AI With Python Full Course 2026 [FREE] | Learn Artificial Intelligence With Python | Simplilearn

Key Takeaways

[music] >> Hey everyone, welcome to this Python for AI course by Simply Learn. If you want to start your journey in AI, machine learning, or even data science, Python is one of the first skills you need to master. But don't worry, we are not going to make it complicated. In this course, we will break Python down into simple, beginner-friendly way, so you can clearly understand how it works and how it becomes the foundation for AI. We'll begin by understanding why Python is so widely used in the world of AI. Then we'll set up the essential tools you'll need, including VS Code, Jupyter Notebook, Anaconda, and Google Colab. After that, we'll move step by step through the core Python concepts such as variables, data types, operators, input and output, list, tuples, dictionaries, sets, conditional statements, loops, and functions. And the best part is that you don't just learn these topics as theory, you'll also understand how these Python basics connect to real AI machine learning workflows. So, by the end of this course, Python will no longer feel confusing. You'll have a strong foundation to confidently move forward into AI, machine learning, and data science. Before we begin, if you're interested in mastering the future of technology, the Professional Certificate course in Generative AI and Machine Learning is a perfect opportunity for you. Offered in collaboration with the E&ICT Academy, IIT Kanpur, this 11-month live and interactive program provides hands-on expertise in cutting-edge areas like Generative AI, machine learning, and tools such as ChatGPT, DALL-E 2, and Hugging Face. You'll gain practical experience through 15-plus projects, integrated labs, and live masterclasses delivered by esteemed IIT faculty. Alongside earning a prestigious certificate from IIT Kanpur, you'll receive official Microsoft badges for Azure AI courses and career support through Simply Learn's Job Assistance program. Hurry up and enroll now, the link is given in the description box below and in the pinned comments. Now, before we begin, here's a quick question for you to answer. Which Python concepts helps us reuse code instead of writing the same logic again and again? Is it variable, function, comment, or operator? Drop your answers in the comment section below. Let's get started. >> Uh we are going to start with this, you know, first course. We're just going to study the basics of Python. Python will be our primary focus for the entire program. Um we will use Copilot. So, there's there will be Copilot material um later on in the program. But, like in this first course, we're going to be focused on Python. And And for most um things, we will be using Python. Um even when we use Copilot, it will produce Python code. Everything we do will be in Python. I think one of the things is by you know, by the end of the program, if anything else, you guys will be in a much better position with Python. You'll be better Python coders by the end by the end of the program. If you don't learn anything else, you'll get better at Python, I promise. Uh cuz that's you know, all of our examples, all of our demos, everything we do will be in Python. So, you'll you'll get better at it, uh for sure. And we'll have a lot of practice to do that. Okay. So, this first lesson is all about an introduction to what Python is. So, if you're completely unfamiliar with it, totally fine. We will uh get you up to speed and talk about the fundamental tools and how to set everything up on your own computer and talk about the various ways to um utilize Python. That some of it will involve a setup you can do on your own computer. Some of it will involve some cloud resources. Um so, that you don't need to set anything up on your computer if you don't want to. Um we'll have options there, which will be nice. So, I will show us those and walk us through those. But, this first lesson all about the basics uh and getting set up. So, um what's interesting is like at the beginning of every lesson we usually have this kind of engagement or discussion. Uh but, you know, we've kind of kind of already asked you guys about this so uh if you're familiar with programming, if you're familiar with Python. Um but, one thing I want you to think about a little bit is that um especially as we go along and learn about what Python is is why is Python the chosen language for AI? So, why is it the one that everyone uses to do AI? And I think what you're going to learn is that it has a really amazing ecosystem that has been around for a long time that um supports AI in particular. So, Python is the go-to for anything AI, data science, machine learning, anything in that sort uh because it's been used for so long for that and it has such a community and ecosystem around it. That's something we're going to learn. It's also really easy to learn and use, which makes it nice to to be kind of an introduction to the field. It doesn't take a lot to get started in it because it's so easy to work with. Um I can tell you as someone who's gone through that experience like I studied mathematics in college and in graduate school and studied like probability and statistics, but I was able to teach myself Python primarily and use that to get into kind of data science and machine learning in the industry. So, and I think that's a common story as people and I've seen that for many learners coming from different backgrounds. They've been able to pick up Python pretty easily cuz it's a very easy language to understand and and syntax of it. And there's so many tools within it that make it really easy to work with. So, um I promise it won't be as uh daunting as it may seem, even if you're coming at it from zero experience. Uh I think you'll find this is the perfect way to get into programming and get into data science and and AI and machine learning. Because it's so easy to pick up and learn and it has such a nice rich community ecosystem. So, just wanted to mention that. Okay. So, some of our objectives for this first lesson will be to talk about programming languages in general and um programming in general. So, maybe you know, more generic than Python, just you know, what are what do general programs look like? What are some of the building blocks of programs that are important? What are some of those uh key principles of programming that we will want to follow as well, even if we're doing Python for AI purposes? Um so, just talk about programming in general and then kind of zoom in on Python as we go along. One of the things we'll be interested in doing is just getting you guys set up. So, talk about how we can configure Python for you to use on your own machine. Um but also have some options that don't require installing anything on your own machine, uh which is nice. Um and then as I said, we'll kind of zoom in on Python, talk about its benefits, uh some of the nice features. I've kind of already mentioned it, really big community around it, easy to learn. We'll just talk about those more in detail. Talk about um why it's so popular in the AI world. Um And then we'll get into some very fundamental things specific to Python. So, once we talk about the background, get you guys set up, we'll go into uh some of the syntax basics, things like identifiers, things like indentation, comments, um some of the basics of the code that are going to be important for you to kind of get started with. Um and then talk about some of the basic data types that Python offers to manipulate and work with data, which of course is important um when you know, as we go forward and when do anything with data, which of course with AI we will be interested in doing. Um but that's These are the objectives of just the this first lesson. As we go forward, we're going to learn about many other basic topics within Python. So, things like how to write functions, how to build objects, how to manipulate our flow of the program with like things like if else statements, things like loops. We'll learn all about that in kind of the next lessons after this one. But, this is all the content for this lesson. I anticipate today we will get through all of this today and then get into the second lesson, which will um get into those kind of if else and loops. So, we'll we'll get we'll I'm sure by today we'll get into those. All right. Any questions on kind of what we're going to learn in this first lesson? So, mainly trying to get you guys set up, give you some background on Python, and then towards the end of the lesson, um get into some basics of the syntax. It it's kind of the goals, I would say. Okay, so when we talk about programming, um what do we mean by programming in general? It's really uh synonymous with instruction. So, programming really means giving or writing instructions for a computer to perform tasks. Um so, these instructions we write down in what we call code, but those those are just telling the computer what to do. And of course, the computer is not going to do anything unless we write down these instructions. So, these instructions can do really powerful things. They can power, you know, whole applications, things that we use every day like Microsoft Word, PowerPoint, Excel, those kind of things. Um they can automate tasks, they can um power websites. Um they can do AI, right? So, we can have um things like ChatGPT and Alexa and Siri, etc., etc. Um these are all powered by instructions telling computer what to do. One of the things that we will get better at as we go along is figuring out how to write these instructions in Python. Python is going to be the language we write those instructions in. Um and and they will be executed by a a Python um program, but we should think of programming in general as just instructing the computer what to do. Just at a high level, right? So, when we talk about these instructions, they have two ways of being executed by the the computer. Um and roughly these break down into what we call interpreted languages and compiled languages. So, that the code that we write, which is um representing the instructions that we write, can be executed um in in one of these two ways. Let me start with the left. So, the interpreted languages, this means that the computer is literally executing the the instructions line by line by line when we run the program. So, there is no translation of anything. It's just literally taking our instructions and running it line by line instruction by instruction essentially. Um now, the advantage to doing this is that it's uh easier to debug because the instructions are going to be executed one by one, so it can hit an error pretty quick if there's a mistake in one instruction, nothing else will run. Um however, it's also slower because we're going to take it one instruction at a time. Um and so the the uh this way of running programs tends to be slower, but it's also easier to work with, which is why we're so interested in Python. It's in this bucket of what we call interpreted languages. So, a lot of scripting languages find themselves in this bucket of being executed one line at a time, no translation needed by the machine. It just reads our instructions and executes it. The thing that does the execution is called an interpreter. Um and Python has an interpreter that we will get you guys set up with on your own machine that can execute Python code. So, you need an interpreter. The interpreter just executes your instructions line by line by line. Um so, some examples would be like Python, that's what we're going to study in this um entire program, but there's other languages like JavaScript, Ruby, um Pearl, many others that are uh interpreted. They require an interpreter, but they execute line by line by line, and there's no intermediate translation of anything. Um it's kind of executed as is. Now, contrast this with compiled languages, which are uh kind of a different piece. They These These instructions have to be translated into something the machine can understand in order to execute. So, there is an intermediate step of what we call compiling the code um into uh basically a translated version of your instructions so that the machine can execute it. Now, there's a trade-off there. Doing that can make it more difficult to develop, and it can take longer to debug because you have to go through this translation step every single time through the compiler. But, when you run the code, because it's already been translated into this machine format, it's a lot faster. Um so, some examples of languages like this are C, C++, Java. Um Go. But, uh we won't really be working with those. We'll just be sticking with Python. But, if you have experience with those languages, those you're probably familiar with this. You have to compile the program first before you can execute it. But, we are going to be in this interpreted world. If you know, and it's okay, like none of this makes sense, that's okay. Just understand that um generally interpreted languages are going to be more user-friendly because they're they're easier to execute. They don't require as many moving parts as what a compiled language would require. Which is nice for us, right? Nice for Python. That's what we're going to be interested in working with. Uh they're kind of um yeah, they're kind of related So, So, the question is are JavaScript and Java related? Kind of. Um JavaScript is kind of like the um the the scripting version of um some of the same concepts we see in Java, but Java is the compiled um it it requires a a special kind of what's called a Java runtime, which is a a compiler to translate the Java code into um machine code that the Java runtime will execute. JavaScript is not like that at all. It's It can actually be ran in a web browser. Which is um JavaScript usually powers a lot of like front-end websites. They're usually powered by JavaScript and Java usually powers more like back-end um applications. Like actual software programs are usually would be coded in Java. JavaScript is going to be used more for like building a website. But, you know, I'm not an expert on that really, but that's kind of my understanding of it. And if anyone is an expert on those differences, feel free to let us know in the chat, but uh that's my That's my basic summary of that. Okay, so we have interpreted languages. That's where Python falls under. So, it just um summarizing that, it's going to be easier to work with those, which is great for us. That's another reason why Python is so easy. It's interpreted, meaning that everything executes. We don't need to worry about compiling things, which is nice. Um but also in terms of programming, there's also uh categories of how the instructions are written that you can bucket different languages into. So, for example, um some language are are more um procedural in nature, meaning that you write out all the instructions exactly kind of line by line by line. You don't really organize things at all in your instructions. Um so, some examples would be like C and Pascal are more like that. Um then on the opposite end of the spectrum is kind of object-oriented, in which case you uh build your code and organize it around the idea of everything being an object. And so, uh Python actually falls into this category where um uh most things in Python are objects and you manipulate objects and objects have data to them. They have things they can do and interact with other objects. Um so, think of it just as a way we will organize our instructions. Python allows us to organize it around the concept of an object. We'll learn about what that means [clears throat] as we go along, but just realizing that some programming languages break down along these um kind of buckets here. Um Python is also a scripted language, meaning you can write out your code in an individual script, and you can that you can have an interpreter that executes that script. Um so you don't need to organize all your code inside of an object. So for that reason, Python super [snorts] flexible. That's another reason why it's so nice to use. It actually falls into both of these buckets on the right, which is very convenient. We can have basically this means we can have a lot of organization or very little organization, depending on how we want to set it up. Yeah, Roberto, so even though they're different types, so Java is compiled and Python is interpreted, um they are both object-oriented, meaning So think of the this slide as telling you how the instructions are organized. So how they are executed is different. So Java requires a a compiler to execute things. Python requires an interpreter. This is more about how the instructions are organized. So Java and Python both allow you to organize your code into objects. Um but what's nice about Python is it also falls under the bucket of scripting, meaning that it allows you to organize things into scripts, which is less organization than it would be in into objects. We're actually going to learn about objects later on in a future lesson. Like how to build objects and what they mean. So yeah, even though they're different, they're both object-oriented, which just means that you can organize your code into objects. Python allows that, so does Java, so does C++. Um many many languages allow for um organizing your your code into objects. So, we're going to learn about that. It's It's not that one's better. They're just um I I would put them at different So, let me draw this. I would put them at different spectrum different ends of the spectrum on organization. So, scripting is very loose. Basically, you it's more like us an individual um uh set of instructions to do one task. You can just have and you can have many individual scripts to do many small tasks. Um and then on the other end of the spectrum, think about it as like you've organized your cabinet into many folders and many like uh you know, many pieces of organization that are we would call objects. Um so, object-oriented programming OOP is kind of on the other end of the spectrum when it comes to like level level of organization. Does that make sense? So, scripting very loose. It usually scripting is is um reserved for like one task and it's um you're just writing out your instructions to accomplish that one task. Um which is helpful for like automation of things because you're going you're usually automating like a single task. Um so, it's very loose. It's not very organized and nothing is organized necessarily in objects. Um very loose organization. Object-oriented is much more structured to it and things being put into objects um in order to manipulate and work with objects throughout the program. Yeah. It's not that one's better. I think it's more just use case dependent. Um there are times where it actually will benefit us from using objects. Um and I think the thing to pay attention to on this slide is that look at where Python falls into. It actually falls into both, meaning that we can have things very loose and easy to work with cuz scripting usually will be faster and easier to just write something to to accomplish one task. But we have the flexibility to organize our code into objects if we want to. Which will be better for bigger tasks that require more organization. Like training a neural network or building an LLM. Those bigger tasks would benefit from more organization. And then finally on this slide there are languages that are built on the concept of their entire way of writing instructions is more in a functional way, meaning everything is based on operating functions and variables. Um and so there are some languages like that. Haskell and Scala are very popular ones. Um but that is can be very difficult to learn. It's It can be difficult but very nice in some ways because it can be very natural to think of um you manipulate like giving instructions to computer in a functional way. Think about it as like applying a function to a variable. Um that makes sense, but writing your all of your instructions in that way can be kind of difficult to learn. So for that reason I think these languages are more difficult to learn, but they can be very powerful. Um and they find themselves very useful in like operating on big data. Um so if you've ever heard of like Spark um Spark operates with Scala for instance. Um but uh we won't really focus on functional. It's kind of its own paradigm. Um but uh again, like Python is where our focus will be. It allows us to be really organized, loosely organized. Nice flexibility there. So, so far based on these two slides, I'm showing you that Python is interpreted, which is easier and faster to work with. Um not faster to run, but faster to get up and running cuz you don't need to compile things. That's nice from our perspective. And it's also has very good flexibility when it comes to organizing our instructions, organizing our code. Could be very loose in scripts, could be very structured in in objects. Okay, so generally, no matter how uh no matter what language it is, um when you process those instructions, generally things are going to be organized e- even if it's in a script or if it's object-oriented, um you're generally going to have the very beginning of the program um kind of setting up the input, then the middle of it really processing that and doing something with that. So, that's usually like the bulk of the logic is in the processing phase, and then generally you're producing some output. So, that could be like a model prediction, that could be um a a graph that you've built from your code, um whatever that output is. But generally, it flows this way. This is this is makes sense, right? Of course, there's input, you're manipulating that input in some way, and then you're producing some output. I think that all makes sense. That's a very logical way to flow. Um now, that's not to say that within this processing step, there may not be um iteration. Like of course, there may may be times where we need to, as part of the processing, kind of iterate and do multiple passes of processing. Um So, the processing could be a lot. We could be doing a lot. We could be doing a little. Just depends on what we're actually doing. So, if we're reading in some data as the input, um and then we're just doing some simple, um slicing and dicing of it, that's some easy processing and maybe producing a graph or producing a metric, something of that sort, that's pretty easy to do. But, if we're training a neural network or training a model, the processing step can take a while and it may, you know, be very iterative in nature. So, it just depends on what we're doing and those instructions, but no matter what, most of our programs will flow in this way, kind of input, processing, output. It makes sense. It's very logical. So, what are some principles that we should abide by when we're writing our code? So, this this would really be for any language, but of course, for Python that we are interested in. Um So, something we're going to be interested in doing is um basically avoiding repetition where we can. So, instead of having copy-paste everywhere, we will generally favor organizing our code to some degree, meaning we will utilize functions where it makes sense and objects where it makes sense to organize things. And also, instead of um having very repetitive code, we will favor using uh loop structures that can iterate over um things many times instead of us us having to write all those out one by one by one. So, we're going to learn about these tools that we have at our disposal, but they will help us organize our code, avoid repetition all over the place. One of the things we want to avoid is having the same code repeated all over the place. If if we find ourselves doing that, we should really put that code into a function or maybe into an object so that we can reuse it. So, we're really going to favor like reusability of things. Re- recycle, reuse, you know. So, we're going to learn how to do that. How to build functions, how to build objects, but that's something we're going to favor when we're when we're programming. It's something you should be on the lookout for. If you find yourself writing the same code over and over just in different spots, um that's probably a clue you should organize that into a function so you can just call that function wherever you need to rather than copying all that code. Okay, so we're going to avoid repetition. Now, the the reason we're going to do that is to uh you know, keep everything simple. We want to make sure things are clean, simple, understandable. Um we don't want to have we don't want to have overly complex things that are very difficult to follow. So, one of the things that is going to be really nice about Python is it lends itself very well to being simple because it's