The Python Programming Language

Want to learn about the Python programming language, where it's used and what are its advantages? This blog is just the right place for you. Know all about being a Python programmer.

What is Python?

Python is a high-level, object-oriented programming language with dynamic semantics that is interpreted. It has a reputation for being a beginner-friendly language, replacing Java as the most popular programming language in the world and allowing beginners to focus on fully understanding programming basics rather than minute details. This popular programming language is extensively used in web development, data analytics, data science, and machine learning.

Now that we are familiar with Python, let's examine why it is preferred by many programmers today.

Python History

Guido van Rossum initially created Python as a side project because he was looking for something entertaining to do over the holidays. The name "Python" is a tribute to the British comedy group Monty Python and is meant to be simple and enjoyable.

In 1991, the language was finally made public. Compared to Java, C++, and C, it used fewer lines of code when it was first released to express ideas. Its design ethos was also excellent and fully achieved its primary goal, i.e., to offer advanced developer production capabilities and code readability.

Why Python?

Python is a popular programming language for system scripting, software development, and server-side web development. It is the go-to language for many experienced programmers and is widely used for Rapid Application Development and as a scripting language or glue to connect existing modules or components. Moreover, Python's simple syntax and its emphasis on readability results in lower program maintenance costs.

Although Python has several well-known flaws, it is still considered as one of the world's most important and widely used languages. Here is a list of reasons why Python is so popular:

  • Easy to Learn and Use:
    Learning Python is incredibly simple for newcomers and beginners. Because of its straightforward syntax, lack of complexity, and emphasis on natural language, Python is more approachable compared to other programming languages. It is easy to learn. For instance, built in data structures like dictionaries and arrays with several efficient methods are available to users. Even concepts like object oriented programming are easier to grasp in the Python programming language. Furthermore, it takes very little time to set up Python, and one can always search the internet for free tutorials for different operating systems. It is also interesting how there are several ways to achieve the same task in Python; for example, one can use normal lists and list comprehensions to get things done more efficiently.
  • Mature and Supportive Python Community:
    Python was developed more than 30 years ago, which is a lot of time for any programming language's community to develop and mature appropriately. Learners and even experienced programmers can improve their knowledge of Python programming language basics by using abundant documentation, guides, and video Python tutorials.
  • Hundreds of Python Libraries and Frameworks:
    Thanks to corporate sponsorship and a large, supportive community, Python has great libraries you can choose from to save time and effort during the initial development cycle. For instance, Django is a very useful Python web development framework.
  • Versatility, Efficiency, Reliability, and Speed:
    Python can be used in almost any environment, and regardless of the platform being used, there will be no performance loss. It is adaptable and can be used in various settings, including web development, hardware programming, desktop applications, mobile applications, and much more.
  • Big data, Machine Learning, and Cloud Computing:
    Big data, machine learning, and cloud computing are some of the hottest trends in computer science. Python’s mature libraries and frameworks assist numerous organizations in transforming and enhancing their processes and workflows. This is apparent as Python is the second most used language for data science and analytics after R.
  • Automation:
    Python is perhaps the best performance enhancer for software testing automation. It is amazing to see how little time and how few lines of code are needed to create automation tools.
  • Open-Source:
    Python is regarded as one of the best development alternatives for developers who take cost into extreme consideration. Python is available for download and uses at no cost whatsoever. Additionally, Python-using commercial platforms do not require any additional licensing fees and are mostly free.

What Does a Python Developer Do?

A dedicated Python developer is expected to understand the language properly and to be able to use Python to execute a variety of tasks. Data collecting and analytics, database generation, web development and design, programming, and automation are all part of this.

The coding, designing, deploying, and debugging of development projects, typically on the server side, are the duties of a Python developer (or back end). However, they might also support businesses' technological infrastructure.

A Python Developer might be asked to build tools for the job, design the framework for your code, build applications for your employer, build websites and integrable systems, or release new services on any given day.

While multiple companies employ some Python developers, others are independent contractors. Therefore, the specifics of this position vary depending on your employer's needs, as most programming positions do.

As you can see, the duties of a Python developer can be very diverse. The job role and responsibilities of a Python developer are quite varied due to the wide range of possible applications for Python.

