Industries including web development, data analysis, artificial intelligence, and scientific computing use Python, a robust and adaptable programming language. Even for beginners, learning and using it is simple thanks to its clear syntax and sizable standard library.
We’ll take you on a tour through Python’s fundamentals and more advanced subjects in this blog series to help you grasp the language. This course will help you develop your abilities and advance your Python knowledge, whether you’re a total novice or an experienced coder.
By the end of this series, you’ll be able to write clear, effective, and maintainable code and have a firm grasp of Python and its ecosystem. Join us on this trip and let’s conquer Python together whether you want to acquire a new skill or start a career in programming.
First, we will cover the fundamentals of Python grammar, data types, and control structures, followed by more complex subjects such as object-oriented programming, functional programming, and multithreading. We’ll also go through well-known frameworks and modules like Django, Flask, Pandas, and NumPy and demonstrate how to utilize them to create practical applications.
Each chapter will cover its respective topic in depth, starting from the basics and gradually building up to more advanced concepts. The appendices will provide additional resources and support for the reader, including installation instructions, a glossary of terms, and solutions to selected exercises.
1. Python Basics
- Go to the official website at https://www.python.org/downloads/ and download the latest version.
- Once the download is complete, run the installer by double-clicking on the downloaded file.
- In the installer, you should select the option to “Add Python X.X to PATH”, where X.X represents the version number that you downloaded. This will allow you to run Python from the command line.
- Choose the installation directory for Python. The default directory is usually fine.
- Select the components you want to install. It’s usually safe to leave the defaults selected.
- Wait for the installation process to complete. This may take a few minutes.
- After installing, open Command Prompt or PowerShell and type “python” to ensure it is installed correctly. You should see the version number and the Python shell prompt “>>>”.
Python Data Types:
Data types refer to the type of data that a variable can store.
The most commonly used data types are :
- Integers: Whole numbers without a decimal point (e.g., 1, 2, 3).
- Floats: Numbers with a decimal point (e.g., 1.0, 2.5, 3.14).
- Strings: Text enclosed in quotes (e.g., “hello”, ‘world’).
- Booleans: True or False values.
Python Variables:
A variable is a named location in memory that stores a value. You can assign variable values of any data type, and you can change the value throughout the program. Here’s an example of how to create a variable:
x = 5
This example assigns the value 5 to the variable “x”.
Examples: Here are some examples of how to use data types and variables :
# Integers
x = 5
y = 10
print(x + y) # Output: 15
# Floats
a = 1.5
b = 2.5
print(a + b) # Output: 4.0
# Strings
hello = "Hello"
world = 'World'
print(hello + " " + world) # Output: Hello World
# Booleans
x = True
y = False
print(x and y) # Output: False
In these examples, we provide variable values of various data kinds and carry out actions on them. The print() method is used to output the results of the operations to the console.
Python Control Structures:
In programming, control structures are used to manage how a program runs. There are three different categories of control structures:
Code can be executed based on conditions by using conditional statements.
Loops are used to continually run code.
Code is organized into reusable chunks using functions.
Python Conditional Statements:
Python’s conditional statements let you run code in response to a condition. If, else, and elif are the three conditional statements that are most often
age = 18
if age >= 18:
print("You are old enough to vote.")
else:
print("You are not old enough to vote.")
In this example, an if statement is used to determine whether the value of the age variable is greater than or equal to 18. If so, the computer software prints, "You are old enough to vote." In the absence of that, it says, "You are not old enough to vote."
Python Loops:
Loops let you run code repeatedly. For and while loops are the most commonly used loops. Here is an example of using loops:
For loop
for i in range(5):
print(i)
While loop
x = 0
while x < 5:
print(x)
x += 1
In this example, the numbers 0 to 4 are printed using a for loop, and the same is done with a while loop.
Python Function
You may organize code into reusable chunks using functions. The def keyword is used to define functions. A function definition and call example are provided here:
def say_hello(name):
print("Hello, " + name)
say_hello("John")
In this example, we define a function called “say_hello” that takes a parameter called “name“. The function prints “Hello, ” followed by the value of “name“. We then call the function with the argument “John”.
Python Modules
You can import and use Python modules in other Python programs, and they contain Python code, usually in the form of functions. You may construct your modules with Python, and it comes with a predefined library of modules that offer helpful functionality. Below is an example of using Python modules:
import math
result = math.sqrt(16)
print(result) # Output: 4.0
In this example, we import the “math” module, which provides various mathematical functions. We then use the sqrt function from the math module to calculate the square root of 16, and assign the result to a variable called “result. Finally”, we print the value of result to the console.
You can also create your modules by defining functions in a separate file and then importing that file into your main program. Here’s an example of how to create and use a custom module in Python:
my_module.py
def say_hello(name):
print("Hello, " + name)
main.py
import my_module
my_module.say_hello("John") # Output: Hello, John
In this example, we define a function called say_hello in a file called my_module.py. We then import the my_module module into our main program and call the say_hello function with the argument “John”.
