Python 3 Tutorial

Python is a powerful programming language ideal for scripting and rapid application development. It is used in web development (like: Django and Bottle), scientific and mathematical computing (Orange, SymPy, NumPy) to desktop graphical user Interfaces (Pygame, Panda3D).

This tutorial introduces all the core concepts and features of Python 3. After reading the tutorial, you will be able to read and write basic Python programs, and explore Python in-depth on your own.

This tutorial is intended for people who have knowledge of other programming languages and want to get started with Python quickly.

Python for Beginners

If you are a programming newbie, we suggest you visit:

  1. Python Interactive Course - Learn to code through bite-size lessons, quizzes and 100+ challenges
  2. Python Programming - A comprehensive guide on what's Python, how to get started in Python, why you should learn it, and how you can learn it.
  3. Python Examples - Simple examples for beginners to follow.

What's covered in this tutorial?

Run Python on Your computer

If you want to install Python on your computer, follow these resources.

You can also use our online Python editor to get started in Python without installing anything on your computer.

Python Introduction

Let's write our first Python program, "Hello, World!". It's a simple program that prints Hello World! on the standard output device (screen).

"Hello, World!" Program

print("Hello, World!")

When you run the program, the output will be:

Hello, World!

In this program, we have used the built-in print() function to print Hello, world! string.

Variables and Literals

A variable is a named location used to store data in the memory. Here's an example:

a = 5  

Here, a is a variable. We have assigned 5 to variable a

We do not need to define the type of variables in Python. You can do something like this:

a = 5
print("a =", 5)
a = "High five"
print("a =", a)

Initially, integer value 5 is assigned to the variable a. Then, the string High five is assigned to the same variable.

By the way, 5 is a numeric literal and "High five" is a string literal.

When you run the program, the output will be:

a = 5
a = High five

Visit Python Variables, Constants and Literals to learn more.


Operators are special symbols that carry out operations on operands (variables and values).

Let's talk about arithmetic and assignment operators in this part.

Arithmetic operators are used to perform mathematical operations like addition, subtraction, multiplication etc.

x = 14
y = 4

# Add two operands
print('x + y =', x+y) # Output: x + y = 18

# Subtract right operand from the left
print('x - y =', x-y) # Output: x - y = 10

# Multiply two operands
print('x * y =', x*y) # Output: x * y = 56

# Divide left operand by the right one 
print('x / y =', x/y) # Output: x / y = 3.5

# Floor division (quotient)
print('x // y =', x//y) # Output: x // y = 3

# Remainder of the division of left operand by the right
print('x % y =', x%y) # Output: x % y = 2

# Left operand raised to the power of right (x^y)
print('x ** y =', x**y) # Output: x ** y = 38416

Assignment operators are used to assign values to variables. You have already seen the use of = operator. Let's try some more assignment operators.

x = 5

# x += 5 ----> x = x + 5
x +=5
print(x) # Output: 10

# x /= 5 ----> x = x / 5
x /= 5
print(x) # Output: 2.0

Other commonly used assignment operators: -=, *=, %=, //= and **=.

Visit Python Operators to learn about all operators in detail.

Get Input from User

In Python, you can use input() function to take input from user. For example:

inputString = input('Enter a sentence:')
print('The inputted string is:', inputString)

When you run the program, the output will be:

Enter a sentence: Hello there.
The inputted string is: Hello there. 

Python Comments

There are 3 ways of creating comments in Python.

# This is a comment
  """This is a 
  '''This is also a

To learn more about comments and docstring, visit: Python Comments.

Type Conversion

The process of converting the value of one data type (integer, string, float, etc.) to another is called type conversion. Python has two types of type conversion.

Implicit Type Conversion

Implicit conversion doesn't need any user involvement. For example:

num_int = 123  # integer type
num_flo = 1.23 # float type

num_new = num_int + num_flo

print("Value of num_new:",num_new)
print("datatype of num_new:",type(num_new))

When you run the program, the output will be:

Value of num_new: 124.23
datatype of num_new: datatype of num_new: <class 'float'>

Here, num_new has float data type because Python always converts smaller data type to larger data type to avoid the loss of data.

