Book Image

Hands-On Data Science and Python Machine Learning

By : Frank Kane
Book Image

Hands-On Data Science and Python Machine Learning

By: Frank Kane

Overview of this book

Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.
Table of Contents (11 chapters)

Python basics - Part 2

In addition to Python Basics - Part 1, let us now try to grasp more Python concepts in detail.

Functions in Python

Let's talk about functions in Python. Like with other languages, you can have functions that let you repeat a set of operations over and over again with different parameters. In Python, the syntax for doing that looks like this:

def SquareIt(x):
return x * x
print (SquareIt(2))

The output of the above code is as follows:

4

You declare a function using the def keyword. It just says this is a function, and we'll call this function SquareIt, and the parameter list is then followed inside parentheses. This particular function only takes one parameter that we'll call x. Again, remember that whitespace is important in Python. There's not going to be any curly brackets or anything enclosing this function. It's strictly defined by whitespace. So we have a colon that says that this function declaration line is over, but then it's the fact that it's tabbed by one or more tabs that tells the interpreter that we are in fact within the SquareIt function.

So def SquareIt(x): tab returns x * x, and that will return the square of x in this function. We can go ahead and give that a try. print squareIt(2) is how we call that function. It looks just like it would be in any other language, really. This should return the number 4; we run the code, and in fact it does. Awesome! That's pretty simple, that's all there is to functions. Obviously, I could have more than one parameter if I wanted to, even as many parameters as I need.

Now there are some weird things you can do with functions in Python, that are kind of cool. One thing you can do is to pass functions around as though they were parameters. Let's take a closer look at this example:

#You can pass functions around as parameters
def DoSomething(f, x):
return f(x)
print (DoSomething(SquareIt, 3))

The output of the preceding code is as follows:

9

Now I have a function called DoSomething, def DoSomething, and it will take two parameters, one that I'll call f and the other I'll call x, and if I happen, I can actually pass in a function for one of these parameters. So, think about that for a minute. Look at this example with a bit more sense. Here, DoSomething(f,x): will return f of x; it will basically call the f function with x as a parameter, and there's no strong typing in Python, so we have to just kind of make sure that what we are passing in for that first parameter is in fact a function for this to work properly.

For example, we'll say print DoSomething, and for the first parameter, we'll pass in SquareIt, which is actually another function, and the number 3. What this should do is to say do something with the SquareIt function and the 3 parameter, and that will return (SquareIt, 3), and 3 squared last time I checked was 9, and sure enough, that does in fact work.

This might be a little bit of a new concept to you, passing functions around as parameters, so if you need to stop for a minute there, wait and let that sink in, play around with it, please feel free to do so. Again, I encourage you to stop and take this at your own pace.

Lambda functions - functional programming

One more thing that's kind of a Python-ish sort of a thing to do, which you might not see in other languages is the concept of lambda functions, and it's kind of called functional programming. The idea is that you can include a simple function into a function. This makes the most sense with an example:

#Lambda functions let you inline simple functions
print (DoSomething(lambda x: x * x * x, 3))

The output of the above code is as follows:

27

We'll print DoSomething, and remember that our first parameter is a function, so instead of passing in a named function, I can declare this function inline using the lambda keyword. Lambda basically means that I'm defining an unnamed function that just exists for now. It's transitory, and it takes a parameter x. In the syntax here, lambda means I'm defining an inline function of some sort, followed by its parameter list. It has a single parameter, x, and the colon, followed by what that function actually does. I'll take the x parameter and multiply it by itself three times to basically get the cube of a parameter.

In this example, DoSomething will pass in this lambda function as the first parameter, which computes the cube of x and the 3 parameter. So what's this really doing under the hood? This lambda function is a function of itself that gets passed into the f in DoSomething in the previous example, and x here is going to be 3. This will return f of x, which will end up executing our lambda function on the value 3. So that 3 goes into our x parameter, and our lambda function transforms that into 3 times 3 times 3, which is, of course, 27.

Now this comes up a lot when we start doing MapReduce and Spark and things like that. So if we'll be dealing with Hadoop sorts of technologies later on, this is a very important concept to understand. Again, I encourage you to take a moment to let that sink in and understand what's going on there if you need to.