2. Advanced Python Programming
A programming paradigm known as object-oriented programming (OOP) emphasizes objects as the fundamental building elements of the software. Everything is an object, and the language has strong capabilities for generating and modifying objects. We’ll look at the fundamentals of object-oriented programming in this tutorial.
Classes and Objects:
A class serves as a guide for building objects. A class specifies a set of properties and operations that objects derived from it will possess. Here is an illustration of a class definition:
class Dog:
def __init__(self, name, age):
self.name = name
self.age = age
def bark(self):
print("Woof!")
dog1 = Dog("Rufus", 5)
dog2 = Dog("Buddy", 3)
print(dog1.name) # Output: Rufus
print(dog2.age) # Output: 3
dog1.bark() # Output: Woof!
In this example, we define a class called Dog that has two attributes, name and age, and one method, bark().
We then create two objects, dog1 and dog2, from the Dog class and set their attributes using the __init__() method.
At last, we access the attributes of the objects and call the bark() method.
Inheritance:
Thanks to the powerful feature of inheritance in object-oriented programming, it is possible to create new classes that are modified versions of existing classes. You may invoke parent class methods in Python by using the super() function. An example of Python inheritance is shown below:
class Animal:
def __init__(self, species):
self.species = species
class Dog(Animal):
def __init__(self, name, age):
super().__init__("Canine")
self.name = name
self.age = age
dog1 = Dog("Rufus", 5)
print(dog1.species) # Output: Canine
In this example, we construct a Dog class that derives from the Animal class and has an attribute species. Name and age are two more properties for the Dog class. To set the specific attribute of the Dog object, we utilize the super() function to call the init() method of the Animal class. Finally, we print the special property of the dog1 object.
Polymorphism:
“Polymorphism” refers to using the same method or operator on several object types. Method overloading and method overriding in Python are used to create polymorphism. Here is an illustration of how to make use of polymorphism:
class Cat:
def make_sound(self):
print("Meow")
class Dog:
def make_sound(self):
print("Woof")
def make_animal_sound(animal):
animal.make_sound()
cat1 = Cat()
dog1 = Dog()
make_animal_sound(cat1) # Output: Meow
make_animal_sound(dog1) # Output: Woof
In this illustration, two classes—Cat and Dog—are defined and each has a make_sound() function. Additionally, we define a function called make_animal_sound() that takes an animal’s make_sound() method as a parameter. In the end, we produce a cat1.
Exception Handling In Python
Any programming language must handle exceptions. It enables you to manage mistakes that may arise during code execution and take suitable measures to keep the application from crashing. To manage exceptions in Python, the try-except block can be used. Here’s an example of using Python exception handling:
try:
num1 = int(input("Enter a number: "))
num2 = int(input("Enter another number: "))
result = num1 / num2
print("The result is:", result)
except ValueError:
print("Please enter a valid number.")
except ZeroDivisionError:
print("Cannot divide by zero.")
In this example, the try block is used to run code that may throw an exception. We ask the user to enter two integers and then divide the first by the second. The matching exception is triggered if the user inputs a non-numeric number or attempts to divide by zero. The except block is used to catch the exception and output the relevant error message. If no exception is thrown, the function in the try block runs without error.
You may also use the otherwise block to execute code that should only be executed if no exception is thrown, and the finally block to execute code that should be executed whether or not an exception is triggered. Here’s an illustration:
try:
num1 = int(input("Enter a number: "))
num2 = int(input("Enter another number: "))
result = num1 / num2
except ValueError:
print("Please enter a valid number.")
except ZeroDivisionError:
print("Cannot divide by zero.")
else:
print("The result is:", result)
finally:
print("Thank you for using this program.")
In this example, we use the else block to print the result if no exception is raised, and the finally block to print a message after the program has finished executing, regardless of whether an exception was raised or not.
In addition to catching built-in exceptions, you can also define your custom exceptions by creating a new class that inherits from the Exception class. Here’s an example of how to define a custom exception in Python:
class NegativeNumberError(Exception):
pass
def square_root(num):
if num < 0:
raise NegativeNumberError("Cannot calculate square root of a negative number.")
return num ** 0.5
try:
num = int(input("Enter a number: "))
result = square_root(num)
print("The square root of", num, "is", result)
except ValueError:
print("Please enter a valid number.")
except NegativeNumberError as e:
print(e)
In this example, we construct a custom exception named NegativeNumberError that is thrown when a user attempts to calculate the square root of a negative integer. We raise the exception with the raise keyword and assign the error message to the variable e with the as keyword. The except block catches the NegativeNumberError exception and prints the problem message.
Writing to a File:
To write a file, you can use the open() function in write mode (“w”). Here’s an example of how to write a file in Python:
# Open a file in write mode
f = open("example.txt", "w")
# Write to the file
f.write("Hello, world!\n")
f.write("This is an example file.\n")
# Close the file
f.close()
In this example, we will open the file example.txt in write mode (“w”). The write() function is used to write two lines of text to the file, while the close() method is used to shut the file.
