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In this lesson we're going to combine our knowledge of functions with a new topic - objects.
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Objects are the basis for every Python program that you will ever write.
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Not to oversimplify, but when programming in Python we do things with stuff.
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Please pardon my formality.
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By
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"do things"
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I mean calling functions - functions that add two numbers, functions that import data, functions that fit
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regressions or generate charts or train machine learning models. And by "stuff" I mean objects.
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This lesson will be all about the stuff part.
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So let's talk about objects. You see,
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pretty much everything in Python is an object.
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Remember those modules that we talked about in previous lessons - modules like pandas, math or our own
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life module?
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Those are objects.
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We also had data frames that we put inside our X and y variables. Data frames are objects too.
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And the plot inside our plt variable is also an object. More formally, all the data in Python
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take the form of objects and the goal of this lesson is to explain the code that we wrote in previous
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lessons in more detail. By the end of this lesson,
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I want you guys to have a deeper understanding of how 
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pd.read_csv and plt.show and regr.intercept actually work.
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My goal is to make these lines of code make a lot more sense.
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Let's make a real life analogy to objects in Python.
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Now suppose you have a car.
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If you don't own a car, don't worry.
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Owning a car is not a prerequisite for this course.
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Actually, I don't own a car myself but I've watched enough Top Gear to get very good at talking about cars
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and pretending I own one.
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Anyhow, we're going to talk about your red car like it's a Python object.
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First off, your car needs to have a name.
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After all, every decent car has a name.
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David Hasselhoff's car in Knight Rider was called Kit and in Pixar's movie, the protagonist was called
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Lightning McQueen.
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So in honor of Pixar we're gonna name your red racecar
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"lightning_mcqueen" all lowercase and with an underscore to keep with Python naming conventions and also
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avoid expensive American lawsuits.
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Now if you have to describe this car to your friends, you might talk about your car's color,
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the number of seats it has, the power of the engine or how much petrol you have left in the tank.
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All these things are attributes of your car.
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Now there's something quite significant about this - namely these attributes capture information about
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the state of your car and your car's state can change.
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For example, as you're driving along, the amount of fuel in the tank will decrease. In the same way,
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if you've got a computer game the state of the game will also change, right?
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Most obviouslym the level that the player is on will change as the program runs.
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This is the kind of thing I'm alluding to when I'm talking about state. But coming back to these attributes,
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let's talk about these attributes in a bit more detail and look at the Python's syntax.
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The thing is much of Python programming boils down to this simple pattern "object.attribute".
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So if you were writing a piece of Python code, accessing the attributes of your car then you might write
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something like "lightning_mcqueen.color" or "lightning_mcqueen.engine_power" or "lightning_mcqueen
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.mileage".
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We've actually encountered this object.attribute pattern many times before.
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One of the best examples is when we pulled out the coefficient of our regression or when we accessed the intercept
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of the regression. In both of these cases, the linear regression inside our regr variable was the object -
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and this object had an attribute called "intercept_".
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Now don't worry about the underscore at the end of this name.
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It's just part of the name of the attribute.
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The underscore has no special meaning - they could have called his attributes something else but they
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tagged on the underscore at the end of the name.
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The point is that we also used the same "object
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.attribute" pattern when we were talking about our Python modules; with "hitchhikersGuide
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.theAnswer" and "math.pi". In all of these cases we were able to access information from inside our
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object using the dot notation and it didn't matter if that object was a linear regression or if it was
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a Python module.
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Now accessing information from objects through their attributes is very well and good but remember how
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we said that Python is all about doing things with stuff.
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This is where our functions come in. However, because we really like computer programming jargon,
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we're gonna use a different word to refer to functions in this context. Functions that are used with
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an object are called methods.
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Now oftentimes you'll hear the word function and method used interchangeably
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and trust me I'd be the last person to nitpick about this use of language but it's still a good idea
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to get familiar with all this vocabulary because you'll definitely come across these words online and
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also nobody enjoys getting corrected by strangers on the Internet in online forums.
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So use methods when you're talking about functions that belong to an object.
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Let's return to our car example. Before we talked about how our car has a state.
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It can be a new car with low mileage; it can be an old car with high mileage; the car's fuel tank can
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be full or the car can be almost out of petrol.
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The state of the car object is captured through the attributes of the car.
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An attribute is a piece of information about the car like the mileage or the amount of fuel.
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Now, unless you're playing Top Trumps, the most interesting thing about a car is not its stats but what
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the car does.
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And the coolest part of the car is definitely its behavior.
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Remember that we said programming is about doing things? Well, things get done by calling methods and
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methods are the name for functions that belong to an object.
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So, an example of a method on our car object would be something like drive().
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Now the question is, how do we call one of these functions that's associated with our car?
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Well, the format is exactly the same as with the attributes - we use the dot notation.
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In other words, we have our object, then a dot and then name of the method.
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So for example, if your red race car can drive then we'd write "lightning_mcqueen.drive()", and if
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your car can do other things like talk, then we'd follow the same pattern.
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That's enough theory.