Here is an example where the Python interpreter cannot implicitly type convert.

num_int = 123     # int type
num_str = "456"   # str type


When you run the program, you will get

TypeError: unsupported operand type(s) for +: 'int' and 'str'

However, Python has a solution for this type of situation which is known as explicit conversion.

Explicit Conversion

In case of explicit conversion, you convert the datatype of an object to the required data type. We use predefined functions like int(), float(), str() etc. to perform explicit type conversion. For example:

num_int = 123  # int type
num_str = "456" # str type

# explicitly converted to int type
num_str = int(num_str) 


To lean more, visit Python type conversion.

Python Numeric Types

Python supports integers, floating point numbers and complex numbers. They are defined as int, float and complex class in Python. In addition to that, booleans: True and False are a subtype of integers.

# Output: <class 'int'>

# Output: <class 'float'>

c = 5 + 3j

# Output: <class 'complex'>

To learn more, visit Python Number Types.

Python Data Structures

Python offers a range of compound datatypes often referred to as sequences. You will learn about those built-in types in this section.


A list is created by placing all the items (elements) inside a square bracket [] separated by commas.

It can have any number of items and they may be of different types (integer, float, string etc.)

# empty list
my_list = []

# list of integers
my_list = [1, 2, 3]

# list with mixed data types
my_list = [1, "Hello", 3.4]

You can also use list() function to create lists.

Here's how you can access elements of a list.

language = ["French", "German", "English", "Polish"]

# Accessing first element

# Accessing fourth element

You use the index operator [] to access an item in a list. Index starts from 0. So, a list having 10 elements will have index from 0 to 9.

Python also allows negative indexing for its sequences. The index of -1 refers to the last item, -2 to the second last item, and so on.

Check these resources for more information about Python lists:


Tuple is similar to a list except you cannot change elements of a tuple once it is defined. Whereas in a list, items can be modified.

Basically, lists are mutable whereas tuples are immutable.

language = ("French", "German", "English", "Polish")

You can also use tuple() function to create tuples.

You can access elements of a tuple in a similar way to a list.

language = ("French", "German", "English", "Polish")

print(language[1]) #Output: German
print(language[3]) #Output: Polish
print(language[-1]) # Output: Polish

You cannot delete elements of a tuple, however, you can entirely delete a tuple itself using del operator.

language = ("French", "German", "English", "Polish")
del language

# NameError: name 'language' is not defined

To learn more, visit Python Tuples.


A string is a sequence of characters. Here are different ways to create a string.

# all of the following are equivalent
my_string = 'Hello'

my_string = "Hello"

my_string = '''Hello'''

# triple quotes string can extend multiple lines
my_string = """Hello, welcome to
           the world of Python"""

You can access individual characters of a string using indexing (in a similar manner to lists and tuples).

str = 'programiz'
print('str = ', str)

print('str[0] = ', str[0]) # Output: p

print('str[-1] = ', str[-1]) # Output: z

#slicing 2nd to 5th character
print('str[1:5] = ', str[1:5]) # Output: rogr

#slicing 6th to 2nd last character
print('str[5:-2] = ', str[5:-2]) # Output: am

Strings are immutable. You cannot change elements of a string once it is assigned. However, you can assign one string to another. Also, you can delete the string using del operator.

Concatenation is probably the most common string operation. To concatenate strings, you use + operator. Similarly, the * operator can be used to repeat the string for a given number of times.

str1 = 'Hello '
str2 ='World!'

# Output: Hello World!
print(str1 + str2)

# Hello Hello Hello
print(str1 * 3)

Check these resources for more information about Python strings:


A set is an unordered collection of items where every element is unique (no duplicates).

Here is how you create sets in Python.

