WEBVTT 1 00:00:00.040 --> 00:00:02.600 All right, buckle up everyone, because today we're taking a 2 00:00:02.640 --> 00:00:04.639 deep dive into Python two oh one. 3 00:00:04.799 --> 00:00:07.559 We're going way past Python one oh one here. Think 4 00:00:07.599 --> 00:00:09.279 of this as your insider's. 5 00:00:08.720 --> 00:00:13.199 Guide to some seriously powerful Python techniques. We've got excerpts 6 00:00:13.199 --> 00:00:16.280 from the book, the table of contents, the chapter intros, 7 00:00:16.359 --> 00:00:19.320 even some code snippets. By the time we're done, you'll 8 00:00:19.359 --> 00:00:22.120 be writing Python code that I'll make your colleagues say, whoa, 9 00:00:22.519 --> 00:00:23.280 how they do that. 10 00:00:23.600 --> 00:00:26.320 Python two one is broken down into four parts. We've 11 00:00:26.359 --> 00:00:29.760 got intermediate modules, odds and ends, Web and testing. 12 00:00:30.519 --> 00:00:34.399 Hmmm, that odds and ends section sounds intriguing. What's in there? 13 00:00:34.679 --> 00:00:38.560 That's where we're focusing today. Covers unicode generators and iterators 14 00:00:38.560 --> 00:00:39.399 and benchmarking. 15 00:00:39.439 --> 00:00:41.600 All right, let's jump right in. I know Python two 16 00:00:41.640 --> 00:00:44.280 oh one has a whole section on modules, and I'm 17 00:00:44.320 --> 00:00:46.840 really curious about ar parse. I've heard it can be 18 00:00:46.880 --> 00:00:49.039 a game changer for building command line tools. 19 00:00:49.119 --> 00:00:51.439 Yeah. Arc parse is awesome for that. It makes handling 20 00:00:51.479 --> 00:00:54.799 command line arguments so much easier, especially if you're dealing 21 00:00:54.840 --> 00:00:56.600 with things like usernames and passwords. 22 00:00:56.679 --> 00:00:58.520 Uh huh, sensitive data makes sense. 23 00:00:58.679 --> 00:01:00.960 The book has a really good example on page sixteen, 24 00:01:01.479 --> 00:01:06.519 where they walk you through defining arguments setting defaults. They 25 00:01:06.560 --> 00:01:09.400 even use chain map to manage those defaults in a 26 00:01:09.400 --> 00:01:10.239 really smart way. 27 00:01:10.480 --> 00:01:12.480 I've heard of chain map but never really used it. 28 00:01:12.519 --> 00:01:13.719 What's so special about it? 29 00:01:13.840 --> 00:01:16.120 Well, instead of having a bunch of IFL statements to 30 00:01:16.120 --> 00:01:19.480 handle defaults, you've used chain map to kind of layer 31 00:01:19.560 --> 00:01:21.560 your defaults and control where they come from. 32 00:01:22.000 --> 00:01:23.920 Oh. That sounds a lot cleaner than the way I've 33 00:01:23.959 --> 00:01:26.879 been doing it. The example also highlights some specific lines 34 00:01:26.879 --> 00:01:29.200 of code. You've got line four marked right. 35 00:01:29.239 --> 00:01:31.840 Line four is where they import chain map from the 36 00:01:31.879 --> 00:01:32.719 collections module. 37 00:01:32.879 --> 00:01:36.200 Ah. Collections, Now that's a module I have used and 38 00:01:36.319 --> 00:01:38.319 loved so many useful tools in there. 39 00:01:38.359 --> 00:01:40.879 It's a gold mine for sure. And they use chain 40 00:01:40.920 --> 00:01:43.640 map in this example to really streamline how the defaults 41 00:01:43.640 --> 00:01:44.200 are handled. 42 00:01:44.280 --> 00:01:46.519 I'm seeing how all these pieces fit together. Now, I 43 00:01:46.560 --> 00:01:49.319 see you've also got lines fourteen and fifteen highlighted. 44 00:01:49.680 --> 00:01:52.680 Those lines are key to They use a dictionary comprehension 45 00:01:53.000 --> 00:01:56.920 and the VIRS function. It's a really slick way to 46 00:01:57.000 --> 00:01:59.599 grab the arguments that the users actually provided. 47 00:02:00.000 --> 00:02:03.120 So basically, arc parse is like the velvet rope for 48 00:02:03.200 --> 00:02:06.319 your command line tools. Letting the right data in and 49 00:02:06.400 --> 00:02:07.560 keeping the riff raff out. 50 00:02:07.760 --> 00:02:10.599 Uh huh, yeah, I like that analogy. Okay, So moving 51 00:02:10.639 --> 00:02:13.120 on from arc pars, we've got collections on the docket. 