Reading from a File:
You may use the open() method in read mode (“r”) to read from a file. Here’s a Python example of how to read from a file:
# Open a file in read mode
f = open("example.txt", "r")
# Read the contents of the file
contents = f.read()
# Print the contents
print(contents)
# Close the file
f.close()
The “example.txt” file is opened in read mode (“r”) in this example. We may read the whole contents of the file and put them in a variable named contents by using the read() function. Using the print() function, we output the contents of the file before closing it with the close() function.
Appending to a File:
Use the open() method in append mode (“a”) to add to a file. An illustration of how to add to a file in Python is given below:
# Open a file in append mode
f = open("example.txt", "a")
# Append to the file
f.write("This line was appended to the file.\n")
# Close the file
f.close()
We open the example.txt file in append mode (“a” in this example). Using the write() method, we add a fresh line of text to the document using the write () method, and the close() method is used to finish it.
Context Managers:
Using context managers is a good idea when working with files in Python. By creating a block of code within which a resource is accessible and shutting the resource when the block is exited, context managers aid in resource management. An illustration of using Python context managers is provided here:
# Open a file using a context manager
with open("example.txt", "r") as f:
# Read the contents of the file
contents = f.read()
# Print the contents
print(contents)
Python Regular Expressions
Regular expressions, or “regex” for short, are a useful tool in Python for modifying and searching for text. They let you provide patterns that match specified text, which may be utilized for a variety of applications ranging from data validation to text file processing.
Regular expressions are, at their most basic, a series of characters that constitute a search pattern. The regular expression hello, for example, will match the string “hello” anyplace in a chunk of text.
But regular expressions can be much more intricate than this. To represent particular character kinds, like numerals or spaces, for instance, you can utilize special characters. Quantifiers can be used to describe how many times a pattern should be matched, and alternation can be used to match one of multiple possible patterns.
To work with regular expressions in Python, utilize the repackage. This module offers a wide range of regular expression-based text search and manipulation operations.
import re
text = "The quick brown fox jumps over the lazy dog."
# Search for the word "brown" in the text using regular expressions
pattern = r"brown"
match = re.search(pattern, text)
if match:
print("Found the word 'brown' in the text.")
else:
print("Did not find the word 'brown' in the text.")
Python Libraries and Frameworks
Frameworks
Python is a popular and productive programming language with extensive libraries and frameworks for practically every technological sector.
Python frameworks automate the execution of many operations and provide developers with a foundation for application development.
Each framework has its own set of modules or packages that considerably shorten development time. Python frameworks are classified as full-stack, micro, or asynchronous.
Before delving into the popular Python frameworks in-depth, let’s first define the different sorts of frameworks
Libraries
A module is a file containing Python code, while a package is a directory containing sub-packages and modules. However, the distinction between a package and a Python library is hazy.
A Python library is a reusable piece of code that you may use in your applications or projects.
In contrast to languages such as C++ or C, Python libraries are not context-specific. A ‘library’ in this context refers to a collection of essential components.
A library is, in essence, a collection of modules. A package is a library that can be installed with the help of package management such as RubyGems or npm.
NumPy and Pandas for Data Analysis
Pandas and NumPy are two of the most popular Python data analysis packages. They provide a wide range of capabilities, from simple actions like slicing and dicing to more complicated operations like reshaping and grouping. Finding a quick and effective approach to examine your data is the most important challenge in data science. It might be difficult to choose one library over another, especially when they are comparable. Both provide a wide range of functionality, but their architecture, function, syntax, and language fundamentally differ. Let’s look at the fundamental distinctions between Pandas and NumPy.
Matplotlib for Data Visualization
Matplotlib is a multi-platform data visualization package based on NumPy arrays and designed to operate with the SciPy stack as a whole. John Hunter invented it in 2002, and the Space Telescope Science Institute adopted it in 2003. It is well-known for its compatibility with a wide range of operating systems and graphics backends, and it supports dozens of backends and output formats. It has a significant user base as well as an active developer community. Matplotlib’s interface and aesthetics have begun to show their age in recent years.
Matplotlib is a tried-and-true cross-platform graphics engine designed to make it easy to define new global charting styles. New R languages tools like ggplot and ggvis, as well as online visualization toolkits based on D3js and HTML5 canvas, may make Matplotlib feel clumsy and out-of-date. However, it remains an important component of the data visualization stack, even as new tools force the community to abandon direct use of the Matplotlib API.
Flask for Web Development
Python is a lightweight Python web framework that allows developers to construct Python-based online applications. It is extensible and does not need a certain directory structure or boilerplate code. Flask may also be used to provide additional capabilities to online applications, such as database storage and web form validation.
Django for Web Development
Django is a high-level Python web framework that promotes quick development and clean, pragmatic design. It was built by professional developers to take care of most of the pain of web development, so you can focus on building your app instead of reinventing the wheel. It’s free and open source.
Django was created to assist developers in completing applications as rapidly as possible.
Some of the busiest websites on the internet make use of Django’s ability to expand swiftly and flexibly.
Django takes security seriously and assists developers in avoiding numerous typical security blunders.