Hands-On Data Science and Python Machine Learning

4.3 (3 reviews total)
By Frank Kane
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  1. Getting Started

About 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.

Publication date:
July 2017
Publisher
Packt
Pages
420
ISBN
9781787280748

 

Getting Started

Since there's going to be code associated with this book and sample data that you need to get as well, let me first show you where to get that and then we'll be good to go. We need to get some setup out of the way first. First things first, let's get the code and the data that you need for this book so you can play along and actually have some code to mess around with. The easiest way to do that is by going right to this - Getting Started.

In this chapter, we will first install and get ready in a working Python environment:

  • Installing Enthought Canopy
  • Installing Python libraries
  • How to work with the IPython/Jupyter Notebook
  • How to use, read and run the code files for this book
  • Then we'll dive into a crash course into understanding Python code:
  • Python basics - part 1
  • Understanding Python code
  • Importing modules
  • Experimenting with lists
  • Tuples
  • Python basics - part 2
  • Running Python scripts

You'll have everything you need for an amazing journey into data science with Python, once we've set up your environment and familiarized you with Python in this chapter.

 

Installing Enthought Canopy

Let's dive right in and get what you need installed to actually develop Python code with data science on your desktop. I'm going to walk you through installing a package called Enthought Canopy which has both the development environment and all the Python packages you need pre-installed. It makes life really easy, but if you already know Python you might have an existing Python environment already on your PC, and if you want to keep using it, maybe you can.

The most important thing is that your Python environment has Python 3.5 or newer, that it supports Jupyter Notebooks (because that's what we're going to use in this course), and that you have the key packages you need for this book installed on your environment. I'll explain exactly how to achieve a full installation in a few simple steps - it's going to be very easy.

Let's first overview those key packages, most of which Canopy will be installing for us automatically for us. Canopy will install Python 3.5 for us, and some further packages we need including: scikit_learn, xlrd, and statsmodels. We'll need to manually use the pip command, to install a package called pydot2plus. And that will be it - it's very easy with Canopy!

Once the following installation steps are complete, we'll have everything we need to actually get up and running, and so we'll open up a little sample file and do some data science for real. Now let's get you set up with everything you need to get started as quickly as possible:

  1. The first thing you will need is a development environment, called an IDE, for Python code. What we're going to use for this book is Enthought Canopy. It's a scientific computing environment, and it's going to work well with this book:
  1. To get Canopy installed, just go to www.enthought.com and click on DOWNLOADS: Canopy:
  1. Enthought Canopy is free, for the Canopy Express edition - which is what you want for this book. You must then select your operating system and architecture. For me, that's Windows 64-bit, but you'll want to click on corresponding Download button for your operating system and with the Python 3.5 option:
  1. We don't have to give them any personal information at this step. There's a pretty standard Windows installer, so just let that download:
  1. After that's downloaded we go ahead and open up the Canopy installer, and run it! You might want to read the license before you agree to it, that's up to you, and then just wait for the installation to complete.
  2. Once you hit the Finish button at the end of the install process, allow it to launch Canopy automatically. You'll see that Canopy then sets up the Python environment by itself, which is great, but this will take a minute or two.
  3. Once the installer is done setting up your Python environment, you should get a screen that looks like the one below. It says welcome to Canopy and a bunch of big friendly buttons:
  1. The beautiful thing is that pretty much everything you need for this book comes pre-installed with Enthought Canopy, that's why I recommend using it!
  2. There is just one last thing we need to set up, so go ahead and click the Editor button there on the Canopy Welcome screen. You'll then see the Editor screen come up, and if you click down in the window at the bottom, I want you to just type in:
!pip install pydotplus 
  1. Here's how that's going to look on your screen as you type the above line in at the bottom of the Canopy Editor window; don't forget to press the Return button of course:
  1. One you hit the Return button, this will install that one extra module that we need for later on in the book, when we get to talking about decision trees, and rendering decision trees.
  2. Once it has finished installing pydotplus, it should come back and say it's successfully installed and, voila, you have everything you need now to get started! The installation is done, at this point - but let's just take a few more steps to confirm our installation is running nicely.

