First of all, what is Python? According to its creator, Guido van Rossum, Python is a:
“high-level programming language, and its core design philosophy is all about code readability and a syntax which allows programmers to express concepts in a few lines of code.”
For me, the first reason to learn Python was that it is, in fact, a beautiful programming language. It was really natural to code in it and express my thoughts.
Another reason was that we can use coding in Python in multiple ways: data science, web development, and machine learning all shine here. Quora, Pinterest and Spotify all use Python for their backend web development. So let’s learn a bit about it.
You can think about variables as words that store a value. Simple as that.
In Python, it is really easy to define a variable and set a value to it. Imagine you want to store number 1 in a variable called “one.” Let’s do it:
one = 1
How simple was that? You just assigned the value 1 to the variable “one.”
two = 2 some_number = 10000
And you can assign any other value to whatever other variables you want. As you see in the table above, the variable “two” stores the integer 2, and “some_number” stores 10,000.
Besides integers, we can also use booleans (True / False), strings, float, and so many other data types.
# booleans true_boolean = True false_boolean = Falsestring
my_name = “Leandro Tk”
float
book_price = 15.80
“If” uses an expression to evaluate whether a statement is True or False. If it is True, it executes what is inside the “if” statement. For example:
if True:
print(“Hello Python If”)if 2 > 1:
print(“2 is greater than 1”)
2 is greater than 1, so the “print” code is executed.
The “else” statement will be executed if the “if” expression is false.
if 1 > 2:
print(“1 is greater than 2”)
else:
print(“1 is not greater than 2”)
1 is not greater than 2, so the code inside the “else” statement will be executed.
You can also use an “elif” statement:
if 1 > 2:
print(“1 is greater than 2”)
elif 2 > 1:
print(“1 is not greater than 2”)
else:
print(“1 is equal to 2”)
In Python, we can iterate in different forms. I’ll talk about two: while and for.
While Looping: while the statement is True, the code inside the block will be executed. So, this code will print the number from 1 to 10.
num = 1while num <= 10:
print(num)
num += 1
The while loop needs a “loop condition.” If it stays True, it continues iterating. In this example, when num
is 11
the loop condition equals False
.
Another basic bit of code to better understand it:
loop_condition = Truewhile loop_condition:
print(“Loop Condition keeps: %s” %(loop_condition))
loop_condition = False
The loop condition is True
so it keeps iterating — until we set it to False
.
For Looping: you apply the variable “num” to the block, and the “for” statement will iterate it for you. This code will print the same as while code: from 1 to 10.
for i in range(1, 11):
print(i)
See? It is so simple. The range starts with 1
and goes until the 11
th element (10
is the 10
th element).
Imagine you want to store the integer 1 in a variable. But maybe now you want to store 2. And 3, 4, 5 …
Do I have another way to store all the integers that I want, but not in millions of variables? You guessed it — there is indeed another way to store them.
List
is a collection that can be used to store a list of values (like these integers that you want). So let’s use it:
my_integers = [1, 2, 3, 4, 5]
It is really simple. We created an array and stored it on my_integer.
But maybe you are asking: “How can I get a value from this array?”
Great question. List
has a concept called index. The first element gets the index 0 (zero). The second gets 1, and so on. You get the idea.
To make it clearer, we can represent the array and each element with its index. I can draw it:
Using the Python syntax, it’s also simple to understand:
my_integers = [5, 7, 1, 3, 4]
print(my_integers[0]) # 5
print(my_integers[1]) # 7
print(my_integers[4]) # 4
Imagine that you don’t want to store integers. You just want to store strings, like a list of your relatives’ names. Mine would look something like this:
relatives_names = [
“Toshiaki”,
“Juliana”,
“Yuji”,
“Bruno”,
“Kaio”
]print(relatives_names[4]) # Kaio
It works the same way as integers. Nice.
We just learned how Lists
indices work. But I still need to show you how we can add an element to the List
data structure (an item to a list).
The most common method to add a new value to a List
is append
. Let’s see how it works:
bookshelf = []
bookshelf.append(“The Effective Engineer”)
bookshelf.append(“The 4 Hour Work Week”)
print(bookshelf[0]) # The Effective Engineer
print(bookshelf[1]) # The 4 Hour Work Week
append
is super simple. You just need to apply the element (eg. “The Effective Engineer”) as the append
parameter.
Well, enough about Lists
. Let’s talk about another data structure.
Now we know that Lists
are indexed with integer numbers. But what if we don’t want to use integer numbers as indices? Some data structures that we can use are numeric, string, or other types of indices.
