The Ultimate Python Beginner's Handbook

Python er blevet et af de hurtigst voksende programmeringssprog de sidste par år.

Ikke kun er det meget udbredt, det er også et fantastisk sprog at tackle, hvis du vil komme ind i programmeringsverdenen.

Denne Python-guide til begyndere giver dig mulighed for at lære sprogets kerne på få timer i stedet for uger.

Hurtig info: Du kan downloade en PDF-version af denne Python-guide til begyndere.

Klar til at dykke ind?

Indholdsfortegnelse

  1. Introduktion til Python
  2. Installation af Python 3
  3. Kørselskode
  4. Syntaks
  5. Kommentarer
  6. Variabler
  7. Typer
  8. Typecasting
  9. Brugerinput
  10. Operatører
  11. Betingelser
  12. Lister
  13. Tuples
  14. Sæt
  15. Ordbøger
  16. mens Loops
  17. til sløjfer
  18. Funktioner
  19. Anvendelsesområde
  20. Listeforståelser
  21. Lambda-funktioner
  22. Moduler
  23. hvis navn == ' hoved '
  24. Filer
  25. Klasser og objekter
  26. Arv
  27. Undtagelser
  28. Konklusion

Introduktion til Python

Python blev oprettet i 1990 af Guido van Rossum i Holland.

Et af målene med sproget var at være tilgængeligt for ikke-programmører.

Python blev også designet til at være et andet sprog for programmører at lære på grund af sin lave læringskurve og brugervenlighed.

Python kører på Mac, Linux, Windows og mange andre platforme.

Python er:

  • Fortolket: det kan udføres ved kørsel, og ændringer i et program er øjeblikkeligt synlige. For at være meget teknisk har Python en compiler. Forskellen sammenlignet med Java eller C ++ er, hvor gennemsigtig og automatisk den er. Med Python behøver vi ikke bekymre os om kompileringstrinnet, da det gøres i realtid. Afvejningen er, at fortolkede sprog normalt er langsommere end kompilerede.

  • Semantisk dynamisk: du behøver ikke at specificere typer for variabler, og der er ikke noget, der får dig til at gøre det.

  • Objektorienteret: alt i Python er et objekt. Men du kan vælge at skrive kode på en objektorienteret, proceduremæssig eller endda funktionel måde.

  • Højt niveau: du behøver ikke håndtere maskinoplysninger på lavt niveau.

Python har vokset meget for nylig, dels på grund af dets mange anvendelser inden for følgende områder:

  • System scripting: det er et godt værktøj til at automatisere hverdags gentagne opgaver.

  • Dataanalyse: det er et godt sprog at eksperimentere med og har masser af biblioteker og værktøjer til at håndtere data, oprette modeller, visualisere resultater og endda implementere løsninger. Dette bruges i områder som finans, e-handel og forskning.

  • Webudvikling: rammer som Django og Flask tillader udvikling af webapplikationer, API'er og websteder.

  • Machine Learning: Tensorflow and Pytorch are some of the libraries that allow scientists and the industry to develop and deploy Artificial Intelligence solutions in Image Recognition, Health, Self-driving cars, and many other fields.

You can easily organize your code in modules and reuse them or share them with others.

Finally, we have to keep in mind that Python had breaking changes between versions 2 and 3. And since Python 2 support ended in 2020, this article is solely based on Python 3.

So let's get started.

Installing Python 3

If you use a Mac or Linux you already have Python installed. But Windows doesn't come with Python installed by default.

You also might have Python 2, and we are going to use Python 3. So you should check to see if you have Python 3 first.

Type the following in your terminal.

python3 -V 

Notice the uppercase V.

If your result is something similar to 'Python 3.x.y', for instance, Python 3.8.1, then you are ready to go.

If not, follow the next instructions according to your Operating System.

Installing Python 3 on Windows

Go to //www.python.org/downloads/.

Download the latest version.

After the download, double-click the installer.

On the first screen, check the box indicating to "Add Python 3.x to PATH" and then click on "Install Now".

Wait for the installation process to finish until the next screen with the message "Setup was successful".

Click on "Close".

Installing Python 3 on Mac

Install XCode from the App Store.

Install the command line tools by running the following in your terminal.

xcode-select --install 

I recommend using Homebrew. Go to //brew.sh/ and follow the instructions on the first page to install it.

After installing Homebrew, run the following brew commands to install Python 3.

brew update brew install python3 

Homebrew already adds Python 3 to the PATH, so you don't have to do anything else.

Installing Python 3 on Linux

To install using apt, available in Ubuntu and Debian, enter the following:

sudo apt install python3 

To install using yum, available in RedHat and CentOS, enter the following:

sudo yum install python3 

Running Code

You can run Python code directly in the terminal as commands or you can save the code in a file with the .py extension and run the Python file.

Terminal

Running commands directly in the terminal is recommended when you want to run something simple.

Open the command line and type python3

[email protected]:~$ python3 

You should see something like this in your terminal indicating the version (in my case, Python 3.6.9), the operating system (I'm using Linux), and some basic commands to help you.

The >>> tells us we are in the Python console.

Python 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>>

Let's test it by running our first program to perform basic math and add two numbers.

>>> 2 + 2 

The output is:

4 

To exit the Python console simply type exit().

>>> exit() 

Running .py files

If you have a complex program, with many lines of code, the Python console isn't the best option.

The alternative is simply to open a text editor, type the code, and save the file with a .py extension.

Let's do that, create a file called second_program.py with the following content.

print('Second Program') 

The print() function prints a message on the screen.

The message goes inside the parentheses with either single quotes or double quotes, both work the same.

To run the program, on your terminal do the following:

[email protected]:~$ python3 second_program.py 

The output is:

Second Program 

Syntax

Python is known for its clean syntax.

The language avoids using unnecessary characters to indicate some specificity.

Semicolons

Python doesn't use semicolons to finish lines. A new line is enough to tell the interpreter that a new command is beginning.

The print() method will display something.

In this example, we have two commands that will display the messages inside the single quotes.

print('First command') print('Second command') 

Output:

First command Second command 

But the following is wrong due to the semicolons in the end:

print('First command'); print('Second command'); 

Indentation

Many languages use curly-brackets to define scope.

Python's interpreter uses only indentation to define when a scope ends and another one starts.

This means you have to be aware of white spaces at the beginning of each line -- they have meaning and might break your code if misplaced.

This definition of a function works:

def my_function(): print('First command') 

This doesn't work because the indentation of the second line is missing and will throw an error:

def my_function(): print('First command') 

Case sensitivity and variables

Python is case sensitive. So the variables name and Name are not the same thing and store different values.

name = 'Renan' Name = 'Moura' 

As you can see, variables are easily created by just assigning values to them using the = symbol.

This means name stores 'Renan' and Name stores 'Moura'.

Comments

Finally, to comment something in your code, use the hash mark #.

The commented part does not influence the program flow.

# this function prints something def my_function(): print('First command') 

This was just an overview. The details of each of these will become clearer in the next chapters with examples and broader explanations.

Comments

The purpose of comments is to explain what is happening in the code.

Comments are written along with your code but do not influence your program flow.

When you work by yourself, maybe comments don't feel like something you should write. After all, at the moment, you know the whys of every single line of code.

But what if new people come on board your project after a year and the project has 3 modules, each with 10,000 lines of code?

Think about people who don't know a thing about your app and who are suddenly having to maintain it, fix it, or add new features.

Remember, there is no single solution for a given problem. Your way of solving things is yours and yours only. If you ask 10 people to solve the same problem, they will come up with 10 different solutions.

If you want others to fully understand your reasoning, good code design is mandatory, but comments are an integral part of any codebase.

How to Write Comments in Python

The syntax of comments in Python is rather easy: just use the hash mark # symbol in front of the text you want to be a comment.

