'll go ahead and 5:17 find those keys in the groupdict, it'll use those as keyword arguments. 5:19 All right, so let's try this out, we should have email addresses for everybody. 5:26 Check it out, we've got email addresses. 5:30 There's the name, and an email address. 5:32 That works pretty well, and it's a lot simpler than I 5:35 would have actually expected it to be if you were just like hey, 5:38 turn this into a bunch of email addresses. 5:41 Hopefully this will give you some ideas on how to take our address book further. 5:43 You can build all sorts of things out of dictionaries. 5:47 Made from strings through regular expressions. 5:50 In this course, compiling our pattern doesn't actually save us a lot of time or 5:53 memory, but it's a good habit to get into, so 5:56 you won't forget to do it when it actually matters. 5:59 And using things like finditer helps us to save even more memory. 6:01 Compiling patterns also lets you make patterns available for 6:06 import in other parts of your applications, or even in other packages. 6:08 This way, you can make your pattern as perfect as possible in just one place and 6:12 reap the benefits anywhere else that you need it. 6:16 I think we're done. 6:18 We've gone over pretty much every area of regular expressions in Python, and 6:20 we've turned a block of text that no one would want to wade through into super 6:24 useful dictionaries that we can put into classes or transform into a new text file. 6:27 In fact, that's your extra credit for this course. 6:32 Take the regex we wrote and the text and make a class for 6:34 a person to find by the data. 6:37 Give them names and a phone number and an email address. 6:39 Maybe make another class for an address book that collects all these 6:41 people together and let's the user search them. 6:44 Be sure to share it on the forums. 6:46 Thanks so much for being part of this course. 6:48 I'll see you next time. 6:50