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Let's put this into practice and add a function to our 
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life.py file. So open the life.py file from your project's folder. In here,
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we're going to create a function by using the keyword def and we're going to call this function
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"quote_marvin".
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A function is not going to take any arguments so we'll just put empty parentheses and then a
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colon and inside the function, we'll put a print statement.
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We're gonna print out a single sentence, so we'll have two single quotes and then we'll write.
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"Ive calculated your chance of survival,
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but I don't think
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you'll like it".
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Now check out what happens if we add an apostrophe before the t
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in "don't". Putting that single quote there terminates our string, so anything after the single quote is
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considered to be
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Python code and then we have another single quote here.
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So this is a problem, right?
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We've got a special character, that single quote, inside of our piece of text.
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The obvious problem is that in Python single quotes also go at the beginning and at the end of a string
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to tell Python that something is a piece of text.
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So, we need to tell Python that it should ignore the single quote in don't.
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In other words, we have to escape the single quote character. To escape a special character like this
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we have to add a backslash in front of it.
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This is what a backslash looks like.
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Notice how as soon as I put it there the syntax highlighting changes, this single quote is ignored
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and now this single quote at the end is what terminates this string.
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Now if you're looking at your keyboard you don't want to use this one here.
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This is the forward slash.
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And this does not escape the single quote.
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You want to use the backslash.
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I am going to add two more backslash us in single quotes.
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I might add one here, and I'm going to add one here.
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Now let's save the file and then return to the Python intro notebook that we've been working in.
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We go to File > Save. I'm going to switch tabs to the Python intro notebook,
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and this is where we've imported life as hitchhikersGuide.
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Now if you returning to this notebook and have not run the code in here for a while, you can either click
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inside this cell here and press Shift+Enter on your keyboard to rerun the cell or you can go to "Cell"
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and then "Run All" to run all the cells in the notebook.
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After you've done that go to the bottom of your notebook and there as a challenge see if you can call
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the quote_marvin method.
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If you call this method successfully, then you should see a quote from the Hitchhiker's Guide To The Galaxy
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appear below the cell after you run it.
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I'll give you a few seconds to pause the video.
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And here's the solution.
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You'd write "hitchhikersGuide.quote_
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marvin()" and hit Shift+Enter.
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Remember, to call the quote_marvin method,
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we need to use our module object.
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In this case, our module has the name hitchhikersGuide.
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Put a dot after hitchhikersGuide and then write the method name.
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Now, suppose you just tried to run quote_marvin on its own, without the object.
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In that case you'd get an error.
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The error would read "name 'quote_marvin' is not defined".
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In other words, our Python notebook doesn't recognize a function called quote_marvin and this is because
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quote_marvin belongs to the life.py file.
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It belongs to the module, not our Python intro notebook; and this neatly illustrates the difference between
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a function and a method. To use quote_marvin,
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we need to have that object and use the dot notation. In contrast, for a function like print or for a
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function like type,
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we didn't have to have anything before that function name. Print and type are functions, while quote_marvin
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is a method.
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So let's revisit our old notebook with the linear regression again. Looking at this code,
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pd.read_csv
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we now see pd as the object and read_csv as the method.
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And below,
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we can now recognize plt as the object and scatter as the method.
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In fact, we're calling a whole host of methods on this plt object, including xlabel, ylabel, title
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and show.
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Now a question that we get a lot and something that can be a little bit confusing is the link between
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variables and objects.
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So let's talk about this a little bit and link how this lesson ties in with what we said about variables.
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Back at the top of our Python intro notebook,
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we had a variable called myAge.
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The question you might ask at this point is "Is myAge an object?" and the way to think about it is this -
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variables are containers. Variables are boxes that hold on to data.
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The thing inside the variable is the actual object. In this case,
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the object is the number 32. The object is the data and the variable is the name that is referencing
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the number 32. Looking at the linear regression notebook,
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we had a variable called capital X. X contained our movie budgets as a data frame.
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The data frame contained inside of X is the object. And X is just the name for the data frame.
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So in summary, a variable is a way to refer to an object at some point in time.
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Variables are containers for data, but a variable is not the data itself. And this is actually quite important,
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because it links back to our lesson on data types. You see, in Python the type actually lives with the
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object and not with the variable.
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Check this out.
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When I scroll to the bottom here and in this cell right here I'm going to write
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myAge = '200' and then I'm going to write type(myAge) and hit Shift+Enter.
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This now shows me that myAge is a string at this point in time of the program. At the top of the Python
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notebook,
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myAge was still an integer - an int, but at the bottom of this notebook myAge is now a string.
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In other words, I can change the type of myAge so let's change it again, I'm going to say "myAge =
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20.53", then I say type(myAge) and hit Shift+Enter.
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Now we see that myAge is a float so in our notebook myAge starts out as an integer equal to 32.
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Then it becomes a string and then it becomes a floating point number. Now this piece of code will actually
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really, really freak out a Java or C programmer, by the way. The Java and C guys watching this probably
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just had a cold shiver run down their spine like when they watched the girl come out of the well in
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the horror movie The Ring.