going to be so easy to actually read and understand um you know, understand what's going on. But, one of the things that falls in line with this is like um for instance, naming things appropriately. So, instead of just calling everything in our code like X, Y, and Z, if somebody comes along and reads, "Oh, I see your code has an X, Y, and Z." That may not make sense. You know, we would want to be more thoughtful with the names of our variables and names of our functions. So, instead of X, Y, Z, maybe we would use something like name or place or you know, something appropriate to identify this is what this is. So, think about that when you're writing your code is try to make it understandable. Name things that somebody else reading it would understand what it is if they see that name. So, that's that's a mistake I see a lot of people make when they first start. It's okay like when you're first getting started and practicing to name things like X, Y, and Z. I think that's fine or like ABC. Um but does that make sense like with somebody else was reading it? They see X, Y, Z in the program, that may not make sense, you know? So, but if it has a good name to it, you could say, "Oh, like I see this is somebody's name that this variable's referring to or this is um a particular object that this is referring to. Um it's not just kind of an abstract X or Y or Z. >> [laughter] >> Yeah. That was spaghetti. Yeah, that's that's what uh that's what a lot of people refer to that as. Uh just sloppy, unorganized, um hard to understand code. One of the things that's great about Python is it's naturally very understandable. So, like I don't think we will have that issue as much as if we had other languages, but it's still possible. So, these are things we'll learn as we go along. I'm just trying to get it into your mind a little early here. Name things appropriately is one of the main piece of advice I can give here. Um so the next tip is to organize things. This goes along with avoiding repetition. So, organize. Um let's put things into functions. Let's put things into objects where it makes sense. If we know we're going to reuse that, um let's put it into a function. And so, we're going to learn about how to do that, but generally this is good practice. If you find yourself writing um uh code to do something and it turns out to be um it turns out to be uh something you know you're going to reuse or it turns out to be more than a handful of lines of code, you generally want to organize that into a function so that uh it's clear this is what this code is doing, this is what it's responsible for. It's obvious um you know, that it's organized into into uh that unit of work, essentially. So, we are going to practice this. This is something we're [snorts] going to get good at, I think, as we go along cuz we're going to favor organization where it makes sense. Okay. So, readability, one of the things is using meaningful names. I kind of already mentioned that. The other thing is using good comments. So, we're going to learn probably today how to make comments in our Python code, which is going to be helpful to orient yourself or another reader of it to hey, this is what this function does, this is what this line of code is doing. Um I can't tell you how many times, you know, people write code and then it they themselves come back to it a week later and have no idea what it's doing. That happens all the time, and it's even happened to me. So, uh comments are your friend in that regard in that um they don't really cost you anything to put comments in there um to say to to kind of highlight this is what this piece of code is doing, and you can make a note to yourself right within the code. That's what comments are. They're basically notes to yourself. Um So, we're going to learn about that today, how to write comments and and what that looks like in the code. The other thing is indentation. You know, Python supports uh in like you have to indent, so that's not really going to be an issue. Some languages don't really support that, especially the compiled ones. They don't enforce strictly indentation. They enforce other things like braces and and semicolons and such, but um our our Python code will be properly indented uh by necessity because otherwise it won't work. So, um that's something we're going to learn about too today is how we indent things and why that matters. We'll talk about that. Um I see a question from Sherry. Uh is Python a program that can be programmed with simple language? Yes, it's very easy to uh it it's Python is a very natural language to program in because um yeah, it's very simple. Uh simple language is used all over the place. I think it's going to be really easy to learn. I think it'll be really easy to pick up. At least that's my hope and I think it from my experience it is. As I said, I was someone who did that and I've worked with many learners who've done the same. So, yes, I think it'll be pretty easy to pick up. Very simple. Um and then the other thing is we can do uh we can find our errors very quickly. Now, because this is an interpreted language, we can run things one line at a time and we we will quickly hit errors uh early on in our code if if we have them. So, this will be nice and Python provides really good um error messages um to say, "Hey, like this is what's wrong with your code. You should fix it this way." Um essentially like it's giving you a clue into what needs to be fixed. Um so so this is something uh that we will practice with as we go along is kind of um finding errors and what to do with them. Um but because it's interpreted, we will run across those very quickly unlike with compiled language which is harder to debug because you basically have to compile everything, hope that it compiles. If it does, then you have to run things. Um it just takes longer to get through that debugging phase. But with the with Python it's very quick. You get a very quick feedback loop on if your code's working or not. Which is nice. A lot of votes for C. I agree. C is the correct answer here. So, the interpreter is the thing that will execute the code line by line. So, it doesn't do everything at once. It actually goes line by line. Which is why you can stumble onto your errors quickly. Because if you're going line by line, um, and you have an error on this first line, you're never going to reach these other lines, right? You're it's just going to show you this is where your error is. It's on line 101 or whatever it is. And, you know, it's going to show you where the error is. So, it's going to go one at a time and execute those. Um, it's not going to convert the code into machine language. That's what a compiled language would do. Not an interpreted one. Um, and uh they do require an interpreter. So, D is just completely wrong. It's the opposite of that. It does require it. So, the interpreter is the thing that is executing the uh code line by line. So, what is Python in particular? So, it is a as we've already seen an interpreted language, meaning that it requires an interpreter to execute. It's going to be executed line by line by that interpreter. Um, it has capability to be object-oriented. It also has capability to be scripted. Um, which is just in relation to how it's organized. One of the really nice things is it is what we call dynamically typed or what you would say dynamic semantics. We will see what this means, but basically it means that we don't have to declare what every piece of uh what every variable or every piece of data is [clears throat] inside of Python, we can let the interpreter interpret that. Which is nice, it makes things really easy to work with. We don't need to say, "Okay, this is an integer, this is a floating point number, this is an array, this is you know, with a lot of programming especially compiled languages, programming languages, you have to do that. Cuz you have to tell the compiler, "This is what this piece of data is." But with an interpreter, the interpreter can, as the name suggests, interpret that. It doesn't need to know what everything is in terms of its data type, which is which makes it really easy to code. On the cons of that, it can make it more prone to error because you're not really enforcing types. So, there's it is somewhat of a trade-off there, but for our purposes, the dynamic semantics make make it so that the interpreter can dynamically understand what data is based on how it's being used, which is great for us. Like, it makes it just quicker to get up and running and started and and working with data. We don't need to declare what its type is, which is static semantics. Um Now, Python itself, amazing programming language, it's used across many different applications such as data science, automation, machine learning, AI. It's also used in to build [clears throat] software even. I'm not sure if you guys know this, but there's some really famous software that's written in Python. One of the most famous is Instagram at Meta. It is completely coded in Python, which is it's over like 20,000 lines of Python code, which is pretty amazing. But so, of course it's been really um used in AI and machine learning and such, but it's also as a programming language been used for other things like more pure software applications, which is what makes Python really nice. It's so simple, so easy to learn. Um so for that reason, uh it is going to be great for us to get started with, especially if you're coming in with basically no programming experience. The other thing about Python is it has uh as I said earlier like a really big ecosystem, uh meaning that there's many different packages and modules within those package packages that do things already. So, we don't What's great about Python is we won't need to reinvent the wheel on so many different things. Like if we need to build a plot, if we need to train a model, and and use a specific type of model, that likely already exists in a package somewhere. And what's great is they're almost always open source, meaning we don't have to pay for anything. You just use it out of the box, which is fantastic. So, there's within Python, there's so many ways to do things, especially in the AI machine learning world, that we'll just borrow those and use them in our own code, um which helps uh you know, with um getting up and running very quickly. We don't need to reinvent things. We can just use things that already exist. Um which is fantastic. So, that ecosystem really benefits machine learning AI um because they they already exist. We don't need to spend our time rewriting all those things. Um and so that's something we're going to learn as we go along is like how to install those, how to import those, how to use those in our own code, those those packages that already do something for us, so we don't need to come up with it on our own. We just need to use it properly. Okay, so there's a little bit of history. Python was first invented in the late 1980s by a guy named Guido van Rossum in Amsterdam. Um where it gets its name is after the old comedy series. You guys might be familiar with it, the Monty Python Flying Circus show. Um and so that's where it's got its name. Um you know, it was first created then, but has since taken on a really big role in the especially, you know, I keep saying in the AI community. So much so that it has its own software foundation that kind of is responsible for maintaining it. They meet regularly. They come up with improvements. Um they come up with new versions [clears throat] of Python. Uh for example, Python 3.14 just released in October, which is a major release. Uh They hadn't had one in a while. And that one is uh 3.14, so it's kind of known as Pi- thon. Um which was a big milestone. Um but, you know, they have uh they've had many different versions over the years. It's been maintained and developed by the software foundation. Um and people are actively working on it at many large companies. So, for instance, Meta has a big group that is working on um Python improvements, Microsoft as well. Um Google, all of those guys have groups kind of working to improve Python cuz they all use it. And so, what they typically do is work on it, open source it, and then the community gets to use those tools, those packages, those tools, those improvements. Um so, it's it's actively um utilized across many big companies, actively uh maintained by them, or contributed to by them, so that's that's really great. Um you know, Python was originally derived from other language um other languages uh as kind of a trying to find like a mixture of some of the best of all worlds. But, it's main like driving force and why Python came to existence from these other languages is it just this ease of use. People really wanted something like super easy to get up and running and something really natural. Um and so, we will as we start learning the syntax of it, I think you guys will understand why it's so easy. But, um that's that's what led to the inspiration is just people wanted something easier to work with, not as and not as a strenuous to kind of get up and running. What open-source license is it? Um that's a good question. I think it's the MIT license, but I could be wrong on that. The you can look it up. If you go to python.org Yeah, if you go to python.org, I think it might talk more about what the license structure is there. I want to say it's MIT open license, but I've I'm really not 100% sure on that. Okay, so what are some of the benefits of working with Python? And these are things you will experience as we go along, but just wanted to call them out. Um the flexibility of it. As I said, it can be really organized into object-oriented or it can be loosely organized into scripts. So, that flexibility alone is really awesome. Um which has allowed it to power many different things like um APIs, web pages, full-blown applications like Instagram. Um chat GPTs, like actual AI LLMs. Um you know, it has so much flexibility there to power so many different applications. Um probably the biggest benefit especially to us is its ease of use. Um uh Oh, thank you. Some of Tim just posted it. It's the the GNU uh public license. Yes. Oh, never mind. It's a Python software front. It has its own. Okay, perfect. Thanks for sharing that. Thanks for sharing that. Yeah, I wasn't a completely sure which which license it was. But it is open source. Um and people do make their own kind of derivations of Python. But as I was saying, one of the benefits of Python is how easy it is to learn. I keep emphasizing that cuz it's true. Once we get into it, you will see this. I promise. It'll be easy to learn, easy to pick up. Um and it's designed in that way, designed to be very minimalist as a language, which is great. Um it has a lot of things that come with it and it's kind of built into Python, a lot of capability. So that we call that the standard library. It's just the things built into Python. It has a lot of capability out of the box. Um you know, not only that, but it has a large community that's developed so many different packages that do things for us, especially in the AI world. So that's another great thing, kind of a robust community developing these packages that help us get things done. Um readability. So because the code is so simple, it's also easy to read. So you can usually read other Python code and quickly understand what it's doing, which you know, makes for easy um easy understanding of other people's code, easy understanding of code in the community, and kind of almost like it's self-documenting cuz it's so easy to read. So that that simplicity, that ease of use lends itself well to being really readable. You can usually just take a look at the code, easily read it, understand what it's doing. Which is great. Like great for you guys learning. Great for taking a look at the demos and examples that we will do. They're very readable. Okay, so why has Python really dominated AI? So this is a valid question. Like even So it's used for many different things. The programming language. So it can build applications. That I've given you the example of Instagram and there's many others um that are built off of Python code. Why is it so useful for AI in particular? Mainly uh some of the reasons we've already talked about. Mainly how easy it is to use. Lends itself well for AI because um that has allowed people to kind of quickly get up and running and test out their algorithms, test out their models just really quickly with Python. That's great. The other things listed on here are certainly big reasons as well. So for example, it has so many community libraries, those those packages that um have AI models and AI tools that we can reuse. That people have built these up over years and years and years. Um so it's to our benefit to reuse those and not have to reinvent everything. We can get quickly up and running with those. It'll be great. The other thing is Python it lends itself very very well to working with data. In general, very easy to work with data, very easy to load it in from external sources, query it, work with it, visualize it. Python is so adept at that. Um so, that's what makes it really nice at doing machine learning and AI because so much of it is manipulating data. So, um for that reason alone, Python is so popular in the AI community just because of its ability to work with data. It's so easy. This is something we're going to really focus in on like in our next course when we talk about data science. But, um just the ability and the power of it to work with data makes lends itself well to AI uh capabilities. Um the other thing is I mentioned the rapid prototyping. You can quickly build a model in Python cuz the code is so easy and there's so many libraries already. You can quickly prototype. Um it has obviously a big community around it that's building out these packages, writing documentation, maintaining it from an open-source level. So, that's another reason very popular. Um Python's also used with other technologies. So, it does have capability to integrate with other languages. So, for instance, Python can One of the most popular integrations is Python can work with C and C++. So, sometimes that's necessary to integrate with those to do certain things. Um so, Python has been extended to work with other languages. So, sometimes there's other uh necessary support from other like things in other languages that are necessary to power something in AI. Um for example, working with GPUs and doing things in deep learning, um there's been a lot of integration with uh working with um C tools. Now, will we do that? No. It's already been done for us in some of these packages. But, um the pure ability of Python to do that is really powerful and it gets taken for granted honestly because you don't see that. It's underneath the hood and it's abstracted away from you when you work with those Python packages, but there was a lot of work that went into it to integrate it with other kind of other programming languages. Okay. So, as an example, like I mentioned the Instagram one, so Netflix for instance, all of their recommendation is powered by Python. So, when you open up Netflix or really any streaming service for that matter, they're going to use Python to deliver those recommendations and produce those personalized recommendations. Um Spotify as well for like music. Um nearly all recommendation algorithms are written in Python. And in this program, we are actually going to learn about recommendation systems. So, that'll be pretty fun. Way down the road when you get into machine learning, we'll talk about how do we build a recommendation engine, but um they're all done through Python for for example. So, really cool uh use cases there. So, one of the things I wanted to address is how AI itself is changing coding. So, you guys may be aware of this, but obviously there's been a huge um kind of explosion in generative AI tools that can help write documents and write emails and write texts and all these things. One of the things they can do is write code. So, um one of the big areas where AI is changing coding is its its ability to generate code for us. And so, um throughout this program like we won't shy away from that necessarily and I encourage you guys to use AI tools as you see fit to help your own understanding and help your own productivity. Um you know, we still will go through the fundamentals so you can understand it. But the AI tools can definitely be a supplement to help. Um it's just that I think you guys will understand it better going through the examples that we do we do together. And so that when AI generates code, you will be able to understand it and also be able to debug it, right? Cuz it's not always going to be perfect. So that's always the catch with AI is that you know, it doesn't always produce perfect answers. Um but the at the very least we will be able to, you know, debug things and understand things better so that uh we can catch those errors. Um so obviously like AI's also besides flat out generating, it's also suggesting what should be there. So uh some of the code editors really do a good job at that, suggesting things and picking up on what you should produce next. That's going to be um very interesting as we get into uh some of the platforms that you guys will work with to write your Python code. They will have that ability. Um so uh the other thing is like there's some cloud tools that um don't require writing much code at all and they can just do things. So in other words, you can power them by prompts. You're not really writing code, you're just writing natural language and then they do something. Um they generate the code in the background and they execute something. Um we will learn about those things uh later on in the program. We especially cuz we we will cover generative AI in the future. Um towards the end of our program. So if you're wondering like are we going to cover LLMs? Are we are we going to cover how all things get generated? Yes, it just will be um later on in the program. Okay, a lot of votes for B. Yeah, pretty unanimous on B. I think I agree with that. Yeah, B is definitely the right answer. So, all of the recommendation systems, which we will learn how to build ourselves later on, are written in Python, and um they uh are machine learning models that make the recommendations, and that machine learning is driven by data, um and all of that data is manipulated in Python, um and used to train uh models that do the recommendations. That's all happening in Python. So, we're going to talk about getting you guys set up on your own machine, and talking about the different development environments we can use to actually work with Python code. Um before we go into that, any questions about anything we covered so far? Everything's good so far. Yep. And if you know, again, if you have experience with Python, I recognize that. It is going to be a little slow in beginning. Um it's mostly to get us really oriented to some background on around Python, and get us set up. And then we will be doing, you know, uh getting into the syntax and all that uh coming up shortly. So, we will actually be learning Python specifics coming up soon, but you know, we're going to um get everything set up first. All right, so let's continue then. Thank you guys for that. So, um it turns out that there are many tools in the community for developing Python code. And so, um you might hear this word IDE. It is short for Integrated Development Environment. This is a piece of software that helps you write and test Python code. So, and there's many out there. There's a bunch on this list. We are going to focus on a few options. There's even more than what's on this list. But, we're going to focus on a few options. These IDEs are designed to really help you write Python. They provide many tools in the background that make your life easier when you're working with Python. So, for example, they can provide syntax highlighting. They can tell you when you have a syntax error. Um almost like a spell check for Python. Um they can help you run Python code right within the window. Um they can help you organize your projects. Uh they can do a lot of different things. Um and so, there's many tools out there that can do it and it's really a personal preference which one you use. But, in this program we're really going to focus on a a few of them to to showcase those options cuz they're very popular options. Um and then uh allow you guys the flexibility to choose which option makes most sense for you. So, generally that's going to be mostly a a preference. Um mostly a preference as to which one you're the most comfortable with. But, I want to give you guys the option to uh explore the various options that are available. Uh Roberto, is there one