Giving the installation a test run

  1. Let's now give your installation a test run. The first thing to do is actually to entirely close the Canopy window! This is because we're not actually going to be editing and using our code within this Canopy editor. Instead we're going to be using something called an IPython Notebook, which is also now known as the Jupyter Notebook.
  2. Let me show you how that works. If you now open a window in your operating system to view the accompanying book files that you downloaded, as described in the Preface of this book. It should look something like this, with the set of .ipynb code files you downloaded for this book:

Now go down to the Outliers file in the list, that's the Outliers.ipynb file, double-click it, and what should happen is it's going to start up Canopy first and then it's going to kick off your web browser! This is because IPython/Jupyter Notebooks actually live within your web browser. There can be a small pause at first, and it can be a little bit confusing first time, but you'll soon get used to the idea.

You should soon see Canopy come up and for me my default web browser Chrome comes up. You should see the following Jupyter Notebook page, since we double-clicked on the Outliers.ipynb file:

If you see this screen, it means that everything's working great in your installation and you're all set for the journey across rest of this book!

If you occasionally get problems opening your IPNYB files

Just occasionally, I've noticed that things can go a little bit wrong when you double-click on a .ipynb file. Don't panic! Just sometimes, Canopy can get a little bit flaky, and you might see a screen that is looking for some password or token, or you might occasionally see a screen that says it can't connect at all.

Don't panic if either of those things happen to you, they are just random quirks, sometimes things just don't start up in the right order or they don't start up in time on your PC and it's okay.

All you have to do is go back and try to open that file a second time. Sometimes it takes two or three tries to actually get it loaded up properly, but if you do it a couple of times it should pop up eventually, and a Jupyter Notebook screen like the one we saw previously about Dealing with Outliers, is what you should see.

 

Using and understanding IPython (Jupyter) Notebooks

Congratulations on your installation! Let's now explore using Jupyter Notebooks, which is also known as IPython Notebook. These days, the more modern name is the Jupyter Notebook, but a lot of people still call it an IPython Notebook, and I consider the names interchangeable for working developers as a result. I do also find the name IPython Notebooks helps me remember the notebook file name suffix which is .ipynb as you'll get to know very well in this book!

Okay so now let's take it right from the top again - with our first exploration of the IPython/Jupyter Notebook. If you haven't yet done so, please navigate to the DataScience folder where we have downloaded all the materials for this book. For me, that's E:DataScience, and if you didn't do so during the preceding installation section, please now double-click and open up the Outliers.ipynb file.

Now what's going to happen when we double-click on this IPython .ipynb file is that first of all it's going to spark up Canopy, if it's not sparked up already, and then it's going to launch a web browser. This is how the full Outliers notebook webpage looks within my browser:

As you can see here, notebooks are structured in such a way that I can intersperse my little notes and commentary about what you're seeing here within the actual code itself, and you can actually run this code within your web browser! So, it's a very handy format for me to give you sort of a little reference that you can use later on in life to go and remind yourself how these algorithms work that we're going to talk about, and actually experiment with them and play with them yourself.

The way that the IPython/Jupyter Notebook files work is that they actually run from within your browser, like a webpage, but they're backed by the Python engine that you installed. So you should be seeing a screen similar to the one shown in the previous screenshot.

You'll notice as you scroll down the notebook in your browser, there are code blocks. They're easy to spot because they contain our actual code. Please find the code box for this code in the Outliers notebook, quite near the top:

%matplotlib inline 
import numpy as np 
 
incomes = np.random.normal(27000, 15000, 10000) 
incomes = np.append(incomes, [1000000000]) 
 
import matplotlib.pyplot as plt 
plt.hist(incomes, 50) 
plt.show() 

Let's take a quick look at this code while we're here. We are setting up a little income distribution in this code. We're simulating the distribution of income in a population of people, and to illustrate the effect that an outlier can have on that distribution, we're simulating Donald Trump entering the mix and messing up the mean value of the income distribution. By the way, I'm not making a political statement, this was all done before Trump became a politician. So you know, full disclosure there.