52 00:02:13.240 --> 00:02:15.800 Yes, I've got to sing the praises of counter. It's 53 00:02:15.800 --> 00:02:20.479 made counting occurrences in like text files so incredibly easy. 54 00:02:20.560 --> 00:02:23.280 Oh. Counter is a life saver. But it's more than 55 00:02:23.319 --> 00:02:26.879 just counting. It's got all these helpful methods like most common, 56 00:02:27.120 --> 00:02:29.919 which can be super handy for things like figuring out 57 00:02:29.960 --> 00:02:32.080 the most frequent words in a document right. 58 00:02:31.960 --> 00:02:34.840 And it handles all the tedious counting logic behind the scenes, 59 00:02:35.199 --> 00:02:37.240 so your code stays clean and readable. 60 00:02:37.479 --> 00:02:39.960 Speaking of clean code, have you ever run into those 61 00:02:40.120 --> 00:02:42.759 pesky kier exceptions when working with dictionaries? 62 00:02:42.840 --> 00:02:44.599 Oh? Yeah, all the time. It's like they sneak up 63 00:02:44.639 --> 00:02:45.919 on you when you least expect it. 64 00:02:46.080 --> 00:02:48.919 Well, default dickt is your new best friend. It lets 65 00:02:48.919 --> 00:02:51.120 you set a default value for any key that doesn't 66 00:02:51.120 --> 00:02:52.439 already exist in the dictionary. 67 00:02:52.879 --> 00:02:56.000 Ah. So instead of crashing with a keyr, it just 68 00:02:56.159 --> 00:02:59.080 gracefully provides a default value exactly. 69 00:02:59.360 --> 00:03:02.120 The example page fourteen really shows how elegant it can be. 70 00:03:02.360 --> 00:03:05.000 Lines one, two, and four especially. 71 00:03:04.680 --> 00:03:07.280 Okay, I'm definitely bookmarking that example for later. Now. DK 72 00:03:07.400 --> 00:03:09.759 is one I haven't used much. What's the skip on that? 73 00:03:10.159 --> 00:03:13.960 Think of DKE as a list that's optimized for adding 74 00:03:13.960 --> 00:03:16.120 and removing elements from both ends, so. 75 00:03:16.199 --> 00:03:18.719 Like a queue, but you can add things to the 76 00:03:18.719 --> 00:03:19.479 front or the back. 77 00:03:19.639 --> 00:03:23.039 Yeah, it's really efficient if you need to process data 78 00:03:23.719 --> 00:03:26.479 in a specific order, like first in, first out or 79 00:03:26.560 --> 00:03:27.639 last in first out. 80 00:03:27.719 --> 00:03:30.159 Gotcha. Page nineteen has an example of a deck that's 81 00:03:30.280 --> 00:03:33.639 created from the lowercase letters right lines one, two, and three. 82 00:03:33.719 --> 00:03:36.560 I think those lines set up the deck. The cool 83 00:03:36.560 --> 00:03:38.479 part is that it has meteds like a pen left 84 00:03:38.520 --> 00:03:41.039 and pop left, so you have a lot of control 85 00:03:41.159 --> 00:03:42.479 over where things go in the deck. 86 00:03:42.879 --> 00:03:45.159 Okay, I'm starting to see how deep could be useful 87 00:03:45.159 --> 00:03:48.639 in some specific situations. What about named tupole. I haven't 88 00:03:48.680 --> 00:03:49.759 used that one much either. 89 00:03:49.879 --> 00:03:53.479 If you like clean and readable code, you'll love named tuple. 90 00:03:53.560 --> 00:03:56.360 It's basically a tuple, but you can access elements by 91 00:03:56.439 --> 00:03:58.520 name instead of just their position, so it's. 92 00:03:58.400 --> 00:04:01.840 Like a regular tuople, but with more meaningful labels. 93 00:04:01.919 --> 00:04:04.840 Exactly. It makes your code so much easier to understand, 94 00:04:05.000 --> 00:04:08.240 especially when you're dealing with data that has multiple fields. 95 00:04:08.639 --> 00:04:12.199 The book uses that autoparts example again on page twenty. 96 00:04:12.319 --> 00:04:14.479 You've marked lines one, three, five, and six. 97 00:04:14.680 --> 00:04:17.959 Yeah, that's going. They define a name tuple called parts 98 00:04:18.279 --> 00:04:20.879 with feels like make, model, and year. 99 00:04:21.600 --> 00:04:24.160 So instead of remembering that, say the year is at 100 00:04:24.199 --> 00:04:26.439 index two, you can just access it as parts dot 101 00:04:26.560 --> 00:04:27.439 year exactly. 