that stands out as an industry standard? Um there's a couple that you see. Like, honestly the two of them that we will study uh in this coming up in the next few slides are the industry standard which are going to be VS Code, Microsoft VS Code, and then Jupyter Notebooks. >> [snorts] >> So, these two are going to be uh ones that we will study in particular and use throughout um So so yes, we will those will be industry standards. PyCharm's also very popular. Um so I don't want to rule out PyCharm. I know a lot of people who use it. So um I would encourage you to explore PyCharm as well if you want to, but we are not going to do that uh in in these slides, but um I would check it out and see if you like it. Um it's another very I'm putting a an asterisk next to it cuz I think it's one of the more popular. Uh yes. Uh yeah, we're going to do descriptions. Um requirements uh I'll try my best to give those, but honestly the requirements will be given when you install them. Um So the other thing I want to say is we will have a couple of options that don't require you to install anything. So I'm going to showcase those as well. So there's a couple of options that are um we won't have to install anything cuz they're going to be cloud-based. Okay? I'll show you those. Okay. So but yeah, VS Code I think VS Code and Jupyter Notebooks are are probably the industry standard most popular uh IDEs. Okay. So what we would recommend in this program and the ones that we will use the most uh throughout are going to be these three: Visual Studio Code, also known as VS Code, Jupyter Notebooks, and Google Co- Colab, which is Google's hosted um Google's hosted version of notebooks essentially. Um so I will showcase each one of these and give you some examples of how to set it up and examples of how to work with it. Um and that's what we'll do over the course of the next few slides and the next uh bit of time is I'm going to go through each one of these and kind of show you what you would need to do to get it set up. Um now, that being said >> [cough] [clears throat] >> Excuse me, these two are ones that you will install These two you would install locally on your on your own machine. And this one is um uh cloud-hosted by Google and it's free. Um all of these are free. But uh the first two VS Code and Jupyter Notebook you would install on your own machine. Colab you would just access through your web browser. It is hosted by Google. So, that's an advantage. You don't really need to install anything and for that reason um sometimes we will favor Colab uh and for other reasons, too. Colab has some really nice features if you've never used it. Um but notebooks um Jupyter Notebook and Colab are very similar. They're very similar. Colab just has its own spin-off on on the notebook um type of file that Jupyter Notebooks work with. And it's um like I said, kind of cloud-hosted. So, I'm going to I'm going to walk us through each one of these and [clears throat] explain to you what they do what they look like and then we will um I'll set up each one of them uh kind of in a live demo so you guys can see. Um but uh we throughout the program it will really be up to you which one of these you want to use. There's no hard requirement to use any one of them. It's really going to be your preference which one of these tools you want to use to work with Python. Whatever one you feel comfortable working with, that's the one you should use. All three of these are very popular in the industry, so you're not missing out by using one versus the other. Um they're all very popular, even Colab. I know it was on the screen, but it is widely used in in the community and industry. Uh no system recommendations for training LLMs. Um no, because we don't we won't really focus on that until the end. When we get to when we get into generative AI, we'll talk about that. When we get into generative AI, we'll talk about that. So yeah, we're not we're not focusing on LLMs in the beginning. That's that's an advanced topic for us. What is my personal preference? Um I like Visual Studio Code um personally. I that's what I use for my day-to-day work is uh Visual Studio Code. I like Visual Studio Code and I like Colab a lot. Um so you know, we'll talk about this, but one of the advantages to Colab is that it has free access to GPUs, which is huge for doing things like uh neural nets. Um so we will lean on Colab quite a bit later on. Uh later on when we um s- actually get to deep learning and neural nets, we'll because Colab has free access to GPUs. I'll show us that. It's it's really nice. And when you do anything with neural nets, it usually benefits you to have a GPU access. Um so that'll be nice. But I usually do most Python coding inside of VS Code. It supports Python pretty pretty well. What is more commonly used in the industry? Um the two most popular are Visual Studio Code and and notebooks, Jupyter notebooks. They're both like you can't go wrong with either one. Those two are really popular. Jupyter notebooks and Visual Studio Code are really popular. Uh there's there's both of those you would be okay with. Either one. Let me start with Visual Studio Code. So, um now Visual Studio Code is a more general code editor. So, it's actually you can edit lots of different languages inside of VS Code. Um so, you could do Java, you could do C, you could do Scala, you can do Go, you can do all kinds of languages are supported inside of Visual Studio Code. So, it's a really fantastic product for programming in general, not just Python. Um it has built-in terminal support. It has Copilot integrated into it, which is nice for AI like generative AI assistants working with your code, which is nice. Um of course, it supports Python, which is what we are interested in. Um it has it has Python tools. I will show us which ones we should install as part of VS Code so that we can work with Python files and notebooks. Um so, it's it's a really great code editor in general, which is why I like using it. Um but in particular, it's pretty good at working with Python. It it supports Python pretty uh deeply. Um so, uh for that reason, VS Code is really, really popular. But just keep in mind, you can actually use it for many different types of code that uh that people write. Uh JavaScript, um Java, as I said, like many languages are supported inside of Visual Studio Code. So, it's a more general code editor. It happens to be really great at working with Python, though. All right. So, I'm going to show us a demo on setting up VS Code. Now, we are going to do this for each one of these, for Jupyter and for Colab, I'm going to I'm going to do similar demos. So, um don't worry, we'll get to those, but I want to start with VS Code to show you kind of how to get that set up and what it looks like. Um so, where you can find this demo is inside of the demos that I mentioned earlier in the reference material. So, I'm going to I'm going to jump over to that. Let me show you guys. So, I'm back in the LMS. You guys will want to download the demos. I think somebody linked it earlier, in case this didn't show up for you, but we're going to be inside of the demos and we're going to do demo one for lesson one. We're going to do lesson one, demo one, which is going to be the VS Code demo. So, the main steps that we're going to do I is just going to be to point you to where to install Visual Studio Code. So, it is a it is an application, is a free application you can install on your machine. Um so, uh you will want to follow this link that is within the demo file, this code.visualstudio.com/download, and download it for your particular platform. So, if you're on Windows, obviously choose the Windows. If you're on a Mac, um choose Mac, and make sure that you choose the right One of the precautions is to choose the right Mac platform. So, if you have like an M1, M2, M3, M4 Mac, choose the Apple Silicon um button. If you're on an older Mac, um, then you want to use the Intel chip one. Um, uh, if you're on if you happen to be on Linux, which I don't probably most of you are not, but if you are, um, you want to download the right, uh, distribution, uh, version. But, uh, follow this link first. That's the first step. Very easy step. Just go to that site, pick your right platform, and uh, go ahead and download the installer. And mostly we will be walking through the steps in the installer, and then, um, I will show us what it looks like once it's installed. And then show you a couple additional steps that are actually not mentioned in this file that I think are worth doing to get you set up. Uh, yes, we will be doing Jupiter next. Yes. We'll we'll we're going to be covering green VS Code, Jupiter, and Colab. I'm going to show us examples of all of those. Okay, let me ask you guys. Were you guys able to get to the download page and start that download and installation of VS Code? Able to do that? Any issues with that? Okay. Yeah, it's just like any Yeah, I love I love the optimism. Yet. Uh, already having both of them installed. Okay. Yeah, I know if you already have it installed, I mean, great. I'll show So, if you if you already have VS Code installed, great. You can sit tight. I will show you, um, a couple of extensions that you'll want to add for Python support. If you have it installed already, I'll show us how you can use it with Python in particular. Okay? If you already have it installed, perfect. Looks like you guys have it launched. Still working on it. Okay. So, these These instructions um show an example of someone that would be on a Microsoft platform um walking through the installation. Uh if you're on a Windows, you probably want to create a desktop icon. You definitely want to add it to your path. And this just shows what's being installed. So, this is all the install wizard on Windows. Nothing that exciting there. So, this If you follow all these steps, you will have it installed. I hope you have enough disk space. Uh I don't think it's too big. I don't think it's too too massive. I forget how much space it takes. I don't think it's that much. I don't think it's that much, but um yeah. Hopefully you have enough. It So, if if you don't uh if you do not have enough disk space, um don't worry because we're going to do Colab, which doesn't require you installing anything. So, you can always use that option. All right? So, if if for some re- Let me just say that, too. Just even if if it's not a disk space issue, if you have an any installation issues, no worries because we will work with Colab in Google that is going to be cloud-hosted that you don't need to install anything. You just need a Google account. Okay? A free Google account. Um so, no worries at all if you cannot get any of these things installed. The which are going to be Jupiter and uh Jupiter and VS code. Where do we go? I haven't said yet. It just I'm just making sure it's installed for folks. I'm going to I'm going to go over to it in a second. But did we generally get it installed? And do you have it open? So, if you get Once you get it installed, uh once you get it installed, then open it. Yeah, you need to get it installed. Uh it should be this first It should be this link here. Follow this link to get it installed. Oops, I pasted the wrong link. Let me find I'll copy and paste the link. But yeah, take take a moment to get it open. Once you have it open, just sit tight. If you want to What does it say? Yeah, so it for you guys seeing the co-pilot features, um click click use AI features. I think that's okay. Yes. Um you'll you'll likely want co-pilot. Yes. >> [snorts] >> Click Click okay on that. That's the link, by the way, for for download. In case uh we needed to get to it. Okay. So, I'm going to go over to VS Code and show you what it looks like on my end. Okay, so you should have something that looks roughly like this. I don't have anything open. I don't have any files open. I just kind of have a blank screen here. But, if you I would recommend using the AI features if you can. I think that'll come in handy later on. Are we comfortable moving forward? I want to show us the extensions that support Python. So, right now, when you first when you first install this, it does not work with Python out of the box. We have to install a couple extensions inside of here to get it to work with Python. I'm going to show us how to do that. Don't worry about tuning any settings. No, don't worry about doing any of that at this stage. Don't really need to tune anything. We just need to get Python support. Okay. So, you guys with me on this main page? You can use your corporate Yeah, sure. Sure. Yeah, you can use If you have it If you have Copilot and want to use your corporate, you can use that. That's fine. But, you guys are with me on the main page because I'm about to show us I'm about to show us the extensions we we to install to work with Python. Okay? Really important cuz this isn't this is not in the documentation. Um no no need to reinstall. Um you can I'll show you how to add that through the extensions. No need to reinstall. You can add it as an extension. Yeah. Okay. So, let me ask you guys on the left do you see this little box icon that if you hover over it says extensions? Do you see that? You There may be other things here, too, but at least that one with the extensions. Okay. >> [clears throat] >> So, we do see that one. Okay. So, what we want to do No, I wouldn't I wouldn't uninstall. That's okay because we're actually going to install Anaconda to get Jupiter. I wouldn't I wouldn't I would cancel that if you can cuz you're going to want that for Jupiter, as well. I wouldn't uninstall Anaconda. I wouldn't uninstall. But, I mean, if it's already going if it's already doing it, that's okay. We'll just reinstall it later. All right. So, back to the extensions. So, let's click on the extensions. Okay. So, do we see something like this that has a search bar for extensions? Do we see the search bar for the extensions? Okay. What do you think we're going to search for? Python. Python. We're going to search for Python. Yep. So, you are going to want to install the official Python extension from Microsoft. It is this one that has the blue check mark next to Python. Uh so, there now there are other ones here, but we just want the one that says Python from Microsoft. Do we see that extension when you type in Python? Do we see that one? So, just So, it should just say Python. It should be Microsoft. Uh it's really popular. It has a lot of downloads, over 192 million downloads as an extension. It's from Microsoft. Okay, click on that. Click on that. And then you should see an install button. I already have it installed, so it says uninstall, but right here there should be an install button. Install the Python extension. It's out of 192 million installs. Really popular extension. Are you guys able to install it? You want to install that. It should be pretty quick. It should be pretty quick. It's not that big of an extension. So, what this does is it a Python Sherry is just a Python one. If you go into the extensions and then search for Python, it is just the one it's just this one that says Python and it's from Microsoft. Python blue check mark Microsoft. You want that one. And then you want to click on that one and then hit the install. Um Roberto, is that for uh Copilot? Is that for Copilot? I Maybe try closing it and reopening it. Try closing VS Code, reopening and retrying the install. Um no, we're not opening any folders right now. We're not opening it. We're just installing the extension. That's all. We're just installing the extension. We're not opening any project folders. Just installing the extension. Were we Were we able to install that? I know there's a lot by Microsoft, but there should just be one that that says Python. There So, see how the name like this name is this name here is I swear this name is Python Debugger. This one is Pylance. Just the one that says Python. Just that one. That's the one we want. Only that one right now. Okay, perfect. Perfect. Okay. Great. Okay. So, one more extension for you guys. So, once you install that one, I have one more for you that you want to install. Are we ready for that one? One more we want to install. Okay. Ready for the next one? So, the next one you want to install is the Jupyter extension. Which is the Jupyter. It's this one. It's the very first one here on my screen. So, it It says Jupyter and it's from Microsoft. Okay, we want to install that one. Jupyter and it's from Microsoft. Want to install that one. So, this one has 98 million uh installs. You want to install this one. Did you guys find that one? So, you want to type in j u p y t e r and it should be the Jupyter extension here that is uh from Microsoft. So, you want to install that one. Great. Now, what does this one do? This extension will allow you to work with Jupyter Notebooks inside of VS Code if you want to. So, Jupyter Notebook has its own standalone program, which we will look at next. But, you can open you can have those files, those Jupyter Notebook files, be compatible with VS Code and open them and edit them and run them inside of VS Code if you want to. So, this extension gives you the flexibility to work with notebooks inside of VS Code. So, you never have to leave VS Code if you want to work with notebooks. Um So, this is a good extension if you really want to work with notebooks and stay inside of VS Code. Yes. Uh when you Yeah, when you install install an extension, it might it might install a couple other dependency extensions. Yes. That's okay. Those are required. That's okay. That's That's That's okay. All right. How do we feel? Good? Uh did we get those installed? Do we Do we were you able to get those installed? Okay. Here is how we will test that it all worked. So, we're going to do something really simple. Here's how we will test that it worked. Let me go out of here and back to our files. So, out of the extensions, I just went to the top button where it's the little file um icon. And um I am going to um go up to the very, very top where it um So, you you guys see on your VS Code window where it says file, edit, selection, view. I'm just going to create I'm just going to create a new new file. So, do you guys see that where it's where you say file, edit, selection, view? Click on file, and then click on new file. You should see what I see on this screen right here. If you see If you see Python and Jupyter Notebook, then you know those are installed correctly. Do you guys see these options? Text, Python, and Jupyter Notebook? Great. So, what that means is we we can now create those kind of files in the future. We can create notebooks. We can create Python files, and VS Code will be able to work with those. If you don't see Python, that means your Python extension didn't install yet, or you didn't install it. So, you want to go back to You want to go back to your extensions and make sure you install Python. So, go go to this button over here, the extensions. Type in Python. And then make sure you install this Python extension. Okay? So, you're going to install the Python extension, and you're going to install the Jupyter extension. Which is this. And install both of those. Make sure those are installed. If they're installed and you still didn't see that when you went to file um new file, if you don't see those, then um try exiting VS Code and relaunching it. Okay, try exiting VS Code and reopening it and seeing if you can make a new file. Okay, but it should be under uh at the top file and then new file. And then you should see those options, Python and Jupyter. Once you have those extensions installed, you may need to close out of VS Code and reopen it to see that. Okay, perfect. After you relaunched, okay, perfect. Yeah, you may need to relaunch so that it can show the it can show the extensions. Yeah. Perfect. Okay, perfect. So, that's set up for you guys. So, um perfect. It's set up for you guys. Uh we will work with it in the future, but just wanted to make sure it was installed and set up. Once we start working with Python, um I will show you guys how to how to work with it. Um but glad that's set up for now. Uh what issue are you having, uh Ramiro? Is it not showing it's not showing Python or Jupyter for you when you do file new file? It's not showing those? You may need to exit VS Code and reopen it. Uh Sunil, yeah, you can you can make one. We're not going to do anything with it right now. It's not going to you're not going to do anything with it right now, but um It's Make sure you're searching for it with a Y. It's J U P Y T E R. You have to search You have to So, when you go when you click on the extension, search for J U P Y. It should be the first thing that shows up with J U P Y T E R. It's this Jupiter one for Microsoft. Uh kernel I'll So, the Let me show us Let me show us that later. The kernel you have to um you have to have a Python interpreter. So, you may need to install a Python interpreter to to be able to run the kernel. So, I need to show us that. Um but I I don't want to get into that right now. Save what to? Oh, wherever you want wherever you want on your own machine. It's up to you. It doesn't really matter. Just wherever you want. It doesn't matter. It's up to you. All right. So, what I want to do is uh I want want take a break um because now, you know I said after 2 hours we'll take a longer break. So we will now we'll take a 10-minute break now. If you're still having any issues, we can try to get you set up at the end of class. But we are going to set up so coming up after our break we're going to take a 10-minute break coming up after that. We'll we'll go and install Jupiter notebook. And then after that we will look at colab. So you're going to have multiple options to run python not so if this wasn't working for you. That's okay. We'll try a different route. Okay, we'll try a different route. I I know colab will work for you cuz that is hosted by Google and really easy to get working with so at the worst case scenario colab will work for you. I know it. But we'll try to get Jupiter notebooks installed for you as well. But if you're having issues with BS code, let me know at the end of class. We'll try to get you set up. Okay. You're still having issues with it. But I'm what we're going to do right now is take take a 10-minute break. So let's try to be back in about uh 10 minutes. Let's call it an even um Let's call it an even Uh what will be covering? Um installing the other installing the other python setups so Jupiter notebook and working with colab and then we will get into the basics of python the syntax. So we're going to talk about indentation identifiers maybe if we have time basic variable types data types. Yep. So we'll get into python. We will get into python today. All right. So let's jump over to uh Jupiter notebooks. So um what's so special about Jupiter? Well, it turns out that uh Jupiter is a platform for running what are called notebook files. So, obviously we just installed the Jupiter extension in VS Code, which will allow us to run notebooks in VS Code, but Jupiter has its own notebook platform, and that's what you will install in this setup. Um notebooks are special. They are um really great um Python code files that give us the ability to execute isolated what are called cells of code. So, we can run one cell at a time and test and debug the execution of that single cell without affecting any of the other cells. So, um notebooks are great for uh running code live and interactive. When we do a lot of our demos in this program, they're all going to be in notebooks, um so that we can kind of run things one cell at a time. Um uh No, so without notebooks, you either have to run you run like a Python script, like a Python file, um which is a .py file, and uh usually you have to either run that through a debugger or run the entire script at once. You don't really get code isolated into individual cells, which is really nice with notebooks. The other thing is notebooks are easily shareable. So, you can share a notebook with somebody else, and they can open it and see all of your inputs and outputs in the notebook, which is really nice. Like all of the outputs get saved into the notebook, um which is nice. So, and notebooks uh especially in the Jupiter platform, are going to have all the data science libraries available to them. So, uh, if you're people usually love doing notebooks for working with data. Um, really easy to work with data inside of notebooks and and build things like plots. You can display your, uh, you can display your graphs really easily inside of the notebook and then share your notebook so other people can see your graphs. Um, so notebooks are really awesome like interactive environments for running code. Um, and we will favor notebooks, uh, as our primary way of