#This is a comment and it won't influence my program flow 

You can use a comment to explain what some piece of code does.

#calculates the sum of any given two numbers a + b 

Multiline Comments

Maybe you want to comment on something very complex or describe how some process works in your code.

In these cases, you can use multiline comments.

To do that, just use a single hash mark # for each line.

#Everything after the hash mark # is a comment #This is a comment and it won't influence my program flow #Calculates the cost of the project given variables a and b #a is the time in months it will take until the project is finished #b is how much money it will cost per month a + b * 10 

Variables

In any program, you need to store and manipulate data to create a flow or some specific logic.

That's what variables are for.

You can have a variable to store a name, another one to store the age of a person, or even use a more complex type to store all of this at once like a dictionary.

Creating, also known as Declaring

Declaring a variable is a basic and straightforward operation in Python

Just pick a name and attribute a value to it using the = symbol.

name='Bob' age=32 

You can use the print() function to show the value of a variable.

print(name) print(age) 
Bob 32 

Notice that in Python there is no special word to declare a variable.

The moment you assign a value, the variable is created in memory.

Python also has dynamic typing, which means you don't have to tell it if your variable is a text or a number, for instance.

The interpreter infers the typing based on the value assigned.

If you need it, you can also re-declare a variable just by changing its value.

#declaring name as a string name="Bob" #re-declaring name as an int name = 32 

Keep in my mind, though, that this is not recommended since variables must have meaning and context.

If I have a variable called name I don't expect it to have a number stored in it.

Naming Conventions

Let's continue from the last section when I talked about meaning and context.

Don't use random variable names like x or y.

Say you want to store the time of a party, just call it party_time.

Oh, did you notice the underscore _?

By convention, if you want to use a variable name that is composed of two or more words, you separate them by underscores. This is called Snake Case.

Another option would be using CamelCase as in partyTime. This is very common in other languages, but not the convention in Python as stated before.

Variables are case sensitive, so party_time and Party_time are not the same. Also, keep in mind that the convention tells us to always use lower case.

Remember, use names that you can recall inside your program easily. Bad naming can cost you a lot of time and cause annoying bugs.

In summary, variable names:

  • Are Case sensitive: time and TIME are not the same
  • Have to start with an underscore _ or a letter (DO NOT start with a number)
  • Are allowed to have only numbers, letters and underscores. No special characters like: #, $, &, @, etc.

This, for instance, is not allowed: party#time, 10partytime.

Types

To store data in Python you need to use a variable. And every variable has its type depending on the value of the data stored.

Python has dynamic typing, which means you don't have to explicitly declare the type of your variable -- but if you want to, you can.

Lists, Tuples, Sets, and Dictionaries are all data types and have dedicated sections later on with more details, but we'll look at them briefly here.

This way I can show you the most important aspects and operations of each one in their own section while keeping this section more concise and focused on giving you a broad view of the main data types in Python.

Determining the Type

First of all, let's learn how to determine the data type.

Just use the type() function and pass the variable of your choice as an argument, like the example below.

print(type(my_variable)) 

Boolean

The boolean type is one of the most basic types of programming.

A boolean type variable can only represent either True or False.

my_bool = True print(type(my_bool)) my_bool = bool(1024) print(type(my_bool)) 

Numbers

There are three types of numeric types: int, float, and complex.

Integer

my_int = 32 print(type(my_int)) my_int = int(32) print(type(my_int)) 

Float

my_float = 32.85 print(type(my_float)) my_float = float(32.85) print(type(my_float)) 

Complex

my_complex_number = 32+4j print(type(my_complex_number)) my_complex_number = complex(32+4j) print(type(my_complex_number)) 

String

The text type is one of the most commons types out there and is often called string or, in Python, just str.

my_city = "New York" print(type(my_city)) #Single quotes have exactly #the same use as double quotes my_city = 'New York' print(type(my_city)) #Setting the variable type explicitly my_city = str("New York") print(type(my_city)) 

You can use the + operator to concatenate strings.

Concatenation is when you have two or more strings and you want to join them into one.

word1 = 'New ' word2 = 'York' print(word1 + word2) 
New York 

The string type has many built-in methods that let us manipulate them. I will demonstrate how some of these methods work.

The len() function returns the length of a string.

print(len('New York')) 
8 

The replace() method replaces a part of the string with another. As an example, let's replace 'New' for 'Old'.

print('New York'.replace('New', 'Old')) 
Old York 

The upper() method will return all characters as uppercase.

print('New York'.upper()) 
NEW YORK 

The lower() method does the opposite, and returns all characters as lowercase.

print('New York'.lower()) 
new york 

Lists

A list has its items ordered and you can add the same item as many times as you want. An important detail is that lists are mutable.

Mutability means you can change a list after its creation by adding items, removing them, or even just changing their values. These operations will be demonstrated later in the section dedicated to Lists.

my_list = ["bmw", "ferrari", "maclaren"] print(type(my_list)) my_list = list(("bmw", "ferrari", "maclaren")) print(type(my_list)) 

Tuples

A tuple is just like a list: ordered, and allows repetition of items.

There is just one difference: a tuple is immutable.

Immutability means you can't change a tuple after its creation. If you try to add an item or update one, for instance, the Python intepreter will show you an error. I will show that these errors occur later in the section dedicated to Tuples.

my_tuple = ("bmw", "ferrari", "maclaren") print(type(my_tuple)) my_tuple = tuple(("bmw", "ferrari", "maclaren")) print(type(my_tuple)) 

Sets

Sets don't guarantee the order of the items and are not indexed.

A key point when using sets: they don't allow repetition of an item.

my_set = {"bmw", "ferrari", "maclaren"} print(type(my_set)) my_set = set(("bmw", "ferrari", "maclaren")) print(type(my_set)) 

Dictionaries

A dictionary doesn't guarantee the order of the elements and is mutable.

One important characteristic in dictionaries is that you can set your own access keys for each element.

my_dict = {"country" : "France", "worldcups" : 2} print(type(my_dict)) my_dict = dict(country="France", worldcups=2) print(type(my_dict)) 

Typecasting

Typecasting allows you to convert between different types.

This way you can have an int turned into a str, or a float turned into an int, for instance.

Explicit conversion

To cast a variable to a string just use the str() function.

# this is just a regular explicit intialization my_str = str('32') print(my_str) # int to str my_str = str(32) print(my_str) # float to str my_str = str(32.0) print(my_str) 
32 32 32.0 

To cast a variable to an integer just use the int() function.

# this is just a regular explicit intialization my_int = int(32) print(my_int) # float to int: rounds down to 3 my_int = int(3.2) print(my_int) # str to int my_int = int('32') print(my_int) 
32 3 32 

To cast a variable to a float just use the float() function.

# this is an explicit intialization my_float = float(3.2) print(my_float) # int to float my_float = float(32) print(my_float) # str to float my_float = float('32') print(my_float) 
3.2 32.0 32.0 

What I did above is called an explicit type conversion.

In some cases you don't need to do the conversion explicitly, since Python can do it by itself.

Implicit conversion

The example below shows implicit conversion when adding an int and a float.

Notice that my_sum is float. Python uses float to avoid data loss since the int type can not represent the decimal digits.

my_int = 32 my_float = 3.2 my_sum = my_int + my_float print(my_sum) print(type(my_sum)) 
35.2  

On the other hand, in this example, when you add an int and a str, Python will not be able to make the implicit conversion, and the explicit type conversion is necessary.

my_int = 32 my_str = '32' # explicit conversion works my_sum = my_int + int(my_str) print(my_sum) #implicit conversion throws an error my_sum = my_int + my_str 
64 Traceback (most recent call last): File "", line 1, in  TypeError: unsupported operand type(s) for +: 'int' and 'str' 

The same error is thrown when trying to add float and str types without making an explicit conversion.

my_float = 3.2 my_str = '32' # explicit conversion works my_sum = my_float + float(my_str) print(my_sum) #implicit conversion throws an error my_sum = my_float + my_str 
35.2 Traceback (most recent call last): File "", line 1, in  TypeError: unsupported operand type(s) for +: 'float' and 'str' 

User Input

If you need to interact with a user when running your program in the command line (for example, to ask for a piece of information), you can use the input() function.

country = input("What is your country? ") #user enters 'Brazil' print(country) 
Brazil 

The captured value is always string. Just remember that you might need to convert it using typecasting.

age = input("How old are you? ") #user enters '29' print(age) print(type(age)) age = int(age) print(type(age)) 

The output for each print() is:

29   

Notice the age 29 is captured as string and then converted explicitly to int.