Understanding boolean expressions

Boolean expression syntax is a little bit weird or unusual, at least in Python:

print (1 == 3)

The output of the above code is as follows:

False

As usual, we have the double equal symbol that can test for equality between two values. So does 1 equal 3, no it doesn't, therefore False. The value False is a special value designated by F. Remember that when you're trying to test, when you're doing Boolean stuff, the relevant keywords are True with a T and False with an F. That's a little bit different from other languages that I've worked with, so keep that in mind.

print (True or False)

The output of the above code is as follows:

True

Well, True or False is True, because one of them is True, you run it and it comes back True.

The if statement

print (1 is 3)

The output of the previous code is as follows:

False

The other thing we can do is use is, which is sort of the same thing as equal. It's a more Python-ish representation of equality, so 1 == 3 is the same thing as 1 is 3, but this is considered the more Pythonic way of doing it. So 1 is 3 comes back as False because 1 is not 3.

The if-else loop

if 1 is 3:
print "How did that happen?"
elif 1 > 3:
print ("Yikes")
else:
print ("All is well with the world")

The output of the above code is as follows:

All is well with the world

We can also do if-else and else-if blocks here too. Let's do something a little bit more complicated here. If 1 is 3, I would print How did that happen? But of course 1 is not 3, so we will fall back down to the else-if block, otherwise, if 1 is not 3, we'll test if 1 > 3. Well that's not true either, but if it did, we print Yikes, and we will finally fall into this catch-all else clause that will print All is well with the world.

In fact, 1 is not 3, nor is 1 greater than 3, and sure enough, All is well with the world. So, you know, other languages have very similar syntax, but these are the peculiarities of Python and how to do an if-else or else-if block. So again, feel free to keep this notebook around. It might be a good reference later on.

Looping

The last concept I want to cover in our Python basics is looping, and we saw a couple of examples of this already, but let's just do another one:

for x in range(10):
print (x),

The output of the previous code is as follows:

0 1 2 3 4 5 6 7 8 9

For example, we can use this range operator to automatically define a list of numbers in the range. So if we say for x in range(10), range 10 will produce a list of 0 through 9, and by saying for x in that list, we will iterate through every individual entry in that list and print it out. Again, the comma after the print statement says don't give me a new line, just keep on going. So the output of this ends up being all the elements of that list printed next to each other.

To do something a little bit more complicated, we'll do something similar, but this time we'll show how continue and break work. As in other languages, you can actually choose to skip the rest of the processing for a loop iteration, or actually stop the iteration of the loop prematurely:

for x in range(10):
if (x is 1):
continue
if (x > 5):
break
print (x),

The output of the above code is as follows:

0 2 3 4 5

In this example, we'll go through the values 0 through 9, and if we hit on the number 1, we will continue before we print it out. We'll skip the number 1, basically, and if the number is greater than 5, we'll break the loop and stop the processing entirely. The output that we expect is that we will print out the numbers 0 through 5, unless it's 1, in which case, we'll skip number 1, and sure enough, that's what it does.

The while loop

Another syntax is the while loop. This is kind of a standard looping syntax that you see in most languages:

x = 0
while (x < 10):
print (x),
x += 1

The output of the previous code is as follows:

0 1 2 3 4 5 6 7 8 9

We can also say, start with x = 0, and while (x < 10):, print it out and then increment x by 1. This will go through over and over again, incrementing x until it's less than 10, at which point we break out of the while loop and we're done. So it does the same thing as this first example here, but just in a different style. It prints out the numbers 0 through 9 using a while loop. Just some examples there, nothing too complicated. Again, if you've done any sort of programming or scripting before, this should be pretty simple.

Now to really let this sink in, I've been saying throughout this entire chapter, get in there, get your hands dirty, and play with it. So I'm going to make you do that.

Exploring activity

Here's an activity, a little bit of a challenge for you:

Here's a nice little code block where you can start writing your own Python code, run it, and play around with it, so please do so. Your challenge is to write some code that creates a list of integers, loops through each element of that list, pretty easy so far, and only prints out even numbers.

Now this shouldn't be too hard. There are examples in this notebook of doing all that stuff; all you have to do is put it together and get it to run. So, the point is not to give you something that's hard. I just want you to actually get some confidence in writing your own Python code and actually running it and seeing it operate, so please do so. I definitely encourage you to be interactive here. So have at it, good luck, and welcome to Python.

So that's your Python crash course, obviously, just some very basic stuff there. As we go through more and more examples throughout the book, it'll make more and more sense since you have more examples to look at, but if you do feel a little bit intimidated at this point, maybe you're a little bit too new to programming or scripting, and it might be a good idea to go and take a Python revision before moving forward, but if you feel pretty good about what you've seen so far, let's move ahead and we'll keep on going.