running code throughout the program. Now, where you open those notebooks is up to you. You can open them in VS Code, you can open them in the Jupiter Notebook platform. Uh, you can open them inside of Colab and run notebooks in Colab. Uh, notebooks are very very popular. Why isn't running in notebooks the default? It's because, uh, not all code runs inside of cells. Like applications are not going to be well-suited for notebooks. Like Instagram is not running in a notebook. Uh, it's more structured into actual Python files and actual, uh, more structured programs are going to be not in a notebook. Notebook is more for prototyping and debugging and, uh, executing small chunks of code to test it out. It's not for writing larger programs. Like an like a, uh, an LLM application like a chatbot would generally be in not in a notebook, it'd be in like a Python file. Uh, cells versus class objects. So, cells are just small, uh, think of them as small little environments to execute our code. Class objects are actual chunks of code that define an object. They're They're different things. Yeah, different things. We'll We'll learn about objects and we will certainly see what cells are as we go through. I'm going to show you an example of a cell coming up when we install Jupyter. But uh let's talk about let's go through the installation of Jupyter Notebook so you can see what a notebook looks like. I think that'll be helpful to orient. So, let's go over to that demo. So, this is going to be demo two. Uh demo two inside of um uh lesson one. So, we're going to go over to that. Everybody has this one. Okay, perfect. Okay, so you're going to follow this instruction. Now, what this is going to do is first um No, this has no This is not going to be anything to do with VS Code. It's going to be a different platform. It's going to be Jupyter. Where is this? This is the This is the demos. This is uh demo two inside of that demos folder that we said uh to to uh grab all the demos from your LMS. Does anybody have that uh demo two PDF? They can upload. I I think somebody uploaded all of them earlier, but if you have demo two want to upload it real quick. I don't have the PDFs. If somebody wants to share that. So, there So, they're different. Um VS So what I was saying is you can open notebooks inside of VS Code, and the thing that allows you to open notebooks in VS Code is the extension. So yes, if you're going to work with notebooks in VS Code, you need the extension installed. But you can use the standalone Jupyter platform to work with notebooks. It's up to you. If you like using VS Code, um if you like using VS Code, you can do it that way. If you like uh the Jupyter platform, you can do it that way. It's up to you. It's just a preference. I'm giving you guys options. That's my goal is to give you options and let you guys choose what you're most comfortable with. Okay? And we're And we're taking time to do that now in the beginning of the program, right? Cuz we're going to be doing a lot of Python examples coming up as we start in learning Python. So it's it's valuable to spend that time now. I know it can seem a little slow, but I promise it'll be worth it so that you guys have options for running your running your code. Yes, thank you guys for uploading those. Appreciate it. Those are the demos you want to uh follow along with. Okay, so the first step here is going to be to install Anaconda. Now you may be wondering, "What is Anaconda? I thought we were talking about Jupyter." And that's a valid question. Anaconda is a what's called a distribution of Python. So Anaconda is a program, a software, a collection of software programs that give you a version of Python with a bunch of packages, uh with a bunch of packages already installed. Um and then uh one of those is the Jupiter package so that you can run Jupiter notebooks. And what Jupiter notebooks will be is a uh basically a web browser application that will open up a notebook editor in your web browser. So, that's ultimately what we're going to do, but we are going to install it via the Anaconda distribution uh via the Anaconda distribution of Python. So, that's where we're going to start is with the initial download of Anaconda. Uh it's no no skip registration. Okay, let me let me uh open the link. I think there I think there's a way to find it without having to do the registration. There's a way to get to it without having to do that. I'm going to find it real quick. Uh you can't Okay, so if you can't install it, that's okay. We will be able to work with notebooks in Colab and and you can work with notebooks inside of VS Code. That's fine, too. Yeah, I'm getting I will I'm going through the registration process so I can um I can show you the install. Okay, let me share my screen. Did you guys get to Once you go through the like setting up your account, do you get to this page? Do you get to this page? For those of you going through Yeah, that looks right for you, Ashish. that looks right. Do you guys get to this page though when you get through your like account setup? Okay, you got to this page. Okay, so then choose your correct Windows or Mac. Down You want to be over here on the left. You want to do the Anaconda distribution. This is what you want to do. So choose the right one. If you're on an M1, M2, M3, you're going to do the silicon. If you're on an older Mac, you're going to do the 64. And then obviously if you're on a Windows, you should be clicking over here to do Windows. But you want to do the Anaconda distribution, not Miniconda. Okay, so click on the installer for Anaconda distribution. Okay. And then let that install. Now, while that's installing, let me explain something about the difference between uh I think it was asked earlier, what's the difference between um Anaconda uh as the default Python. So Anaconda as I was saying earlier, is a version of Python that has a bunch of data science and machine learning packages already installed for you. So it uh it comes with a bunch of packages that are already installed. So if you use that Python, um that Python has a bunch of packages built in with it that you don't need to go out and install. So Anaconda is a very popular version of Python for people to install that are working in data science, AI, ML. Very popular version because it already comes with a bunch of packages that you would use for manipulating data, for doing machine learning, or doing anything in AI. So it's it's a very um popular distribution. It also comes with Jupiter. Which is why we wanted to use it because it comes with the notebook capability out of the box. Okay. So, I'm going to launch So, when this is done installing, you want to launch the program that gets installed called the uh Anaconda Navigator. So, it should install a program on your machine called the Anaconda Navigator. Do you guys have that? Did anybody get through and and you have that program, the Anaconda Navigator? You don't need any advanced ones. You don't need any advanced options. Just the just the defaults, all the defaults. Should be good. Still downloading? Okay, I'm going to show you I'm going to show you what the navigator looks like once you once you have it. That's okay. If it takes a little bit of time to download, that's okay. Um basically, once you download it, um you just have to click a couple more buttons and then you can access Jupiter. Okay. Let me share my screen and show you what you should like once it installs, this is what it should look like. It's okay if it's taking a little bit of time. You should have something that kind of looks like this, which is the um dashboard that has the different programs available to to you. Um Do you guys see something like this if you have the navigator? Do you see something like this? Which is the which is the like when you open the navigator program, you should see something like this that has a bunch of different um You do, okay. It's If it's taking a little bit of time, that's okay. Yes, Anaconda Navigator is how you launch. Yes. Anaconda Navigator is how you launch it. So, yeah, you want to open that. Now, the the whole reason to come here is so we can launch Jupyter Notebooks. So, we can launch Jupyter Notebooks. Um This is the program we ultimately want to launch. This is going to uh allow us to open notebook files, edit them, run code cells. I'll show you what a notebook looks like in a second. But but once you have Anaconda installed, open the Navigator and then launch Jupyter Notebook. It's just one extra step. Launch the Jupyter Notebook. What that should do is launch the the notebook uh It should launch the web browser of your like whatever you have as your default web browser, it should open the notebook program in ever in your web browser. So, if it's Chrome, Firefox, Edge, whatever your default web browser, it's going to launch the notebook program in the browser. Okay? So, I'm going to launch it and then I'll show you what it looks like. Again, if it's taking you a little bit of time, that's okay. Whenever it's done, how did I get to these icons? Just launch Do you have the Anaconda Navigator program? It should have got It should be installed. Open the Anaconda Navigator program. It should have been installed with the Anaconda installation. All right, was anybody able to get to this? The Jupiter This So, it should launch in your browser. Anybody able to get to that? Fantastic. Fantastic. I'm glad some of you guys were able to get to it. If it's it's not yet, that's okay. Remember, when it's done installing, you're going to go to Anaconda Navigator and then uh launch Jupyter Notebook. That's That's what you're going to do. That's okay, Roberto. It's okay. All right. I do want to I want to show you guys a notebook. I just want to show you what it looks like. What I'm going to do is I'm going to um open a notebook by going to new and then Python 3 notebook. So, you can open a folder, you can open a terminal, you can open a text file. I'm going to open a Python 3, which is a notebook you So, it's a it's a a notebook powered by Python. So, I'm going to click on that, which will launch a new notebook in a new tab. And here I am in the notebook editor. So, now I am in a notebook editor screen. So, if you go when you first launch Jupiter, you you can navigate to Notice that that notebook got created here where I currently am on my machine. I could navigate to I can navigate to documents and then I could um you know, create a new file there or I could make a new folder here and and do it that way. Um but uh I am uh just editing this notebook right here within this um current folder that I'm in. Okay. So, do you guys remember when I said that code gets executed in a in a isolated cell? Do you remember that? Um this is what a cell looks like. And you can make new cells by hitting this plus button. So, you hit this plus button over here, you can make new cells. So, if you hit plus plus plus plus plus plus, I'm making a bunch of cells. Now, what's really cool about cells, yeah, Tim just discovered this. What's really cool about cells is you can change them to be text or code. So, if you change it to markdown, I can write markdown text in here to say, "This is my notebook." And then if I run this, it's going to display as text. So, if I run that cell, which uh when I'm editing it, I can click run, and it will render that as text because I changed the cell type to markdown. Markdown is just a flavor of text style. So, otherwise, we can write some Python code. Now, what I want you guys to type, I'll type this in the chat, to verify everything is working, is I want you to type print "Hello world." I want you to type that. Inside of a cell. And then and then hit run. And it should run that code. And you should see you should be able to see uh you should be able to see that. Shift enter, yep. You can whenever you're on a cell you can hit shift enter it'll run the cell. You can That's okay. You can always you can go back and watch the video. So, this is being recorded. You can go back and watch the video. I know I know it's a little frustrating it's still installing for you. But go back and watch the video and I definitely encourage once it's installed to go back and try this. Which would be just launching your Anaconda Navigator and then launching Jupiter. If you don't have By the way, if you don't have Python 3 um you may need to uh exit your navigator and reopen it. Okay? You may need to exit your your navigator reopen it so that you can launch Jupiter again. Were you guys able to run this in a cell? For those of you that have Jupiter running, were you able to run this? >> [snorts] >> Nice, and it worked for you? Okay, perfect. Perfect. So, this is what I meant by this is an isolated cell. So, notice that we can run this and it doesn't affect um Sure, sure. I hear you. I I hear you. Update the doc. Uh I can how about I post it in our um our Slack channel. By the way, do you guys have access to the to the Slack channel? Okay, I can post it there. I can post the instructions to get there. Okay, I can post it in our our uh cohorts uh channel. I hear you. You don't want to search through a 4-hour video. I I hear you. Uh I don't have the link on hand, but you can get to it through the LMS. So, if you go to the LMS and go to uh there should be um there should be a link to get to it within there. It should be like over here on the right. I don't have the link I don't have the link to it off hand. Yeah. But, there should be a way to get to it from the LMS. Yeah, there should be a banner here. I don't know why I don't have it, but should be there. Okay, so if you're just getting things installed, how do you get to here? Um you open the navigator. Open the navigator. Syntax is hello world. It's just inside of It's just that. Print hello world. Um open the navigator. Open the navigator, which looks like this. Let me share my screen. Okay, open the Anaconda navigator that got installed. Then, uh, click launch on the Jupyter Notebook program. So, you should have this at least. You may have other ones. Click on this launch. Uh, click on this launch, and then you can launch the, uh, Anaconda navigator. Okay. If it If it's a little stuck, that's okay. We're going to move on. We're going to go to Colab, which can run notebooks as well. So, if it seems a little stuck, that's okay. I will post in our Slack instructions on how to run this. It's okay. All right, but what I wanted to do uh, what I wanted to do before we move on to Colab is I just wanted to show you, I wanted to call out a couple things about notebooks. Um, is that, uh, a couple things about notebooks. One is that notice that these cells are very isolated. Whatever I put here, um, does not affect what I had before. So, I can add numbers like that, and it can, um, compute that. And this does not affect this. So, So, this is why notebooks are so great is you can document the notebooks with with mixing in text and code like we do here. Um, you can run code in its own cells. Uh, you can run code in its own cells, and then you can, um, have that very isolated. So I could jump down here and run something and that doesn't matter that there's nothing here. Like it doesn't need to be in order. I can you know, I can run stuff out of I can overwrite this. And run that and it produces the output. So you know, many things we could do inside of notebooks that are really fantastic for just quickly prototyping and running Python code inside of cells. So it's very nice that way. Notebooks are notebooks are really nice. You can also like I could share this file. So this produces a file on my machine. If I go back to the navigator Anaconda navigator Jerry Anaconda navigator. Um Can you read value of variables from another cell? You you have to store them in a variable. So I could I could call this X. And then I can refer to X later. We'll learn about that. We'll learn about that with variables. But yes, you can kind of do that with variables. >> [clears throat] >> All right. Um What are the numbers after Which numbers? These the ones in the brackets? Oh, so those are which cells we've executed. So I executed this one first, so it it's number one. And then I executed um uh this I think I did this second, so it text. It doesn't really get one of those. And then I did this one third. And then I did um I think I did this one fourth and then it get overwritten with the fifth. So, it just tells you like how many executions you've done and what is which number execution that was. Then I did this one sixth. It just keeps track of your executions. Okay, so somebody asked about a kernel. What is a kernel? So, uh the kernel is um the kernel is basically the interpreter. So, it's the thing that that The kernel is just a a um a copy of the interpreter that the notebook is attaching to in order to run. So, the notebook can't run anything because remember a pi Python needs Python needs an interpreter to run its code. So, in notebooks we basically create like a virtual copy of the interpreter called a kernel. Um and you can actually have many kernels based on your um your base interpreter. So, what's nice is Anaconda um Anaconda comes with uh an interpreter for you. And then you create kernels that are virtual copies of that um that are virtual copies of that uh interpreter so that you can run your Python code against it. Remember, you need an interpreter, but notebooks attach to kernels. Kernels are like virtual interpreters. Um and you can have many kernels based on the original interpreter. So, the kernel is literally just think of it like the computer that's powering the notebook. That's all. It's just the compute engine that's allowing you to execute your Python code. So, every notebook has an associated kernel. And what's interesting is if you restart your kernel, you lose all your data. So, all of these outputs that we have, um, you would lose if you restarted your kernel. So, if I restart, um, now I like since I restarted, this is not going to know what X is. So, if I try to print X again, it's going to say, "I don't know what X is." cuz I restarted my kernel. It lost all that data. But, I can redefine it, and then there it is. And notice that my iterations restart. Um, my iterations restart when I uh restart my kernel. So, if if I go back and restart the kernel again, and now if I run this, this will be first. So, notice how that restarts to first. This will be second. This will be third. Try restarting I don't know what that is. For I don't know why that Yeah, choose the Anaconda. Either one. Choose You want to use Anaconda as your But, what that's saying is what do you want to use as your interpreter to to build your kernels. So, choose one of those. That's fine. So, yeah, Anaconda requirements, laptop requirements, um, you need a little bit of You need a little bit of RAM. Uh You need a little bit of RAM to run the notebooks cuz you need some memory. Um, you don't need a lot of it though. I'd be surprised if you didn't meet the requirements. It's not that much, but you do need some. I'm not sure the exact. I'd have to look that up on the Anaconda website. Oh, that it it must have been full to start with or pretty full. I'd be This doesn't take up that much space, I don't think. >> [snorts] >> Was it pretty full to begin with? I would assume. I don't think this takes up that much space. Uh, what I want to do then, I want to go over to Colab. Okay, how do we feel about the notebooks? I Maybe if it's still installing for you, give it a little time. Open that Open the navigator. Let's try Colab. I guarantee you Colab will work for you if you're still having issues with If you're having issues with Jupiter, no worries. Let's just try Colab. I promise that will be a lot easier. Be a million times easier, I think, than than working with Jupiter. Okay, great. So, we will continue then. All right. Let me jump over to our final um demo with setting up a Colab notebook. So, I'm just going to jump into doing that on in the interest of time. Uh So, would you be taking up additional sessions, too, other than Uh, so we're going I'm going to be the instructor for all of the courses in this program. So, you're you're stuck with me >> [laughter] >> for all of those. Does that make sense? Like all of the all of the uh AI engineer program uh courses. Stuck. Yeah. Yeah, stuck or be excited. It's going to be one of the other. Probably not an in-between feeling. Hopefully cool. Hopefully hopefully good. Yeah. Like I said, I've taught this many times. Uh I think it would be uh I think it'll be good. You are stuck. Okay, well, we're going to get you unstuck with Colab. I would not worry about getting Jupiter set up. If it's not working for you, we're going to ditch it and we're going to use something else that works. I promise it's not a I promise getting Jupiter set up is not that important relative to getting at least one of these options that works. So, if it's not working over on Jupiter, I'm not worried in the slightest about it cuz there's going to be plenty of options to run run Python code. In fact, we're going to do one next. Which is going to be with um with Colab. So, we'll do that. Um so, let me jump into that. Let me share my screen here. Um Learning a lot already. Great. That's great. Glad to hear that. Thank you. >> [clears throat] >> Okay. Thank you, guys. Appreciate it. All right, let me go to the demo. Demo three. All right. So, what you want to do essentially is to go to this website um which I have here. I'm going to paste it in the chat. Um so, what you want to do is go to Google's website for their Colab platform uh which is So, Colab is a um notebook platform that Google hosts. So, you don't need to install anything. You just go there in your favorite web browser, log in with your Google account. In fact, I don't even think you need to be necessarily logged in. You can in order to save your notebooks to your drive. But, um you go there and you basically open up a notebook and start working with it right away. And it's fantastic. Their notebook environment already has a lot of packages installed into it for AI and machine learning. So, that's that's really great. Um uh once you get to the page, um you should log in though if you have a Google account. The only reason I say that is because it will save your notebooks to your drive automatically so that you it will automatically save your notebooks just like as if you're working in a Google Doc. So, that's great so that um it saves your work automatically. Um so, plea you know, I would recommend getting a Google account if you don't have one for free, logging in using Colab is completely free. Um so, it's a fantastic platform. Um when you go to that site, uh assuming you've logged in, you want to click on that lower left blue button where it says new notebook. You can see it in the screenshot. And I'll I'll open up one in a moment on on my screen, but do you guys see this screen right here that's in this screenshot that has the new notebook on the bottom in the lower left? No, from that site? What do you see? Oh, so you're already in a notebook. It You're already in a notebook like it says welcome to Colab? Oh, okay. So, you it already opened the notebook for you. Okay, that's that's fine. That's fine. I'll show you I'll show you what that looks like. That's no problem. That means you're already inside of it. Okay. Okay, so then we're pretty much in the notebook environment and we can start running code. Let me let me hop over to Colab and show you guys what it looks like. Let me stop sharing that and jump over to Colab here. Okay. So, if you're in the welcome to Colab, um that's fine or you can start a new notebook. Let me assume that we've opened up welcome to Colab, so you're in this screen. What you want to do if you're in this screen is just go to go up to file and then do new notebook. Just go to file new notebook and drive. Just do that. File new notebook. And this will create a new notebook for you, which will start fresh, a blank notebook. Okay. Were you able to do that? If you guys were folks able to get here to this blank notebook? One way or the other, you click the blue button to hit a new one or you went up to file and did new notebook. Yes, okay. So, there we are. Without doing all the Jupiter install steps, we're in a notebook. Look how easy that was, right? So, [laughter] why didn't we just start with this? Um So, yeah, so we're in Google's notebook platform, uh which is a fantastic platform and uh what's great about this is you can export these. Now, these are dot ipynb, which is which is if you're curious what that extension means, it's short for interactive Python notebook. Okay, ipynb. So, these are the files that you can open in Jupiter. If you have If you notice when you open up Jupiter notebook earlier, it was a ipynb. Um inside of VS code, when