Python Jumpstart by Building 10 Apps Transcripts
Chapter: Course Conclusion
Lecture: Course and app review

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0:00 Let's review what you've learned in each one of these apps.
0:03 When I first introduced the apps I told you what you are going to learn
0:06 but you had never seen it so you know,
0:08 it probably didn't really resonate the way it will now.
0:11 So let's go through and just kind of review really quickly
0:13 and if you had to reinforce one of these concepts,
0:15 just jump back to either the concept video from that app or the code,
0:19 or play around with it some more.
0:21 So in app 1, this was our simplest app, it was the Hello World app,
0:25 remember, the goal was really to test your Python environment get your editor set up,
0:30 things like that, not really to prove that you can write print hello world, that's silly, right.
0:34 So, we sort of introduced PyCharm here,
0:37 we talked about variables, strings, and accepting user input
0:41 and this was really sort of getting our feet wet, getting started.
0:44 Next app was, what I kind of considered our first real app,
0:47 that's Guess That Number Game, and remember,
0:50 the computer randomly select a number between 0 and 100
0:53 and then it would tell the user hey, you are too high, too low, as it took in the guesses.
0:56 So in order to do that, we had to understand boolean conditions,
1:00 we had if, else, elif statement, while loops
1:03 and when we start talking about these concepts like if else statements and while loops
1:09 we have to talk about the shape of a Python program
1:11 and here we learned about the white space and indentation
1:14 and how the smart editors help with that,
1:16 we also talked about string formatting with the curly braces
1:19 to sort of generate strings in a much nicer and more flexible way,
1:22 and we got the very basics of functions go in there as well.
1:26 App 3, When Is My Birthday this one was all about dates, times and time spans,
1:30 now these can be tricky in Python so I made this special app
1:34 just to help you become comfortable with those
1:36 and sort of reinforce what we learned in the first two apps.
1:39 App number 4 was our Journal or a daily diary and this was pretty big,
1:44 we covered a lot of stuff, this was our first sort of major app I would say.
1:48 And, because it was a bigger app we actually broke our code into multiple files,
1:53 we had our journal, module, and we had our main
1:56 and we had those work together, we did a lot of file I/O and to facilitate that
2:00 we did path management and in OS independent way using you know,
2:05 not surprising the OS.path; we used for in loops and iterators,
2:08 obviously playing a key role in the for in loops,
2:11 because we had these different files,
2:13 we might want to reuse the methods and features of one of those modules,
2:17 and if we have been just calling main,
2:21 or just writing our code directly, like write in line there,
2:24 we wouldn't be able to import those,
2:26 so we talked about the dunder name convention,
2:28 to differentiate between when your program itself is being run
2:32 and when your script is being reused.
2:35 Also we talked about documenting your own code with docstrings.
2:38 App number 5, this was our weather forecast app
2:41 and we went out to weather underground
2:43 and we grabbed the html and we did that using a technique called screen scraping
2:47 by making an http request, using the request package pulling that down,
2:51 taking the html, send it off to the BeautifulSoup package
2:54 and then writing css selectors against that in memory
2:58 to actually pull out the data we want.
3:00 In order to get Requests and BeautifulSoup, which are external packages,
3:04 we used pip and we could further isolate our particular environment in our application
3:11 using virtual environments.
3:13 Along the way I also got to introduce you to a very Pythonic concept called slicing.
3:16 App 6, I hope this one made you laugh, this was the Lol Cat Factory,
3:21 And this was all about consuming services and working with binary data,
3:25 so recall, we went out to our lol cat service,
3:27 we downloaded a bunch of binary images, and we saved them to the file system
3:32 and then we used Python to subprocess capability
3:34 to launch the finder or folder browser explorer depending on your os,
3:40 we'd launch that up to actually show the UI
3:43 since we are not diving into things like PyQt and so on.
3:47 App number 7 was our Dungeon and Dragons Style Wizard Game,
3:50 this was one of my favorites and this one is about
3:52 a very core concept in modern programming
3:55 and Python classes, inheritance, things like that.
