Python for Software Development: Everything You Need to Know

Quick Go-Through: When building software, Python is the number one choice for developers as it excels in multiple domains. Its extensive library ecosystem, readability, and simplicity allow for versatile application and rapid prototyping. In this blog, you’ll learn about the power of Python, its significant aspects, how it helps simplify your development process, and much more.

What matters the most when it comes to software, and what, as a user, do you expect the most out of it?

To over 90% of our clients we have worked with so far, it’s performance.

And which programming language has been persistently delivering top-notch performance in software systems?

That would be Python.

The reason is it’s simple and has a clean syntax.

Compared to other programming languages, Python enables software professionals to write more concise code. This efficiency brings numerous benefits for development. It’s an excellent deal for businesses that seek performance above all.

And that’s not everything. Python for software development is extremely versatile, scales well, integrates easily with other technologies, and has a vast ecosystem of libraries and frameworks. All these factors make it an ideal solution for a wide range of projects.

As such, it has become the most popular language in 2024, according to GitHub’s Octoverse report.

Here in this blog, we will not only talk about the advantages of this language but also walk you through the top frameworks, tools, examples, and all that you should know about Python software development.

Let's get going!

What is Python?

Python is an interpreted, high-level, dynamically semantic, object-oriented programming language. It is particularly suited for Rapid Application Development, AI and ML projects, and data processing due to its high-level built-in data structures, dynamic typing, and dynamic binding. Additionally, for its flexibility and integration capabilities, it often works as a scripting or glue language to connect existing components.

The best part is that Python's simple syntax puts readability first and makes learning more accessible, which ultimately reduces program maintenance costs. Its increased productivity frequently leads to coders falling in love with it. Because there is no compilation phase, the edit-test-debug cycle is quick. Python scripts can be easily debugged; a segmentation fault will never result from a bug or poor input.

As a result, one of the most accessible languages to acquire is Python, even for beginners. It is frequently chosen as a developer's first programming language since it uses intuitive and easy-to-use English grammar.

The Rise of Python in Software Development

Since its launch in 1989, Python has enjoyed steady popularity. It’s always been the easiest to command but developers had equally compelling alternatives like Java and C++. However, in recent times, especially in the last five years, its popularity has surged dramatically, and it has outranked many of the top contemporary languages.

For example, if you look at the Stack Overflow Trends, Python is topping the chart with a huge margin, well ahead of its competitors:

Now in the TOIBE index, Python holds the top spot, and its ranking has improved by a whopping +9.98%, while C++ ranks second, with a modest growth of +0.81%. This shows the coding language is experiencing a significant upward trend in interest and usage like no other languages.

So, what made Python explode in popularity in recent years?

Well, a lot of it has to do with the boom of Artificial Intelligence use cases, Machine Learning platforms, and data-driven projects. These technologies need programming languages that are simple to write yet can handle complex algorithms, have a rich set of libraries to build versatile models, and are great for data crunching. Python seems to be a natural fit for these criteria.

Its popularity is further solidified with the highest number of pull requests on GitHub. As developers and researchers are getting more involved in AI/ML software development, it leads to their frequent contributions to Python repositories – a direct result of its widespread usage.

Why Do You Need Python to Build Software?

As a well-liked open-source programming language, Python provides improved process management capabilities. It helps develop complex multi-protocol network applications while maintaining simple syntax. Not to overlook that popular platforms like Google, Instagram, and Spotify use Python.

Such its widespread adoption that it ranks first on the PYPL Index as the most popular language with a considerable lead:

Therefore, being an all-purpose programming language, Python software development offers a wide range of benefits and advantages, such as:

1. It is a Versatile Language

Python is an innovative programming language with many exciting features and capabilities. Web applications, search engines, games, animation software, networking apps, and other software systems use it as their core technology. For instance, a web developer may use Python to build a website, and a data scientist might use it to make visualizations or modify data.