you work with notebooks, they are ipynb. So, ipynb is a notebook file. And it can be opened in any one of these three platforms, right? The Jupiter from Anaconda, the uh VS code can open ipynb, and you can also upload your own notebooks here if you have them on your own machine. You just go to file, upload notebook. And then and then it will open up a box where you can choose which file on your machine to upload. So, you can upload your own notebooks, which will be uh great when we get into um demos. We have demo notebooks for you guys that we'll work through with our code. You can upload those into Colab and work with them directly inside of here. So, let's try running something. Let's do the print hello world. So, um you want to type that in, and I can paste it in the chat for you guys. And then you want to You want to hit either shift enter or this play button right next to the cell. Okay. So, that might take a moment because it's booting up your uh your kernel. Uh but then it should run, and you should see the output. Now, this is going to look very similar to Jupiter, just slightly different. We're inside of Colab, and we started a new notebook. We're just inside of a blank notebook for now, and we uh are just within this first cell, and I'd I'm doing hello world. Were you guys able to run that? We didn't, but we could. We could open a notebook in VS Code because we installed the extension. We did that. Remember we installed the Jupyter extension? So, we can open notebooks in VS Code and we can run them there. I just didn't show that to us. We might do that later down the road, but you do have that flexibility to run run things there if you want to. Okay. >> [clears throat] >> One thing I want to show you guys that's really cool. So, um one thing I want to show you is if you go up to runtime, and then go down to change runtime type. Do you Do you guys have that? Change runtime type. If you click on runtime, and then change runtime type. Do we have that? Click on that. Click on change runtime type. And look at our options. We can choose a GPU for free. So, we can swap over to a GPU kernel, which is fantastic for training deep learning models, and we can use that GPU for free. This is one of the reasons that uh Colab is so amazing is they give free access to a GPU. So, if you don't have one on your own machine, um you can use the GPUs from Colab for free. Yeah, go ahead. I mean, there's no nothing wrong with it. So, uh what it's going to ask you to do is uh terminate your current kernel because you're connected to a CPU-basic kernel. It wants you to disable that so you can swap over to GPU. Click okay. That's okay. All right. And then we are now um we click save and that will swap us over and connect us to a GPU. So if you how you know that you're connected to a GPU is if you go over to um if you go over to this box on the right, do you guys see that one where it says RAM and disk? If we click that, it will show us our resource resource usage and you should see GPU RAM available at 15 gigs. So you have 15 GB of GPU RAM available that you can use. So remember you just click this RAM um you should click this RAM uh uh Sorry, this RAM and disk. Roberto, did you swap over the runtime to uh Yeah, it should pop up. Okay. Then you should be able to click on this. Yeah, it might take some time to connect to one because what Google has to allocate one to you um and then it has to like connect it over the cloud. It can take a minute. Yeah, it can take a minute. It has to allocate one to you. So the question is which one is better? Um So for the vast majority of things, the CPU the standard CPU runtime which is the default is going to be better for the vast majority of things. The only time the GPU is really going to be beneficial is when we start doing deep learning and training neural networks. Then the GPU will be really beneficial. It will speed up the training time by a significant amount. I can tell you like I trained a neural network for images for image recognition. It took me it took 2 hours on the CPU and then when I swapped it over to GPU it took less than a minute. Took less than a minute and it was taking 2 hours on the CPU. So, yeah, training neural nets on a GPU when we get to that is going to be beneficial. So, if you're not using Colab right now, that's okay, but in the future when you get into deep learning you're likely going to want to use Colab to swap over to the GPU for free. Now, they do rate limit you. So, it's free, but you can max it out in a day and then they cool you off for 24 hours. I have I have done unfortunately. So, like if you max out that RAM and you use it too much in a 24-hour period, they will uh not allow you to connect to a free GPU for another 24 hours. So, I doubt you'll run in that situation, but I have before. If you're just if you're just using it so much. No, so unfortunately uh no. So, unfortunately no, you cannot um Colab doesn't connect to your local resources. It only it does the cloud Google's cloud resources. So, no, you can't use your own through Colab, but yes, you can use your own GPU through VS Code. I will show us how to do that later when we get into deep learning. I I show you that later. We we won't need to worry about that now, but later on, yes, that'll be important. Uh it doesn't show GPU. Make sure you swap over the runtime to go to change runtime type and make sure you pick GPU. Make sure you go away from No, you should use Colab. I wouldn't You don't need to buy anything. You just use Colab. Just use Colab for for sure. Colab's free. It does everything you're going to need for the class. Yeah, I I highly advocate for Colab. It So, by the way, if you're curious like Colab came about because Google wanted the the machine learning research community to have access to GPUs for free to um develop like machine learning and uh deep learning models. So, uh it's been around for a while. I remember using Colab um probably almost 10 years ago. And it used to be it used to be very lucky if you got a GPU. You used to like you used to have to click and then hope that you would get allocated one and sometimes you wouldn't. And you I would sit there and have to refresh and try to hope that I would get a GPU. But now it's it's like readily available. It is fantastic. No, you're but you're joining it at a good time cuz I'm telling you the GPU was very difficult to get. I would always try to switch over to that and I would rarely be able to. So. Pretty good. But like I said, like if the CPU is perfectly fine for everything we're going to do except when we get into deep learning, you're going to want to swap that over to GPU. But that's going to be for We have a while till we get to deep learning. We have a lot to learn between now and then. Okay. What do we think? Do we like Colab? We're comfortable with it. Feel free to use it. Feel free to use VS Code. Feel free to use Jupyter, whatever you want to use, okay? There's options, right? I Hopefully you have options that work for you. Um they are all used in the industry, so you're not missing out on if whatever you use, I people use it. Of these three, people use all of them. So, feel free to use whatever is easiest. Yeah, I can show that real quick. Yeah. Let me go back over to it. I'm going to be real quick on it though because I want to make sure we get over to our other material. Okay, let me show you how you can run a notebook. Let me show you how to run a notebook. So, I'm going to go to file new file and open a Jupyter notebook. Okay. So, it'll open a new Now, notice notice [snorts] the extension of it is .ipynb. That should be no surprise. That is the universal kind of interactive Python notebook file. Okay? So, the biggest thing you have to do when you open a notebook in VS Code is you have to tell it what kernel to connect to. So, I have to go to select kernel. And then, what you have to select is the Python environment. And luckily, if you installed Anaconda, you have a built-in Python environment, which is going to be your uh which is going to be um the you know, which is going to be the uh Anaconda that you installed. So, I have an Anaconda here. Now, I have a lot of other ones, but the uh Anaconda is here. Let's say it's this one. Does that So, when you when you uh Yeah, you have to install you have to install a a Python environment. Yes. So, you want to install Anaconda first, and then you can run your then you can run your notebooks. And then, you just uh run your code as usual. And then, we can run that. Yeah. It but like it's working as if, you know, the same kind of notebook that we have inside of Colab, the same kind of notebook we have in Jupyter. You you have to have a Python installed for this to work. So, you go to you go to Python environments. And then, choose a Python environment. You could try to create one. I'm not sure if that'll work for you. Create Python environment. You could try that too. Yeah, that's fine. Any any one will work. Any Python will work. You just need to pick a Python. Any one will work. Um Okay. Yeah, if it defaulted to something, that's fine too. And then we can generate more cells. And run cells. But yeah, that's the thing is you're going to want to install um Anaconda most likely cuz you need a Python version installed on your machine in order to run this. Perfect. Okay. All right. So, what I want to do is jump back over to our notes so we can continue along. Um again, feel free to use whatever platform works for you. Colab, doing notebooks in VS Code, doing Jupyter notebooks. Whatever works for you, please feel free to use that. There is no wrong way of using it. Whatever is best suited to you and you're most comfortable with, please use that option. Uh it's lowercase p. That's why. Lowercase p. Capital P is not a function in Python. Lowercase. Yeah, go with Colab. Yeah, if you're if you're on a machine you can't install anything, go with Colab. That's totally fine. That's why it's there. It's for the, you know, convenient kind of cloud aspect to it. Um feel free to do Colab for everything. That's totally fine. I will use Colab from time to time as well. All right, let me uh go back to our notes then. And pick up from uh syntax. So, I'm going to go back to Let me share my screen. Go back to Can you use Colab on your phone? I've never tried it. I would be surprised. [clears throat] Maybe an iPad. Maybe like a tablet it could work pretty well. Phone, I'm not so sure. Yeah, go ahead and try it and let me know how it works. Try it and let me know. I really don't know. I'm curious now to try that. All right. So, I'm going back over the notes. We're going to finish up today uh with the time we have left to go through some syntax. So, really uh really getting into um into Python like the actual code of it so we can get started on that and start working our way through it. Uh the difference so the the difference is um you will be executing dot py files with the within the terminal. So you'll be running Python files instead of cells in a notebook. You're just you're running a you're running a Python script rather than individual cells. Okay. So there's there's a difference there. And the reason the reason we choose notebooks is to run individual cells. It's just easier. Same syntax. Same syntax is just the code is not isolated in the cells. Still Python. All right. Um let's go forward into the syntax start learning about it. All right. So something we need to learn about is how do we properly write Python code? What is the syntax to it? So some things we're going to need to learn about are how to write proper identifiers which are names. Identifiers are just names for variables. So we need to know what's allowed what's not allowed. We need to talk about what the indentation means and why do we need it in Python. I want to show you guys how to write comments cuz that's really important to leaving notes to yourself or others about the code. And then talk about um generally how we produce output and how we can accept input um from a user or someone interacting with our code. I want to talk about all of things. We'll see how how much of we get to. But let's start with the identifier. So, what is an identifier in programming? This is really for any programming language. An identifier is just a name we give to something inside of our code. So, it's a name we give to a variable, a name we give to a function, a name we give to an object. Um so, any name we give to something in our code, like when we set something equal to X, like X equals 3 + 3. Um that thing, that X, is the name we are giving or assigning to a result or a variable or an object. So, anytime we write down a name in our code of something, there are certain rules that those names have to follow. And these are something we will um pick up as we go along, but I wanted to call them out here. So, um when we name anything in Python, generally, they have to follow these set of rules, meaning they have to be a combo of lowercase or uppercase letters. Either one's allowed. It can be even be a mixture of lowercase and uppercase. Numerical digits are allowed in the name. That's okay. Any digit 0 to 9 is okay. And underscores are okay. Okay, underscores are okay. And there's no um minimum or maximum length. So, names can be really long. They can be really short. Um of course, they should be meaningful. So, when we name something, it should not Remember, we want to kind of get away from naming everything X or Y or A or B. Um because those names may not mean much when we look back at the code. So, even though those are valid names from an identifier perspective, we want to be really meaningful when we name something, we name a variable, name a function, name an object. Um here's one catch. The name cannot start with a number. So, I can't name something uh just the the number zero or the number one um because I can't start with that. Now, it can include that. So, if I need to include a number in the name, as long as it doesn't start with it, that's okay. But, names cannot start with a digit. That's just one rule of Python. Anything that you're assigning a name to, like a variable, function, whatever, cannot start with a number or else it would be invalid. Okay? So, I'll show us examples of that later. Yes, they can start with underscores. Yes, that's okay. They can start with underscores. Of course, they can be lower, upper case. They can start with It cannot start with a digit. It can have digits, they just can't be the first character of the name. Yes. Um now, special symbols cannot be used in the name. So, you cannot have a percentage, dollar sign, exclamation point, hyphen, pound symbol, at, ampersand symbol, at symbol, none of those can be used in the name. So, those symbols are not recognized. So, if you try to include those in the name of something, that will produce an error. So, we don't want to do that. The other thing we want to avoid is naming something in the same name as something that already exist internal to Python. So, those things are called keywords. So, there are certain keywords that have a meaning in Python. They are built into the language. We cannot reuse those. They're basically reserved. Um so, something like class is reserved because it means something. It means you're declaring a class. We'll see what we'll talk about what that means later. Or something like global cannot be used because it declares something as global. Um So, you know, we'll learn what some of those keywords are. There's a list of them that are in the Python documentation. But, we want to avoid naming things after built-in uh uh functions or built-in keywords. Um so, so in fact, we've already used one, which is the print function. You know, when we printed out hello world, we would want to avoid naming something print. Cuz print means something. It exists as a function. We don't want to name something print. Right? That would that would produce it because it would produce confusion. The interpreter would see that and say, "Oh, do you mean the function print or you trying to name something print?" It wouldn't know. So, we want to avoid naming something that already exists inside of Python, like print or like class, global. Um there's many others. Okay. Lastly, and this one always throws people off, is that when we name something uh that is case sensitive. So, if you name something lowercase A, that is a completely different variable or completely different function than if we were to name something capital A. These are different. They're treated differently. So, Python will think that those are two different uh objects or variables or whatever the case is. So, be really careful with case sensitivity. Python is case sensitive. Lowercase A will not be treated the same as capital A and whenever you're naming something. So, if I have a variable and I I I set lowercase A equal to five and then I set um uh capital A equals to 10 then if I um print A, that would produce five. But, if I print capital A, that will produce 10. It's not the same. So, it is case sensitive. So, these would be two different names, lowercase A and capital A. Okay. So, we're going to do two examples with these, but these are just some rules we have to abide by in the syntax if we're naming anything like naming our variables, naming our functions, naming our objects as we go along in in the course, right? We just cannot The main one that trips people up is we can't start with a digit and we can't use words that already exist like print. Oh, is my video stuck for people? Was it stuck? Oh. Okay. Always let me know cuz it might it might be. Always let me know cuz it definitely could be. So, it's better to know than not to know. Okay. Oh, no worries. Like I said, Uh, always always feel free to to let us know cuz um it would if it is then it's good to call it out. So, no worries about that. Any questions about these names? Do Does it make sense about like how we name things matters and there's just are certain rules that we have to follow. Um we want to avoid these wacky symbols. Um you know, we want to avoid naming things that already exist. We want to avoid starting with a digit. Otherwise, it's going to be a pretty standard like lowercase uppercase mixture. Maybe occasionally with an underscore mixed in there. Um or or digits even. As long as you don't start with one, that's okay. Yeah, Tim, that's a good reference the PEP. So, PEP are the set of guidelines that um the Python Foundation has kind of agreed upon as um here's what you should use as your style guide. Here's what Here's what the community believes is the best style for Python. Those are good to read. Yeah, those are those are uh good references for really like formatting and styling your your Python code uh to be in line with kind of what the community expects. Okay. Okay, so let me give you some examples. Um so, the ones on the left are going to be valid. So, we can name something my class. We can name something var_1. That's okay. count Um that's okay. Uh but if we have um like on the right, if we have something that starts with a digit, that would be bad. So So this one is no good cuz it starts with this number. That's not good. Um this name has this wacky at symbol in it. That's no good. So this would be a bad name for something that would produce an error. Remember, the interpreter is going to see that and reject it essentially and say, "You can't name something this. It's not valid." Um same thing with trying to name something global. This is a keyword that already exists in the language. The interpreter is going to see that and get confused. It's not going to know if you're talking about the keyword that's built in or you're trying to come up with your own name. It's not going to know, so it's just going to throw an error. Um so again, we want to we want to keep things simple. We want to use underscores where it makes sense. We We don't want to start with numbers. Um we can use a mixture of lowercase and uppercase. That's fine. Um this is a good variable name rather than if I just called something X. That again, we're trying to avoid That's something I always see in the beginning. I think it's okay in the beginning, but it's something we really want to be conscious of is naming our variables very meaningfully. Like count is going to be more meaningful if we're keeping track of a count of something. We would rather call that count than if than if I just called it X. Cuz if you read the code, which do you guys believe me like when you see it, you kind of know exactly what it means, X or count? What do we think? Which one like has more meaning to it when you see it? You know exactly what it's keeping track of, X or count? Yeah, count. I would agree with that. Count, yeah. So, it's just an like that's just a single example of trying to keep track of things in a meaningful way. That's a good name to give to a variable that would be uh you know, keeping track of something, the count of something. Rather than if we just called it X or Y or A or B. All right. I want to talk to you guys about indentation next. So, now we know we have to name things appropriately and the interpreter will give us an error if we don't name things appropriately. What about indentation? So, indentation is a way for Python to understand what code gets executed together. Okay? So, and it it also indicates that I am breaking the flow of the code from one section to the next. So, the indentation is really important to signify to the interpreter there is a new section of code that has to be considered um before I move on. So, um you should always use indentation whenever you have a colon like we see a colon here with if else statements. [snorts] Now, we haven't learned about if else statements, but we will. But, notice how we have the colon there and the interpreter would be okay with this. This would work. Okay? Which is a simple statement of saying, "Is five greater than two?" Yes. So, in the case that it is, let's run this code. But, we're only able to run it because the interpreter recognizes it's indented. So, the indentation is really, really critical as it makes the interpreter understand what should be next. The interpreter understands what should be next after this statement. Um like an if statement or a loop statement. Um we will always have indentation. So, this would actually uh throw an error because there's this is not indented. This is at the same level. And if you have Colab open, you could try this for yourself. Um if you had Colab open it, you could try it for yourself. Is like this would this would throw an error where it says, "I am expecting indentation, but you did not have have any." Does it matter how many spaces? Uh you yes, you want to use four spaces. This this should be four spaces here. Uh spaces or tabs. Uh I'm only laughing because it's a it's a kind of a controversial question in the community. Some people get really upset over >> [laughter] >> one or the other. I'm not one of those people. I don't really care. They So, most code editors uh set the tab automatically as four spaces. So, a tab will do the same thing as if you manually just did four spaces. It doesn't really matter in that case. So, either way. >> Yeah, your So, the IDEs will do that for you. The IDEs will generally do that for you because they know it should go on the same line. Would it work as on the same line? Yes. In some cases it will, but not all. It depends on how complex it is. But, what do you think is easier to read? From a readability perspective, which is easier? If it's all in one line, or is it more readable and easier to follow if it's indented? Yeah, the That's the purpose So, yes, I I agree. Indented makes it easier to read. So, that's another reason Python really enforces indentation is to make it easier to read. There's a reason they do that. It's to make it easier to read. Okay. And that's one of the best selling points of Python is how easy it is to read and work with. The indentation really helps. So, to summarize this, we are going to have to use indentation anytime we have uh a statement with a colon. Anytime we have a statement with a colon, we're going to have to have an indentation immediately follow it. And there are certain statements that have a colon like if, else, else if, and any loop. Any looping statement. Now, all of these things we're going to learn about. We'll learn about it in our next lesson. But, anytime we have a colon, this is signaling the interpreter, "Okay, there needs to be a block of code following that, which is" Yeah, as you say, Ramiro, it's like a a hierarchy. Yes, that's a great way of thinking about it. It's saying, "Okay, I should check this, then do this if that's true." That tells the interpreter, "This is only going to be executed in the event that this is actually true. Otherwise, I'm going to keep