Operators

In a programming language, operators are special symbols that you can apply to your variables and values in order to perform operations such as arithmetic/mathematical and comparison.

Python has lots of operators that you can apply to your variables and I will demonstrate the most used ones.

Arithmetic Operators

Arithmetic operators are the most common type of operators and also the most recognizable ones.

They allow you to perform simple mathematical operations.

They are:

  • +: Addition
  • -: Subtraction
  • *: Multiplication
  • /: Division
  • **: Exponentiation
  • //: Floor Division, rounds down the result of a division
  • %: Modulus, gives you the remainder of a division

Let's see a program that shows how each of them is used.

print('Addition:', 5 + 2) print('Subtraction:', 5 - 2) print('Multiplication:', 5 * 2) print('Division:', 5 / 2) print('Floor Division:', 5 // 2) print('Exponentiation:', 5 ** 2) print('Modulus:', 5 % 2) 
Addition: 7 Subtraction: 3 Multiplication: 10 Division: 2.5 Floor Division: 2 Exponentiation: 25 Modulus: 1 

Concatenation

Concatenation is when you have two or more strings and you want to join them into one.

This useful when you have information in multiple variables and want to combine them.

For instance, in this next example I combine two variables that contain my first name and my last name respectively to have my full name.

The + operator is also used to concatenate.

first_name = 'Renan ' last_name = 'Moura' print(first_name + last_name) 
Renan Moura 

Since concatenation is applied to strings, to concatenate strings with other types, you have to do an explicit typecast using str().

I have to typecast the int value 30 to string with str() to concatenate it with the rest of the string.

age = 'I have ' + str(30) + ' years old' print(age) 
I have 30 years old 

Comparison Operators

Use comparison operators to compare two values.

These operators return either True or False.

They are:

  • ==: Equal
  • !=: Not equal
  • >: Greater than
  • <: Less than
  • >=: Greater than or equal to
  • <=: Less than or equal to

Let's see a program that shows how each of them is used.

print('Equal:', 5 == 2) print('Not equal:', 5 != 2) print('Greater than:', 5 > 2) print('Less than:', 5 = 2) print('Less than or equal to:', 5 <= 2) 
Equal: False Not equal: True Greater than: True Less than: False Greater than or equal to: True Less than or equal to: False 

Assignment Operators

As the name implies, these operators are used to assign values to variables.

x = 7 in the first example is a direct assignment storing the number 7 in the variable x.

The assignment operation takes the value on the right and assigns it to the variable on the left.

The other operators are simple shorthands for the Arithmetic Operators.

In the second example x starts with 7 and x += 2 is just another way to write x = x + 2. This means the previous value of x is added by 2 and reassigned to x that is now equal to 9.

  • =: simple assignment
x = 7 print(x) 
7 
  • +=: addition and assignment
x = 7 x += 2 print(x) 
9 
  • -=: subtraction and assignment
x = 7 x -= 2 print(x) 
5 
  • *=: multiplication and assignment
x = 7 x *= 2 print(x) 
14 
  • /=: division and assignment
x = 7 x /= 2 print(x) 
3.5 
  • %=: modulus and assignment
x = 7 x %= 2 print(x) 
1 
  • //=: floor division and assignment
x = 7 x //= 2 print(x) 
3 
  • **=: exponentiation and assignment
x = 7 x **= 2 print(x) 
49 

Logical Operators

Logical operators are used to combine statements applying boolean algebra.

They are:

  • and: True only when both statements are true
  • or: False only when both x and y are false
  • not: The not operator simply inverts the input, True becomes False and vice versa.

Let's see a program that shows how each one is used.

x = 5 y = 2 print(x == 5 and y > 3) print(x == 5 or y > 3) print(not (x == 5)) 
False True False 

Membership Operators

These operators provide an easy way to check if a certain object is present in a sequence: string, list, tuple, set, and dictionary.

They are:

  • in: returns True if the object is present
  • not in: returns True if the object is not present

Let's see a program that shows how each one is used.

number_list = [1, 2, 4, 5, 6] print( 1 in number_list) print( 5 not in number_list) print( 3 not in number_list) 
True False True 

Conditionals

Conditionals are one of the cornerstones of any programming language.

They allow you to control the program flow according to specific conditions you can check.

The if statement

The way you implement a conditional is through the if statement.

The general form of an if statement is:

if expression: statement 

The expression contains some logic that returns a boolean, and the statement is executed only if the return is True.

A simple example:

bob_age = 32 sarah_age = 29 if bob_age > sarah_age: print('Bob is older than Sarah') 
Bob is older than Sarah 

We have two variables indicating the ages of Bob and Sarah. The condition in plain English says "if Bob's age is greater than Sarah's age, then print the phrase 'Bob is older than Sarah'".

Since the condition returns True, the phrase will be printed on the console.

The if else and elif statements

In our last example, the program only does something if the condition returns True.

But we also want it to do something if it returns False or even check a second or third condition if the first one wasn't met.

In this example, we swapped Bob's and Sarah's age. The first condition will return False since Sarah is older now, and then the program will print the phrase after the else instead.

bob_age = 29 sarah_age = 32 if bob_age > sarah_age: print('Bob is older than Sarah') else: print('Bob is younger than Sarah') 
Bob is younger than Sarah 

Now, consider the example below with the elif.

bob_age = 32 sarah_age = 32 if bob_age > sarah_age: print('Bob is older than Sarah') elif bob_age == sarah_age: print('Bob and Sarah have the same age') else: print('Bob is younger than Sarah') 
Bob and Sarah have the same age 

The purpose of the elif is to provide a new condition to be checked before the else is executed.

Once again we changed their ages and now both are 32 years old.

As such, the condition in the elif is met. Since both have the same age the program will print "Bob and Sarah have the same age".

Notice you can have as many elifs as you want, just put them in sequence.

bob_age = 32 sarah_age = 32 if bob_age > sarah_age: print('Bob is older than Sarah') elif bob_age < sarah_age: print('Bob is younger than Sarah') elif bob_age == sarah_age: print('Bob and Sarah have the same age') else: print('This one is never executed') 
Bob and Sarah have the same age 

In this example, the else is never executed because all the possibilities are covered in the previous conditions and thus could be removed.

Nested conditionals

You might need to check more than one conditional for something to happen.

In this case, you can nest your if statements.

For instance, the second phrase "Bob is the oldest" is printed only if both ifs pass.

bob_age = 32 sarah_age = 28 mary_age = 25 if bob_age > sarah_age: print('Bob is older than Sarah') if bob_age > mary_age: print('Bob is the oldest') 
Bob is older than Sarah Bob is the oldest 

Or, depending on the logic, make it simpler with Boolean Algebra.

This way, your code is smaller, more readable and easier to maintain.

bob_age = 32 sarah_age = 28 mary_age = 25 if bob_age > sarah_age and bob_age > mary_age: print('Bob is the oldest') 
Bob is the oldest 

Ternary Operators

The ternary operator is a one-line if statement.

It's very handy for simple conditions.