2. It Has a Simple and Concise Code Structure

Python doesn't require you to use complex syntax. Software development using Python lets novice programmers develop applications quickly. It uses a simple, clean, and readable structure, making the code relatively easy for developers and students to understand, read, and master this language. The code can be shortened and simplified to make it easier for beginners to learn this programming language.

3. It Offers Cloud Computing Support

Python supports cloud computing. It ensures you can easily create websites and custom applications and save everything on the cloud or distributed network. Cloud computing services help your business to grow by delivering rapid access to all the necessary data at any time, and Python excels at that.

4. It Has Dynamic Language Features

We're talking about an interpreted language with dynamic data types that make it relatively easy to use and implement in your projects. Software produced with Python is a good choice for both small and large-scale projects because its dynamic language features provide flexibility, speed, and ease of maintenance. It helps developers build quick prototypes as well as handle complex software requirements.

5. It Offers Integration with Other Languages

Since Python is quite flexible, it enables developers to integrate it well with other programming languages and technologies for developing complex applications. For example, it can directly call C functions and access C libraries, run on Java Virtual Machine (JVM), interact within the .NET environment, connect to SQL databases, and so on.

6. It is an Extensible and Portable Language

Being an open-source programming language, this language allows you to add new functionalities or modify existing ones easily. This makes it a versatile and extensible language that both beginners and experienced programmers can use to create complex projects that meet their specific requirements.

What are the Benefits of Using Python for Software Development?

There are many advantages to using Python to build software, which is a multipurpose language. The development of this technology has been based on the fact that it implements a simple and easy-to-use English syntax that almost anyone can use.

To be precise, the syntax is so simple that it is the most recommended first-children software programming language. Developers admire Python for the same reason, as reflected in the Stack Overflow Developer Survey:

Let's go into the advantages of developing software with Python:

1. Python is Robust

Python is a powerful programming language. This means that it has been used to create complex and difficult-to-maintain code, which makes it ideal for demanding tasks. There are no questions about its effectiveness, and its syntax is straightforward.

This programming language is the most preferred language for fast-track development since it keeps the code concise and precise, making it much easier to manage and debug. In addition, it has some excellent features, like support for dynamic typing and nested scopes.

2. Python is Fast

Building software with time and resource efficiency is possible with Python because it has a fast process-control capability. It can be used for web-based applications since it supports concurrent programming and offers high performance. What's more? Generally, the code you write in it would run fast without any issues or delays.

Also, as we are learning more about Python, do you want a deeper insight into PHP vs. Python?

3. Python is Portable

This means you can use your codes on any platform. Whether it's a start-up project or a large corporate project, writing in Python helps you to distribute the codes to developers efficiently and helps them avoid errors in coding.

4. Python is Easy to Learn and Understand

It is a simple language for new users to learn. It offers simplicity and clarity, making it easier for rookie developers to learn Python. The syntax is such that any programmer can comprehend it. In addition, you will be surprised to know that some excellent resources online help you improve your programming skills with comprehensive and encouraging tutorials.

5. Python Can be Implemented on Any Platform

Python has been made flexible enough to run on Linux-based, Windows, and Mac operating systems. It is the perfect solution for integrating any software project into any platform. It is a better option for those with limited budgets since the open-source community manages its maintenance and development.

Plus, you would be amazed to know that many successful companies use Python in their projects, contributing to its popularity globally.

Popular Python Frameworks and Libraries

Python developers can make the best out of ready-made components in the framework to save time and money and accelerate time-to-market. Plus, not to overlook, it has a great scope of frameworks, tools, and libraries available for developers.

Before diving into the top Python frameworks and tools, let us look into different types of frameworks:

Types of Python Frameworks

1. Full-Stack Frameworks

These frameworks are a one-stop solution to build a fully functional software system. It fully supports the entire development process, from the user interface to the backend architecture.

2. Micro Frameworks

These are lightweight frameworks. They provide the basic tools for software development but do not offer additional features and functionalities such as a database abstraction layer, form validation, specific tools, and libraries. Furthermore, the developers using a micro framework must manually add many codes and additional requirements.