Let’s learn about the Dictionary
data structure. Dictionary
is a collection of key-value pairs. Here’s what it looks like:
dictionary_example = {
“key1”: “value1”,
“key2”: “value2”,
“key3”: “value3”
}
The key is the index pointing to the value. How do we access the Dictionary
value? You guessed it — using the key. Let’s try it:
dictionary_tk = {
“name”: “Leandro”,
“nickname”: “Tk”,
“nationality”: “Brazilian”
}print(“My name is %s” %(dictionary_tk[“name”])) # My name is Leandro
print(“But you can call me %s” %(dictionary_tk[“nickname”])) # But you can call me Tk
print(“And by the way I’m %s” %(dictionary_tk[“nationality”])) # And by the way I’m Brazilian
I created a Dictionary
about me. My name, nickname, and nationality. Those attributes are the Dictionary
keys.
As we learned how to access the List
using index, we also use indices (keys in the Dictionary
context) to access the value stored in the Dictionary
.
In the example, I printed a phrase about me using all the values stored in the Dictionary
. Pretty simple, right?
Another cool thing about Dictionary
is that we can use anything as the value. In the Dictionary
I created, I want to add the key “age” and my real integer age in it:
dictionary_tk = {
“name”: “Leandro”,
“nickname”: “Tk”,
“nationality”: “Brazilian”,
“age”: 24
}print(“My name is %s” %(dictionary_tk[“name”])) # My name is Leandro
print(“But you can call me %s” %(dictionary_tk[“nickname”])) # But you can call me Tk
print(“And by the way I’m %i and %s” %(dictionary_tk[“age”], dictionary_tk[“nationality”])) #
Here we have a key (age) value (24) pair using string as the key and integer as the value.
As we did with Lists
, let’s learn how to add elements to a Dictionary
. The key pointing to a value is a big part of what Dictionary
is. This is also true when we are talking about adding elements to it:
dictionary_tk = {
“name”: “Leandro”,
“nickname”: “Tk”,
“nationality”: “Brazilian”
}dictionary_tk[‘age’] = 24
print(dictionary_tk) # {‘nationality’: ‘Brazilian’, ‘age’: 24, ‘nickname’: ‘Tk’, ‘name’: ‘Leandro’}
We just need to assign a value to a Dictionary
key. Nothing complicated here, right?
As we learned in the Python Basics, the List
iteration is very simple. We Python
developers commonly use For
looping. Let’s do it:
bookshelf = [
“The Effective Engineer”,
“The 4 hours work week”,
“Zero to One”,
“Lean Startup”,
“Hooked”
]for book in bookshelf:
print(book)
So for each book in the bookshelf, we (can do everything with it) print it. Pretty simple and intuitive. That’s Python.
For a hash data structure, we can also use the for
loop, but we apply the key
:
dictionary = { “some_key”: “some_value” }for key in dictionary:
print(“%s --> %s” %(key, dictionary[key]))some_key --> some_value
This is an example how to use it. For each key
in the dictionary
, we print
the key
and its corresponding value
.
Another way to do it is to use the iteritems
method.
dictionary = { “some_key”: “some_value” }for key, value in dictionary.items():
print(“%s --> %s” %(key, value))some_key --> some_value
We did name the two parameters as key
and value
, but it is not necessary. We can name them anything. Let’s see it:
dictionary_tk = {
“name”: “Leandro”,
“nickname”: “Tk”,
“nationality”: “Brazilian”,
“age”: 24
}for attribute, value in dictionary_tk.items():
print(“My %s is %s” %(attribute, value))My name is Leandro
My nickname is Tk
My nationality is Brazilian
My age is 24
We can see we used attribute as a parameter for the Dictionary
key
, and it works properly. Great!
Objects are a representation of real world objects like cars, dogs, or bikes. The objects share two main characteristics: data and behavior.
Cars have data, like number of wheels, number of doors, and seating capacity They also exhibit behavior: they can accelerate, stop, show how much fuel is left, and so many other things.
We identify data as attributes and behavior as methods in object-oriented programming. Again:
Data → Attributes and Behavior → Methods
And a Class is the blueprint from which individual objects are created. In the real world, we often find many objects with the same type. Like cars. All the same make and model (and all have an engine, wheels, doors, and so on). Each car was built from the same set of blueprints and has the same components.
Python, as an Object-Oriented programming language, has these concepts: class and object.
A class is a blueprint, a model for its objects.
So again, a class it is just a model, or a way to define attributes and behavior (as we talked about in the theory section). As an example, a vehicle class has its own attributes that define what objects are vehicles. The number of wheels, type of tank, seating capacity, and maximum velocity are all attributes of a vehicle.
With this in mind, let’s look at Python syntax for classes:
class Vehicle:
pass
We define classes with a class statement — and that’s it. Easy, isn’t it?
Objects are instances of a class. We create an instance by naming the class.
car = Vehicle()
print(car) # <main.Vehicle instance at 0x7fb1de6c2638>
Here car
is an object (or instance) of the class Vehicle
.