102 00:04:27.480 --> 00:04:29.120 It makes the code so much more intuitive. 103 00:04:29.240 --> 00:04:32.879 Okay, last one for collections order dict. This is for 104 00:04:32.920 --> 00:04:36.079 when the order you add things to a dictionary actually matters. 105 00:04:35.839 --> 00:04:38.480 Right, you got it. A regular dictionary doesn't care about 106 00:04:38.519 --> 00:04:41.959 the order of its keys, but sometimes that order is crucial. 107 00:04:42.439 --> 00:04:45.600 Think configuration files or anything where the sequence matters. 108 00:04:45.879 --> 00:04:48.160 Yeah, that makes sense. So we've covered our parts and 109 00:04:48.240 --> 00:04:51.040 taken a tour of collections. What's next on the module menu. 110 00:04:51.199 --> 00:04:54.240 Context slib is up next. It's all about context managers 111 00:04:54.240 --> 00:04:57.720 and resource handling. Think of the with statement in Python. 112 00:04:57.879 --> 00:05:00.879 Ah, yes, the with statement. It's like a safety net 113 00:05:00.879 --> 00:05:03.680 for making sure resources get closed properly exactly. 114 00:05:04.079 --> 00:05:07.000 But contextlib takes it a step further and gives you 115 00:05:07.079 --> 00:05:11.240 tools like suppress, which can silently ignore certain types of exceptions. 116 00:05:11.439 --> 00:05:14.839 So instead of a big try accept block, you can 117 00:05:14.959 --> 00:05:21.360 just use suppress to handle expected exceptions without. 118 00:05:22.879 --> 00:05:24.839 Elevent and easier to be love it. 119 00:05:24.879 --> 00:05:27.879 Okay, speaking of elegant code, what about funk Tools. I'm 120 00:05:27.879 --> 00:05:28.759 intrigued by the name. 121 00:05:29.040 --> 00:05:31.480 Funk Tools is all about taking your functions to the 122 00:05:31.519 --> 00:05:35.160 next level. It's got tools for things like caching, partial, 123 00:05:35.199 --> 00:05:38.000 function application, and even function overloading. 124 00:05:38.120 --> 00:05:39.639 Whoa lots of goodies in there. 125 00:05:39.680 --> 00:05:42.759 For example, there's l rucash, which is a decorator that 126 00:05:42.839 --> 00:05:44.639 catches the results of your function calls. 127 00:05:44.800 --> 00:05:46.959 So if I have a function that does some heavy computation, 128 00:05:47.160 --> 00:05:49.360 all roucash can store the results, so the next time 129 00:05:49.360 --> 00:05:51.199 I call it with the same inputs, it doesn't have 130 00:05:51.240 --> 00:05:52.920 to do all that work again exactly. 131 00:05:52.959 --> 00:05:55.959 It's a brilliant way to boost performance. Page thirty one 132 00:05:56.000 --> 00:05:58.000 has an example where they use it to speed up 133 00:05:58.000 --> 00:05:59.000 fetching web pages. 134 00:05:59.160 --> 00:06:01.800 Ah. So it's perfect for those functions that get called 135 00:06:01.839 --> 00:06:03.600 repeatedly with the same arguments. 136 00:06:03.720 --> 00:06:06.519 Yep. And then there's partial, which is all about creating 137 00:06:06.560 --> 00:06:09.879 specialized versions of your functions with some of the arguments 138 00:06:09.920 --> 00:06:10.639 pre set. 139 00:06:11.000 --> 00:06:13.040 So it's like a function template where some of the 140 00:06:13.079 --> 00:06:14.199 blanks are already filled in. 141 00:06:14.319 --> 00:06:17.639 You got it. The example on page thirty two uses 142 00:06:17.680 --> 00:06:19.800 a simple ad function to show how it works. 143 00:06:20.120 --> 00:06:23.240 Let me guess they use partial to create a version 144 00:06:23.279 --> 00:06:26.040 of AD where one of the numbers is already fixed. 145 00:06:26.240 --> 00:06:29.240 Spot on, they create a version of AD that always 146 00:06:29.240 --> 00:06:31.800 adds two to whatever number you give it. It's a 147 00:06:31.839 --> 00:06:32.360 knee trick. 148 00:06:32.600 --> 00:06:35.399 Okay, I'm starting to see how partial could come in handy, 149 00:06:35.959 --> 00:06:39.600 especially for things like callbacks or event handlers. 150 00:06:39.759 --> 00:06:42.879 Now for something really powerful, we've got single dispatch. 