We can select any code block in the notebook by clicking on it. So if you now click in the code block that contains the code we just looked at above, we can then hit the run button at the top to run it. Here's the area at the top of the screen where you'll find the Run button:

Hitting the Run button with the code block selected, will cause this graph to be regenerated:

Similarly, we can click on the next code block a little further down, you'll spot the one which has the following single line of code :

incomes.mean() 

If you select the code block containing this line, and hit the Run button to run the code, you'll see the output below it, which ends up being a very large value because of the effect of that outlier, something like this:

127148.50796177129

Let's keep going and have some fun. In the next code block down, you'll see the following code, which tries to detect outliers like Donald Trump and remove them from the dataset:

def reject_outliers(data): 
    u = np.median(data) 
    s = np.std(data) 
    filtered = [e for e in data if (u - 2 * s < e < u + 2 * s)] 
    return filtered 
 
filtered = reject_outliers(incomes) 
plt.hist(filtered, 50) 
plt.show() 

So select the corresponding code block in the notebook, and press the run button again. When you do that, you'll see this graph instead:

Now we see a much better histogram that represents the more typical American - now that we've taken out our outlier that was messing things up.

So, at this point, you have everything you need to get started in this course. We have all the data you need, all the scripts, and the development environment for Python and Python notebooks. So, let's rock and roll. Up next we're going to do a little crash course on Python itself, and even if you're familiar with Python, it might be a good little refresher so you might want to watch it regardless. Let's dive in and learn Python.

 

Python basics - Part 1

If you already know Python, you can probably skip the next two sections. However, if you need a refresher, or if you haven't done Python before, you'll want to go through these. There are a few quirky things about the Python scripting language that you need to know, so let's dive in and just jump into the pool and learn some Python by writing some actual code.

Like I said before, in the requirements for this book, you should have some sort of programming background to be successful in this book. You've coded in some sort of language, even if it's a scripting language, JavaScript, I don't care whether it is C++, Java, or something, but if you're new to Python, I'm going to give you a little bit of a crash course here. I'm just going to dive right in and go right into some examples in this section.

There are a few quirks about Python that are a little bit different than other languages you might have seen; so I just want to walk through what's different about Python from other scripting languages you may have worked with, and the best way to do that is by looking at some real examples. Let's dive right in and look at some Python code:


If you open up the DataScience folder for this class, which you downloaded earlier in the earlier section, you should find a Python101.ipynb file; go ahead and double-click on that. It should open right up in Canopy if you have everything installed properly, and it should look a little bit something like the following screenshot:

New versions of Canopy will open the code in your web browser, not the Canopy editor! This is okay!

One cool thing about Python is that there are several ways to run code with Python. You can run it as a script, like you would with a normal programming language. You can also write in this thing called the IPython Notebook, which is what we're using here. So it's this format where you actually have a web browser-like view where you can actually write little notations and notes to yourself in HTML markup stuff, and you can also embed actual code that really runs using the Python interpreter.

 

Understanding Python code

The first example that I want to give you of some Python code is right here. The following block of code represents some real Python code that we can actually run right within this view of the entire notebook page, but let's zoom in now and look at that code:


Let's take a look at what's going on. We have a list of numbers and a list in Python, kind of like an array in other languages. It is designated by these square brackets:

We have this data structure of a list that contains the numbers 1 through 6, and then to iterate through every number in that list, we'll say for number in listOfNumbers:, that's the Python syntax for iterating through a list of stuff and a colon.

Tabs and whitespaces have real meaning in Python, so you can't just format things the way you want to. You have to pay attention to them.

The point that I want to make is that in other languages, it's pretty typical to have a bracket or a brace of some sort there to denote that I'm inside a for loop, an if block, or some sort of block of code, but in Python, that's all designated with whitespaces. Tab is actually important in telling Python what's in which block of code:

for number in listOfNumbers: 
    print number, 
    if (number % 2 == 0): 
        print ("is even")
    else: 
        print ("is odd") 
         
print ("Hooray! We're all done.")