Remember that our vehicle class has four attributes: number of wheels, type of tank, seating capacity, and maximum velocity. We set all these attributes when creating a vehicle object. So here, we define our class to receive data when it initiates it:
class Vehicle:
def init(self, number_of_wheels, type_of_tank, seating_capacity, maximum_velocity):
self.number_of_wheels = number_of_wheels
self.type_of_tank = type_of_tank
self.seating_capacity = seating_capacity
self.maximum_velocity = maximum_velocity
We use the init
method. We call it a constructor method. So when we create the vehicle object, we can define these attributes. Imagine that we love the Tesla Model S, and we want to create this kind of object. It has four wheels, runs on electric energy, has space for five seats, and the maximum velocity is 250km/hour (155 mph). Let’s create this object:
tesla_model_s = Vehicle(4, ‘electric’, 5, 250)
Four wheels + electric “tank type” + five seats + 250km/hour maximum speed.
All attributes are set. But how can we access these attributes’ values? We send a message to the object asking about them. We call it a method. It’s the object’s behavior. Let’s implement it:
class Vehicle:
def init(self, number_of_wheels, type_of_tank, seating_capacity, maximum_velocity):
self.number_of_wheels = number_of_wheels
self.type_of_tank = type_of_tank
self.seating_capacity = seating_capacity
self.maximum_velocity = maximum_velocitydef number_of_wheels(self): return self.number_of_wheels def set_number_of_wheels(self, number):
self.number_of_wheels = number
This is an implementation of two methods: number_of_wheels and set_number_of_wheels. We call it getter
& setter
. Because the first gets the attribute value, and the second sets a new value for the attribute.
In Python, we can do that using @property
(decorators
) to define getters
and setters
. Let’s see it with code:
class Vehicle:
def init(self, number_of_wheels, type_of_tank, seating_capacity, maximum_velocity):
self.number_of_wheels = number_of_wheels
self.type_of_tank = type_of_tank
self.seating_capacity = seating_capacity
self.maximum_velocity = maximum_velocity@property def number_of_wheels(self): return self.number_of_wheels @number_of_wheels.setter def number_of_wheels(self, number):
self.number_of_wheels = number
And we can use these methods as attributes:
tesla_model_s = Vehicle(4, ‘electric’, 5, 250)
print(tesla_model_s.number_of_wheels) # 4
tesla_model_s.number_of_wheels = 2 # setting number of wheels to 2
print(tesla_model_s.number_of_wheels) # 2
This is slightly different than defining methods. The methods work as attributes. For example, when we set the new number of wheels, we don’t apply two as a parameter, but set the value 2 to number_of_wheels
. This is one way to write pythonic
getter
and setter
code.
But we can also use methods for other things, like the “make_noise” method. Let’s see it:
class Vehicle:
def init(self, number_of_wheels, type_of_tank, seating_capacity, maximum_velocity):
self.number_of_wheels = number_of_wheels
self.type_of_tank = type_of_tank
self.seating_capacity = seating_capacity
self.maximum_velocity = maximum_velocitydef make_noise(self):
print(‘VRUUUUUUUM’)
When we call this method, it just returns a string “VRRRRUUUUM.”
tesla_model_s = Vehicle(4, ‘electric’, 5, 250)
tesla_model_s.make_noise() # VRUUUUUUUM
Encapsulation is a mechanism that restricts direct access to objects’ data and methods. But at the same time, it facilitates operation on that data (objects’ methods).
“Encapsulation can be used to hide data members and members function. Under this definition, encapsulation means that the internal representation of an object is generally hidden from view outside of the object’s definition.” — Wikipedia
All internal representation of an object is hidden from the outside. Only the object can interact with its internal data.
First, we need to understand how public
and non-public
instance variables and methods work.
For a Python class, we can initialize a public instance variable
within our constructor method. Let’s see this:
Within the constructor method:
class Person:
def init(self, first_name):
self.first_name = first_name
Here we apply the first_name
value as an argument to the public instance variable
.
tk = Person(‘TK’)
print(tk.first_name) # => TK
Within the class:
class Person:
first_name = ‘TK’
Here, we do not need to apply the first_name
as an argument, and all instance objects will have a class attribute
initialized with TK
.
tk = Person()
print(tk.first_name) # => TK
Cool. We have now learned that we can use public instance variables
and class attributes
. Another interesting thing about the public
part is that we can manage the variable value. What do I mean by that? Our object
can manage its variable value: Get
and Set
variable values.
Keeping the Person
class in mind, we want to set another value to its first_name
variable:
tk = Person(‘TK’)
tk.first_name = ‘Kaio’
print(tk.first_name) # => Kaio
There we go. We just set another value (kaio
) to the first_name
instance variable and it updated the value. Simple as that. Since it’s a public
variable, we can do that.