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The important point I'm trying to make here is that in Python that type lives with the object inside
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the variable, not the variable itself.
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So in this code we've simply changed what the object is inside of my age, inside of the variable; we've
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changed which object the variable is referencing. And there's actually another important link to our
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lesson on types - namely the fact that the behavior of the object will depend on its type.
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So our car can drive because it's of type car.
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And we could add one to myAge because it was a number.
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In other words the operations that you can do on an object depend on its type.
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So even your simple integer is an object in Python an integer has a value say like 199, and then some
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operations that we can do on it like multiplying or adding.
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And this is why programmers like to think of objects as pieces of computer memory with a value and a
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set of operations. In Python we actually care more about what an object does rather than what it is.
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Python programmers care more about an object's behavior and its methods than what its data type is.
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This is what makes Python's types dynamic and this is at the root of Python's flexibility
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as a programming language. On pythons Wikipedia page,
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this is what people mean when they talk about Python's dynamic type system. That said, I harp on and on about
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types because, in my experience, even though they give Python a lot of flexibility they're also a big
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stumbling block
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when you first starting out with Python. Now, as a challenge, let's put a couple of the past lessons together.
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You're back as the engineering lead at the calculator company in Silicon Valley.
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You've been laughing at your boss's terrible jokes and you've got a big bonus, but your company needs
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to ship its calculators to its first paying customers ASAP.
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So it's crunch time for you and your development team, and it's on you to implement the square root functionality
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of the calculator.
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Here's what you got to do.
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You got to open that life.py file and you've got to import the math module and then you're
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going to try to figure out how to use the python square root functionality from the math module. You're
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going to create your own function called square_root,
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and that function is going to take one input and return the square root of whatever that function has
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been given as an argument. After you're all done,
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you're going to save the life.py file and in your Jupyter notebook, you're gonna go to Restart and
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Run All. Finally in the bottom cell of your python intro notebook,
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you can calculate the square root of 63.14. I'll give you a few seconds to pause the
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video and have a go at this challenge. Ready?
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Here's the solution. At the top of our life.py file, we're gonna import math
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and then we're gonna create a function called def square_root and it's gonna take a single
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input, say x, semicolon and then in the body of the function we're going to return the result of the calculation.
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So the question is how do we make that calculation? The way we figure this out is that we're going to
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Google for the official Python documentation on the math module.
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So we're going to write Python math module documentation into Google and then we're gonna go to the
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Python 3, which is what we're using,
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documentation. The documentation will look something like, this and it's a pretty long page, but let's
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see if we can find square root and if we search for the words square on this page we can see that the
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guidance for using the square root in the math module looks like this.
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We'd have to use the math module then put a dot after it and then write sqrt and provide a single
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argument. Let's use this functionality back in our life.py module. On the same line where we have
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our return statement,
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we're going to write math.sqrt, and we're going to provide the same input, the same parameter
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that we have for our function.
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So the name of that parameter in our case is x.
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Now again the exact name of this parameter doesn't really matter.
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We could've called this thing something else.
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In this case we'd have to be consistent.
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We'd have to call this something else as well.
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That way we're using the same parameter. Back in our Jupyter notebook,
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we said we'd go to Kernel > Restart & Run All to make sure that this works no matter what it is that
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you've been doing prior to getting to this point in the video.
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So here in our last cell we can write hitchhikersGuide.square_root, and then provide
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the value 63.14.
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This was the last part of the challenge. Hitting Shift+Enter,
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I can see an attribute error.
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Life has no attribute called square root.
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What did we forget to do?
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If you're getting this as well, it's because you haven't saved your life.py
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file and you can actually tell that they are unsafe changes by the little star in the name
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of the tab.
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So go to file, hit Save, that little star will disappear and it'll say that your file was saved a few
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seconds ago. And back in our Python notebook,
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we're gonna have to go again to Kernel > Restart & Run All.
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Scrolling down now we can see that our cell has evaluated correctly.
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And the square root of 63.14 is about 7.95.
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So there you go.
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We've got the solution right below our cell, but since our method here is returning a value, let's save
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this value in a variable.
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So when I say "result = hitchhikersGuide.square_root". And then below I am just going to print
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the result and hit Shift+Enter. So I hope this lesson helped demystify the code that we've written previously
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and explain what's going on behind the scenes.
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Looking at this line of code, reading "result = hitchhikersGuide.square_root(63
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.14)"
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We can see that it's exactly the same pattern as with the Python code that we wrote when we're doing
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our linear regression.
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There we wrote
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data = pd.read_csv
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We've got an object called pd.
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It has a method called read_csv
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And that method is returning a value that we're storing in a variable called theta.
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Cool.
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So at this point you pretty much know Python.
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So now Ryan Sawyer's comic here can probably bring a smile to your face as well.
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Also, I bet you're itching to use your Python knowledge to run another regression.
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I'm gonna head off and grab a bite to eat now, but I do look forward to seeing you in the next lesson.
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Take care.