# set of integers
my_set = {1, 2, 3}

# set of mixed datatypes
my_set = {1.0, "Hello", (1, 2, 3)}

You can also use set() function to create sets.

Sets are mutable. You can add, remove and delete elements of a set. However, you cannot replace one item of a set with another as they are unordered and indexing has no meaning.

Let's try commonly used set methods: add(), update() and remove().

# set of integers
my_set = {1, 2, 3}

print(my_set) # Output: {1, 2, 3, 4}

print(my_set) # Output: {1, 2, 3, 4}

my_set.update([3, 4, 5])
print(my_set) # Output: {1, 2, 3, 4, 5}

print(my_set) # Output: {1, 2, 3, 5}

Let's tryout some commonly used set operations:

A = {1, 2, 3}
B = {2, 3, 4, 5}

# Equivalent to A.union(B) 
# Also equivalent to B.union(A)
print(A | B) # Output: {1, 2, 3, 4, 5}

# Equivalent to A.intersection(B)
# Also equivalent to B.intersection(A)
print (A & B) # Output: {2, 3}

# Set Difference
print (A - B) # Output: {1}

# Set Symmetric Difference
print(A ^ B)  # Output: {1, 4, 5}

More Resources:


Dictionary is an unordered collection of items. While other compound data types have only value as an element, a dictionary has a key: value pair. For example:

# empty dictionary
my_dict = {}

# dictionary with integer keys
my_dict = {1: 'apple', 2: 'ball'}

# dictionary with mixed keys
my_dict = {'name': 'John', 1: [2, 4, 3]}

You can also use dict() function to create dictionaries.

To access value from a dictionary, we use keys. For example:

person = {'name':'Jack', 'age': 26, 'salary': 4534.2}
print(person['age']) # Output: 26

Here's how you can change, add or delete dictionary elements.

person = {'name':'Jack', 'age': 26}

# Changing age to 36
person['age'] = 36 
print(person) # Output: {'name': 'Jack', 'age': 36}

# Adding salary key, value pair
person['salary'] = 4342.4
print(person) # Output: {'name': 'Jack', 'age': 36, 'salary': 4342.4}

# Deleting age
del person['age']
print(person) # Output: {'name': 'Jack', 'salary': 4342.4}

# Deleting entire dictionary
del person

More resources:

Python range()

range() returns an immutable sequence of numbers between the given start integer to the stop integer.

print(range(1, 10)) # Output: range(1, 10)

The output is an iterable and you can convert it to lists, tuples, set and so on. For example:

numbers = range(1, 6)

print(list(numbers)) # Output: [1, 2, 3, 4, 5]
print(tuple(numbers)) # Output: (1, 2, 3, 4, 5)
print(set(numbers)) # Output: {1, 2, 3, 4, 5}

# Output: {1: 99, 2: 99, 3: 99, 4: 99, 5: 99} 
print(dict.fromkeys(numbers, 99))

We have omitted optional step parameter for range() in above examples. When omitted, step defaults to 1. Let's try few examples with step parameter.

# Equivalent to: numbers = range(1, 6)
numbers1 = range(1, 6 , 1)
print(list(numbers1)) # Output: [1, 2, 3, 4, 5]

numbers2 = range(1, 6, 2)
print(list(numbers2)) # Output: [1, 3, 5]

numbers3 = range(5, 0, -1)
print(list(numbers3)) # Output: [5, 4, 3, 2, 1]

Python Control Flow

if...else Statement

The if...else statement is used if you want perform different action (run different code) on different condition. For example:

num = -1

if num > 0:
    print("Positive number")
elif num == 0:
    print("Negative number")
# Output: Negative number

There can be zero or more elif parts, and the else part is optional.

Most programming languages use {} to specify the block of code. Python uses indentation.

A code block starts with indentation and ends with the first unindented line. The amount of indentation is up to you, but it must be consistent throughout that block.

Generally, four whitespaces are used for indentation and is preferred over tabs.

