Getting Started with Python

Understand key data structures and use Python in object-oriented programming

Fabrizio Romano, Benjamin Baka, Dusty Phillips

• Éditeur: Packt Publishing
• Année: 2019
• ISBN:
  9781838559984
• Édition: 1
• Sujet: Computer Science
• Sous-sujet: Programming in Python

Harness the power of Python objects and data structures to implement algorithms for analyzing your data and efficiently extracting information

Key Features

• Turn your designs into working software by learning the Python syntax
• Write robust code with a solid understanding of Python data structures
• Understand when to use the functional or the OOP approach

Book Description

This Learning Path helps you get comfortable with the world of Python. It starts with a thorough and practical introduction to Python. You'll quickly start writing programs, building websites, and working with data by harnessing Python's renowned data science libraries. With the power of linked lists, binary searches, and sorting algorithms, you'll easily create complex data structures, such as graphs, stacks, and queues. After understanding cooperative inheritance, you'll expertly raise, handle, and manipulate exceptions. You will effortlessly integrate the object-oriented and not-so-object-oriented aspects of Python, and create maintainable applications using higher level design patterns. Once you've covered core topics, you'll understand the joy of unit testing and just how easy it is to create unit tests.

By the end of this Learning Path, you will have built components that are easy to understand, debug, and can be used across different applications.

This Learning Path includes content from the following Packt products:

• Learn Python Programming - Second Edition by Fabrizio Romano
• Python Data Structures and Algorithms by Benjamin Baka
• Python 3 Object-Oriented Programming by Dusty Phillips

What you will learn

• Use data structures and control flow to write code
• Use functions to bundle together a sequence of instructions
• Implement objects in Python by creating classes and defining methods
• Design public interfaces using abstraction, encapsulation and information hiding
• Raise, define, and manipulate exceptions using special error objects
• Create bulletproof and reliable software by writing unit tests
• Learn the common programming patterns and algorithms used in Python

Who this book is for

If you are relatively new to coding and want to write scripts or programs to accomplish tasks using Python, or if you are an object-oriented programmer for other languages and seeking a leg up in the world of Python, then this Learning Path is for you. Though not essential, it will help you to have basic knowledge of programming and OOP.

Built-in Data Types

"Data! Data! Data!" he cried impatiently. "I can't make bricks without clay."

– Sherlock Holmes – The Adventure of the Copper Beeches

Everything you do with a computer is managing data. Data comes in many different shapes and flavors. It's the music you listen to, the movies you stream, the PDFs you open. Even the source of the chapter you're reading at this very moment is just a file, which is data.

Data can be simple, an integer number to represent an age, or complex, like an order placed on a website. It can be about a single object or about a collection of them. Data can even be about data, that is, metadata. Data that describes the design of other data structures or data that describes application data or its context. In Python, objects are abstraction for data, and Python has an amazing variety of data structures that you can use to represent data, or combine them to create your own custom data.

In this chapter, we are going to cover the following:

• Python objects' structures
• Mutability and immutability
• Built-in data types: numbers, strings, sequences, collections, and mapping types
• The collections module
• Enumerations

Everything is an object

Before we delve into the specifics, I want you to be very clear about objects in Python, so let's talk a little bit more about them. As we already said, everything in Python is an object. But what really happens when you type an instruction like age = 42 in a Python module?

If you go to http://pythontutor.com/, you can type that instruction into a text box and get its visual representation. Keep this website in mind; it's very useful to consolidate your understanding of what goes on behind the scenes.

So, what happens is that an object is created. It gets an id, the type is set to int (integer number), and the value to 42. A name age is placed in the global namespace, pointing to that object. Therefore, whenever we are in the global namespace, after the execution of that line, we can retrieve that object by simply accessing it through its name: age.

If you were to move house, you would put all the knives, forks, and spoons in a box and label it cutlery. Can you see it's exactly the same concept? Here's a screenshot of what it may look like (you may have to tweak the settings to get to the same view):

So, for the rest of this chapter, whenever you read something such as name = some_value, think of a name placed in the namespace that is tied to the scope in which the instruction was written, with a nice arrow pointing to an object that has an id, a type, and a value. There is a little bit more to say about this mechanism, but it's much easier to talk about it over an example, so we'll get back to this later.

