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In this lecture, I'm going to talk about Jupyter Notebook.

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Occasionally, I get this strange question from students asking why I don't do things in Jupyter notebook.

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Let me explain why I think this is strange.

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Firstly, it's my position that it's completely unnecessary and actually doesn't change anything to

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use Jupyter Notebook or not.

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Let me repeat that using Jupyter Notebook is exactly the same as not using Jupyter Notebook.

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There's no difference other than the fact that it looks different.

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In other words, the screen is a different color.

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Obviously, it's such a difference is trivial.

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In this lecture, I'm going to demonstrate how that's the case.

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One major reason I dislike Jupyter Notebook is because it causes too many students to be unaware of

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how Python really works.

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If you're only comfortable inside notebook and when you see Python code in a text file or anywhere else

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and you get scared or intimidated, that's not good.

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That Python code that's in the text file is exactly the same as what would be in the notebook.

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There's really nothing scary about it.

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Programmers working at real jobs eventually need to write code that's going to be deployed and run automatically.

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In other words, your final code is going to sit in a Python file that runs by itself without Jupyter

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notebook.

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So if you're going to have any hope of using these skills in a real job, you had better be comfortable

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with writing Python code outside of Jupiter notebook.

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You would also better be aware that there's actually zero difference between writing code in a Jupiter

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notebook and writing code in Python or the console.

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Here's one example I like of how you might use Python in the quote unquote real world.

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Let's say you write a script that emails your boss to tell him you're going to be late for work.

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And let's say you don't actually want to send this email manually, but you want it to get sent automatically

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every Friday morning so that your boss doesn't yell at you for coming in to work late after you party

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too hard Thursday night?

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Well, that's very simple.

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All I have to do is on my server create what's called a crontab in it.

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I just enter the code for when I want this command to run and then to the right of that, I specify

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the command that I want to run.

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That's just Python space.

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And then the script name, as you can see, it's simply how you would run this Python scripts from the

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command line.

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And now, every Friday at 9:45 a.m., this script is going to send the same email to your boss.

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Tell him you'll be late.

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Well, let's not get off track here.

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The point of this is you really don't want to be using Jupyter Notebook for something like this.

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I think one perceived advantage of Jupiter notebook is that you can see the results of intermedia calculations.

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However, this is merely a perceived advantage and not a real advantage because you can do the exact

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same thing even when you're not inside notebook.

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Firstly, as I'm sure you've seen by now, I Python also prints out the results after you enter a command.

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I Python is called a reptile, which stands for Read Avow Print Loop, and that's generally what they

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all do.

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No matter what language you're in.

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So whether that's Python, Ruby or any other language, the key word here is print.

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Why is that?

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Well, one of my golden rules for writing and debugging code is when in doubt, print it out.

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I can't tell you how many times I've gotten a question on the Q&A when it could have been easily answered

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by inserting a print statement into the existing code.

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Anyway, what's the point of this long discussion about printing things out?

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Well, it's that if you think Jupyter Notebook is the only program that helps you do this, you need

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to expand your horizons a little bit.

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You should, in fact, always be doing this if you're not using an abundant amount of print statements

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while quoting, you are not doing it right.

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Remember, programming is not philosophy.

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You're not supposed to be running a program in your head.

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That's like trying to do long division in your head when you have a calculator in your hand.

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So just by printing things out, you can be more efficient.

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Stop trying to guess what a program will do and just let the program itself tell you what it's doing.

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The key takeaway here is you should always be printing things out.

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The fact that Jupyter Notebook shows you the result of each block of code is not simply a happy surprise.

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But another important lesson here is that if you want to use Jupyter Notebook, there's absolutely nothing

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stopping you from doing so.

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In other words, using Jupyter Notebook is 100 percent compatible with everything we are already doing.

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In fact, if you recall, your goal in these courses is not to run my code, but to write your own code.

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And of course, since it's your code, you can write it however you want, including Jupyter Notebook

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and the rest of his lecture.

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I'm going to prove to you that you can take any scripts from our course repository, which we know runs

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in the console because that's how I always demonstrate it and show you that this exact same code runs

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in Jupyter Notebook.

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Let's begin.

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OK, so let's say I'm in the folder numpy class, and I'm interested in the code inside classification

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example that PI, as you can see, what I have right now is this code inside a text editor.

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Now, if you're not aware of what a text editor is, it's just a program that shows you the contents

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of a text file and lets you edit those contents.

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It's the ideal program for writing code.

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Now, occasionally, if you're writing in a language like Java or Swift, you might want to use an I.D..

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But even then, it's totally optional.

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These days, I prefer to write Java in a plain text editor like sublime text as well.

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In any case, normally one does not need to use an I.D. for writing Python code.

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Now about Jupyter Notebook.

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Well, let's start up a Jupyter notebook, so I'm going to go Jupyter Notebook.

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All right, so now I've got Jupyter notebook running, so I'm going to start a new notebook.

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Also, notice that we started the notebook in the same directory as the relevant Python file, so that's

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something you want to keep in mind for the future.

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Now, at this point, what I'm going to do is I'm going to prove that everything in this Python file

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works exactly the same in the notebook as it does in the console.

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So let's start with the imports.

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Let's grab this one to.

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All right.

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Paste that in.

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Run it.

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So everything's fine so far.

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Now let's load in the data.

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OK, we've loaded in the data.

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Now, I'm not sure what its type is, so I can check it by using the tight function.

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So let me try that.

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Cool, so I get S.K. learned utils bunch, so that's the type of the variable data.

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Now, previously you recall that we did this example in Python, but as you can see, the result is

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the same in Jupyter notebook.

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Alternatively, if you wanted to run this Python file in the console, so you wanted to type.

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Let's say.

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So let's say you wanted to type in Python classification, example that PI, then you could just add

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some print statements if you wanted to show those same lines while this file was running.

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Now, I'm not going to go through such elaborate detail for the rest of this example, since you've

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already seen it.

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So let's just get through the rest.

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Line by line.

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Let's say I want to check out the keys in the data variable.

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OK.

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Looks good so far.

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Let's check the shape of the data attribute.

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OK.

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Looks good so far.

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Check the target's.

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OK.

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Still, what we expect the target names looks good.

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Target shape, it should be five, sixty nine, yeah.

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And let's check out the feature names.

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OK.

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That's the same as well.

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Now let's do our train to split.

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OK.

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Now, let's instantiate and fit our model.

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All right, now, let's check the train score.

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And the test score.

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All right.

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Now, let's see how we can make new predictions.

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So you see, have you assigned to a variable, it doesn't print the result, but if you have an expression,

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then it does print the result.

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So are some predictions, and this was an alternative way to calculate the accuracy of the predictions.

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So we have the same answer as before, and we also have this other example where we can use a neural

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network to do the same thing.

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So let's build the model, train it and print the train score.

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And let's print the test score as well.

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All right, so everything works the exact same way as it does without using notebook.

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Now, another thing I could have done was I could have just taken this whole thing and copied and pasted

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it in and run it.

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So that's another possible thing you can do.

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But the downside of that is you don't get to see the entire media outputs, but again, that's what

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print statements are for.

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All right, so what can we conclude from this exercise?

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Well, we can see that this code runs exactly the same inside Jupyter notebook as it does everywhere

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else.

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That's why I always say Python code is Python code, and no matter where it is, if you want to use

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Jupyter Notebook to run the course code, there's absolutely nothing stopping you from doing so.