Let's try another example:

if False:
  print("I am inside the body of if.")
  print("I am also inside the body of if.")
print("I am outside the body of if")

# Output: I am outside the body of if.

Before you move on to next section, we recommend you to check comparison operator and logical operator.

Also, check out Python if...else in detail.

while Loop

Like most programming languages, while loop is used to iterate over a block of code as long as the test expression (condition) is true. Here is an example to find the sum of natural numbers:

n = 100

# initialize sum and counter
sum = 0
i = 1

while i <= n:
    sum = sum + i
    i = i+1    # update counter

print("The sum is", sum)

# Output: The sum is 5050

In Python, while loop can have optional else block that is executed if the condition in the while loop evaluates to False. However, if the loop is terminated with break statement, Python interpreter ignores the else block.

To learn more, visit Python while Loop

for Loop

In Python, for loop is used to iterate over a sequence (list, tuple, string) or other iterable objects. Iterating over a sequence is called traversal.

Here's an example to find the sum of all numbers stored in a list.

numbers = [6, 5, 3, 8, 4, 2]

sum = 0

# iterate over the list
for val in numbers:
  sum = sum+val

print("The sum is", sum) # Output: The sum is 28

Notice the use of in operator in the above example. The in operator returns True if value/variable is found in the sequence.

In Python, for loop can have optional else block. The else part is executed if the items in the sequence used in for loop exhausts. However, if the loop is terminated with break statement, Python interpreter ignores the else block.

To learn more, visit Python for Loop

break Statement

The break statement terminates the loop containing it. Control of the program flows to the statement immediately after the body of the loop. For example:

for val in "string":
    if val == "r":

print("The end")

When you run the program, the output will be:

The end

continue Statement

The continue statement is used to skip the rest of the code inside a loop for the current iteration only. Loop does not terminate but continues on with the next iteration. For example:

for val in "string":
    if val == "r":

print("The end")

When you run the program, the output will be:

The end

To learn more on break and continue with detail explanation, visit Python break and continue.

pass Statement

Suppose, you have a loop or a function that is not implemented yet but want to implement it in the future. They cannot have an empty body. The interpreter would complain. So, you use the pass statement to construct a body that does nothing.

sequence = {'p', 'a', 's', 's'}
for val in sequence:

Python Function

A function is a group of related statements that perform a specific task. You use def keyword to create functions in Python.

def print_lines():
  print("I am line1.")
  print("I am line2.")

You have to call the function to run the codes inside it. Here's how:

def print_lines():
  print("I am line1.")
  print("I am line2.")

# function call

A function can accept arguments.

def add_numbers(a, b):
  sum = a + b

add_numbers(4, 5)

# Output: 9

You can also return value from a function using return statement.

def add_numbers(a, b):
  sum = a + b
  return sum

result = add_numbers(4, 5)

# Output: 9

Here are few resources to check:

Recursion (Recursive function)

A function that calls itself is known as recursive function and this process is called recursion.

Every recursive function must have a base condition that stops the recursion or else the function calls itself infinitely.

# Recursive function to find the factorial of a number

def calc_factorial(x):

    if x == 1:
        return 1
        return (x * calc_factorial(x-1))

num = 6
print("The factorial of", num, "is", calc_factorial(num)) 

# Output: The factorial of 6 is 720

Visit Python recursion to learn more.

Lambda Function

In Python, you can define functions without a name. These functions are called lambda or anonymous function. To create a lambda function, lambda keyword is used.

square = lambda x: x ** 2

# Output: 25

We use lambda functions when we require a nameless function for a short period of time. Lambda functions are used along with built-in functions like filter(), map() etc.

To learn more, visit:


Modules refer to a file containing Python statements and definitions.

A file containing Python code, for e.g.:, is called a module and its module name would be example.

Let us create it and save it as

# Python Module example

def add(a, b):
   return a + b

To use this module, we use import keyword.