3. Asynchronous Frameworks

This framework has been gaining a lot of attention lately. Any asynchronous framework is a micro framework that handles many concurrent connections. To be precise, this framework built for Python uses the programming language's asyncio library.

4. GUI Frameworks

As the name suggests, these frameworks are useful for building graphical user interfaces for software systems, especially for mobile and desktop apps. They handle the complexities of UI rendering and manage user interactions.

5. Testing Frameworks

Python testing frameworks are quite popular among developers for their efficiency in writing and managing tests. Developers can test various parts of the software using different testing methods like unit testing, functional testing, and UI and acceptance testing.

Now let us explore the top frameworks of Python that have proved to be essential for software development:

Best Python Frameworks

1. Django

Django is a free and open-source web framework written in Python. It follows the Model-View-Template architectural pattern and comes with many built-in features like database ORM, authentication, routing, and admin interface.

Key Features of Django

• Supports many web-hosting platforms.
• Provides built-in encryption modules for security.
• Offers a "batteries included" approach for common use cases.

2. Flask

This micro framework, which draws inspiration from the Sinatra Ruby framework, depends on the Werkzeug WSGI toolkit and Jinja2 template. Flask is easily adjustable because of its lightweight and modular construction.

Key Features of Flask

• Handles HTTP request handling.
• Uses secure cookies to establish client-side sessions.
• Allows integration with any ORM.

3. PyTorch

Developed by Favebook, PyTorch is one of the heavily favored Python framework, known for its Machine Learning capabilities and dynamic computation graph. Researchers and data scientists love this library and commonly use it for language processing and image recognition.

Key Features of PyTorch

• Supports GPU, CPU, and parallel processing.
• Offers a comprehensive library of tensor operations.
• Provides APIs for deep learning models.

4. Numpy

As a foundational package, Numpy is very useful for numerical computing in Python. It offers a collection of mathematical functions to work on multidimensional arrays. It's an open-source project with regular updates and has become a standard for scientific and mathematical applications.

Key Features of Numpy

• Performs and manipulates operations on arbitrary dimensions.
• Allow fast computation and manipulation of large datasets.
• More memory-efficient than standard Python lists

5. Pandas

It's a powerful library for data analysis, data manipulation, and statistical modeling. Pandas allows developers and engineers to manipulate, clean, and analyze structured data by using data structures like Series or DataFrame.

Key Features of Pandas

• Provides automatic data alignment.
• Allows efficient data indexing and selection.
• Optimizes memory usage with categorical data types.

6. AIOHTTP

This framework relies on Python 3.5+ features, such as async & awaits. It makes use of asyncio library, and it is an asynchronous framework. Furthermore, besides being a server web framework, it can also serve as a client framework. It also provides a request object and router to enable the redirection of queries to function developed to handle the same.

Key Features of AIOHTTP

• Supports both clients and HTTP servers.
• Enables developers to build the views effectively.
• Offers pluggable routing.

7. Bottle

Bottle is one of the best Python web frameworks known for being lightweight, fast, and reliable. It uses the WSGI application call interface, one of the most popular ways to run a web server. It is relatively easy to use but will require some knowledge of WSGI application execution.

Key Feature of Bottle

• Ajax and REST framework support.
• Easy to extend by using the plugin system.
• Complete support of WSGI-based server frameworks.

8. CherryPy

This framework is another lightweight and fast framework that is easy to install and able to handle any size of processing. It supports threads, sockets, and various protocols, while also offering automatic process management in the background. Additionally, it provides complete access to low-level modules for enhanced control and flexibility.

Key Features of CherryPy

• Support for various WSGI servers.
• Logging handler to handle errors in any part of the application.
• Very minimal syntax to write reusable code.

9. CubicWeb

It is also a lightweight web framework. It is easy to install, supports multiple protocols, and can handle any processing size. You can build your application using the pre-built modules from Nginx or Apache Webserver. CubicWeb provides complete access to low-level modules, including external libraries like JSON and XML.