You'll notice that within this for block, we have a tab of one within that entire block, and for every number in listOfNumbers we will execute all of this code that's tabbed in by one Tab stop. We'll print the number, and the comma just means that we're not going to do a new line afterwards. We'll print something else right after it, and if (number % 2 = 0), we'll say it's even. Otherwise, we'll say it's odd, and when we're done, we'll print out All done:

You can see the output right below the code. I ran the output before as I had actually saved it within my notebook, but if you want to actually run it yourself, you can just click within that block and click on the Play button, and we'll actually execute it and do it again. Just to convince yourself that it's really doing something, let's change the print statement to say something else, say, Hooray! We're all done. Let's party! If I run this now, you can see, sure enough, my message there has changed:

So again, the point I want to make is that whitespace is important. You will designate blocks of code that run together, you know, such as a for loop or if then statements, using indentation or tabs, so remember that. Also, pay attention to your colons too. You'll notice that a lot of these clauses begin with a colon.

 

Importing modules

Python itself, like any language, is fairly limited in what it can do. The real power of using Python for machine learning and data mining and data science is the power of all the external libraries that are available for it for that purpose. One of those libraries is called NumPy, or numeric Python, and, for example, here we can import the Numpy package, which is included with Canopy as np.

This means that I'll refer to the NumPy package as np, and I could call that anything I want. I could call it Fred or Tim, but it's best to stick with something that actually makes sense; now that I'm calling that NumPy package np, I can refer to it using np:

import numpy as np

In this example, I'll call the random function that's provided as part of the NumPy package and call its normal function to actually generate a normal distribution of random numbers using these parameters and print them out. Since it is random, I should get different results every time:

import numpy as np
A = np.random.normal(25.0, 5.0, 10)
print (A)

The output should look like this:

Sure enough, I get different results. That's pretty cool.

Data structures

Let's move on to data structures. If you need to pause and let things sink in a little bit, or you want to play around with these a little bit more, feel free to do so. The best way to learn this stuff is to dive in and actually experiment, so I definitely encourage doing that, and that's why I'm giving you working IPython/Jupyter Notebooks, so you can actually go in, mess with the code, do different stuff with it.

For example, here we have a distribution around 25.0, but let's make it around 55.0:

import numpy as np
A = np.random.normal(55.0, 5.0, 10)
print (A)

Hey, all my numbers changed, they're closer to 55 now, how about that?

Alright, let's talk about data structures a little bit here. As we saw in our first example, you can have a list, and the syntax looks like this.

Experimenting with lists

x = [1, 2, 3, 4, 5, 6]
print (len(x))

You can say, call a list x, for example, and assign it to the numbers 1 through 6, and these square brackets indicate that we are using a Python list, and those are immutable objects that I can actually add things to and rearrange as much as I want to. There's a built-in function for determining the length of the list called len, and if I type in len(x), that will give me back the number 6 because there are 6 numbers in my list.

Just to make sure, and again to drive home the point that this is actually running real code here, let's add another number in there, such as 4545. If you run this, you'll get 7 because now there are 7 numbers in that list:

x = [1, 2, 3, 4, 5, 6, 4545]
print (len(x))

The output of the previous code example is as follows:

7

Go back to the original example there. Now you can also slice lists. If you want to take a subset of a list, there's a very simple syntax for doing so:

x[3:]

The output of the above code example is as follows:

[1, 2, 3]

Pre colon

If, for example, you want to take the first three elements of a list, everything before element number 3, we can say :3 to get the first three elements, 1, 2, and 3, and if you think about what's going on there, as far as indices go, like in most languages, we start counting from 0. So element 0 is 1, element 1 is 2, and element 2 is 3. Since we're saying we want everything before element 3, that's what we're getting.

So, you know, never forget that in most languages, you start counting at 0 and not 1.