151 00:06:42.959 --> 00:06:46.560 Single dispatch that rings a bell. Isn't that for function overloading? 152 00:06:46.920 --> 00:06:49.879 You know your stuff. It lets you define multiple versions 153 00:06:49.920 --> 00:06:53.079 of the same function that handle different input types differently. 154 00:06:53.399 --> 00:06:55.199 So I could have one AD function that works for 155 00:06:55.279 --> 00:06:58.319 numbers and another one that works for strings, and single 156 00:06:58.360 --> 00:07:00.519 dispatch figures out which one to use based on the 157 00:07:00.519 --> 00:07:01.240 input type. 158 00:07:01.360 --> 00:07:04.079 Yeah, it's a really elegant way to handle those situations 159 00:07:04.079 --> 00:07:06.920 where you want the same function to behave differently depending 160 00:07:06.920 --> 00:07:08.240 on the type of data it's given. 161 00:07:08.360 --> 00:07:11.199 The code on page thirty five shows how to overload 162 00:07:11.199 --> 00:07:13.879 the AD function for injurers, strings, and lists. 163 00:07:14.319 --> 00:07:17.959 Right, each version of AD has its own specific logic 164 00:07:18.040 --> 00:07:21.759 for handling those types. Lines ten, sixteen, and twenty two 165 00:07:21.879 --> 00:07:23.560 show those different implementations. 166 00:07:24.120 --> 00:07:27.279 This is making me rethink how I approach function design. 167 00:07:27.360 --> 00:07:32.519 And Python. Okay, last stop on our module tour. Importlib, ah, 168 00:07:32.639 --> 00:07:35.079 import lib. It's like the control panel for your imports 169 00:07:35.079 --> 00:07:37.360 in Python. You can do all sorts of fancy things 170 00:07:37.360 --> 00:07:37.680 with it. 171 00:07:37.839 --> 00:07:39.199 Fancy in a good way, I hope. 172 00:07:39.240 --> 00:07:42.680 Definitely. You can do dynamic imports where you only load 173 00:07:42.680 --> 00:07:44.800 a module when you actually need it. You can also 174 00:07:44.839 --> 00:07:47.120 do safety checks to make sure a module exists before 175 00:07:47.160 --> 00:07:48.040 you try to import it. 176 00:07:48.120 --> 00:07:51.759 That sounds incredibly useful for preventing those module not found errors. 177 00:07:52.319 --> 00:07:54.600 And it can even let you import modules directly from 178 00:07:54.639 --> 00:07:56.759 source files, which can be handy in some cases. 179 00:07:56.839 --> 00:07:59.040 Page fifty one shows how to check if a module 180 00:07:59.079 --> 00:08:02.439 exists using import lb right. Lines three, nine, twelve, and 181 00:08:02.519 --> 00:08:03.720 fourteen are the key ones. 182 00:08:03.879 --> 00:08:07.439 Those lines demonstrate how to gracefully handle the situation where 183 00:08:07.439 --> 00:08:09.519 a module might not be available. 184 00:08:09.319 --> 00:08:11.600 So instead of crashing, your code can check if the 185 00:08:11.639 --> 00:08:13.639 module exists and then take appropriate action. 186 00:08:13.839 --> 00:08:15.959 That's the goal. It's all about writing a robust and 187 00:08:16.040 --> 00:08:18.920 reliable Python code. Okay, Before we move on, I want 188 00:08:18.920 --> 00:08:21.160 to circle back to a couple of concepts that I'm 189 00:08:21.199 --> 00:08:24.319 still a bit fuzzy on. Iterators and generators. 190 00:08:24.399 --> 00:08:26.839 All right, good ones. They can be a little tricky 191 00:08:26.839 --> 00:08:30.720 to wrap your head around it. First, let's start with iterables. Basically, 192 00:08:30.759 --> 00:08:33.759 anything you can loop over in Python is an iterable, 193 00:08:34.039 --> 00:08:35.039 like a list or a tupple. 194 00:08:35.279 --> 00:08:39.559 Right, I get that. So where do iterators fit in Well. 195 00:08:39.320 --> 00:08:42.360 An iterator is what actually makes the looping possible. It's 196 00:08:42.360 --> 00:08:44.919 like a little machine that keeps track of where you 197 00:08:44.919 --> 00:08:47.279 are in the sequence and gives you the next element 198 00:08:47.279 --> 00:08:48.039 when you ask for it. 199 00:08:48.240 --> 00:08:51.559 So the iterable is the data source and the iterator 200 00:08:51.639 --> 00:08:53.840 is like a cursor moving through that data. 201 00:08:53.879 --> 00:08:56.879 A perfect analogy. And in Python you can get an 202 00:08:56.919 --> 00:09:00.360 iterator from an iterable using the itter function, and then 203 00:09:00.360 --> 00:09:02.480 you use the next function to fetch the next element. 