Key Features of CubicWeb

• Enables multiple protocols (HTTP, etc.) in one application.
• Supports threads, sockets, and other programming languages (PHP, Ruby, etc.).
• Automatic process management using in-process cache management facilities.

10. Dash

This library framework provides a high-level development environment to build web applications. It can be used as a standalone library or integrated with nginx, Apache, or any other large-scale server. If you have been looking for a framework for creating software with Python, you can consider relying on Dash.

Key Features of Dash

• Simple user interface that includes better control structure and improved support for custom objects.
• Complete access to low-level modules, including external libraries like JSON and XML.
• Compatible with data science libraries for data analysis and visualization.

11. Falcon

It is another widely used Python framework for software development. Unlike other frameworks that require loading a lot of dependencies for building HTTP APIs, Falcon allows developers to create a cleaner design that enables HTTP and REST architectures.

Key Features of Falcon

• It has an extensible, highly optimized code base.
• Offers ease of excess for headers and bodies via request and response classes.
• Encourages the REST architectural style and tries to do as little as possible while remaining highly effective.

How to Develop Software Using Python?

The process of building software with Python is not drastically different from using other programming languages. However, its simplicity and ability to handle complex tasks make the overall software development life cycle faster and better.

While traditional development processes can feel rigid and time-consuming, Python speeds things up with its extensive library support, easy-to-read code, and integration-friendly nature.

With that in mind, let’s understand how to create a software application using Python:

1. Outline Your Python Software Goals

Successful projects start with a clear specification of requirements. You need to identify what you’re building, why you’re building it, and who it’s for.

Here are some defining project criteria to pinpoint:

• The problem you want to solve – challenges you’re facing, tasks you want to automate, or tools you need to help users.
• People who will use the software – identifying user groups (primary and secondary), creating user personas (demographics. preferences, habits), etc.
• Key features to integrate – basic features like dashboard, login/signup and advanced features like predictive analytics or machine learning models.
• Final objectives of the software – determining the outcomes such as accelerating workflows, improving customer experience, or reducing manual work.

If you’re not sure about this phase, a software consulting team might help you set the project goals and structure it according to your exact requirements.

2. Hire the Right Development Team

The developers you choose to work on your project will directly determine how seamless the process is, how quickly it’s completed, and how well the final product performs.

Knowing how to write Python code is not enough. You should also consider if they have worked on similar projects. For example, if your software is going to integrate AI, developers should be familiar with libraries like TensorFlow or Scikit-learn.

Additionally, look for their soft skills as well. Problem-solving abilities, knowing how to work smartly to save time and money, adhering to code quality and coding standards, communication skills, and so forth – all these factors play a crucial role in selecting the ideal dev team and pays off in the long term.

3. Plan the Tech Stack and Frameworks

Your team now needs to choose the right tools and frameworks for the software you’re planning to build. The good news is that Python offers solutions for almost all kinds of projects. For example, Django or Flask are the best choices for web apps. If you’re looking at ML or AI, go with PyTorch or TensorFlow. And for data analysis, there’s no better option than Pandas or NumPy.

Note that the development team might also suggest other technologies to integrate with Python. Databases like MongoDB or PostgreSQL and cloud platforms such as Azure or AWS are the most effective for code in this language.

4. Break It Down: Development and Testing

Development and testing are closely coordinated. In fact, your developers should not wait to test the software after it’s completely built. With Python, you can and should test the software modules frequently to avoid major fixes and lengthy iterations.

Now for development, it is amazingly fast because it’s simple, readable, and has a lot of pre-built libraries. Here's how the development process typically works:

• Initial Setup – The team sets up the project environment depending on the chosen technology.
• Building Features – Developers and designers start coding the essential features like the user interface, database, and backend. At this point, the software is in its basic form and might only have a few core features.
• Iterative Workflows – Since Python is highly flexible, developers can gradually build the software, add more features and functionalities in stages, and test them as they go.