We don’t use the term “private” here, since no attribute is really private in Python (without a generally unnecessary amount of work). — PEP 8
As the public instance variable
, we can define the non-public instance variable
both within the constructor method or within the class. The syntax difference is: for non-public instance variables
, use an underscore (_
) before the variable
name.
“‘Private’ instance variables that cannot be accessed except from inside an object don’t exist in Python. However, there is a convention that is followed by most Python code: a name prefixed with an underscore (e.g. _spam) should be treated as a non-public part of the API (whether it is a function, a method or a data member)” — Python Software Foundation
Here’s an example:
class Person:
def init(self, first_name, email):
self.first_name = first_name
self._email = email
Did you see the email
variable? This is how we define a non-public variable
:
tk = Person(‘TK’, ‘tk@mail.com’)
print(tk._email) # tk@mail.com
We can access and update it. Non-public variables are just a convention and should be treated as a non-public part of the API.
So we use a method that allows us to do it inside our class definition. Let’s implement two methods (email
and update_email
) to understand it:
class Person:
def init(self, first_name, email):
self.first_name = first_name
self._email = emaildef update_email(self, new_email): self._email = new_email def email(self):
return self._email
Now we can update and access non-public variables
using those methods. Let’s see:
tk = Person(‘TK’, ‘tk@mail.com’)
print(tk.email()) # => tk@mail.com
tk._email = ‘new_tk@mail.com’
print(tk.email()) # => tk@mail.com
tk.update_email(‘new_tk@mail.com’)
print(tk.email()) # => new_tk@mail.com
first_name
TK and email
tk@mail.comnon-public variable
with a methodemail
out of our classnon-public variable
as non-public
part of the APInon-public variable
with our instance methodWith public methods
, we can also use them out of our class:
class Person:
def init(self, first_name, age):
self.first_name = first_name
self._age = agedef show_age(self):
return self.age
Let’s test it:
tk = Person(‘TK’, 25)
print(tk.show_age()) # => 25
Great — we can use it without any problem.
But with non-public methods
we aren’t able to do it. Let’s implement the same Person
class, but now with a show_age
non-public method
using an underscore ().
class Person:
def init(self, first_name, age):
self.first_name = first_name
self._age = agedef _show_age(self):
return self._age
And now, we’ll try to call this non-public method
with our object:
tk = Person(‘TK’, 25)
print(tk._show_age()) # => 25
We can access and update it. Non-public methods are just a convention and should be treated as a non-public part of the API.
Here’s an example for how we can use it:
class Person:
def init(self, first_name, age):
self.first_name = first_name
self._age = agedef show_age(self): return self._get_age() def _get_age(self): return self._age
tk = Person(‘TK’, 25)
print(tk.show_age()) # => 25
Here we have a _get_age
non-public method
and a show_age
public method
. The show_age
can be used by our object (out of our class) and the _get_age
only used inside our class definition (inside show_age
method). But again: as a matter of convention.
With encapsulation we can ensure that the internal representation of the object is hidden from the outside.
Certain objects have some things in common: their behavior and characteristics.
For example, I inherited some characteristics and behaviors from my father. I inherited his eyes and hair as characteristics, and his impatience and introversion as behaviors.
In object-oriented programming, classes can inherit common characteristics (data) and behavior (methods) from another class.
Let’s see another example and implement it in Python.
Imagine a car. Number of wheels, seating capacity and maximum velocity are all attributes of a car. We can say that an ElectricCar class inherits these same attributes from the regular Car class.
class Car:
def init(self, number_of_wheels, seating_capacity, maximum_velocity):
self.number_of_wheels = number_of_wheels
self.seating_capacity = seating_capacity
self.maximum_velocity = maximum_velocity
Our Car class implemented:
my_car = Car(4, 5, 250)
print(my_car.number_of_wheels)
print(my_car.seating_capacity)
print(my_car.maximum_velocity)
Once initiated, we can use all instance variables
created. Nice.
In Python, we apply a parent class
to the child class
as a parameter. An ElectricCar class can inherit from our Car class.
class ElectricCar(Car):
def init(self, number_of_wheels, seating_capacity, maximum_velocity):
Car.init(self, number_of_wheels, seating_capacity, maximum_velocity)
Simple as that. We don’t need to implement any other method, because this class already has it (inherited from Car class). Let’s prove it:
my_electric_car = ElectricCar(4, 5, 250)
print(my_electric_car.number_of_wheels) # => 4
print(my_electric_car.seating_capacity) # => 5
print(my_electric_car.maximum_velocity) # => 250
Beautiful.
For more stories and posts about my journey learning & mastering programming, follow my publication The Renaissance Developer.
Have fun, keep learning, and always keep coding.
Originally published by TK at https://medium.freecodecamp.org
#python