Thus, a Python Developer's job would encompass different responsibilities, including:

  • Create and design effective websites and applications.
  • Python code should be reusable, testable, and efficient.
  • Connect and integrate data storage solutions.
  • Design integrative systems.
  • Integrate user-facing elements and comprehend end-user needs.

How to Become a Python Developer

There's no better time than now to start your career as a Python developer. It's hard to find a tech domain that isn't extensively using Python. 

Learn the Fundamentals

Although becoming a Python developer is simple, you must have the discipline to develop the necessary Python skills over time. To become a skilled Python developer, you must develop a structured learning process. You must first be an expert in the fundamentals. Therefore, you need to understand each of these fundamental ideas (which serve as the cornerstone for other programming languages, too), starting with the fundamentals of Python:

  • Variables And Data Types (including static types)
  • Data Structures and Algorithms 
  • Object Oriented Programming
  • Loops, Conditional and Control Statements 
  • I/O Operations and Exception Handling 
  • Modules and File Handling 
  • Database Knowledge 

Choose Your Career Path

There are numerous potential fields for a Python developer, and the one you intend to specialize in will inform your process more closely. For example, a Python developer's potential career paths include:

  • Data scientist
  • Machine learning engineer
  • Software application developer
  • Web app developer
  • Software automation engineer
  • Game developer
  • Data analyst

Choosing a career path will help you move towards becoming a great Python developer.

Build Your GitHub Repository

When applying for a job as a Python developer, your GitHub repository doubles as your resume; instead of sending samples of your work in zip files, recruiters can view it on the developer platform.

Your GitHub repository should now contain your Python projects and practice work. Even if it's just a piece of straightforward code you wrote or a piece of existing code you slightly modified. Moreover, it is always good to share your progress with the world and to even learn from the Python community by participating in discussions.

Learn Supporting Frameworks and Technologies 

Develop your skill set if you want a dependable way to improve your Python experience and stay competitive. In addition to reading related books, you ought to be familiar with the following machine learning and data science Python libraries:

  • NumPy
  • SciPy
  • Keras
  • TensorFlow

If you want to learn Python web development, learning HTML and CSS can help you better grasp web frameworks like Django and Flask. However, even if you're new to Python, HTML and CSS may help you develop little web pages to amaze your friends. Also, HTML pages are very easy to make.

While mastering every library would be ideal, you can still advance significantly by learning at least a few. To hone their Python skills, Python programmers can do courses that introduce a certain framework well or give simplified Python tutorials regarding that technology.

Advantages of Python

Best Language For Machine Learning

AI researchers are enthusiasts of Python. Because of their usability and versatility for Python users, Google TensorFlow and other libraries lay the groundwork for AI development. These libraries and their availability are essential because they allow developers to concentrate on development and construction. Today, one can Google Python tutorials and courses covering all the basics.

Earning Potential

According to Indeed, Python is the second-highest-paid programming language after Java. With an annual salary in 2022 of about 111,899 US dollars for mid-level Python developers, which is much higher than the mean salary in the US and even in the world. These salaries are even higher in the domains of web development, machine learning, and data science.

Ease of Comprehension

Python's ease of use and enjoyment are two of its top advantages. Its syntax reads like English, unlike most computer languages, making learning easier than other programming languages. In addition, it manages complexity well, allowing you to concentrate on learning Python programming's advantages rather than the difficulties of the minute intricacies. Error handling is also good in Python, which means that a developer can easily succeed in the translation of an error and remove the bug.


Python is not only versatile, but it is also easy to learn. Python may be used for web processing, machine learning, and even biology, thanks to over 125,000 third-party Python packages. It is favored in data science because of its data-focused modules, including pandas, NumPy, and matplotlib, which are particularly adept at processing, manipulating, and displaying data. It is sometimes referred to as the "Swiss Army Knife" of computer languages due to its adaptability.

Used in Many Industries

Python is employed in almost every sector because of its numerous advantages and versatility. New Python programmers' abilities go beyond data science.

Vast Libraries Support

The vast Python standard library contains practically all the functions required for your task. Therefore, you are not dependent on external libraries.

Nevertheless, a Python package manager (pip) simplifies importing additional excellent packages from the Python package index even if you manage to do so (PyPi). There are more than 200,000 packages in it, including several for web development, machine learning, and data science.