204 00:09:02.600 --> 00:09:05.200 And what about that stop iteration exception I've seen. 205 00:09:05.279 --> 00:09:07.679 Ah yeah, that just means the iterator has reached the 206 00:09:07.759 --> 00:09:10.279 end of the sequence, like reaching the last page as 207 00:09:10.320 --> 00:09:10.639 a book. 208 00:09:10.679 --> 00:09:13.559 Okay, that makes sense. Now how do generators fit into 209 00:09:13.559 --> 00:09:14.120 this picture? 210 00:09:14.399 --> 00:09:17.799 Think of generators as iterators with a memory saving superpower. 211 00:09:19.120 --> 00:09:21.639 They don't generate all the values up front, They produce 212 00:09:21.720 --> 00:09:23.720 them one at a time on demand. 213 00:09:23.960 --> 00:09:25.759 So it's like having a recipe that tells you how 214 00:09:25.799 --> 00:09:27.639 to make each dish as you need it instead of 215 00:09:27.639 --> 00:09:29.720 cooking everything in advance and letting it go cold. 216 00:09:29.919 --> 00:09:32.240 That's a great way to put it. It's super efficient, 217 00:09:32.360 --> 00:09:34.240 especially if you're working with large data sets. 218 00:09:34.360 --> 00:09:36.960 Okay, generators are starting to click from it now. And 219 00:09:37.120 --> 00:09:40.120 Python two oh one mentions a module called itter tools, 220 00:09:40.200 --> 00:09:42.600 which is like a toolbox for working with iterators. 221 00:09:42.679 --> 00:09:45.200 Itter tools is awesome. It's packed with all these pre 222 00:09:45.279 --> 00:09:48.240 built iterators that you can mix and match to create 223 00:09:48.320 --> 00:09:49.399 really powerful code. 224 00:09:49.720 --> 00:09:52.000 Sounds dangerous. What are some of the highlights. 225 00:09:52.080 --> 00:09:55.279 Well, there's count, which can generate an endless sequence of numbers. 226 00:09:55.399 --> 00:09:58.080 Page sixty two has a simple example using. 227 00:09:57.879 --> 00:10:00.000 Count lines one in two, right, yep. 228 00:10:00.360 --> 00:10:03.720 And then there's cycle, which endlessly loops through an iterable. 229 00:10:04.080 --> 00:10:06.799 It's great for things like round robin scheduling, or anything 230 00:10:06.799 --> 00:10:08.879 where you need to keep repeating a sequence. 231 00:10:09.000 --> 00:10:10.840 Okay, I can see how that could be useful. Where's 232 00:10:10.840 --> 00:10:11.879 the example for cycle. 233 00:10:12.000 --> 00:10:13.879 It's on page sixty three, lines one in three. 234 00:10:14.000 --> 00:10:16.000 Got it? What else is in there? 235 00:10:16.159 --> 00:10:18.600 Group B is a really powerful one. It lets you 236 00:10:18.600 --> 00:10:21.720 group consecutive items in an iterable based on a key, 237 00:10:22.080 --> 00:10:23.720 Like if you have a list of transactions, you could 238 00:10:23.720 --> 00:10:25.480 group them by date or by customer. 239 00:10:25.720 --> 00:10:29.039 Ooh, that sounds perfect for data analysis. I see they 240 00:10:29.120 --> 00:10:32.360 use group B to group vehicles by make. On page sixty. 241 00:10:32.080 --> 00:10:34.240 Eight right, lines one and nine show how to use 242 00:10:34.279 --> 00:10:37.000 group B, and then lines ten and thirteen show how 243 00:10:37.039 --> 00:10:37.960 to access the groups. 244 00:10:38.080 --> 00:10:40.799 This is making me realize how often I've written custom 245 00:10:40.840 --> 00:10:43.440 code to do things that iter tools can already do 246 00:10:43.559 --> 00:10:43.799 for me. 247 00:10:44.000 --> 00:10:46.159 Huh huh. Yeah, it's a real timesaver once you get 248 00:10:46.159 --> 00:10:46.559 the hang of it. 249 00:10:47.000 --> 00:10:49.039 Last one, and I'm just going by your notes, here 250 00:10:49.200 --> 00:10:49.879 is product. 251 00:10:50.080 --> 00:10:52.600 Oh yeah. Product is a fun one. It generates all 252 00:10:52.720 --> 00:10:57.080 possible combinations of elements from multiple iterables. It's like a 253 00:10:57.120 --> 00:10:58.440 combinatorial explosion. 