Now this can confuse matters, but in this case, it does make intuitive sense. You can think of that colon as meaning I want everything, I want the first three elements, and I could change that to four just again to make the point that we're actually doing something real here:

x[:4]

The output of the above code example is as follows:

[1, 2, 3, 4]

Post colon

Now if I put the colon on the other side of the 3, that says I want everything after 3, so 3 and after. If I say x[3:], that's giving me the third element, 0, 1, 2, 3, and everything after it. So that's going to return 4, 5, and 6 in that example, OK?

x[3:]

The output is as follows:

[4, 5, 6]

You might want to keep this IPython/Jupyter Notebook file around. It's a good reference, because sometimes it can get confusing as to whether the slicing operator includes that element or if it's up to or including it or not. So the best way is to just play around with it here and remind yourself.

Negative syntax

One more thing you can do is have this negative syntax:

x[-2:]

The output is as follows:

[5, 6]

By saying x[-2:], this means that I want the last two elements in the list. This means that go backwards two from the end, and that will give me 5 and 6, because those are the last two things on my list.

Adding list to list

You can also change lists around. Let's say I want to add a list to the list. I can use the extend function for that, as shown in the following code block:

x.extend([7,8])
x

The output of the above code is as follows:

[1, 2, 3, 4, 5, 6, 7, 8]

I have my list of 1, 2, 3, 4, 5, 6. If I want to extend it, I can say I have a new list here, [7, 8], and that bracket indicates this is a new list of itself. This could be a list implicit, you know, that's inline there, it could be referred to by another variable. You can see that once I do that, the new list I get actually has that list of 7, 8 appended on to the end of it. So I have a new list by extending that list with another list.

The append function

If you want to just add one more thing to that list, you can use the append function. So I just want to stick the number 9 at the end, there we go:

x.append(9)
x

The output of the above code is as follows:

[1, 2, 3, 4, 5, 6, 7, 8, 9]

Complex data structures

You can also have complex data structures with lists. So you don't have to just put numbers in it; you can actually put strings in it. You can put numbers in it. You can put other lists in it. It doesn't matter. Python is a weakly-typed language, so you can pretty much put whatever kind of data you want, wherever you want, and it will generally be an OK thing to do:

y = [10, 11, 12]
listOfLists = [x, y]
listOfLists

In the preceding example, I have a second list that contains 10, 11, 12, that I'm calling y. I'll create a new list that contains two lists. How's that for mind blowing? Our listofLists list will contain the x list and the y list, and that's a perfectly valid thing to do. You can see here that we have a bracket indicating the listofLists list, and within that, we have another set of brackets indicating each individual list that is in that list:

[[ 1, 2, 3, 4, 5, 6, 7, 8, 9 ], [10, 11, 12]]

So, sometimes things like these will come in handy.

Dereferencing a single element

If you want to dereference a single element of the list you can just use the bracket like that:

y[1]

The output of the above code is as follows:

11

So y[1] will return element 1. Remember that y had 10, 11, 12 in it - observe the previous example, and we start counting from 0, so element 1 will actually be the second element in the list, or the number 11 in this case, alright?

The sort function

Finally, let's have a built-in sort function that you can use:

z = [3, 2, 1]
z.sort()
z

So if I start with list z, which is 3,2, and 1, I can call sort on that list, and z will now be sorted in order. The output of the above code is as follows:

[1, 2, 3]

Reverse sort

z.sort(reverse=True)
z

The output of the above code is as follows:

[3, 2, 1]

If you need to do a reverse sort, you can just say reverse=True as an attribute, as a parameter in that sort function, and that will put it back to 3, 2, 1.

If you need to let that sink in a little bit, feel free to go back and read it a little bit more.

Tuples

Tuples are just like lists, except they're immutable, so you can't actually extend, append, or sort them. They are what they are, and they behave just like lists, apart from the fact that you can't change them, and you indicate that they are immutable and are tuple, as opposed to a list, using parentheses instead of a square bracket. So you can see they work pretty much the same way otherwise:

#Tuples are just immutable lists. Use () instead of []
x = (1, 2, 3)
len(x)

The output of the previous code is as follows:

3

We can say x= (1, 2, 3). I can still use length - len on that to say that there are three elements in that tuple, and even though, if you're not familiar with the term tuple, a tuple can actually contain as many elements as you want. Even though it sounds like it's Latin based on the number three, it doesn't mean you have three things in it. Usually, it only has two things in it. They can have as many as you want, really.