How to Install Python?

There are only a few steps to complete while installing Python 3 on Windows. Following these steps, we can use Python's integrated package management to install software on all operating systems from any location.

Download the Installer

Go to the official Python website in your browser and download the Windows installer.

Run the Installer

Install Python for all users by double-clicking the downloaded file, and make sure Python 3 is added to your path. To start, click Install now. Next, click Disable path length limit and then To complete the installation, click Close.

Verify Installation

The following command can now be entered at the command prompt to confirm that Python 3 has been successfully installed and even reveal the specific version:

python --version

However, it is important to know that operating systems can have multiple Python versions installed simultaneously.

Components of Python

Python's components are necessary since they create the Python code. Every Python program block uses them; they are the fundamentals of Python coding.

The list of Python components consists of the following:

Python Virtual Machine (PVM):

Because the Python virtual machine is stack-based, values for operations and their results are stored on a stack. The Python Virtual Machine (PVM) is responsible for converting the byte code commands into machine code, which the computer can then use to execute those commands and show the results.

Pip - PyPi:

Pip is a Python-based package management tool to install and manage software packages. For installing Python programs and their dependencies during deployment, the Python Software Foundation suggests using pip.

Python Standard Library:

The exact syntax, semantics, and tokens of Python are contained in the Python Standard Library. It has integrated modules that give users access to system features like I/O and a few more core modules. C is the programming language used to create most Python libraries. More than 200 core modules and built in functions make up the Python standard library.

Python Shell:

You write your Python programs in the CLI straight in the shell. The Python interpreter is then called when you write code in the shell. 

Development Best Practices

It can be challenging to learn something new at first. The best practices for Python code serve as guidelines. These will aid your code navigation and facilitate learning a new programming language.

Using Python 3 Instead of Python 2

Always choose Python 3 over Python 2 when picking between the two. Python 2 has been in "End of Life" status since January 2020, which means it won't receive any more progress, including those that address bugs or increase security.

Documenting and Commenting Code Properly

Documenting and appropriately commenting on every code are essential components of Python best practices. This point is crucial if several people are simultaneously working on the same code. Clarity can set in and leave everyone scratching their brains when they attempt to submit their code with clear documentation and succinct comments. Therefore learn to add better block and inline comments, simplified expressions, and proper documentation for functions:

# This comment is a block comment
print("Flexiple Python")
print("Python is a helpful programming language")
print ("Hello World!") # This is an inline comment
# We have printed strings
def addition(x, y):
    """ Return addition of two numbers """
    result = x + y
    return result

Using Proper Naming Conventions

Using the right naming convention when creating Python code helps speed up the process. Choosing a sensible naming convention will shorten a project's timeframe and make it simpler for team members to upload their code because there are so many different variables, functions, classes, packages, and other things.

# Good Coding Practice
InputVar = 24
Constant = 14
Result = InputVar + Constant

Using Virtual Environments

Every developer should have a virtual environment for any project currently in process. Since many projects could need different versions of a single library, this virtual environment makes it much simpler to avoid issues with library conflicts. Virtual environments are especially useful in machine learning and data science projects.

Testing Code Frequently

Always remember the value of regular testing. Code auditing can change the game. Writing test code and looking for bugs is an excellent habit. So spend some time testing the code.

Using Logs Instead of Prints

Logging is a mechanism for monitoring events that occur when using the software. Logging becomes increasingly important for planning, debugging, operating, and tracking performance as applications grow in size and complexity.

To cope with logging operations, Python's Standard Library has included the logging module since version 2.3. Logs give information comparable to print functions, but they are far more powerful because they can provide much more specific information.

Although the print function may be tempting, logs are usually the safest option, especially if you need to become more familiar with logging operations. They can handle more sophisticated applications and scale more effectively.

Consider the following code:

import logging'This is just information')
logging.debug('I will help you debug')
logging.critical("There is some major issue in the code")

Above, .info() acknowledges that the code does not contain any bugs. Lastly, .debug() is used for diagnosing issues in the code, and .critical() logs the message when there is an error causing the program to stop functioning or exit abruptly.

Using Loops Correctly

A loop is used in computer programming to repeat a section of code. Python has two kinds of loops, for and while, and one should know when to use which.

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