# importing example module
import example 

# accessing the function inside the module using . operator
example.add(4, 5.5) 

Python has a ton of standard modules readily available for use. For example:

import math

result = math.log2(5) # return the base-2 logarithm
print(result) # Output: 2.321928094887362

You can import specific names from a module without importing the module as a whole. Here is an example.

from math import pi
print("The value of pi is", pi)

# Output: The value of pi is 3.141592653589793

More Resources:

Python File I/O

A file operation takes place in the following order.

  1. Open a file
  2. Read or write (perform operation)
  3. Close the file

How to open a file?

You can use open() function to open a file.

f = open("test.txt")    # open file in current directory
f = open("C:/Python33/README.txt")  # specifying full path

We can specify the mode while opening a file.

Mode Description
'r' Open a file for reading. (default)
'w' Open a file for writing. Creates a new file if it does not exist or truncates the file if it exists.
'x' Open a file for exclusive creation. If the file already exists, the operation fails.
'a' Open for appending at the end of the file without truncating it. Creates a new file if it does not exist.
't' Open in text mode. (default)
'b' Open in binary mode.
'+' Open a file for updating (reading and writing)
f = open("test.txt")      # equivalent to 'r' or 'rt'
f = open("test.txt",'w')  # write in text mode
f = open("img.bmp",'r+b') # read and write in binary mode

How to close a file?

To close a file, you use close() method.

f = open("test.txt",encoding = 'utf-8')
# perform file operations

How to write to a file?

In order to write into a file in Python, we need to open it in write 'w', append 'a' or exclusive creation 'x' mode.

with open("test.txt",'w',encoding = 'utf-8') as f:
   f.write("my first file\n")
   f.write("This file\n\n")
   f.write("contains three lines\n")

Here, we have used with statement to open a file. This ensures that the file is closed when the block inside with is exited.

How to read files?

To read a file in Python, you must open the file in reading mode.

There are various methods available for this purpose. We can use the read(size) method to read in size number of data.

f = open("test.txt",'r',encoding = 'utf-8')    # read the first 4 data

Visit Python File I/O to learn more.

Python Directory

A directory or folder is a collection of files and subdirectories. Python has the os module, which provides many useful methods to work with directories and files.

import os

os.getcwd()  // present working directory
os.chdir('D:\\Hello') // Changing current directory to D:\Hello
os.listdir()  // list all sub directories and files in that path
os.mkdir('test') // making a new directory test
os.rename('test','tasty') // renaming the directory test to tasty
os.remove('old.txt')  // deleting old.txt file

Visit Python Directory to learn more.

Python Exception Handling

Errors that occur at runtime are called exceptions. They occur, for example, when a file we try to open does not exist FileNotFoundError, dividing a number by zero ZeroDivisionError etc.

Visit this page to learn about all built-in exceptions in Python.

If exceptions are not handled, an error message is spit out and our program comes to a sudden, unexpected halt.

In Python, exceptions can be handled using try statement. When exceptions are caught, it's up to you what operator to perform.

# import module sys to get the type of exception
import sys

randomList = ['a', 0, 2]

for entry in randomList:
        print("The entry is", entry)
        r = 1/int(entry)
        print("Next entry.")
print("The reciprocal of",entry,"is",r)

When you run the program, the output will be:

The entry is a
Oops! <class 'ValueError'> occurred.
Next entry.

The entry is 0
Oops! <class 'ZeroDivisionError'> occurred.
Next entry.
The entry is 2
The reciprocal of 2 is 0.5

To learn about catching specific exceptions and finally clause with try statement, visit Python exception handling.

Also, you can create user-defined exceptions in Python. For that, visit Python Custom Exceptions

Python OOP

Everything in Python is an object including integers, floats, functions, classes, and None. Let's not focus on why everything in Python is an object. For that, visit this page. Rather, this section focuses on creating your own classes and objects.

Class and Objects

Object is simply a collection of data (variables) and methods (functions) that act on data. And, class is a blueprint for the object.