going." Okay. All right. Any questions about the indentation? This is something we're going to learn about more as we go along. When do we use indentation and when do we not? We're going to learn about it when we get into the if else and the loops, which we will study. But, do we do we Let me ask you guys this. Do we understand the idea or the intent behind indentation? Do we roughly get that idea? We don't We don't know yet when to use it. I get that. But, more of the intent or the purpose of using it. It's to really like section things off. Yeah. Okay. Good. Good. Good. Good. Glad to hear that. Okay. Okay. Let's wrap up today by talking about comments. So, uh what are comments? These are like annotations or notes to yourself that are completely ignored by the interpreter. So, when your code gets executed, the comment does not play any role in what gets executed. The interpreter will actually just completely ignore it the moment it sees the comment. It will just ignore it and go to the next line. So, the purpose of it is for humans to leave a note to other humans reading the code. And that is very powerful is to be able to read those to to leave those notes and not have it affect the actual code that's uh that's actually executed. So, there's multiple ways to make comments inside of Python. The most basic is to use the pound symbol. So, that remember, we cannot use pound symbols to name anything. This is why, because the pound symbol is reserved for making comments. So, you you when you have a pound symbol like this uh that immediately signals to the interpreter everything else that follows that on this line is a comment. Any other text that follows that on that line is a comment, and usually your editor like VS Code, Jupyter, Colab will color that differently. Maybe like a a like you can see in here, this is a Jupyter example. You can see it's kind of a light gray greenish gray um to signal that this is a comment. Um Now, you may be asking, why would we have comments? Again, you are going to look back at code weeks later. Especially in this program, you're going to look at code and review and be like, what what was I doing there? If you leave a comment, you'll remember what you were doing there, why you had that line. Um and not only that, like when you share your code with others, which in the real world you would be doing, collaborating with others right? Adding in those comments can be really beneficial to do. So, you will see me throughout the program, I'm going to leave a lot of comments on our demos and our notebooks that we work on together in the live sessions. I will leave comments, mainly to call out certain things. Like I will say this is a really important step. Or we are doing this because I will leave a lot of comments, and I encourage you guys to do the same in your own code. Um remember, they're free. They're They get ignored by the interpreter. They don't affect anything. They are notes to yourself. So, use them accordingly. Um and you know, there's actually multiple ways to leave comments, but this is I'll show us those as we go along. But this is the uh most basic is you just you you type in a pound symbol, and then everything else that follows that is uh is your comment. Okay. All right, guys. That's it for today. Um what a great first session. Thank you guys. Thank you guys for being patient um going through the setup of some of those tools. I hope you landed on one that worked for you. Um you know, use that one going forward, please. If it's Colab, use that. Jupyter, use that. VS Code, use that. Whatever you you uh feel most comfortable with, please use that. Um we have a lot to cover still. You know, we're going to um continue on Wednesday. Uh we were we will uh continue talking about Python. We're just getting our feet wet a little bit on on Python. A lot more to cover. We're going to get into the the more nitty-gritty of the code. So, it'll be really fun. We'll cover if else, loops, um how to control the flow of our program. Um we will do that. This is where we left off was writing comments uh in Python code, which I I did want to remind us of how to do that. It is going to be using the uh pound symbol to initiate a comment, and basically the Python interpreter will ignore everything else on that line. It treats all of that text as a comment. And again, like comments are free. You might as well use them to your advantage to kind of leave a note to yourself of hey, this is what this code is doing. So that when you come back and read it you can understand it better. And so I encourage you guys like when we do demos and we will do a lot of demos especially today. Leave comments. You know, put comments in there so you so you can make a note to yourself what this code is doing. So you will get I think it'll be good to get in the habit of leaving comments to kind of mark up the code to kind of remind yourself of this is what it was doing when you look back at it in the future. Okay, so we had ended with that. What I wanted to do is move on into the next slide. So talk about basically how we display output to the screen, which we've already seen an example of when we did the Hello World, which is the print function on the right. So this is a By the way, this is a Python function and you know it's a function because of these parentheses. So these parentheses signal that this is a function because it expects some sort of input to go inside of those parentheses. And And the input that would go inside of there is going to be text, like some sort of uh some sort of text that belongs inside of quotes. And whatever we put there will display on the screen. So that's useful for us to like display information. Print We would say we are printing out information to the screen. So if we want to know the value of a variable or the value of something that we are doing a calculation with or uh you know, sanity check something in our code, we we can print it out. Which would be using the print function and it will display that value onto the screen. So, we will use the print function quite a bit. Um you know, we haven't learned what functions are, but uh functions in Python are, you know, um designed to be uh chunks of code that execute and do something. And they take arguments and you know it takes an argument because of the parentheses. That is um signaling that there should be some something inside of this parentheses here, which is going to be uh text. So, whatever text you want to display or maybe some variable you want to display, um that would go inside of there. So, we'll get the hang of using the print function as we go along, but just wanted to call out that's the primary methodology of kind of um displaying something on the screen if print function. Um now, the reverse of that is uh asking a user to uh input some data. So, that would be this input function and um this is something that uh as you can see in example below is we can put some text inside of this parentheses. So, again, a function has those parentheses that signals it's a it's a function. Um and we can put some text in there, which would be kind of what displays and to the user as kind of a prompt. Like, here uh enter your name and that would display on the screen and then there would be a box next to it. I'm going to show us this. I'm going to actually run this inside of uh a notebook in a minute. But, then there would be a box that displays that that would say um hey, you know, enter your name and then you can type in uh and and then when you hit enter it will save that input into into this variable called name. So, and remember name, this is a valid identifier because it starts with a lowercase n um which is fine and it has all valid characters. It doesn't have any wacky, you know, uh pound symbol or at or anything crazy, so it's it's a decent identifier. Um So, name name would be okay and So, input is whenever you want to get whenever you want to allow the user to input something like it'll bring up a text box and they can enter some data um and that will be saved in this whatever variable you name this you set equal to input. Um and then you can see like as soon as we put that in we immediately just we can display it. So, we we print hello and then comma name which references whatever we stored whatever the user input there. So, I'm I'm going to show you some example of that. Um but input is what get is our primary way of getting input from the from the the user in a text box so we can use that data in our program. Print is our primary way of displaying data that we already have in our code. We can print it which will display it. Um So, we're going to see many examples of these as well on but I just wanted to call out those two. These functions by the way are built into Python. So, we don't need to create them ourselves. They already exist. They're already built into Python. Um nothing special we need to do to use them. We can just use them right out of the box. So, again we'll see this in our in our code examples that we're going to do in a minute. Um where exactly would a end user be? So, maybe we ask them for some input um and then we do like so we ask them for some like their name, their email, their uh date of birth, those kind of things we can ask in the input and then we maybe we store them in a database or we do something with it in the Python code. So, um whenever you want to accept input from a end user, that's when you would use this input. It just depends on the application. Right? It depends on the application like what kind of input data you you want to accept from the from the user. Okay, so I'm going to show us this. Um, before we do that demo, um let me ask you guys, which of the following do do we remember from Monday? Which of the following identifier names follows Python's rules and best practices for readability? So, not only so you should be looking for the answer choice here that follows the rules but also is a meaningful name. A lot of different choices. Okay. By the way, which let me ask let me I'll come back to the answer to the original question but let me ask this alternative question, which one of these is not valid? Meaning it would Python would throw an error if you use it. Which one of these is not valid in general? Cool, great. It is C. You guys were right on top of that. Very good. So, C is not valid. It names of things cannot start with a number. So so that's um C is completely invalid in general and that would produce uh an error. Right, it starts with a number, exactly, which we cannot do. Now, starting with an underscore is okay. That's allowed, so that's not an issue, and having a number be second after the underscore is okay as well. So, technically A and B would follow the rules. So, those definitely follow the rules. Um now, are they readable and meaningful is the question. I would argue that possibly not. Var 123 is pretty generic. I would argue that even though it's valid, like Python would not have any errors with that uh variable name, it's not very meaningful. It's too generic. It's almost as if we just called something X. We just called something var 123. That's probably not going to be meaningful to us, and we're not going to understand what that really represents. If somebody were to come along and read it and see var 123, that's probably not that great of a name. It's not telling us exactly what that represents. So, I would say A is likely not um A is likely not uh a good choice. And C we know is invalid. So, really I think the only two options you could argue are B and D. I think D is a really good answer. It um it it follows the rules. Uh underscores are fine. Everything is lowercase, that's fine. Um so, it's valid, but it also is meaningful as a name, right? So, final result value. Um we we should probably like inner code, we would have context, we would know what that means. Okay, this is our final result. Um so, that's a that's a pretty good name for something. Um you know, this one is okay. It's just not that readable. 321 customer details DB table It's okay. I don't think it's um it's not the worst. It it definitely would work. But it's um kind of a clunky name. I'm sure we could come up with something better, but it would work technically. There'd be no issues with that. Okay. So, I think D is probably the best choice, but B is valid, too. I think B could work for this. D and B, I think you're okay. Okay, good. Good. You guys were right on top of that. You have a good I think you have a good feel for what the allowed names for things are, which is good. Okay. Um I'm going to then swap over to this demo. So, you guys should have the uh demos um and we kind of went through some of those first few last time to get you set up on Colab and Jupiter and VS Code. Um I am going to be using Colab for most of these, but feel free to use whatever you want to use. If you want to use Jupiter, if you want to use VS Code, and run your notebooks on your own machine, feel free to use whatever you want to use. I'm going to be using Colab just for the simplicity of it. Um So, so this demo will walk us through um opening up a new Colab notebook and then running those input and print. So, some examples with input and print. Um so, we'll do that together. Let me go over to that demo. So, if you're following along, we are going to be doing um demo four. So, it should be lesson one demo four. Um do you guys have this? Give you a moment to to that up. Lesson one demo four. Do you guys have access to this one? So, we did we did one, two, and three on Monday, which were just getting those environments set up. So, this is demo four. Um which again, I know step one says open collab. Feel free to open your own notebook in VS code or open your own notebook in Jupiter as well. Whatever you're most comfortable with. Um I'm going to be using the collab to to do this, but feel free to use whatever works. You're We really just need a notebook to be able to run this code. So, however you're running notebooks, whether that's in VS code or Jupiter or collab, I I any of those, either one is uh perfectly fine. So, so step one is to open up a notebook. I'm going to do it in collab, which is what this says. Um and then you can make a new notebook and then rename it to my first program. I'm going to do that in a second. And then um So, I'm going to walk through this live with you by just showing you some of the steps we're going to do. Um the first thing we're going to do is is just practice doing the print hello world again, so that we can um execute a print statement. So, we'll practice that. We're going to make a second cell um which we can do in collab or VS code or Jupiter by hitting the plus button. There's usually a plus uh you can see it here uh in multiple places. In collab, you can do it right below an existing cell or there's always a plus code here, which is kind of what you have in Jupiter. Usually, in Jupiter, you have a plus button. So, you can just hit hit that plus button, it'll make a new cell. Um and so, we'll make a new cell, so we can write some more code. Um And then in this new one, we are going to practice doing some comments. We're going to practice doing some comments and then um see how we can do uh some more print statements, okay? So, let's do that. Let me jump over to Colab. Let's walk through these first few steps together. Um and then uh we'll come back to this and finish out the rest of the steps because we're also going to do input. So, I'm going to show you how to do these uh input, which will you can see here like the input is going to create a text box where you can put input and it will you hit enter it will save it for you. So, input allows you to get input from the keyboard and save that into a variable to use for later. Okay. So, let's jump over to um let's jump over to Um I'll show us I'll show us in a second. How do you rename it? I'll show you. Let me jump over to Colab. Um Okay. So, I am over lesson one demo four. Yep, that's the one we're doing. Okay, so I am in Colab. I'm going to start a new notebook. Start a new notebook in Colab. Uh so, now I'm here. I'm just on a fresh notebook. Um nothing that interesting going on. Here's how you rename it. Just go up to this box on the left. And almost like a Google Doc, just just uh click into that name and then start typing to erase it. So, see how I'm like hovering over that name and then I'm clicking on it and then I can start typing to erase it. So, I can we can name this my first program. And then hit enter and it will save that. Oh, yeah. So, if you're in VS Code, um do to to rename it, do file and then save as. And then you can give a new name to it. File, save as. Okay, that's how you can rename it in VS Code. All right, let's do let's do the first step. Um let's do print. So, we're going to do print. So, type in print. And we can uh we can do parentheses. Um remember, this is a function, so we need the we need the parentheses to signal that we want to put some text inside of this print function. And then you want to do uh you want to do quotes. You want to do quotes, the double quotes there, in order to allow us to put in some text. So, Python will interpret what's inside of the quotes as text and it will display that text. So, we can do hello world my first Python program. Okay, and then we can run it. So, feel free to put whatever text in here. It doesn't really matter exactly what it is, but you put some text in there between the parentheses and then hit run. And the notebook will take a second to connect and then there it is, right? So, then you see that the text displayed on the screen. Try that out. Are you guys able to run the print in your Jupiter whatever whether it's Colab, whether it's uh Jupiter Notebook, whether it's VS Code, can you run the print? Install. Yeah, install the um in VS Code, yep. Try installing that. Okay, great. You guys were able to run that. Very good. Very good. Okay. No, you don't want to save it as a JSON file. You want to save it as a dot ipynb, just like this. See how this one is uh ipynb? That's the format you want. Remember, that is interactive Python Notebook. You want that file, ipynb. Uh perfect. Yeah, you got you got it to run. You don't have any extension No, if you're in VS Code, remember from Monday, you need to install the the Jupiter extension. If you're in VS Code, you got to install the Jupiter extension. You have to manually So, manually save it. You can save it as a dot py. I would do ipynb, so you can open it in Colab. Type it yourself. Type overwrite what's there and type it your type in my notebook, whatever the name is. Type it out yourself if you can. Like save as and then type out the full file name yourself. Now, let's practice a comment. Let's practice a comment. So, let's build let's do a new code cell. So, we made it you can either do it here. If you hover over your cell, you can hit plus to build a new code cell or you can hit plus here to make a new code cell. So, let's do that. You should be saving Don't worry about the type. Just type in the name .ipynb. I don't think you need to. Or you could just hit What you could do is you could just hit save and then in your file explorer, you could just rename it. If you just save, it will save it to the default location and then just and then just rename it. So, maybe try that route. Just hit Just do save. Just save it and then it should it should try to save it as a .ipynb. Okay. Okay, let's practice um So, the next step in the demo, if you're following along in the demo document, it wants us to do So, we did the print. We want to do um a practice some comments. Okay, perfect. Uh let's practice some comments. So, um remember I told you that we can do uh comments with the pound symbol. So, this is a comment. So, practice writing a comment. Remember, you start a comment with a pound symbol. Um it will get ignored by the interpreter. Interpreter. So, feel free to type in whatever text you want. I'm just reminding us that whatever the comment is is going to be ignored. And we can have whatever code below that that we want to have, and that comment will get completely ignored. So, let's do another print. So, write a comment, hit enter, immediately below that in a new line, let's do another print. This code gets executed. So, we know this print statement's going to get executed, but this comment is going to be ignored by the interpreter. So, let's run that. So, this code gets executed. This comment gets completely ignored. Right? That comment gets completely ignored, which is great. Try writing a comment. Are you guys able to write comments? So, write a comment and then try writing a print statement right after it. And And feel free to put whatever text you want inside the comment, and feel free to Uh for the comment, is the space after the pound symbol required? No, it's not. So, we can test that out. So, I removed the space. Doesn't matter. It It's just for readability. I usually like doing that so that I have some space after it, and this is a little It's just a little bit more readable, right? It's not mixed together. It's just for readability. Great, you guys wrote a comment. Okay, perfect. Perfect. We're able to write comments. Really great. Okay. Okay. If you put multiple code lines, do we need any separator like No, they just go on new lines. So, do you mean like a second print statement? Let's We could try that. Let's do a secondary print statement. So, we can do print Um this one is on the next line. No separator needed. Do you see that? See how it's on its own? I did a print right below this other print, and as long as they're on their own line, that's okay. They just need to be on their own lines. They don't need any separator. If we run this, then this one gets actually then see how this is now printed out below it right there. Is there any character limit on the comments? Uh no, there's no character limit. Um but there's no character limit, but a good practice is to not Like you don't want this to be super long and to take to take up the whole screen, right? Cuz then it's not really readable. So, there's no limit, but you don't want to have overly long comments. You want to keep them kind of concise and short. So, just so you can read them and they're they don't take up a lot of space. Not able to add print statement below. Why Why is that? You should be able to should be able to have a print right below this print. Shouldn't be anything that Make sure you close this parentheses. Make sure every print needs to close the parentheses. And it they all you also need to close the quotes. You close this quote, close this quote within within the print. That needs to be done. So, you should be able to run I'll paste this for you guys in the chat. You should be able to run this. All right. One thing I want to show you guys is just like the demo says in the word document, um you can do multi-line comments. So, if you need to do a lot of comments, all you need to do is triple quotes. So, triple quote then um triple quote and then everything in between That's interesting that it did that. Yeah, so we can do uh pound symbol pound symbol pound symbol pound symbol and that all that should all work. So, we can do that. Yeah, I think it's a Colab thing, but normally in in like Jupiter or in Python, it it will work just fine. But, like in Colab, I think they don't like the triple quotes. But, yeah, do you guys see Do you guys see how I just did it like this with the pound symbols? That's okay, too. Everything between these uh pound symbols is a comment and is ignored. So, now we should be able to run that. So, there we go. Everything gets ignored there. Does that make sense to us? The pound symbol comments. Does it Does it using the pound symbols? So, notice how we use that to do multiple lines of comments. So, we did one here. We did one here. We can have as many We can have um as many uh comment lines as we want and they will all get ignored. What is those? It's supposed to be multi-line comments, but for some reason it's not working. Um it so the the triple quote is supposed to be like representing that you can have a whole block of comments. I don't know why it's not working in collab for me. It's working for you? Okay, I don't know why it's not working. Single quote? It still sh- it still displays here, which I don't get why that's happening. It's kind of weird to me. Yeah, I don't get why the inconsistency. It usually It usually works for me. I don't get that at all. Still still doesn't work for me. I don't know why that doesn't. >> Yeah. I don't get why that's not really liking those triple quotes. Oh, well. I mean, not a big deal. We can just do Okay, we can do we can try single. Still doesn't work. Yeah. Oh, well. We can do a pound symbol. That will always work. Pound symbol is honestly more popular anyway. Most code that you see in the wild