254 00:10:58.600 --> 00:11:00.320 Sounds intense. Is there an example for one? 255 00:11:00.399 --> 00:11:02.720 It's on page seventy just a single line of code 256 00:11:02.720 --> 00:11:04.519 to demonstrate the power of combinations. 257 00:11:04.639 --> 00:11:07.399 Okay, I'm definitely adding iter tools to my list of 258 00:11:07.440 --> 00:11:09.960 modules to explore further. It's like a secret weapon for 259 00:11:10.080 --> 00:11:11.519 data manipulation in Python. 260 00:11:11.799 --> 00:11:14.919 It really is. All Right, we've talked a lot about modules. 261 00:11:15.080 --> 00:11:17.480 Let's switch gears a bit and talk about unicode. It's 262 00:11:17.519 --> 00:11:20.759 such a fundamental concept when working with text in Python, 263 00:11:21.039 --> 00:11:22.480 but it can also be a bit confusing. 264 00:11:22.720 --> 00:11:25.440 Yeah. I've definitely run into some character encoding issues in 265 00:11:25.440 --> 00:11:27.159 the past that made me pull my hair out. 266 00:11:27.519 --> 00:11:31.320 Unicode is all about representing characters from every language in 267 00:11:31.360 --> 00:11:33.399 the world in a consistent way. 268 00:11:33.440 --> 00:11:36.159 So it's like a universal language for characters. 269 00:11:36.600 --> 00:11:38.720 That's a good way to think about it. It helps 270 00:11:38.720 --> 00:11:42.120 to avoid those annoying encoding errors and ensures that your 271 00:11:42.159 --> 00:11:45.320 text is displayed correctly no matter what language it's in. 272 00:11:45.720 --> 00:11:50.480 The book mentions something called Unicode decode error on page ninety. 273 00:11:50.240 --> 00:11:52.679 Nine, Right, that's an error you might encounter if you 274 00:11:52.720 --> 00:11:55.320 try to work with text that's not properly encoded. 275 00:11:55.559 --> 00:11:58.559 So if I'm reading data from a file or the web, 276 00:11:58.639 --> 00:12:00.759 I need to be careful about how it's coded and 277 00:12:00.840 --> 00:12:03.799 use the decode method to convert it to Unicode correctly. 278 00:12:04.039 --> 00:12:06.759 You got it. The ignore and replaced flags can be 279 00:12:06.840 --> 00:12:08.600 handy for dealing with tricky characters. 280 00:12:08.840 --> 00:12:11.919 This is making me appreciate the importance of understanding Unicode. 281 00:12:12.039 --> 00:12:14.440 It's like the foundation for working with text in a 282 00:12:14.480 --> 00:12:15.519 globalized world. 283 00:12:15.759 --> 00:12:18.960 Absolutely, okay. One last concept for this part of our 284 00:12:18.960 --> 00:12:22.600 deep dive benchmarking AH benchmarking. 285 00:12:22.639 --> 00:12:25.399 That's about measuring code performance, right exactly. 286 00:12:25.440 --> 00:12:28.960 It's about figuring out how fast your code runs and 287 00:12:29.039 --> 00:12:31.080 identifying areas where you can make it faster. 288 00:12:31.279 --> 00:12:33.720 So it's not just about writing code that works, it's 289 00:12:33.759 --> 00:12:35.840 about writing code that works efficiently. 290 00:12:36.320 --> 00:12:38.720 You got it. Python two one covers a few different 291 00:12:38.720 --> 00:12:42.360 tools for benchmarking. There's timite, which is great for measuring 292 00:12:42.360 --> 00:12:44.559 the execution time of small pieces of code. 293 00:12:44.720 --> 00:12:47.279 I've heard of timeite, but never really used it. What's 294 00:12:47.320 --> 00:12:47.759 it good for? 295 00:12:47.919 --> 00:12:50.919 It's perfect for those times when you have a function 296 00:12:51.120 --> 00:12:53.320 or a block of code that seems to be running slow. 297 00:12:54.080 --> 00:12:56.919 You can use timite to pinpoint exactly how slow it 298 00:12:56.960 --> 00:12:58.519 is and where the bottleneck might be. 299 00:12:58.600 --> 00:13:01.120 Okay, that makes sense. What about profile? Is that for 300 00:13:01.200 --> 00:13:02.480 profiling entire programs? 