Dereferencing an element

We can also dereference the elements of a tuple, so element number 2 again would be the third element, because we start counting from 0, and that will give me back the number 6 in the following screenshot:

y = (4, 5, 6)
y[2]

The output to the above code is as follows:

6

List of tuples

We can also, like we could with lists, use tuples as elements of a list.

listOfTuples = [x, y]
listOfTuples

The output to the above code is as follows:

[(1, 2, 3), (4, 5, 6)]

We can create a new list that contains two tuples. So in the preceding example, we have our x tuple of (1, 2, 3) and our y tuple of (4, 5, 6); then we make a list of those two tuples and we get back this structure, where we have square brackets indicating a list that contains two tuples indicated by parentheses, and one thing that tuples are commonly used for when we're doing data science or any sort of managing or processing of data really is to use it to assign variables to input data as it's read in. I want to walk you through a little bit on what's going on in the following example:

(age, income) = "32,120000".split(',')
print (age)
print (income)

The output to the above code is as follows:

32
120000

Let's say we have a line of input data coming in and it's a comma-separated value file, which contains ages, say 32, comma-delimited by an income, say 120000 for that age, just to make something up. What I can do is as each line comes in, I can call the split function on it to actually separate that into a pair of values that are delimited by commas, and take that resulting tuple that comes out of split and assign it to two variables-age and income-all at once by defining a tuple of age, income and saying that I want to set that equal to the tuple that comes out of the split function.

So this is basically a common shorthand you'll see for assigning multiple fields to multiple variables at once. If I run that, you can see that the age variable actually ends up assigned to 32 and income to 120,000 because of that little trick there. You do need to be careful when you're doing this sort of thing, because if you don't have the expected number of fields or the expected number of elements in the resulting tuple, you will get an exception if you try to assign more stuff or less stuff than you expect to see here.

Dictionaries

Finally, the last data structure that we'll see a lot in Python is a dictionary, and you can think of that as a map or a hash table in other languages. It's a way to basically have a sort of mini-database, sort of a key/value data store that's built into Python. So let's say, I want to build up a little dictionary of Star Trek ships and their captains:

I can set up a captains = {}, where curly brackets indicates an empty dictionary. Now I can use this sort of a syntax to assign entries in my dictionary, so I can say captains for Enterprise is Kirk, for Enterprise D it is Picard, for Deep Space Nine it is Sisko, and for Voyager it is Janeway. Now I have, basically, this lookup table that will associate ship names with their captain, and I can say, for example, print captains["Voyager"], and I get back Janeway.

A very useful tool for basically doing lookups of some sort. Let's say you have some sort of an identifier in a dataset that maps to some human-readable name. You'll probably be using a dictionary to actually do that look up when you're printing it out.

We can also see what happens if you try to look up something that doesn't exist. Well, we can use the get function on a dictionary to safely return an entry. So in this case, Enterprise does have an entry in my dictionary, it just gives me back Kirk, but if I call the NX-01 ship on the dictionary, I never defined the captain of that, so it comes back with a None value in this example, which is better than throwing an exception, but you do need to be aware that this is a possibility:

print (captains.get("NX-01"))

The output of the above code is as follows:

None

The captain is Jonathan Archer, but you know, I'm get a little bit too geeky here now.

Iterating through entries

for ship in captains:
print (ship + ": " + captains[ship])

The output of the above code is as follows:

Let's look at a little example of iterating through the entries in a dictionary. If I want to iterate through every ship that I have in my dictionary and print out captains, I can type for ship in captains, and this will iterate through every single key in my dictionary. Then I can print out the lookup value of each ship's captain, and that's the output that I get there.