This is how it looks:

 if  else  

Consider the following Python code:

a = 25 b = 50 x = 0 y = 1 result = x if a > b else y print(result) 
1 

Here we use four variables, a and b are for the condition, while x and y represent the expressions.

a and b are the values we are checking against each other to evaluate some condition. In this case, we are checking if a is greater than b.

If the expression holds true, i.e., a is greater than b, then the value of x will be attributed to result which will be equal to 0.

However, if a is less than b, then we have the value of y assigned to result, and result will hold the value 1.

Since a is less than b, 25 < 50, result will have 1 as final value from y.

The easy way to remember how the condition is evaluated is to read it in plain English.

Our example would read: result will be x if a is greater than b otherwise y.

Lists

As promised in the Types section, this section and the next three about Tuples, Sets, and Dictionaries will have more in depth explanations of each of them since they are very important and broadly used structures in Python to organize and deal with data.

A list has its items ordered and you can add the same item as many times as you want.

An important detail is that lists are mutable.

Mutability means you can change a list after its creation by adding items, removing them, or even just changing their values.

Initialization

Empty List

people = [] 

List with initial values

people = ['Bob', 'Mary'] 

Adding in a List

To add an item in the end of a list, use append().

people = ['Bob', 'Mary'] people.append('Sarah') print(people) 
['Bob', 'Mary', 'Sarah'] 

To specify the position for the new item, use the insert() method.

people = ['Bob', 'Mary'] people.insert(0, 'Sarah') print(people) 
['Sarah', 'Bob', 'Mary'] 

Updating in a List

Specify the position of the item to update and set the new value

people = ['Bob', 'Mary'] people[1] = 'Sarah' print(people) 
['Bob', 'Sarah'] 

Deleting in a List

Use the remove() method to delete the item given as an argument.

people = ['Bob', 'Mary'] people.remove('Bob') print(people) 
['Mary'] 

To delete everybody, use the clear() method:

people = ['Bob', 'Mary'] people.clear() 

Retrieving in a List

Use the index to reference the item.

Remember that the index starts at 0.

So to access the second item use the index 1.

people = ['Bob', 'Mary'] print(people[1]) 
Mary 

Check if a given item already exists in a List

people = ['Bob', 'Mary'] if 'Bob' in people: print('Bob exists!') else: print('There is no Bob!') 

Tuples

A tuple is similar to a list: it's ordered, and allows repetition of items.

There is just one difference: a tuple is immutable.

Immutability, if you remember, means you can't change a tuple after its creation. If you try to add an item or update one, for instance, the Python interpreter will show you an error.

Initialization

Empty Tuple

people = () 

Tuple with initial values

people = ('Bob', 'Mary') 

Adding in a Tuple

Tuples are immutable. This means that if you try to add an item, you will see an error.

people = ('Bob', 'Mary') people[2] = 'Sarah' 
Traceback (most recent call last): File "", line 1, in  TypeError: 'tuple' object does not support item assignment 

Updating in a Tuple

Updating an item will also return an error.

But there is a trick: you can convert into a list, change the item, and then convert it back to a tuple.

people = ('Bob', 'Mary') people_list = list(people) people_list[1] = 'Sarah' people = tuple(people_list) print(people) 
('Bob', 'Sarah') 

Deleting in a Tuple

For the same reason you can't add an item, you also can't delete an item, since they are immutable.

Retrieving in a Tuple

Use the index to reference the item.

people = ('Bob', 'Mary') print(people[1]) 
Mary 

Check if a given item already exists in a Tuple

people = ('Bob', 'Mary') if 'Bob' in people: print('Bob exists!') else: print('There is no Bob!') 

Sets

Sets don't guarantee the order of items and are not indexed.

A key point when using sets: they don't allow repetition of an item.

Initialization

Empty Set

people = set() 

Set with initial values

people = {'Bob', 'Mary'} 

Adding in a Set

Use the add() method to add one item.

people.add('Sarah') 

Use the update() method to add multiple items at once.

people.update(['Carol', 'Susan']) 

Remember, Sets do not allow repetition, so if you add 'Mary' again, nothing changes.

people = {'Bob', 'Mary'} people.add('Mary') print(people) 
{'Bob', 'Mary'} 

Updating in a Set

Items in a set are not mutable. You have to either add or delete an item.

Deleting in a Set

To remove Bob from the dictionary:

people = {'Bob', 'Mary'} people.remove('Bob') print(people) 
{'Mary'} 

To delete everybody:

people.clear() 

Check if a given item already exists in a set

people = {'Bob', 'Mary'} if 'Bob' in people: print('Bob exists!') else: print('There is no Bob!') 

Dictionaries

The dictionary doesn't guarantee the order of the elements and it is mutable.

One important characteristic of dictionaries is that you can set your customized access keys for each element.

Initialization of a Dictionary

Empty Dictionary

people = {} 

Dictionary with initial values

people = {'Bob':30, 'Mary':25} 

Adding in a Dictionary

If the key doesn't exist yet, it is appended to the dictionary.

people['Sarah']=32 

Updating a Dictionary

If the key already exists, the value is just updated.

#Bob's age is 28 now people['Bob']=28 

Notice that the code is pretty much the same.

Deleting in a Dictionary

To remove Bob from the dictionary:

people.pop('Bob') 

To delete everybody:

people.clear() 

Retrieving in a Dictionary

bob_age = people['Bob'] print(bob_age) 
30 

Check if a given key already exists in a Dictionary

if 'Bob' in people: print('Bob exists!') else: print('There is no Bob!') 

while Loops

Loops are used when you need to repeat a block of code a certain number of times or apply the same logic over each item in a collection.

There are two types of loops: for and while.

You will learn about for loops in the next section.

Basic Syntax

The basic syntax of a while loop is as below.

while condition: statement 

The loop will continue while the condition is True.

The square of a number is

The example below takes each value of number and calculates its squared value.

number = 1 while number <= 5: print(number, 'squared is', number**2) number = number + 1 
1 squared is 1 2 squared is 4 3 squared is 9 4 squared is 16 5 squared is 25 

You can use any variable name, but I chose number because it makes sense in the context. A common generic choice would be simply i.

The loop will go on until number (initialized with 1) is less than or equal to 5.

Notice that after the print() command, the variable number is incremented by 1 to take the next value.

If you don't do the incrementation you will have an infinite loop since number will never reach a value greater than 5. This is a very important detail!

else block

When the condition returns False, the else block will be called.

number = 1 while number <= 5: print(number, 'squared is', number**2) number = number + 1 else: print('No numbers left!') 
1 squared is 1 2 squared is 4 3 squared is 9 4 squared is 16 5 squared is 25 No numbers left! 

Notice the phrase 'No numbers left!' is printed after the loop ends, that is after the condition number <= 5 evaluates to False.

How to break out of a while loop in Python

Simply use the break keyword, and the loop will stop its execution.

number = 1 while number <= 5: print(number, 'squared is', number**2) number = number + 1 if number == 4: break 
1 squared is 1 2 squared is 4 3 squared is 9 

The loop runs normally, and when number reaches 4 the if statement evaluates to True and the break command is called. This finishes the loop before the squared value of the numbers 4 and 5 are calculated.

How to skip an item in a while loop

The continue will do that for you.

I had to invert the order of the if statement and the print() to show how it works properly.

number = 0 while number < 5: number = number + 1 if number == 4: continue print(number, 'squared is', number**2) 
1 squared is 1 2 squared is 4 3 squared is 9 5 squared is 25 

The program always checks if 4 is the current value of number. If it is, the square of 4 won't be calculated and the continue will skip to the next iteration when the value of number is 5.

*

for Loops

for loops are similar to while loops in the sense that they are used to repeat blocks of code.

The most important difference is that you can easily iterate over sequential types.