301 00:13:02.519 --> 00:13:05.159 You got it? Ce Profile gives you a detailed report 302 00:13:05.200 --> 00:13:07.720 of how much time is spent in each function call 303 00:13:07.799 --> 00:13:10.200 within your program. You can run it from the command 304 00:13:10.240 --> 00:13:13.320 line as mentioned on page one oh six, line one, so. 305 00:13:13.240 --> 00:13:15.360 It's like an X ray for your code, showing you 306 00:13:15.440 --> 00:13:16.840 where all the time is being spent. 307 00:13:17.200 --> 00:13:20.399 And finally, there's line profiler, which takes things to a 308 00:13:20.440 --> 00:13:23.159 whole new level. It actually shows you how much time 309 00:13:23.240 --> 00:13:25.480 is spent on each individual line of code. 310 00:13:25.519 --> 00:13:27.519 WHOA, that's granular. How does it work? 311 00:13:27.720 --> 00:13:30.240 You use the current prop script and the app profile 312 00:13:30.279 --> 00:13:32.879 decorator is shown on page one oh seven to instrument 313 00:13:32.879 --> 00:13:34.799 your code. Then you can run it and get a 314 00:13:34.799 --> 00:13:36.320 line by line breakdown of performance. 315 00:13:36.399 --> 00:13:38.879 That's amazing. It's like having a magnifying glass for your 316 00:13:38.960 --> 00:13:39.799 code performance. 317 00:13:39.919 --> 00:13:42.600 It can be super helpful for finding those subtle performance 318 00:13:42.600 --> 00:13:44.759 bottlenecks that you might not otherwise notice. 319 00:13:44.840 --> 00:13:47.720 Okay, I'm feeling inspired to go and profile all my 320 00:13:47.799 --> 00:13:50.200 Python code now. I think we've covered a lot of 321 00:13:50.279 --> 00:13:52.519 ground in this first part of our Python two oh 322 00:13:52.519 --> 00:13:57.399 one deep dive. We've explored modules like art parse, collections, contextlib, 323 00:13:57.480 --> 00:14:02.759 funct tools, and import lib. We've delved into iterators, generators, Unicode, 324 00:14:02.919 --> 00:14:04.840 and even touched on benchmarking. 325 00:14:04.960 --> 00:14:07.759 We've barely scratched the surface, though, there's so much more 326 00:14:07.799 --> 00:14:10.679 to uncover in Python two oh one. Next time, we'll 327 00:14:10.720 --> 00:14:13.600 dive into the world of regular expressions, type hinting, and 328 00:14:13.679 --> 00:14:16.519 the powerful super built in. Get ready for another round 329 00:14:16.519 --> 00:14:17.600 of Python awesomeness. 330 00:14:17.879 --> 00:14:21.279 Back for more Python fun. Last time, we dug into 331 00:14:21.320 --> 00:14:24.559 some awesome modules and really got to handle on iterators 332 00:14:24.559 --> 00:14:28.159 and generators. What's next on our Python two oh one journey. 333 00:14:28.519 --> 00:14:31.799 Let's explore the world of regular expressions, or, as the 334 00:14:31.799 --> 00:14:33.440 cool kids call it, rejects. 335 00:14:33.919 --> 00:14:36.519 Jacts. I've seen them, those strings of characters that look 336 00:14:36.600 --> 00:14:38.080 like they're straight out of the matrix. 337 00:14:38.200 --> 00:14:40.879 They can seem a bit crypic at first, but trust me, 338 00:14:41.240 --> 00:14:43.519 once you get the basics, rejex is like having a 339 00:14:43.600 --> 00:14:47.039 superpower for working with text. Python two oh one breaks 340 00:14:47.080 --> 00:14:47.840 it down really well. 341 00:14:47.960 --> 00:14:50.120 Okay, I'm willing to give it another shot. What are 342 00:14:50.159 --> 00:14:51.879 the building blocks of rejects? 343 00:14:51.960 --> 00:14:54.519 Well, it all starts with meta characters. They're kind of 344 00:14:54.559 --> 00:14:58.480 like special characters, but they represent patterns rather than literal characters. 345 00:14:58.679 --> 00:15:02.200 So instead of matchings, say the letter A, you can 346 00:15:02.240 --> 00:15:04.000 match a whole category. 347 00:15:03.480 --> 00:15:06.960 Of characters, exactly like the dot can match any character 348 00:15:07.000 --> 00:15:09.320 except a new line. Page seventy six of the book 349 00:15:09.320 --> 00:15:10.320 has a bunch of examples. 350 00:15:10.360 --> 00:15:12.399 Okay, I'm starting to see how this could be powerful. 