There you have it. This is basically the main data structures that you'll encounter in Python. There are some others, such as sets, but we'll not really use them in this book, so I think that's enough to get you started. Let's dive into some more Python nuances in our next section.

 

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.

 

Running Python scripts

Throughout this book, we'll be using the IPython/Jupyter Notebook format (which are .ipynb files) that we've been looking at so far, and it's a great format for a book like this because it lets me put little blocks of code in there and put a little text and things around it explaining what it's doing, and you can experiment with things live.

Of course, it's great from that standpoint, but in the real world, you're probably not going to be using IPython/Jupyter Notebooks to actually run your Python scripts in production, so let me just really briefly go through the other ways you can run Python code, and other interactive ways of running Python code as well. So it's a pretty flexible system. Let's take a look.

More options than just the IPython/Jupyter Notebook

I want to make sure that you know there's more than one way to run Python code. Now, throughout this book, we'll be using the IPython/Jupyter Notebook format but in the real world, you're not going to be running your code as a notebook. You're going to be running it as a standalone Python script. So I just want to make sure you know how to do that and see how it works.

So let's go back to this first example that we ran in the book, just to illustrate the importance of whitespace. We can just select and copy that code out of the notebook format and paste it into a new file.

This can be done by clicking on the New button at the extreme left. So let's make a new file and paste it in and let's save this file and call it, test.py, where py is the usual extension that we give to Python scripts. Now, I can run this in a few different ways.

Running Python scripts in command prompt

I can actually run the script in a command prompt. If I go to Tools, I can go to Canopy Command Prompt, and that will open up a command window that has all the necessary environment variables already in place for running Python. I can just type python test.py and run the script, and out comes my result:

So in the real world, you'd probably do something like that. It might be on a Crontab or something like that, who knows? But running a real script in production is just that simple. You can now close the command prompt.

Using the Canopy IDE

Moving back, I can also run the script from within the IDE. So from within Canopy, I can go to the Run menu. I can either go to Run | Run File, or click on the little play icon, and that will also execute my script, and see the results at the bottom in the output window, as shown in the following screenshot:

So that's another way to do it, and finally, you can also run scripts within this interactive prompt present at the bottom interactively. I can actually type in Python commands one at a time down, and have them just execute and stay within the environment down there:

For example, I could say stuff, make it a list call, and have 1, 2, 3, 4, and now I can say len(stuff), and that will give me 4:

I can say, for x in stuff:print x, and we get output as 1 2 3 4:

So you can see you can kind of makeup scripts as you go down in the interactive prompt at the bottom and execute things one thing at a time. In this example, stuff is a variable we created, a list that stays in memory, it's kind of like a global variable in other languages within this environment.

Now if I do want to reset this environment, if I want to get rid of stuff and start all over, the way you do that is you go up to the Run menu here and you can say Restart Kernel, and that will strike you over with a blank slate:

So now I have a new Python environment that's a clean slate, and in this case, what did I call it? Type stuff and stuff doesn't exist yet because I have a new environment, but I can make it something else, such as [4, 5, 6]; run it and there it is:

So there you have it, three ways of running Python code: the IPython/Jupyter Notebook, which we'll use throughout this book just because it's a good learning tool, you can also run scripts as standalone script files, and you can also execute Python code in the interactive command prompt.

So there you have it, and there you have three different ways of running Python code and experimenting and running things in production. So keep that in mind. We'll be using notebooks throughout the rest of this book, but again, you have those other options when the time comes.

 

Summary

In this chapter, we started our journey with building the most important stepping stone of the book - Installing Enthought Canopy. We then moved to installing other libraries and installing different types of packages. We also grasped some of the basics of Python with the help of various Python code. We covered basic concepts such as modules, lists along with Tuples, and eventually moved on to understanding more of Python basics with a better knowledge of functions and looping in Python. Finally, we started with running some of our simple Python scripts.

In the next chapter, we will move on to understand concepts of statistics and probability.

About the Author

  • Frank Kane

    Frank Kane spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.

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