Mutable or immutable? That is the question

A first fundamental distinction that Python makes on data is about whether or not the value of an object changes. If the value can change, the object is called mutable, while if the value cannot change, the object is called immutable.

It is very important that you understand the distinction between mutable and immutable because it affects the code you write, so here's a question:

>>> age = 42
>>> age
42
>>> age = 43 #A
>>> age
43

In the preceding code, on the line #A, have I changed the value of age? Well, no. But now it's 43 (I hear you say...). Yes, it's 43, but 42 was an integer number, of the type int, which is immutable. So, what happened is really that on the first line, age is a name that is set to point to an int object, whose value is 42. When we type age = 43, what happens is that another object is created, of the type int and value 43 (also, the id will be different), and the name age is set to point to it. So, we didn't change that 42 to 43. We actually just pointed age to a different location: the new int object whose value is 43. Let's see the same code also printing the IDs:

>>> age = 42
>>> id(age)
4377553168
>>> age = 43
>>> id(age)
4377553200

Notice that we print the IDs by calling the built-in id function. As you can see, they are different, as expected. Bear in mind that age points to one object at a time: 42 first, then 43. Never together.

Now, let's see the same example using a mutable object. For this example, let's just use a Person object, that has a property age (don't worry about the class declaration for now; it's there only for completeness):

>>> class Person():
... def __init__(self, age):
... self.age = age
...
>>> fab = Person(age=42)
>>> fab.age
42
>>> id(fab)
4380878496
>>> id(fab.age)
4377553168
>>> fab.age = 25 # I wish!
>>> id(fab) # will be the same
4380878496
>>> id(fab.age) # will be different
4377552624

In this case, I set up an object fab whose type is Person (a custom class). On creation, the object is given the age of 42. I'm printing it, along with the object id, and the ID of age as well. Notice that, even after I change age to be 25, the ID of fab stays the same (while the ID of age has changed, of course). Custom objects in Python are mutable (unless you code them not to be). Keep this concept in mind; it's very important. I'll remind you about it throughout the rest of the chapter.

Numbers

Let's start by exploring Python's built-in data types for numbers. Python was designed by a man with a master's degree in mathematics and computer science, so it's only logical that it has amazing support for numbers.

Numbers are immutable objects.

Integers

Python integers have an unlimited range, subject only to the available virtual memory. This means that it doesn't really matter how big a number you want to store is: as long as it can fit in your computer's memory, Python will take care of it. Integer numbers can be positive, negative, and 0 (zero). They support all the basic mathematical operations, as shown in the following example:

>>> a = 14
>>> b = 3
>>> a + b # addition
17
>>> a - b # subtraction
11
>>> a * b # multiplication
42
>>> a / b # true division
4.666666666666667
>>> a // b # integer division
4
>>> a % b # modulo operation (reminder of division)
2
>>> a ** b # power operation
2744

The preceding code should be easy to understand. Just notice one important thing: Python has two division operators, one performs the so-called true division (/), which returns the quotient of the operands, and the other one, the so-called integer division (//), which returns the floored quotient of the operands. It might be worth noting that in Python 2 the division operator / behaves differently than in Python 3. See how that is different for positive and negative numbers:

>>> 7 / 4 # true division
1.75
>>> 7 // 4 # integer division, truncation returns 1
1
>>> -7 / 4 # true division again, result is opposite of previous
-1.75
>>> -7 // 4 # integer div., result not the opposite of previous
-2

This is an interesting example. If you were expecting a -1 on the last line, don't feel bad, it's just the way Python works. The result of an integer division in Python is always rounded towards minus infinity. If, instead of flooring, you want to truncate a number to an integer, you can use the built-in int function, as shown in the following example:

>>> int(1.75)
1
>>> int(-1.75)
-1

Notice t...