Basic Syntax

The basic syntax of a for loop is as below.

for item in collection: statement 

Loop over a list

To loop over a list or any other collection, just proceed as shown in the example below.

cars = ['BMW', 'Ferrari', 'McLaren'] for car in cars: print(car) 
BMW Ferrari McLaren 

The list of cars contains three items. The for loop will iterate over the list and store each item in the car variable, and then execute a statement, in this case print(car), to print each car in the console.

range() function

The range function is widely used in for loops because it gives you a simple way to list numbers.

This code will loop through the numbers 0 to 5 and print each of them.

for number in range(5): print(number) 
0 1 2 3 4 

In contrast, without the range() function, we would do something like this.

numbers = [0, 1, 2, 3, 4] for number in numbers: print(number) 
0 1 2 3 4 

You can also define a start and stop using range().

Here we are starting in 5 and a stoping in 10. The number you set to stop is not included.

for number in range(5, 10): print(number) 
5 6 7 8 9 

Finally, it is also possible to set a step.

Here we starting in 10 and incrementing by 2 until 20, since 20 is the stop, it is not included.

for number in range(10, 20, 2): print(number) 
10 12 14 16 18 

else block

When the items in the list are over, the else block will be called.

cars = ['BMW', 'Ferrari', 'McLaren'] for car in cars: print(car) else: print('No cars left!') 
BMW Ferrari McLaren No cars left! 

How to break out of a for loop in Python

Simply use the break keyword, and the loop will stop its execution.

cars = ['BMW', 'Ferrari', 'McLaren'] for car in cars: print(car) if car == 'Ferrari': break 
BMW Ferrari 

The loop will iterate through the list and print each car.

In this case, after the loop reaches 'Ferrari', the break is called and 'McLaren' won't be printed.

How to skip an item in a for loop

In this section, we'll learn how continue can do this for you.

I had to invert the order of the if statement and the continue to show how it works properly.

Notice that I always check if 'Ferrari' is the current item. If it is, 'Ferrari' won't be printed, and the continue will skip to the next item 'McLaren'.

cars = ['BMW', 'Ferrari', 'McLaren'] for car in cars: if car == 'Ferrari': continue print(car) 
BMW McLaren 

Loop over a Loop: Nested Loops

Sometimes you have more complex collections, like a list of lists.

To iterate over these lists, you need nested for loops.

In this case, I have three lists: one of BMW models, another on Ferrari models, and finally one with McLaren models.

The first loop iterates over each brand's list, and the second will iterate over the models of each brand.

car_models = [ ['BMW I8', 'BMW X3', 'BMW X1'], ['Ferrari 812', 'Ferrari F8', 'Ferrari GTC4'], ['McLaren 570S', 'McLaren 570GT', 'McLaren 720S'] ] for brand in car_models: for model in brand: print(model) 
BMW I8 BMW X3 BMW X1 Ferrari 812 Ferrari F8 Ferrari GTC4 McLaren 570S McLaren 570GT McLaren 720S 

Loop over other Data Structures

The same logic that applies for loops over a list can be extended to the other data structures: tuple, set, and dictionary.

I will briefly demonstrate how to make a basic loop over each one of them.

Loop over a Tuple

people = ('Bob', 'Mary') for person in people: print(person) 
Bob Mary 

Loop over a Set

people = {'Bob', 'Mary'} for person in people: print(person) 
Bob Mary 

Loop over a Dictionary

To print the keys:

people = {'Bob':30, 'Mary':25} for person in people: print(person) 
Bob Mary 

To print the values:

people = {'Bob':30, 'Mary':25} for person in people: print(people[person]) 
30 25 

Functions

As the code grows the complexity also grows. And functions help organize the code.

Functions are a handy way to create blocks of code that you can reuse.

Definition and Calling

In Python use the def keyword to define a function.

Give it a name and use parentheses to inform 0 or more arguments.

In the line after the declaration starts, remember to indent the block of code.

Here is an example of a function called print_first_function() that only prints a phrase 'My first function!'.

To call the function just use its name as defined.

def print_first_function(): print('My first function!') print_first_function() 
My first function! 

return a value

Use the return keyword to return a value from the function.

In this example the function second_function() returns the string 'My second function!'.

Notice that print() is a built-in function and our function is called from inside it.

The string returned by second_function() is passed as an argument to the print() function.

def second_function(): return 'My second function!' print(second_function()) 
My second function! 

return multiple values

Functions can also return multiple values at once.

return_numbers() returns two values simultaneously.

def return_numbers(): return 10, 2 print(return_numbers()) 
(10, 2) 

Arguments

You can define parameters between the parentheses.

When calling a function with parameters you have to pass arguments according to the parameters defined.

The past examples had no parameters, so there was no need for arguments. The parentheses remained empty when the functions were called.

One Argument

To specify one parameter, just define it inside the parentheses.

In this example, the function my_number expects one number as argument defined by the parameter num.

The value of the argument is then accessible inside the function to be used.

def my_number(num): return 'My number is: ' + str(num) print(my_number(10)) 
My number is: 10 

Two or more Arguments

To define more parameters, just use a comma to separate them.

Here we have a function that adds two numbers called add. It expects two arguments defined by first_num and second_num.

The arguments are added by the + operator and the result is then returned by the return.

def add(first_num, second_num): return first_num + second_num print(add(10,2)) 
12 

This example is very similar to the last one. The only difference is that we have 3 parameters instead of 2.

def add(first_num, second_num, third_num): return first_num + second_num + third_num print(add(10,2,3)) 
15 

This logic of defining parameters and passing arguments is the same for any number of parameters.

It is important to point out that the arguments have to be passed in the same order that the parameters are defined.

Default value.

You can set a default value for a parameter if no argument is given using the = operator and a value of choice.

In this function, if no argument is given, the number 30 is assumed as the expected value by default.

def my_number(my_number = 30): return 'My number is: ' + str(my_number) print(my_number(10)) print(my_number()) 
My number is: 10 My number is: 30 

Keyword or Named Arguments

When calling a function, the order of the arguments have to match the order of the parameters.

The alternative is if you use keyword or named arguments.

Set the arguments to their respective parameters directly using the name of the parameters and the = operators.

This example flips the arguments, but the function works as expected because I tell it which value goes to which parameter by name.

def my_numbers(first_number, second_number): return 'The numbers are: ' + str(first_number) + ' and ' + str(second_number) print(my_numbers(second_number=30, first_number=10)) 
The numbers are: 10 and 30 

Any number of arguments: *args

If you don't want to specify the number of parameters, just use the * before the parameter name. Then the function will take as many arguments as necessary.

The parameter name could be anything like *numbers, but there is a convention in Python to use *args for this definition of a variable number of arguments.

def my_numbers(*args): for arg in args: print(number) my_numbers(10,2,3) 
10 2 3 

Any number of Keyword/Named arguments: **kwargs

Similar to *args, we can use **kwargs to pass as many keyword arguments as we want, as long as we use **.

Again, the name could be anything like **numbers, but **kwargs is a convention.

def my_numbers(**kwargs): for key, value in kwargs.items(): print(key) print(value) my_numbers(first_number=30, second_number=10) 
first_number 30 second_number 10 

Other types as arguments

The past examples used mainly numbers, but you can pass any type as argument and they will be treated as such inside the function.

This example takes strings as arguments.

def my_sport(sport): print('I like ' + sport) my_sport('football') my_sport('swimming') 
I like football I like swimming 

This function takes a list as argument.

def my_numbers(numbers): for number in numbers: print(number) my_numbers([30, 10, 64, 92, 105]) 
30 10 64 92 105 

Scope

The place where a variable is created defines its availability to be accessed and manipulated by the rest of the code. This is known as scope.

There are two types of scope: local and global.

Global Scope

A global scope allows you to use the variable anywhere in your program.

If your variable is outside a function, it has global scope by default.

name = 'Bob' def print_name(): print('My name is ' + name) print_name() 
My name is Bob 

Notice that the function could use the variable name and print My name is Bob.