3:59 So we talked about how you create classes,
4:01 and model your data structures with classes,
4:04 and inheritance allows us to sort of separate that into layers,
4:06 so it's more reusable, more manageable Python has magic methods,
4:10 sometimes are referred to as dunder methods, like __init__(), __str__(),
4:15 for overwriting behaviors of our classes,
4:18 our objects for example we can write the __init__()
4:21 to take control over the creation and initialization of our objects,
4:24 and speaking of initialization, we saw when we are doing inheritance
4:28 that you need to be careful about chaining these together in the correct order
4:33 so the whole hierarchy get setup correctly.
4:35 Typically, this is where you define your fields and these __init__() methods
4:39 and when we are using our objects you don't have static typing in Python,
4:44 instead we use something called duck typing to achieve polymorphism.
4:47 In application 8, we built a file searcher app and we got to feed a ton of data to it,
4:53 remember we had our 2.27GB of text what we were searching with this thing,
4:58 so in order to make that much more performant and efficient
5:02 we used generator methods using the yield and yield return keywords,
5:07 and because we were trying to process a hierarchy, a tree of data, the file structure,
5:13 we used recursion which models that scenario very well.
5:17 In app 9, we got hold of that comma separated value data
5:23 on the Sacramento real estate market in 2008 I believe it was.
5:27 And we loaded that up, we did all sorts of interesting processing on it,
5:31 we saw that at the heart of Python are dictionaries,
5:34 and dictionaries of course played a key role in our comma separated value processing as well;
5:39 we wanted to sort the data that we got back out of that file
5:41 and we did that by passing a method
5:44 we saw that methods were first class objects in Python,
5:48 and when you want to write really small concise ones, it's much better to use
5:52 a lambda method or lambda expression rather than creating a method somewhere else
5:56 and just so that you can pass the name in where you are.
5:59 We talked about the csv file format and how Python has built in support for processing that,
6:03 we also saw that we can run into a situation
6:06 where Python 2 and Python 3 behave differently,
6:09 remember we had the statistics module that's in Python 3.3 and above
6:13 but not in Python 2 so we wrote special code
6:15 to sort of do a polyfil or fill in that missing piece for Python 2
6:20 and the consumers further down in our app didn't have to worry
6:24 about whether there was the statistics module
6:27 we introduced a less efficient but sufficient one for them to use.
6:30 we also saw that many of those situations that are solved with loops and lists,
6:34 so looping over a bunch of data, doing some tests, things like that,
6:40 can be solved much more concisely with list comprehensions.
6:43 And while solving problems with lists is great,
6:46 in app 8 we saw the performance implications
6:48 that you can run into if you are processing a lot of data in a pipeline.
6:52 So, in app 8 we used generator methods,
6:55 there is a similar concept in this comprehension style called generator expressions
7:00 so we use those generator expressions to create efficient data pipelines.
7:04 The last app we built was app number 10, The Movie Search App.
7:09 This went out to the open movie database api and it pulled back data.
7:13 And that was interesting, we were processing json and things like that,
7:16 but the key focus of this app was to build reliable code.
7:22 When you have a network based code
7:24 chances that the network is down are pretty good,
7:27 especially if you are running on a client machine
7:29 like a laptop where it could be out of wi-fi range or whatever.
7:32 So we need to be able to handle those errors
7:34 we introduced the try except block to do that,we also saw that you can use multiple except blocks
7:39 to handle errors by type, remember, put them both specific one first the most general one last,
7:44 we also talked about validating arguments by raising errors;
7:48 we had some kind of argument pass doing init method for a class
7:52 the init method doesn't have a return value
7:55 that you can pass back and say false or none or something like this,
7:58 but we still want to validate the arguments coming in
8:01 and so we saw that we could raise an exception when invalid data was passed
8:05 and then the object would never even be created unless it started out in a valid state.