Alongside development, the following are the crucial tests your team should run using some of the best Python testing frameworks like unittest, pytest, and nose2:

• Unit Testing – Checking if the functions work as intended.
• Integration Testing – Verifying if the different components of the software work together.
• Continuous Testing – Running tests every time developers make changes to the code.

Once the software is nearly complete, the team does more thorough testing like User Acceptance Testing where real users try out the software, Performance Testing where developers audit how the software performs under pressure, and Security Testing to make sure the software is secure from data breaches and unauthorized accesses.

5. Launch and Scale

It's time to go live. The development team will deploy the software on servers or the cloud for the users to access it.

Depending on your needs for traffic, storage, and security, the hosting platform could be traditional web hosting services or cloud services like Azure, AWS, Or GCP. Your team set up the production environment by configuring the server and setting up databases. They can also use automation tools for deployment such as Docker or Kubernetes.

After launch, the next big task is to keep monitoring the software performance for spotting and fixing any issues. You can go the extra mile and collect user feedback to assess how the software is working in real-world conditions.

Finally, if you need to scale the software in the future to handle more users, traffic, or data, Python enables you to do so effortlessly. Without any huge rewrites or costly overhauls, developers can add more servers, optimize the software for better performance, and update features.

Real-Life Examples of Python Software Applications

Looking under the hood of some top brands, you will find that Python often powers them. While each of these platforms uses a mix of technologies, Python’s versatility is the key reason they use it in their tech stack.

1. YouTube

YouTube uses Python for several aspects of its backend and server-side logic, including video processing and automated transcoding, user data and video metadata management, and recommendation algorithms using ML.

2. Netflix

Netflix makes use of Python for the "full-content life cycle." It includes content distribution, security automation, AI-driven recommendation engine, data pipelines, and automating various tasks in the backend.

3. Spotify

Spotify uses Python for backend development solutions and data analysis. The same is favored due to the speed of development and the benefits of machine learning. Spotify also relies on its ML libraries to analyze listening patterns and generate customized playlists.

4. Amazon

Python is integral to automation, data analysis, and AI features of Amazon. It helps power their infrastructure automation tasks, analyze huge amounts of transaction data, build AI models for product recommendation, and derive insights from sales and customer behavior.

5. Instagram

Instagram relies on Python, especially the Django web framework, for server and database management, user interaction analysis, image recognition, and understanding user preferences.

A Practical Use Case of Python Software Development from Radixweb

As we mentioned earlier, the rise of Python can be attributed to its ability to handle Machine Learning use cases, including predictive analytics, anomaly detection, and time series forecasting.

This was the case with one of our clients specializing in the healthcare industry. They needed a solution to predict heart disease risks based on patient data. As soon as we evaluated the project requirements, we knew Python would be the best fit for it.

Using NumPy for matrix manipulation, Pandas for data analysis, Matplotlib for data visualization, and H20.ai for model training, we built an ML model that could analyze historical data and identify patterns.

The final result was a high-accuracy prediction software able to accurately analyze patient information and predict the likelihood of heart diseases.

For more details on the development process, check out the full resource on how we advanced healthcare through Python.

Your Next Strategic Step for Top-Tier Python DevelopmentDespite Python’s simplicity, using it wisely to build quality software is not something you can whip up in a day. The versatility means there are endless possibilities, but it demands a deep understanding and years-long expertise in its libraries, frameworks, and how to make the best use of them to solve practical problems.This is where we truly stand out from the rest. Python has always been at the heart of our tech stack. Over time, we’ve built an incredible team of developers who represent the top 1% of global talent. This has been proven time and again by 250+ businesses that have consistently trusted us to deliver groundbreaking Python-based software solutions, in addition to 4000+ clients across all tech domains.However, words can only convey so much. To truly understand how we bring its potential to life, we encourage you to connect with us. With our hands-on experience with cutting-edge Python technologies to the success stories we’ve delivered, we’ll demonstrate the value we bring to the table.Let's have a talk?