Local Scope

When you declare a variable inside a function, it only exists inside that function and can't be accessed from the outside.

def print_name(): name = "Bob" print('My name is ' + name) print_name() 
My name is Bob 

The variable name was declared inside the function, so the output is the same as before.

But this will throw an error:

def print_name(): name = 'Bob' print('My name is ' + name) print(name) 

The output of the code above is:

Traceback (most recent call last): File "", line 1, in  NameError: name 'name' is not defined 

I tried to print the variable name from outside the function, but the scope of the variable was local and could not be found in a global scope.

Mixing Scopes

If you use the same name for variables inside and outside a function, the function will use the one inside its scope.

So when you call print_name(), the name='Bob' is used to print the phrase.

On the other hand, when calling print() outside the function scope, name="Sarah" is used because of its global scope.

name = "Sarah" def print_name(): name = 'Bob' print('My name is ' + name) print_name() print(name) 

The output of the code above is:

My name is Bob Sarah 

List Comprehensions

Sometimes we want to perform some very simple operations over the items of a list.

List comprehensions give us a succinct way to work on lists as an alternative to other methods of iteration, such as for loops.

Basic syntax

To use a list comprehension to replace a regular for loop, we can make:

[expression for item in list] 

Which is the same as doing:

for item in list: expression 

If we want some conditional to apply the expression, we have:

[expression for item in list if conditional ] 

Which is the same as doing:

for item in list: if conditional: expression 

Example 1: calculating the cube of a number

Regular way

numbers = [1, 2, 3, 4, 5] new_list = [] for n in numbers: new_list.append(n**3) print(new_list) 
[1, 8, 27, 64, 125] 

Using list comprehensions

numbers = [1, 2, 3, 4, 5] new_list = [] new_list = [n**3 for n in numbers] print(new_list) 
 [1, 8, 27, 64, 125] 

Example 2: calculating the cube of a number only if it is greater than 3

Using the conditional, we can filter only the values we want the expression to be applied to.

Regular way

numbers = [1, 2, 3, 4, 5] new_list = [] for n in numbers: if(n > 3): new_list.append(n**3) print(new_list) 
[64, 125] 

Using list comprehensions

numbers = [1, 2, 3, 4, 5] new_list = [] new_list = [n**3 for n in numbers if n > 3] print(new_list) 
[64, 125] 

Example 3: calling functions with list comprehensions

We can also call functions using the list comprehension syntax:

numbers = [1, 2, 3, 4, 5] new_list = [] def cube(number): return number**3 new_list = [cube(n) for n in numbers if n > 3] print(new_list) 
[64, 125] 

Lambda Functions

A Python lambda function can only have one expression and can't have multiple lines.

It is supposed to make it easier to create some small logic in one line instead of a whole function as is usually done.

Lambda functions are also anonymous, which means there is no need to name them.

Basic Syntax

The basic syntax is very simple: just use the lambda keyword, define the parameters needed, and use ":" to separate the parameters from the expression.

The general forms is:

lambda arguments : expression 

One parameter example

Look at this example using only one parameter

cubic = lambda number : number**3 print(cubic(2)) 
8 

Multiple parameter example

If you want, you can also have multiple parameters.

exponential = lambda multiplier, number, exponent : multiplier * number**exponent print(exponential(2, 2, 3)) 
16 

Calling the Lambda Function directly

You don't need to use a variable as we did before. Instead you can make use of parenthesis around the lambda function and another pair of parenthesis around the arguments.

The declaration of the function and the execution will happen in the same line.

(lambda multiplier, number, exponent : multiplier * number**exponent)(2, 2, 3) 
16 

Examples using lambda functions with other built-in functions

Map

The Map function applies the expression to each item in a list.

Let's calculate the cubic of each number in the list.

numbers = [2, 5, 10] cubics = list(map(lambda number : number**3, numbers)) print(cubics) 
[8, 125, 1000] 

Filter

The Filter function will filter the list based on the expression.

Let's filter to have only the numbers greater than 5.

numbers = [2, 5, 10] filtered_list = list(filter(lambda number : number > 5, numbers)) print(filtered_list) 
[10] 

Modules

After some time your code starts to get more complex with lots of functions and variables.

To make it easier to organize the code we use Modules.

A well-designed Module also has the advantage of being reusable, so you write code once and reuse it everywhere.

You can write a module with all the mathematical operations and other people can use it.

And, if you need, you can use someone else's modules to simplify your code, speeding up your project.

In other programming languages, these are also referred to as libraries.

Using a Module

To use a module we use the import keyword.

As the name implies we have to tell our program what module to import.

After that, we can use any function available in that module.

Let's see an example using the math module.

First, let's see how to have access to a constant, Euler's number.

import math math.e 
2.718281828459045 

In this second example, we are going to use a function that calculates the square root of a number.

It is also possible to use the as keyword to create an alias.

import math as m m.sqrt(121) m.sqrt(729) 
11 27 

Finally, using the from keyword, we can specify exactly what to import instead of the whole module and use the function directly without the module's name.

This example uses the floor() function that returns the largest integer less than or equal to a given number.

from math import floor floor(9.8923) 
9 

Creating a Module

Now that we know how to use modules, let's see how to create one.

It is going to be a module with the basic math operations add, subtract, multiply, divide and it is gonna be called basic_operations.

Create the basic_operations.py file with the four functions.

def add(first_num, second_num): return first_num + second_num def subtract(first_num, second_num): return first_num - second_num def multiply(first_num, second_num): return first_num * second_num def divide(first_num, second_num): return first_num / second_num 

Then, just import the basic_operations module and use the functions.

import basic_operations basic_operations.add(10,2) basic_operations.subtract(10,2) basic_operations.multiply(10,2) basic_operations.divide(10,2) 
12 8 20 5.0 

if __name__ == '__main__'

You are in the process of building a module with the basic math operations add, subtract, multiply, and divide called basic_operations saved in the basic_operations.py file.

To guarantee everything is fine, you can run some tests.

def add(first_num, second_num): return first_num + second_num def subtract(first_num, second_num): return first_num - second_num def multiply(first_num, second_num): return first_num * second_num def divide(first_num, second_num): return first_num / second_num print(add(10, 2)) print(subtract(10,2)) print(multiply(10,2)) print(divide(10,2)) 

After running the code:

[email protected]:~$ python3 basic_operations.py 

The output is:

12 8 20 5.0 

The output for those tests are what we expected.

Our code is right and ready to share.

Let's import the new module run it in the Python console.

Python 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import basic_operations 12 8 20 5.0 >>>

When the module is imported our tests are displayed on the screen even though we didn't do anything besides importing basic_operations.

To fix that we use if __name__ == '__main__' in the basic_operations.py file like this:

def add(first_num, second_num): return first_num + second_num def subtract(first_num, second_num): return first_num - second_num def multiply(first_num, second_num): return first_num * second_num def divide(first_num, second_num): return first_num / second_num if __name__ == '__main__': print(add(10, 2)) print(subtract(10,2)) print(multiply(10,2)) print(divide(10,2)) 

Running the code directly on the terminal will continue to display the tests. But when you import it like a module, the tests won't show and you can use the functions the way you intended.

Python 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import basic_operations >>> basic_operations.multiply(10,2) 20 >>>

Now that you know how to use the if __name__ == '__main__', let's understand how it works.

The condition tells when the interpreter is treating the code as a module or as a program itself being executed directly.

Python has this native variable called __name__.

This variable represents the name of the module which is the name of the .py file.

Create a file my_program.py with the following and execute it.

print(__name__) 

The output will be:

__main__ 

This tells us that when a program is executed directly, the variable __name__ is defined as __main__.

But when it is imported as a module, the value of __name__ is the name of the module.