351 00:15:12.600 --> 00:15:15.200 What if I want to match a specific set of characters, 352 00:15:15.240 --> 00:15:16.639 like all the lowercase letters. 353 00:15:16.919 --> 00:15:19.600 That's where character classes come in. You put the characters 354 00:15:19.600 --> 00:15:23.080 you want to match inside square brackets, like AZ would 355 00:15:23.120 --> 00:15:26.360 match any lowercase letter and zero nine would match any digit. 356 00:15:26.639 --> 00:15:29.080 Makes sense. Page seventy seven talks more about that. 357 00:15:29.080 --> 00:15:31.720 Right, That's the one They go into detail about character 358 00:15:31.759 --> 00:15:34.159 classes and all the different ways you can use them now. 359 00:15:34.159 --> 00:15:37.240 Another important part of rejects is quantifiers. They let you 360 00:15:37.240 --> 00:15:40.120 control how many times a character or a pattern should appear. 361 00:15:40.279 --> 00:15:41.919 No, that's handy, Like if I want to match a 362 00:15:41.960 --> 00:15:43.480 word that appears at least three. 363 00:15:43.279 --> 00:15:46.240 Times, You got it. There's the asterisk for zero or 364 00:15:46.279 --> 00:15:49.879 more occurrences, the plus sign plus for one or more, 365 00:15:50.279 --> 00:15:52.679 and the question mark for zero or one. 366 00:15:53.000 --> 00:15:56.120 So much control packed into just a few symbols. Now, 367 00:15:56.120 --> 00:15:59.279 Python has the remodule for working with rejects. 368 00:15:58.960 --> 00:16:01.600 Right absolutely. The remodule gives you all the tools you 369 00:16:01.679 --> 00:16:05.919 need to search, match, and manipulate strings using rejects. It's 370 00:16:05.919 --> 00:16:07.440 your reject Swiss army knife. 371 00:16:07.639 --> 00:16:12.080 Cool. You've highlighted some lines from page seventy nine, lines eight, nine, ten, twelve, 372 00:16:12.080 --> 00:16:12.679 and thirteen. 373 00:16:13.080 --> 00:16:15.840 Those lines show how to use the redot search function 374 00:16:16.279 --> 00:16:18.799 to find a pattern in a string. It returns a 375 00:16:18.840 --> 00:16:20.960 match object if the pattern is found. 376 00:16:20.879 --> 00:16:23.720 So line eight defines the reject pattern, line nine does 377 00:16:23.759 --> 00:16:25.759 the search, and then the other lines show how to 378 00:16:25.799 --> 00:16:28.039 extract information from the match object. 379 00:16:28.279 --> 00:16:31.320 Exactly you can get the match text it's positioned in 380 00:16:31.360 --> 00:16:33.879 the string, and all sorts of other useful details. And 381 00:16:33.919 --> 00:16:36.919 on page eighty Python two oh one shows how to 382 00:16:36.960 --> 00:16:40.840 compile rejects patterns for better performance using redot compile. 383 00:16:41.200 --> 00:16:43.039 Ah. So, if I'm going to be using the same 384 00:16:43.080 --> 00:16:46.360 pattern multiple times, it makes sense to compile it once 385 00:16:46.639 --> 00:16:48.480 and then reuse the compiled object. 386 00:16:48.679 --> 00:16:50.440 It's like caching the pattern so you don't have to 387 00:16:50.519 --> 00:16:52.759 keep recompiling it over and over. It can really speed 388 00:16:52.799 --> 00:16:53.240 things up. 389 00:16:53.320 --> 00:16:55.639 I'm starting to feel more confident about rejects now. It's 390 00:16:55.679 --> 00:16:59.480 really just about understanding those core components and then using 391 00:16:59.559 --> 00:17:01.399 the remark to put them into action. 392 00:17:01.600 --> 00:17:03.559 You're getting it. With a little practice, You'll be a 393 00:17:03.559 --> 00:17:06.640 retext wizard in no time. Okay, okay, ready to switch 394 00:17:06.680 --> 00:17:07.079 gears again. 395 00:17:07.240 --> 00:17:07.559 Hit me. 396 00:17:07.880 --> 00:17:10.559 Let's talk about type hinting. It's a feature that was 397 00:17:10.599 --> 00:17:13.160 added in Python three point five, and it's all about 398 00:17:13.200 --> 00:17:15.319 making your code more explicit and robust. 399 00:17:15.640 --> 00:17:16.680 Explicit in what way? 400 00:17:16.880 --> 00:17:20.359 Well, you know how Python is dynamically typed, meaning the 401 00:17:20.440 --> 00:17:22.400