will have pound symbols wherever they're doing um wherever they're doing comments. So, that it's fine just to use a pound for now. Uh yeah, that's correct. I don't know why That's That's correct. Um I don't know why Colab doesn't seem to like that. It should be ignored um generally with the triple quotes, but uh that's okay. I'm not too concerned about it for now. I guess what you'll And when I do comments, you're usually going to see me using a pound symbol anyways. It'd be very rare that I would need to do uh quotes. Yeah, it's weird that Colab it doesn't work very consistently. That's okay. All right. What I want to show us is I want to move on to the input. So, I want to I want you guys to see I want you guys to type in this code here that will take input from a text box and save it into a variable called name. So, the code we're going to do is going to be like this. It's going to be name equals input and then we'll put um please enter your name. Okay? So, this by the way, I'm going to comment this code here. Um this code should create a text box for us to put in our name. Okay, so that's what should happen. So, when we run this um it should pop uh open a text box right below this and we can type in our name and hit enter. And when we do that, it will store that result in this variable called name, which we can use uh wherever we want to in the code. So, if I hit run there's that text box. Do you guys see that? There's the text box and see how it says please enter your name. And so, we can type in our name. And we hit enter. And there it's stored in the name. We can even um display name by doing print and then the name which will display uh the name that we stored when we did the input. So, try this one out. Try this code out for yourself. Try typing input parentheses and then you want to have some text there. It doesn't matter exactly what it is, but something like please enter your name or enter your name. Try that out and then it should store uh you should be able to type in the box that shows up, hit enter on your keyboard, it should save that, and then you can um print it out. You can print out that name, which will um it display that whatever we typed in before. What does it look like, Roberto? What does it look like? Were you get Were other people able to run this? Oh, yeah, thank you, Melanie. Yeah, I see that. Perfect. Perfect, that looks good to me. Uh you don't need a space. Um it just looks nice, right? It's So, that's a good practice to have the space, so that uh this code is um evenly spaced out, and it looks nicer on the on the screen. Um name equals input, print hello there, uh name. You need Yeah, so uh Roberto, you need a you need a comma after after the quotes. After the quotes, you need a comma after the quotes. To signal to Python that you're putting in you have uh you have some text and then an additional input. So, it need it needs to be more like it needs to be like this. Print um hello there, and then you need an extra comma. See how I have an extra comma after the quote? You need You need that. Sorry, not Roberto's. Uh Kia Kiati. Hope I'm pronouncing that right. Okay, so do we feel good about input and what it does? Perfect. Do we feel good about input and what it does? It it it brings up a text box. It Did you hit enter, Roberto? To Like were you able to type something in and hit It's going to run until you hit enter. You have to type in the text and then hit enter into the box. So, let me rerun this. So, it See how it's still running? See how this like it's going to keep running forever until I type something in. And then when I hit enter, it will stop. What does your code look like? Okay, that looks right. Try Try stopping it and rerunning it. Try Try hitting the stop button and then rerun it. Um you should So, yeah, you should put that in a different cell. So, if you if you separate your code into individual cells, so you could do like um you could do name so we could we could separate this. So this code is the only code that's running in this cell. That doesn't make sense, something else is That doesn't make sense cuz like this collab tab is only taking up 235 megabytes. So something is chewing up your memory. It's not really I can't imagine. Are you using collab? You can see like it's not using that much only 240 230-ish. Yeah, I don't think I don't think collab is the culprit unless you loaded in some really massive data or something. I can't imagine that's the issue. You did you loaded in data that's I mean yeah, then it's going to it's going to take good memory. Okay, okay, okay. Uh MJ, what are you on? Are you on Yeah, could you screenshot it? If it's not working for you, could you try collab? If you could you try collab just for the sake of like getting it running. Things should work in collab pretty easily. You're using collab and nothing's working. Are you making sure it's a code cell and not a text cell? It's not a text cell like this. Which would be like This is where it will be blue. Do you have that? You need to make sure it's code. Yeah. And when I run that, it's going to be a it's going to display text. Yeah. Okay, great. >> [snorts] [laughter] >> Glad that it's working. Great. Okay. Um all right, one more uh one more example. What I want to show you guys is how to do how to include the name in a print statement. So, if we do something like print So, um we can include the name in a print statement. So, if we do something like print and then we have um hello there and then we have um this and then we have welcome to Python. Um this will uh this will display all of that together. So, notice that we can have as many um pieces of information that we want to display kind of one after the other as long as they're separated by these commas. So, we have this uh text comma this text because text is stored in that variable um then this text and then we print that all out and we can have this whole collection of text displayed to the screen. Try that one out. Oh, they do the same thing. They do the same thing. So, the comma and the sorry, yeah, I just noticed the demo does a plus. They do the same thing in Python. So, we can swap that over to a plus. Both of them work. They have the same I shouldn't say they do the same thing, but they have the same effect. They have the same effect. Actually, there's no you need a little bit more spacing here. So, the comma gives you a little bit better spacing. So, what the Let me break this down. What the So, plus plus um adds together uh text. And so, what we're doing here technically is adding all our text together and then displaying it. Um so, plus adds together text and then the comma um uh prints out multiple pieces of text. So, they they have the same effect. Oh, yeah, you can use you can use either one. Okay. Um one thing I wanted to show you guys, too, by the way, in Colab, if you're working inside of Colab, I want you to hover over your name variable. So, if you just take your mouse and hover over that, do you guys see what it says here? Do you see how it says string name and then it has the value of that, which is which is my name. So, that's something cool about Colab is if you hover over variables, it will tell you what their type is. Now, we haven't learned about types, but any text inside of quotes is a string. It's It's what we would call a string. We're going to learn about that. And um we it also displays what data we currently have stored in that variable. So, all you have to do is hover over a variable um to to see what the value is. Yeah, that's Yeah, that's kind of a limitation of VS Code. That's true. It doesn't show you immediately on hovering. Don't see the value on hovering. So, um click into the cell. You have to click into the cell and then hover over it. Click into the cell and then hover over it. It should It should work. Yeah, you have to click on the cell or whatever cell you're on and then uh hover over that and it should work. Uh Merill asks, "How do we integrate that Python code to a client application for user to enter a value?" Um we would likely have a different set of code to do that. Um there is Python code that can get a UI and uh we we will see that um later on in the in the like way later on towards the end of the program, we'll see that. Um we can we can write Python code to do a UI essentially to to make like a almost like a web a web page for someone to enter some input. We'll see that um much later on. So, we're not going to get to that right now. It's really complex. Um what is the purpose of having multiple cells? It's so that we can run individual pieces of code within those cells. It allows us to isolate. Right? Cuz I can run I can run code inside of these cells and they don't affect any other cell. So, it's it's just for like debugging and isolation, which is nice. Right? I don't need to worry about running all of it at once. I can run one cell at a time. Okay, any other questions? Um can we execute multiple lines together? Yes, we did that here. I had multiple So, I'll I'll show you again. I can do um print um this is one statement and then I can come down and do uh print um this is another and then maybe I can do some math. So, you can have as many lines as you want within a cell. Within a cell you can have as many lines of code as you want. Is there a way to tell it the order the cells execute? Um you No, if you if you go up to um if you go up to run all, it's going to run them all in order from top to bottom. Uh in order to tell which cells to execute, you can rearrange them. You can always like So, I could rearrange these cells by the way by I think there's a way to move it down. So, I can move it down. So, now I'm rearranging. So, you can move cells. I think you can even drag and drop them. So, notice how I took the one that's at the very top and I'm moving it down. Otherwise, you have to click, right? You just have to like I can run them in any order if I just click like if I click here, it will run that one first. If I go back up here, it will run that one next. So, you just click around which ones you want to run. Does that make sense? I can run them in any order as long as I click on whatever order I want to do it in. Okay. All right. Perfect. So, that that wraps up that demo. I hope it was informative. I hope you saw the the print statement. Um we're going to see that many times. The input statement. Um that's pretty cool. Um and you got to run you got to run some Python. So, if it's your first time ever doing programming, congratulations. You ran some Python code. That is really exciting. Um so, glad we got to do that. Um let's go back to our notes. And then we'll um Let Let me share the screen. Okay. So, the next thing on our agenda is to cover variables and data types. So, I just said like text is the string data type, but let's learn about all the different data types that are going to be available to us inside of Python and let's talk about variables. Um it's going to be a good discussion. So, um I think what we'll do is we'll take a a 5-minute break now. And we come back and we can start this uh discussion about variables and data types. Um so, let's take uh a 5-minute break. And so let's try to be back um around uh 8:30. Okay? Try to be back around 8 8:30. Okay, so what are variables? These are um basically our way of storing data to make it easier to reference them and manipulate uh throughout our program. So, we've actually already used a variable. We we used one in our uh demo we just did where we called that input the name. Uh we stored that input into a variable called name. And so um variables just really are a reference to some data. That's all they are. They allow us to reference that data throughout the program. We can store information into a variable and then access it throughout our code. Um so on this screen are some examples of variables. Now, variables have names, which is why I said usually we want those to be meaningful. Like X is a valid name, but it's not that interesting of a name. It doesn't give us that information much information about what it's what it really means. So, probably not the best name. Um but we have things like uh we can we can store some text inside of this variable called name. We can store a number inside of this um variable called price. We can store uh a true or a false value inside of this variable called is {underscore} active. Um, and so variables will show up all over our code, and uh, they are basically our way to reference some values. Now, these things over here are basically different types of data that we need to learn about, right? So, we need to learn about what is a 10 versus what is in something inside of quotes is in a string versus something that has decimals, which is a floating point number, versus something that is true or false, which is a boolean value. We need to learn about those data types, but notice how all of these are being referenced by a um, by a variable that has some name to it. Okay, so the variable is this guy. It is our reference to that data, um, and we will use variables throughout, um, so that we can have, you know, references to information in our code. So, variables are fundamental, um, to to working with Python. Um, now, variables can store different kinds of data, so I just alluded to that. And so, the different types of data available to us in Python kind of fall in these two different categories. One being single values, or what are known as scalar values. So, these are things like integers, so the number 10, the number -1, um, the number 2,323. Those are all whole number integers. Um, floats, which are anything with a decimal. So, 32.3, 3.14, um, -1.2. Those are all floating point numbers. Um, booleans only have two options. They only have true or false, so they represent kind of a binary, uh, value, um, which we say are is true or false. And um then we also have um complex numbers which are which have imaginary uh parts to them. We won't really be dealing with complex numbers too much, so I wouldn't worry about them. But in reality, Python supports working with them uh complex numbers and doing complex math, but uh so so complex numbers just have kind of a real part and an imaginary part to them. Um wouldn't worry too much about that. Again, we're not really going to work with those ever throughout throughout the program, but it does exist. Python supports it. So, scalar data, single values, think numbers, think single numbers like floats, think integers, um single uh true or false values. So, these kinds of data can be stored into variables. On the opposite end of the spectrum are aggregated types that we are storing multiple things. So, we're going to learn about all of those, but um probably the most common and one that we've already dealt with is going to be a string. So, a string is technically an aggregated type because it has multiple characters that form, you know, an overall uh string, which is a string is usually you you you know it's a string cuz it's inside of quotes. Right? It's inside of these double quotes or single quotes. Um Python actually doesn't care about quotes really in terms of if it's single or double as long as you're consistent with it. Like, if you if you start with double quotes, you should end with double quotes. If you start with single, you should end with single. Python doesn't really care either way. Um So, strings are going to represent um collections of characters. Um we are going to talk about sets, which are basically like uh an array of unique values. Um so, we'll talk about sets. We'll talk about lists, which are a really important structure. It's basically an array that can hold many different types of data. Um so, we'll talk about lists. We'll talk about tuples. So, you if you see that word t u p l e, that is um people some people pronounce it topple. I I like to call it topple. But, um that is going to be very similar to an array. It's just going to have slight differences and uh if you can change it or not. Tuples you actually cannot change once you create it. Um versus lists, you can modify lists. You can add things to it. You can remove things from it. Tuples you cannot. So, the we're going to learn about those differences as we go along and start working with these different types of data. Um but, they are designed to hold multiple values. Right? So, you can see in that example that list has integers, it has strings. It can it can hold multiple types, which is if you're coming from other languages is generally not the case. Um like arrays in Java, arrays in C, they can only hold one type of data in the array. They can't hold multiple. Um So, uh then finally a dictionary. A dictionary is if you're coming from other languages, it's like a map, a hash map. Basically, it allows you to have uh keys mapped to values. So, it's a really dictionaries are highly useful for storing information where we want to reference like this value maps to this value. So, for instance in this dictionary, the string A maps to one. And then the string B maps to uh you know, two and what or whatever it maps to. And this will allow us to look up values in the dictionary. So, we could look up, "Hey, what is the value stored at key A?" Or, "What is the value stored at key B?" Those kind of things. Dictionaries will be incredibly useful. We're going to explore all of those more as we go along in the lesson, but um for right now you should be making sense that there are some data types that store multiple values like array or sorry lists, um dictionaries, strings, and then there are some data types that only have a single value like a single number like a float integer um boolean. Okay, so more to come on aggregated data. We're going to work with those, learn about the differences, learn about what it looks like in the code to work with the set dictionary tuple list. But those generally hold multiple values or can hold multiple values, whereas um scalar data is only going to hold one. Okay. All right. So uh So as we said earlier um you know, integers, floats, booleans, they only hold a single value. By the way, inside of Python, if you ever want to see what the type of a variable is, so let's say we know we have a variable called name, we can always check the what data type it is by by using the built-in type function. So we can use type and then pass in that variable. And this will display what data type it is. So um if we stored the value 42 in some variable called int, if we um displayed if we did type um if we did type of this, it would uh produce int, which would say, "Okay, this value is an integer." versus 3.14, that's going to be a float versus capital T true, that's going to be uh the boolean type bool. Okay? So uh Uh, we have integers, we have floats, we have booleans, all of which we will use throughout and we'll see where we will use those one versus the other. We'll learn about that. Um, as I said, complex. So, uh, just showing you here that those exist. Obviously, um, like I said, complex has uh, a real part and an imaginary part, which you can access separately. So, if you store a value as a complex, you can, uh, access its real and imaginary parts separately, which you may need to do for some type of, uh, calculations. Um, again, we won't really work with complex numbers in in this program. So, not a big deal for us, but it is supported. And, you know, a lot of, um, mathematical packages in Python will use uh, complex numbers, uh, if they need to. But, we won't really do it in this program. There's not really a need to for us. All right. So, aggregated data, um, we have those strings, which we've already seen. Those are the things inside of quotes. We have sets, which are going to be collections of data, um, that are unique. Basically, only allowing one, uh, copy of those elements inside the set. We're going to learn about that. Um, list, which is going to be a collection of items which we can change. We can add things to it, and remove. Um, lists are really awesome uh, structure in Python. Um, what I want you to see right now, though, is you can start to see the syntax differences, right? So, like I said, um, a a set is where we have, uh, this brace. Notice that a set is created with a curly brace versus a list, which is created with a bracket. So, right away, like when you see a brace, you should should thinking I a set or a dictionary. Those are the two things that are created with a curly brace. Um and you know it's a dictionary because a dictionary will have the colon, which will map I'll show you that on the next screen, but that will map things from key to value. Um depending on if you know left and right of the colon. Um but do you guys see that like the syntax difference of a list has a bracket, set has uh a curly brace. Um that's just one small difference. You know, we're going to learn like what is the actual difference between a set and a list, but that's just one I'm pointing out right now. Um what does mutable mean? So, mutable uh means that we can change it. It's it's able to be changed. So, immutable would be we cannot change it. Yeah, and and one thing about a list that's really nice is every list has a natural ordering to it, which is actually really beneficial. So, a list has a notion of the first item, the second item, the third item, the fourth. That's really important for accessing data within the list. Okay? So, lists are really powerful. Um Yeah, so a a set the reason it shows it's in a different order is because a set does not maintain order. A set never maintains order because um it a set is not you do not access items by by order. So, that's just something unique to a set is that it doesn't have a natural order. So, every time you print it out, it will display in a different order, potentially. It's random. It's random order when you when you display it. A set is just meant to be a general collection. Think of it like a bucket. Like here's this bucket of items that I have. It's just a collection of items. A list actually maintains an order, a consistent order of items. This is different. So, we'll talk more about that when we get into those. Okay. All right. So, I wanted to show you also the tuple and the dictionary. So, a tuple is also a collection of items. Now, the tuple is ordered. So, it's like a list. It's ordered, but it is immutable, meaning you cannot change a tuple. So, once you create a tuple, you cannot change it, or else you'll get an error. Python will tell you, "Hey, this is immutable. I can't change this." So, if you try Changing being if you try to add something to the tuple, if you try to modify one of the entries in the tuple, like if I try to If I go in and try to change this A um to a D, um this would not be allowed. This would This would throw an error. The interpreter would say, "Hey, you're trying to change something that cannot be changed." So, tuples are immutable, but they have a benefit beyond a set of actually being ordered. So, there There's a natural ordering to a tuple where this is the first item, this is the second item, this is the third. And every time you display a tuple, it will be in a consistent order. But, tuples are not like a list. You can't change it. So, tuples are useful for situations where you want ordering, but you don't want anybody to change any of that data that's in the tuple. It's It's not changeable. Mutable meaning It just means changeable. Like, you can modify it. If If something is mutable, you can modify it. Immutable like a tuple is immutable. We cannot modify it. Once we create it, that's what it is. Okay. Yeah. Okay. So, then finally a dictionary. Now, you By the way, um look at the tuple. See how it's created with the parentheses. So, that's different than the curly brace. That's different than the bracket. Right? So, a tuple, you know it's a tuple because of the parentheses. And the items are separated by a comma just how just how they are in a set and just how they are in a list. Um So, so the the parentheses gives it away that it's a tuple. Um now, look at the dictionary. And the dictionary is um a collection of key-value pairs. So, this is a key-value pair. This is a key-value pair. Um this is a key-value pair. And on and on. We can have as many as we want. And one thing I want you to notice about this is there is no restriction on the data types of the keys and the values. So, keys can be integers. Keys can be strings. Values can be integers. Values can be strings. Values can be floats. Values could even be other dictionaries or lists. So, value like we could have what's called a nested dictionary where we actually have something mapping over to another dictionary. That's totally possible in Python. So, we can have dictionaries that part of the values inside of the dictionary actually have are dictionaries themselves. And and that would represent kind of a nested structure