How to define a class?

class MyClass:
   a = 10
   def func(self):

As soon as you define a class, a new class object is created with the same name. This class object allows us to access the different attributes as well as to instantiate new objects of that class.

class MyClass:
  "This is my class"
  a = 10
  def func(self):

# Output: 10

# Output: <function myclass.func at 0x7f04d158b8b0>

# Output: 'This is my class'

You may have noticed the self parameter in function definition inside the class but, we called the method simply as ob.func() without any arguments. It still worked.

This is because, whenever an object calls its method, the object itself is passed as the first argument. So, ob.func() translates into MyClass.func(ob).

Creating Objects

You can also create objects of the class yourself.

class MyClass:
  "This is my class"
  a = 10
  def func(self):

obj1 = MyClass()
print(obj1.a)        # Output: 10
obj2 = MyClass()
print(obj1.a + 5)    # Output: 15

Python Constructors

In Python, a method with name __init()__ is a constructor. This method is automatically called when an object is instantiated.

class ComplexNumber:
    def __init__(self,r = 0,i = 0):  # constructor
        self.real = r
        self.imag = i

    def getData(self):

c1 = ComplexNumber(2,3) # Create a new ComplexNumber object
c1.getData() # Output: 2+3j

c2 = ComplexNumber() # Create a new ComplexNumber object
c2.getData() # Output: 0+0j

Visit Python Class and Object to learn more.

Python Inheritance

Inheritance refers to defining a new class with little or no modification to an existing class. Let's take an example:

class Mammal:
  def displayMammalFeatures(self):
    print('Mammal is a warm-blooded animal.')

Let's derive a new class Dog from this Mammal class.

class Mammal:
  def displayMammalFeatures(self):
    print('Mammal is a warm-blooded animal.')

class Dog(Mammal):
  def displayDogFeatures(self):
    print('Dog has 4 legs.')

d = Dog()

Notice that we are able to call method of base class displayMammalFeatures() from the object of derived class d.

To learn more about inheritance and method overriding, visit Python Inheritance.

We also suggest you to check multiple inheritance and operator overloading if you are interested.

Miscellaneous and Advance Topics


Iterator in Python is simply an object that can be iterated upon. An object which will return data, one element at a time.

Technically speaking, Python iterator object must implement two special methods, __iter__() and __next__(), collectively called the iterator protocol.

An object is called iterable if we can get an iterator from it. Most of the built-in containers in Python like: list, tuple, string etc. are iterables.

The iter() function (which in turn calls the __iter__() method) returns an iterator from them.

my_list = [4, 7, 0, 3]

# get an iterator using iter()
my_iter = iter(my_list)

print(next(my_iter)) # Output: 4
print(next(my_iter)) # Output: 7

To learn more about infinite iterators and how to create custom iterators, visit: Python Iterators.


There is a lot of overhead in building an iterator in Python; we have to implement a class with __iter__() and __next__() method, keep track of internal states, raise StopIteration when there was no values to be returned etc.

This is both lengthy and counterintuitive. Generators come to rescue in such situations.

Python generators are a simple way of creating iterators.

Learn more about Python Generators.


This technique by which some data gets attached to the code is called closure in Python.

def print_msg(msg): # outer enclosing function
    def printer():  # inner function
    return printer  # this got changed

another = print_msg("Hello") # Output: Hello

Here, the print_msg() function is called with the string "Hello" as an argument and the returned function was bound to the name another. On calling another(), the message was still remembered although we had already finished executing the print_msg() function.

The criteria that must be met to create closure in Python are summarized in the following points.

  • We must have a nested function (function inside a function).
  • The nested function must refer to a value defined in the enclosing function.
  • The enclosing function must return the nested function.

Visit Python closures to learn more about closures and when to use them.


Python has an interesting feature called decorators to add functionality to an existing code.

This is also called metaprogramming as a part of the program tries to modify another part of the program at compile time.

To learn about decorators in detail, visit Python Decorators.

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