Python 3.6.9 (default, Nov 7 2019, 10:44:02) [GCC 8.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import my_program my_program >>>

This is how the Python interpreter differentiates the behavior of an imported module and a program executed directly on the terminal.

Files

Creating, deleting, reading, and many other functions applied to files are an integral part of many programs.

As such, it is very important to know how to organize and deal with files directly from your code.

Let's see how to handle files in Python.

File create

First things first, create!

We are going to use the open() function.

This function opens a file and returns its corresponding object.

The first argument is the name of the file we are handling, the second refers to the operation we are using.

The code below creates the file "people.txt", the x argument is used when we just want to create the file. If a file with the same name already exists, it will throw an exception.

people_file = open("people.txt", "x") 

You can also use the w mode to create a file. Unlike the x mode, it will not throw an exception since this mode indicates the writing mode. We are opening a file to write data into it and, if the file doesn't exist, it is created.

people_file = open("people.txt", "w") 

The last one is the a mode which stands for append. As the name implies, you can append more data to the file, while the w mode simply overwrites any existing data.

When appending, if the file doesn't exist, it also creates it.

people_file = open("people.txt", "a") 

File write

To write data into a file, you simply open a file with the w mode.

Then, to add data, you use the object return by the open() function. In this case, the object is called people_file. Then you call the write() function passing the data as argument.

people_file = open("people.txt", "w") people_file.write("Bob\n") people_file.write("Mary\n") people_file.write("Sarah\n") people_file.close() 

We use \n at the end to break the line, otherwise the content in the file will stay in the same line as "BobMarySarah".

One more detail is to close() the file. This is not only a good practice, but also ensures that your changes were applied to the file.

Remember that when using w mode, the data that already existed in the file will be overwritten by the new data. To add new data without losing what was already there, we have to use the append mode.

File append

The a mode appends new data to the file, keeping the existing one.

In this example, after the first writing with w mode, we are using the a mode to append. The result is that each name will appear twice in the file "people.txt".

#first write people_file = open("people.txt", "w") people_file.write("Bob\n") people_file.write("Mary\n") people_file.write("Sarah\n") people_file.close() #appending more data #keeping the existing data people_file = open("people.txt", "a") people_file.write("Bob\n") people_file.write("Mary\n") people_file.write("Sarah\n") people_file.close() 

File read

Reading the file is also very straightforward: just use the r mode like so.

If you read the "people.txt" file created in the last example, you should see 6 names in your output.

people_file = open("people.txt", "r") print(people_file.read()) 
Bob Mary Sarah Bob Mary Sarah 

The read() function reads the whole file at once. If you use the readline() function, you can read the file line by line.

people_file = open("people.txt", "r") print(people_file.readline()) print(people_file.readline()) print(people_file.readline()) 
Bob Mary Sarah 

You can also loop to read the lines like the example below.

people_file = open("people.txt", "r") for person in people_file: print(person) 
Bob Mary Sarah Bob Mary Sarah 

Delete a File

To delete a file, you also need the os module.

Use the remove() method.

import os os.remove('my_file.txt') 

Check if a File Exists

Use the os.path.exists() method to check the existence of a file.

import os if os.path.exists('my_file.txt'): os.remove('my_file.txt') else: print('There is no such file!') 

Copy a File

For this one, I like to use the copyfile() method from the shutil module.

from shutil import copyfile copyfile('my_file.txt','another_file.txt') 

There are a few options to copy a file, but copyfile() is the fastest one.

Rename and Move a File

If you need to move or rename a file you can use the move() method from the shutil module.

from shutil import move move('my_file.txt','another_file.txt') 

Classes and Objects

Classes and Objects are the fundamental concepts of Object-Oriented Programming.

In Python, everything is an object!

A variable (object) is just an instance of its type (class).

That's why when you check the type of a variable you can see the class keyword right next to its type (class).

This code snippet shows that my_city is an object and it is an instance of the class str.

my_city = "New York" print(type(my_city)) 

Differentiate Class x Object

The class gives you a standard way to create objects. A class is like a base project.

Say you are an engineer working for Boeing.

Your new mission is to build the new product for the company, a new model called 747-Space. This aircraft flies higher altitudes than other commercial models.

Boeing needs to build dozens of those to sell to airlines all over the world, and the aircrafts have to be all the same.

To guarantee that the aircrafts (objects) follow the same standards, you need to have a project (class) that can be replicable.

The class is a project, a blueprint for an object.

This way you make the project once, and reuse it many times.

In our code example before, consider that every string has the same behavior and the same attributes. So it only makes sense for strings to have a class str to define them.

Attributes and Methods

Objects have some behavior which is is given by attributes and methods.

In simple terms, in the context of an object, attributes are variables and methods are functions attached to an object.

For example, a string has many built-in methods that we can use.

They work like functions, you just need to separate them from the objects using a ..

In this code snippet, I'm calling the replace() method from the string variable my_city which is an object, and an instance of the class str.

The replace() method replaces a part of the string for another and returns a new string with the change. The original string remains the same.

Let's replace 'New' for 'Old' in 'New York'.

my_city = 'New York' print(my_city.replace('New', 'Old')) print(my_city) 
Old York New York 

Creating a Class

We have used many objects (instances of classes) like strings, integers, lists, and dictionaries. All of them are instances of predefined classes in Python.

To create our own classes we use the class keyword.

By convention, the name of the class matches the name of the .py file and the module by consequence. It is also a good practice to organize the code.

Create a file vehicle.py with the following class Vehicle.

class Vehicle: def __init__(self, year, model, plate_number, current_speed = 0): self.year = year self.model = model self.plate_number = plate_number self.current_speed = current_speed def move(self): self.current_speed += 1 def accelerate(self, value): self.current_speed += value def stop(self): self.current_speed = 0 def vehicle_details(self): return self.model + ', ' + str(self.year) + ', ' + self.plate_number 

Let's break down the class to explain it in parts.

The class keyword is used to specify the name of the class Vehicle.

The __init__ function is a built-in function that all classes have. It is called when an object is created and is often used to initialize the attributes, assigning values to them, similar to what is done to variables.

The first parameter self in the __init__ function is a reference to the object (instance) itself. We call it self by convention and it has to be the first parameter in every instance method, as you can see in the other method definitions def move(self), def accelerate(self, value), def stop(self), and def vehicle_details(self).

Vehicle has 5 attributes (including self): year, model, plate_number, and current_speed.

Inside the __init__, each one of them is initialized with the parameters given when the object is instantiated.

Notice that current_speed is initialized with 0 by default, meaning that if no value is given, current_speed will be equal to 0 when the object is first instantiated.

Finally, we have three methods to manipulate our vehicle regarding its speed: def move(self), def accelerate(self, value), and def stop(self).

And one method to give back information about the vehicle: def vehicle_details(self).

The implementation inside the methods work the same way as in functions. You can also have a return to give you back some value at the end of the method as demonstrated by def vehicle_details(self).

Using the Class

To use the class in your terminal, import the Vehicle class from the vehicle module.

Create an instance called my_car, initializing year with 2009, model with 'F8', plate_number with 'ABC1234', and current_speed with 100.

The self parameter is not taken into consideration when calling methods. The Python interpreter infers its value as being the current object/instance automatically, so we just have to pass the other arguments when instantiating and calling methods.

Now use the methods to move() the car which increases its current_speed by 1, accelerate(10) which increases its current_speed by the value given in the argument, and stop() which sets the current_speed to 0.

Remember to print the value of current_speed at every command to see the changes.

To finish the test, call vehicle_details() to print the information about our vehicle.

>>> from vehicle import Vehicle >>> >>> my_car = Vehicle(2009, 'F8', 'ABC1234', 100) >>> print(my_car.current_speed) 100 >>> my_car.move() >>> print(my_car.current_speed) 101 >>> my_car.accelerate(10) >>> print(my_car.current_speed) 111 >>> my_car.stop() >>> print(my_car.current_speed) 0 >>> print(my_car.vehicle_details()) F8, 2009, ABC1234 

If we don't set the initial value for current_speed, it will be zero by default as stated before and demonstrated in the next example.