there. So, for instance, this name could map to a dictionary with everybody's name in it. Um Or it could map to a list. Um you know. Could map to a list. It could map to to It could map to a tuple. Uh so, you there's really no restriction in what the keys and values are going to be. Uh Brent, is it more efficient than the other uh is what more efficient than the other methods? Just want to clarify your question. So, so I answer it properly. The tuple versus using an array. Yeah, yeah, yeah. So, uh these are all good questions. So, um the tuple is guaranteed not to be changed. So, it is a little faster when we are looking up items, like when we are referencing items, it's a little bit faster because uh we know that it's not going to be modified ever. So, everything is going to be consistently in the same spot. So, like whatever's first is going to stay first. Whatever's second is going to stay second and on and on. So, the tuple is is nice in that sense. A list can be changed. So, whatever is first may not guaranteed to be first in the future. We can modify it. We can remove things. We can add things to the list. So, we can expand The list is very like dynamic. The list So, the list is less efficient because it's way more dynamic. Does that make sense? Like it can change. You can add You can keep expanding the list by adding things to it. You can shrink the list by removing things from it. So, list is way more dynamic. Which for a lot of scenarios is useful. Right? We want to be able to add and remove and modify things. Um but a tuple is more rigid in the sense that once you create it, you cannot change anything about it. Yeah, yeah. So, a dictionary is good for Yeah, like a phone book would be a good example of a dictionary because you with a dictionary you're usually looking up things. So you're So like a dictionary in a phone book you have a name that maps to a phone number. Um so yes, you have a you have that a dictionary will map a key to a value just like a name would be mapped to a phone number. So yeah, uh uh phone book makes a lot of sense. Um A list A list is like any is like a normal like like your grocery list. Like you may add things to it, you may remove things from it, you may change things on it. It's very dynamic. Um a tuple is kind of like a fixed um set of data that's ordered in some way. So maybe like um what you would see on on a on a letter. Like you have your name, you have your address, you have um your zip code. Like you kind of have those and it it should stay that way in order to mail the letter kind of thing. Uh can you convert a tuple to a list? Yes, you can do vice versa. You can convert a tuple to a list and you can convert um you can convert a list to a tuple. Yes, you can convert between them. I'll show you some examples of that later. Okay, so just to recap there. Tuple is not changeable but it has an order. So it has a natural ordering to it. Whatever is first is first, whatever second is second, third and third. So you can access things based on their position within the tuple. That's really nice. But you cannot modify anything about a tuple once you create it. Okay? A list has an ordering to it. You can access things based on their position, but a list is dynamic. It is mutable, meaning you can change it. You can change values, you can add things to it, you can remove things from it. Okay? So, very dynamic, that's what a list is. Um dictionary, it maps keys to values. No restrictions on what those keys and values can be. All right, and then a set. A set is think of it like a bucket. It just has things in it. A set has no order to it, so you cannot access things based on their order, and every time you uh display the set, you can get a different ordering. Um but a a set only is special in that it only allows unique items. So, if you try to put multiple copies of a piece of data, it's only going to keep one of them. So, a a set is like a bucket with only unique things in it. Okay? So, and sometimes that's really useful. It's to know like what are the unique values. Uh a set would help us maintain that. Any questions about those? You know, we have to we have to work with this and see this in the code, and we will, but just any questions right now about these different types of data that we're talking about. Okay. Very good. All right, let's talk about assignment. So, what that means is um oops, let's talk about assignment, which means that we will be um taking a variable name and assigning data to it. Now, we've already seen this. We already saw it in our demo where we did input we did name equals input. So, the equal symbol is how we assign values to a variable. That makes sense, right? It's very ex- like self-explanatory. But um what we should think about with a variable is really the fact that a variable is is a reference to that data. Okay? So, when we say X equals 34, we are assigning 34 to the name X. So, X becomes a variable, which is referencing the data which is an integer 34. Right? What's really interesting about that, and this is how you can kind of test your intuition of the fact that this is a reference, is if we come along and have another name Y and we set that equal to X, this is just saying that we are creating another reference that is equal to the reference we already have. Now, why would we ever do that? Probably we wouldn't. That's kind of redundant. But this just proves that they're ultimately references because when we display X, we get 34. Of course, that's what we stored the value 34 uh referenced by X. And then when we print Y, we get the same number. Right? We get 34, and why does that happen? Because we we literally declared Y equal to X, meaning Y should reference the same data that X does. Okay? So, as variables, they are equal, meaning that um X is being assigned to Y, meaning Y should reference the same data that X does. So, they they uh contain the same data. Now, what's interesting is if you print out the ID, so the ID is the internal um the internal memory address of of the reference. Um now, it usually we don't care about that, but this is just to prove the point is that you can see these are the same address. These are the same. That's by design because we are saying, "Okay, I have this reference X, which is referencing this data 34. It's stored at this address." Um, and then when I come along and say, "Okay, Y equals X." That's just the same reference. You see how it's the same exact address. Same reference. So, just proving that variables are literally just references to data. They allow us to reference that data, which is really, really, you know, nice. So, we can reuse X throughout the code. Um, we can reuse name. We can, you know, whatever we create, we can reuse. Um, if you look over to the right, we have an alternative example, which, um, now resets Y to store a new value. So, instead of saying Y equals to X, we actually overwrite Y and we assign it to the integer 78. That's a new piece of data, right? 78. So, now if you look at their their, uh, references, they're different. These are different, and that makes sense because now they're pointing to two different, uh, pieces of data. Right? X is pointing to 34. Y is referencing to 78. So, of course, they're going to be different, uh, different addresses. And this is a bit of a typo. This should say ID of Y. Cuz we're ref- we're talking about Y. It's a bit of a typo there. Okay. So, hopefully this Now, this example is just to reinforce the fact that when we use the equal sign, we're setting equal we're setting a variable name equal to a piece of data. Right? And that is creating a reference to that piece of data. That's all we're That's all we're saying with this. So, we are assigning a piece of data to that reference, X or Y or whatever it is. Okay. All right, let me ask you guys, um what is the default data type of a variable assigned using the input function? This is an interesting question. We didn't actually cover this, so I'm really curious to see what you guys think about this. A lot of votes for for string. Let's get a few more. Perfect. Yeah, so a lot of votes for C. It is a string. So, that that begs the question, like what happens if we input a number? Like what happens if we put in a two? What happens to that? You know, that two will actually be read in as the string two. So, it would be So, if we use the input and we it pulls up that text box and we put in a number, like two, um and we set that equal to the variable X, whatever we name that, name, X, whatever, what that really means is X is going to be um equal to the the um X is going to be equal to the uh string two. So, that's something to be cautious about with the input is it always assumes the input data is going to be a string. So, luckily, there's a way to convert between strings and numbers. So, if we wanted to turn this into to actual number, what we would do is use the the data type function int, which would convert uh this would convert it over to the numerical two. Would actually convert it from a string to an integer. We just use int. Or we could use like if we if somebody put in a decimal like 2.5, then um we could do uh float of 2.5, and that would convert that over to uh the the number. Okay. Let me actually show you guys this. Let me go over to Colab real quick and show you guys this. I know it's not in a demo, but I think it'll be better if I just show you what I mean by this, cuz this is an important point with input. So, let me uh stop sharing there. Let me go over to Colab for a second, so I can show you literally what this means. Go back into the notebook here. So, what I want to show you is that um when we do input, the default type is string. So, for instance, when I do um when I do uh uh value equals to input, and let's try um enter your age. Oops. Enter your age. And then we uh run this. So, we enter the age. Now, this is going to be read in as a string. So, even though I'm putting a number there, it's actually going to be read in as a string. So, now look at what the type of value is. It's a string. Do we see that? So string So this number, even though we put in a number, it gets it the the input function always converts it to a string, no matter what we put there. If we put a decimal, if we put a a large number, it's always going to assume it's a it's a string. So luckily, um we can convert to an integer by using int, the int function. So, um we can print Sorry, we can say value uh or we can do int value, which which will convert that 32 string, cuz right now, if I were to um just display value, it's a string 32. You can see it inside of the quotes. But now, when I do this, uh and I can run that, now it's an integer. Do we see that? Now it's actually a number. Which is great. It no longer has those quotes. It's actually going to be treated as an actual integer, which which may be useful for calculations or storing it or whatever whatever we need to do with it. So that's just one piece of caution with the input is if you're working with numerical data, it's going to treat it as a string. We have to convert it. Okay? Questions on that? Does that make sense to us? Like the input's always going to accept the input as a string. So if we want to work with it alternatively, um we should convert it. Uh you can Yeah, so like you could convert um if I did this, if I wrapped this around in the int function, that would automatically take whatever we put whatever this returns would automatically be cast over to an int. So, we could do that. So, let me show you that. So, when I run this, I can put in 32. And it it's like automatically going to be cast it to an integer. So, there now it's an integer. Does that make sense? Like when I wrap this int around the input, it's going to automatically convert. Uh what did you put in the input box? So, yes, you'll get an error if you don't put in a valid integer. So, let's put in like if I put in my name, this is going to be this should be an error because I don't know how to convert this string over to a number. It doesn't make sense to do that, right? So, this should be an error. Right, that'll be an error cuz it's a string. So, why did you get an error input enter your age value int value? Uh did the did the text box show up? Maybe try separating it into a different cell. Try try putting the other two lines in a different cell. Um you need the text box to show up and then you need to enter something. Yeah. Okay. All right. Does this all make sense? Any questions about this? About the input function? Okay. Good. Okay, let me go over back to the notes then. >> [snorts] >> Okay. All right. So, we have another demo. We'll do that now. Uh I was just kind of doing one, but let's go back over to This will be demo five. Let's do that. So, we're going to practice assigning different values um to variables and displaying them. Just so you get in the habit of being able to create your own variables and just go through that kind of one more time. We'll do this one relatively quickly um and then uh move on. But, this will be uh demo five. So, let me pull that one up for you guys. Okay, let me share my screen. All right. So, this is going to be demo five. Um Now, again, like feel free to use whatever platform you've been using, Colab, Jupyter Notebook. I know this instruction says set up a Jupyter Notebook. Feel free to use whatever you want. You can use Colab. Um whatever's been working for you to build your build your notebooks. So, obviously this this looks a little different than Colab, but it's because it's the Jupiter. Um so, we create a notebook. Now, what I want you to see is this takes the approach of everything we just did. Let me zoom in on this. Uh I know that's a little small. But, this is doing everything we just said we could do where we um essentially take So, I just want to zoom in on this. Um notice that we uh take the um input and this will be saved as a string. Um so, this will be saved as a string and this will be saved into this name. And, for instance, this will be saved as a string, but we convert it over to an int. Which is exactly the kind of example I just did, right? Where we take take an input, we convert it over to to uh int. Does somebody have Yeah, does somebody have the demos available? Like, if if somebody doesn't mind sharing those in the chat. I again, I don't have the PDFs. They should be from your LMS. They should be in the reference material. There should be a demos folder that you can download. If somebody has those and doesn't mind sharing them, they have that folder of them, like a zip folder of them, that'd be fantastic. Yeah, thanks. Thanks. This is This is the demo we're going through currently. Perfect. So, for you guys having trouble navigating the demos, please download this zip folder. Download the zip folder that that these guys are uploading. Thank you so much. Download the zip folder so you have all of them. Please take a moment to do that. Okay. Uh Copy the code and got an error at height value use foot not meter. I mean it shouldn't matter. It you know, you should just be the point of that one is to put in a decimal. How to create a new file? Um what platform are you on? Colab? I don't know what platform you're on. Colab uh just go to file new notebook. New notebook in drive I think is what it's called. Do you see that? It should be like it should be at the top. There should be a file and then new notebook. Let me go over to it. Uh do do This one? You don't see this? File it's at the top. The top of the notebook. Do file and then new notebook. You don't see new notebook? Uh if you if you don't see that just go to a new tab. Just go to a new tab and go to um Google Colab. You can always do that. Just go to just start a new um just go to Google Colab and then it will let you like launch a new notebook. So, just just do that. Just do a new tab if it doesn't work. Okay. So, by the way, one of those examples was entering a float. So, it looked kind of like this. So, we had um our our height is equal to uh float and then we had uh input and then we had um enter your height. And then this was um uh some sort of uh this should be some sort of decimal value. So, let's say it is um I don't know uh 5.7, whatever that is, uh feet. It doesn't It's just some decimal. Um and then we hit uh we hit enter, that will store the height as a float so that when we um display the height, uh it will be rendered as a float appropriately, right? That's what that That's what should happen. That's the point of that. So, just needs to be some decimal, it should work. All right, let me go back to the demo document. All right. Were you guys able to run some of these? Like, were you able to run some of the inputs and change them? So, try these out on your own real quick. Like, try doing int and then input for enter your age. It should convert that You should be putting in a number or else you'll get an error. And it should convert that over. I By the way, I wouldn't worry about this last one uh because we haven't learned about the comparison operator yet, which is this equals equals. So, we'll learn about that in a in a little bit in a few minutes. So, don't worry about that one too much right now, but at least these first few should make some sense and we should be able to do. Were you guys able to run one of those? And convert over the the float or int and do the input and convert it? Did that work for you? Give it a try. Let me clear that. Any questions about that? Should look something like this. Good. We're good on that on converting over the input. Okay. Perfect. Sounds like Sounds like we're able to run that and it was okay. What are you entering for the feet? Like are you literally entering like quotes? Yeah, that's not going to work when you do that cuz it's going to There's a string F. There's a character there. It's not going to be able to convert over to So, if you did if you did 6.4, that would work. Any decimal should work, but like the F is a character. So, the the float doesn't know how to convert over a character. Right? Yeah, so so that's not going to work. You need to put in a decimal to to be able to convert over to the number. Okay. >> [clears throat] >> Very good. Very good. Let's go back over to our notes so we can continue along. 1.7, yeah. If you have any if you have any character, it's not going to work. It's not going to work. You need to put in You need to put in a decimal. All right. Let's talk about operators. So these are going to be really important. Um Let's talk about operators so that we can uh uh be able to compare things and work with things. Um so let's let's talk about Python operators. So what are operators? What do we mean by that? In Python, operators are special symbols or keywords that perform operations. So as the name suggests, it's performing some level of operation, um which means that the interpreter should do some sort of logical operation, mathematical operation, relational operation to produce a result. Um so usually that means there's going to be multiple variables that are going to be used to do some operation between. So an example of an operation would be like adding, subtracting, multiplying. That's an operation. But we can have logical operations like taking the um logical and or logical or of things. We'll see what that means. But um in Python, there's many situations where we want to we want to be able to do operations between variables, whether that's simple mathematical or maybe some type of relational like testing if a value is in a list. That's an important operation. Is 10 in my list? Is five in my list? Um those are important operations. So, we want to learn about these operators and they're going to be really important for us going forward is because these will be very standard um things we will use as we uh go along. So, we're going to spend some time talking about operators. Um so, it turns out in Python, um you can kind of group operators into many different categories. Uh there's going to be standard arithmetic operators. Those are your everyday things like plus, minus, um division, multiplication. Um assignment operators, which we've already seen is things like equals where we're setting a reference equal to something. We've already seen that. That's an assignment. Comparison, which is things like greater than or less than. Those are important for comparing values, comparing variables. Um logical operators are going to be something like and and or, which will um do a logical operation between two two Boolean values. That'll be important. And then we have a collection of miscellaneous operators. Um those will be things like is something in a collection? Like is five in a list? That's an operator. So, we'll talk We're going to talk about all of these, but just pointing out that there's many different categories of operators in Python. Okay, let's first talk about the arithmetic operators. So, these are going to be your standard everyday um uh operations between numbers. So, if we have numerical values like integers or floats, we can do math between them. That makes sense. Like that should be a capability of Python and it certainly is. We can add things, we can subtract things, we can multiply things, we can divide things. So, um here are all those operators. We have plus, minus, the asterisk is a multiplication. So, X asterisk Y will multiply those together. So, if we have two variables, one of them is 50, one of them is four, we do X asterisk Y, that's going to multiply them together to get 200. Pretty pretty straightforward. Um division is one that we should be careful of. Of course, like we don't want to divide by zero. So, if you if the uh this secondary value that we end up dividing by is zero, that'll give us an error. Um the interpreter will say, "Hey, you're trying to divide by zero. We can't do that." It'll It'll produce an error. So, that's the only thing we have to be on the lookout for with division. Just don't want to divide by zero. Um So, all these are pretty standard. I think they all make sense. Hopefully they do to you. I think they're all pretty standard, you know, the kinds of things you'd see on a on a basic calculator. They all make sense. They should exist. Now, here's some more exotic ones. Um I don't know if you guys have ever seen the the modulus operator, also known as modulo. This is one that returns the remainder of a division. Okay? So, the the percentage sign is a mathematical operation between two numbers that returns not the quotient, like not the actual division result, but the remainder. So, 50 divided by four, um you know, four goes into 50, um it goes in there uh uh 12 times evenly, but it has two left over, right? So, there the remainder there is two. So, the result of X mod We would read this as X mod Y or modulo Y um returns two. So, if you're if you're unfamiliar with the modulo operation, that seems a little bizarre that you take these two numbers um oops. It seems a little bizarre that you take these two numbers and you like do this operation and you get a remainder result, but it's actually a very powerful operation. Um the reason being is that sometimes we want to know what the remainder is more than we want to know what the quotient is. For instance, things that are very like cyclic in nature. Um so, maybe we cycle through a collection and we want to know like how many times do we cycle through and then we have something left over which is the remainder. Um so, the modulo operation is pretty useful. You'd also check like if a number is even or odd using this. Like so, if you modulo by two and it returns zero, that means it's even, right? Cuz that means there's there's nothing left over when I divide by two. So, modulo is kind of a nice way to check if a number is even or odd. Um So, modulo is a pretty nice uh operation. We'll use it from time to time. Uh but that is the percent operator. So, X % Y will look for that remainder of the division. Um now, there's also a double slash operator which is the integer division operator. This is kind of the reverse of modulo. It takes the largest integer quotient that that uh we can do from a division perspective. So, remember I said 50 / 4 we can divide four into 50 12 times evenly and we have two left over. So, the integer division will just return to us an integer always which will be that quotient. So, this is the quotient um and this is the uh remainder of 50 divided by 4. So, the integer division returns to you that whole number that like the largest number of times that that number goes into the other. So, 12 times evenly. Obviously, there's a remainder there. But, um But, but yeah. So, integer division, that one's useful if we want to know like how many times can I fit a value into another value a whole number of times and that happens from from time to time. We may need to know that. Okay. Last operation here is exponent.