[MUSIC] 0:00 Hey y'all, I'm Kenneth, your Treehouse Python teacher. 0:04 Today I want to talk about an interesting newer feature in Python that has caused 0:08 a bit of a ruckus. 0:11 Python, like JavaScript and Ruby, and many other languages, is dynamically typed. 0:12 This means that a variable like name could change from an int to a string to a list, 0:16 to whatever else it needs to be. 0:20 This is different from languages like Java and C#, where a variable has a type 0:22 declared when it's created and it will always hold some data that fits that type. 0:26 But you probably already know this, so why am I telling you? 0:30 Well, with the acceptance of some recent PEPs, 0:32 Python has an officially supported way of doing type hinting. 0:34 It started ten years ago when PEP 3107 landed in Python 3.0. 0:38 This PEP allowed for function annotations. 0:43 You could provide information about the parameters that a function took in 0:45 its return value, and 0:49 these would show up in the __annotations__ attribute of the function. 0:50 Then came Mypy, a static type checker for Python. 0:54 This tool would look for special formatting in your Python code where you'd 0:57 declare what type a parameter should be, and 1:00 then it would tell you if you ever gave it something else. 1:02 This checker and many others like it didn't and 1:05 won't make Python statically typed. 1:07 They didn't and don't enforce anything at runtime. 1:09 Instead they're used during code reviews and 1:12 in build processes to make sure code fits a standard. 1:14 In Python 3.5, Guido introduced PEP 484, which officially laid out 1:17 a specification for how these type annotations should look and behave. 1:21 This is also when the typing module came into Python, 1:26 which we'll look at more later. 1:29 And for Python 3.6, PEP 526 gave us variable annotations. 1:30 So now you can annotate parameters to functions and methods, and 1:35 state what type of data you'd like a particular variable to hold. 1:39 These PEPs caused quite a stir. 1:42 People jumped to conclusions and declared that Python was abandoning its dynamic and 1:44 duck type roots. 1:48 Of course, that hasn't and won't happen. 1:49 All of this type hinting is just another tool to use when you're developing. 1:51 It's especially useful for larger code bases or 1:54 code where you can't look through all of the source easily. 1:57 PyCharm supports these annotations natively and 1:59 will show you warnings if you violate them. 2:01 If you don't use PyCharm, the Mypy tool will do the same. 2:03 Like I said, type hinting isn't going to change Python. 2:07 It's 100% opt in per file, and even per function or per variable. 2:09 In fact even with the type hint and a static checker, 2:14 you can send any data you want to any function or variable you want. 2:16 Well, assuming it won't cause an exception. 2:19 Like doc strings, 2:21 testing, and naming conventions, it's a tool to make your work easier. 2:22 It's something to discuss and decide on with your team. 2:26 If you decide to use it though, I'm sure will save you more than one headache. 2:28 So let me show you about type hinting. 2:32 We'll start with a really straightforward example, then talk about generics and 2:33 optionals, and how to use stub files for 2:36 compatibility with older versions of Python. 2:38