>>> from vehicle import Vehicle >>> >>> my_car = Vehicle(2009, 'F8', 'ABC1234') >>> print(my_car.current_speed) 0 >>> my_car.move() >>> print(my_car.current_speed) 1 >>> my_car.accelerate(10) >>> print(my_car.current_speed) 11 >>> my_car.stop() >>> print(my_car.current_speed) 0 >>> print(my_car.vehicle_details()) F8, 2009, ABC1234 

Inheritance

Let's define a generic Vehicle class and save it inside the vehicle.py file.

class Vehicle: def __init__(self, year, model, plate_number, current_speed): self.year = year self.model = model self.plate_number = plate_number self.current_speed = current_speed def move(self): self.current_speed += 1 def accelerate(self, value): self.current_speed += value def stop(self): self.current_speed = 0 def vehicle_details(self): return self.model + ', ' + str(self.year) + ', ' + self.plate_number 

A vehicle has attributes year, model, plate_number, and current_speed.

The definition of vehicle in the Vehicle is very generic and might not be suitable for trucks, for instance, because it should include a cargo attribute.

On the other hand, a cargo attribute does not make much sense for small vehicles like motorcycles.

To solve this we can use inheritance.

When a class (child) inherits another class (parent), all the attributes and methods from the parent class are inherited by the child class.

Parent and Child

In our case, we want a new Truck class to inherit everything from the Vehicle class. Then we want it to add its own specific attribute current_cargo to control the addition and removal of cargo from the truck.

The Truck class is called a child class that inherits from its parent class Vehicle.

A parent class is also called a superclass while a child class is also known as a subclass.

Create the class Truck and save it inside the truck.py file.

from vehicle import Vehicle class Truck(Vehicle): def __init__(self, year, model, plate_number, current_speed, current_cargo): super().__init__(year, model, plate_number, current_speed) self.current_cargo = current_cargo def add_cargo(self, cargo): self.current_cargo += cargo def remove_cargo(self, cargo): self.current_cargo -= cargo 

Let's break down the class to explain it in parts.

The class Vehicle inside the parentheses when defining the class Truck indicates that the parentVehicle is being inherited by its child Truck.

The __init__ method has self as its first parameter, as usual.

The parameters year, model, plate_number, and current_speed are there to match the ones in the Vehicle class.

We added a new parameter current_cargo suited for the Truck class.

In the first line of the __init__ method of the Truck class we have to call the __init__ method of the Vehicle class.

To do that we use super() to make a reference to the supperclassVehicle, so when super().__init__(year, model, plate_number, current_speed) is called we avoid repetition of our code.

After that, we can assign the value of current_cargo normally.

Finally, we have two methods to deal with the current_cargo: def add_cargo(self, cargo):, and def remove_cargo(self, cargo):.

Remember that Truck inherits attributes and methods from Vehicle, so we also have an implicit access to the methods that manipulate the speed: def move(self), def accelerate(self, value), and def stop(self).

Using the Truck class

To use the class in your terminal, import the Truck class from the truck module.

Create an instance called my_truck, initializing year with 2015, model with 'V8', plate_number with 'XYZ1234', current_speed with 0, and current_cargo with 0.

Use add_cargo(10) to increase current_cargo by 10, remove_cargo(4), to decrease current_cargo by 4.

Remember to print the value of current_cargo at every command to see the changes.

By inheritance, we can use the methods from the Vehicle class to move() the truck which increases its current_speed by 1, accelerate(10) which increases its current_speed by the value given in the argument, and stop() which sets the current_speed to 0.

Remember to print the value of current_speed at every interaction to see the changes.

To finish the test, call vehicle_details() inherited from the Vehicle class to print the information about our truck.

>>> from truck import Truck >>> >>> my_truck = Truck(2015, 'V8', 'XYZ1234', 0, 0) >>> print(my_truck.current_cargo) 0 >>> my_truck.add_cargo(10) >>> print(my_truck.current_cargo) 10 >>> my_truck.remove_cargo(4) >>> print(my_truck.current_cargo) 6 >>> print(my_truck.current_speed) 0 >>> my_truck.accelerate(10) >>> print(my_truck.current_speed) 10 >>> my_truck.stop() >>> print(my_truck.current_speed) 0 >>> print(my_truck.vehicle_details()) V8, 2015, XYZ1234 

Exceptions

Errors are a part of every programmer's life, and knowing how to deal with them is a skill on its own.

The way Python deals with errors is called 'Exception Handling'.

If some piece of code runs into an error, the Python interpreter will raise an exception.

Types of Exceptions

Let's try to raise some exceptions on purpose and see the errors they produce.

  • TypeError

First, try to add a string and an integer

'I am a string' + 32 
Traceback (most recent call last): File "", line 1, in  TypeError: must be str, not int 
  • IndexError

Now, try to access an index that doesn't exist in a list.

A common mistake is to forget that sequences are 0-indexed, meaning the first item has index 0, not 1.

In this example, the list car_brands ends at index 2.

car_brands = ['ford', 'ferrari', 'bmw'] print(car_brands[3]) 
Traceback (most recent call last): File "", line 1, in  IndexError: list index out of range 
  • NameError

If we try to print a variable that doesn't exist:

print(my_variable) 
Traceback (most recent call last): File "", line 1, in  NameError: name 'my_variable' is not defined 
  • ZeroDivisionError

Math doesn't allow division by zero, so trying to do so will raise an error, as expected.

32/0 
Traceback (most recent call last): File "", line 1, in  ZeroDivisionError: division by zero 

This was just a sample of the kinds of exceptions you might see during your daily routine and what can cause each of them.

Exception Handling

Now we know how to cause errors that will crash our code and show us some message saying something is wrong.

To handle these exceptions just make use of the try/except statement.

try: 32/0 except: print('Dividing by zero!') 
Dividing by zero! 

The example above shows the use of the try statement.

Put the block of code that may cause an exception inside the try scope. If everything runs alright, the except block is not invoked. But if an exception is raised, the block of code inside the except is executed.

This way the program doesn't crash and if you have some code after the exception, it will keep running if you want it to.

Specific Exception Handling

In the last example the except block was generic, meaning it was catching anything.

Good practice it to specify the type of exception we are trying to catch, which helps a lot when debugging the code later.

If you know a block of code can throw an IndexError, specify it in the except:

try: car_brands = ['ford', 'ferrari', 'bmw'] print(car_brands[3]) except IndexError: print('There is no such index!') 
There is no such index! 

You can use a tuple to specify as many exception types as you want in a single except:

try: print('My code!') except(IndexError, ZeroDivisionError, TypeError): print('My Excepetion!') 

else

It is possible to add an else command at the end of the try/except. It runs only if there are no exceptions.

my_variable = 'My variable' try: print(my_variable) except NameError: print('NameError caught!') else: print('No NameError') 
My variable No NameError 

Raising Exceptions

The raise command allows you to manually raise an exception.

This is particularly useful if you want to catch an exception and do something with it -- like logging the error in some personalized way like redirecting it to a log aggregator, or ending the execution of the code since the error should not allow the progress of the program.

try: raise IndexError('This index is not allowed') except: print('Doing something with the exception!') raise 
Doing something with the exception! Traceback (most recent call last): File "", line 2, in  IndexError: This index is not allowed 

finally

Den finallyblok udføres uafhængigt af undtagelser hæves eller ej.

De er normalt der for at tillade programmet at rydde op i ressourcer som filer, hukommelse, netværksforbindelser osv.

try: print(my_variable) except NameError: print('Except block') finally: print('Finally block') 
Except block Finally block 

Konklusion

Det er det!

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