The Definitive Guide to Python App Development in 2024

Python App Development

Initially created in 1991 by Guido van Rossum, Python was released as a general-purpose, high-level, interpreted programming language with modularity and code readability as goals. In this blog, we are explaining everything about Python App Development.

Throughout its nearly 30 years of history, Python has grown to be one of the most widely used programming languages. It is quite a simple language that is easy to understand and learn.

The language can run on several platforms like Linux, Mac, Raspberry Pi, Windows, etc. Python runs on an interpreter system which implies that code can be efficiently executed as soon as it is written.

Python development syntax is also a lot simpler than the syntax of any other programming language.

With a current popularity share of 31.73 percent among contemporary development languages, Python’s popularity consistently achieves an all-time high.

As per the latest developer survey by Stack Overflow, Python is the third most popular programming language.

 Guide to Python App Development

Python language is free, open source, and simple to learn and emphasizes the DRY (Don’t Repeat Yourself) principle and readability.

Being high-level, dynamically typed, and interpreted, Python makes debugging errors much simpler and allows rapid development of application prototypes, distinguishing itself as the language to code with. 

What is Python App Development?

Python app development refers to the process of creating applications, software, or programs using the Python programming language.

Python is a versatile, high-level programming language known for its simplicity, readability, and ease of use.

It supports multiple programming paradigms, including procedural, object-oriented, and functional programming, making it suitable for a wide range of applications.

Some Interesting Python Statistics

According to StackOverflow, Python is a fast-evolving programming language that can be used to solve a broad spectrum of issues. It can meet the requirements of most applications.

  • CPython by GitHub, which is the reference implementation of Python in C, has more than 58K stars.
  • In 2023, the IEEE survey suggested Python to be the top programming language amongst mobile app developers globally.
  • According to the TIOBE Programming Community index, Python ranks first amongst the top 20 programming languages used by experienced developers worldwide.
  • At present, there are over 8.2 million Python developers globally and this number is going to only increase in the coming months.

Some Interesting Python Statistics

Why Should You Use Python for Mobile App Development?

Here are some key aspects of Python for mobile app development and reasons why you might choose Python for your app development projects.

-Ease of Learning and Readability

Python is often praised for its simple and readable syntax. Its code is easy to understand and write, making it an excellent choice for beginners and experienced developers alike. This ease of learning accelerates the development process and reduces the likelihood of errors.

-Extensive Libraries and Frameworks

Python has a vast standard library that includes modules and packages for various functionalities. Additionally, numerous third-party libraries and frameworks simplify and speed up development.

Popular frameworks like Django and Flask are widely used for web development, while Kivy is popular for creating cross-platform mobile applications.

-Versatility

Python is a general-purpose programming language, making it versatile for different types of app development. Whether you’re working on web development, data analysis, machine learning, or artificial intelligence, Python has tools and python libraries available to support your project.

-Community Support

Python has a large and active community of developers. This community support means that you can find solutions to problems, access documentation, and seek help easily.

The collaborative nature of the Python community contributes to the language’s continuous improvement.

-Cross-Platform Compatibility

Python is a cross-platform language, meaning that applications developed in Python can run on various operating systems with minimal modifications. This flexibility is particularly beneficial for projects targeting multiple platforms, such as Windows, macOS, and Linux.

-Rapid Prototyping

Python’s concise syntax and dynamic typing facilitate rapid prototyping. Developers can quickly build and test ideas, allowing for a faster development cycle. This is crucial for startups and projects where time-to-market is a significant factor.

-Scalability

Python can scale well for both small and large projects. While it might not be as fast as low-level languages in certain scenarios, Python’s scalability is often sufficient for many applications, and optimizations can be applied where needed.

-Data Science and Machine Learning

Python has become the language of choice for data science, machine learning, and artificial intelligence. Libraries such as NumPy, pandas, TensorFlow, and PyTorch provide powerful tools for data analysis and machine learning, making Python an ideal choice for projects in these domains.

What are the Best Python App Development Frameworks?

Python frameworks are a collection of modules or packages that make the development process easier for developers by providing a structure for app development.

It automates the implementation of common solutions and thus extends flexibility to the developer to focus on logic rather than doing routine tasks.

Let’s check out the top Python App Development Tools based on their features and ratings. This table will guide you in choosing the best tool considering your project requirements.

What are the Best Python App Development Frameworks

How to Use Python for App Development?

Using Python for app development involves several steps, and the approach can vary based on the type of application you want to build (web, mobile, desktop, etc.). Below is a general guide on how to use Python for app development:

Choose the Type of App:

-Web Development

For web applications, popular frameworks like Django and Flask are commonly used. Django is a high-level web framework that follows the Model-View-Controller (MVC) pattern, while Flask is a microframework that provides more flexibility.

# Example: Installing Django

pip install django

# Example: Creating a new Django project

django-admin startproject projectname

# Example: Running the development server

python manage.py runserver

-Mobile Development

For mobile app development with Python, you can use frameworks like Kivy, BeeWare’s Toga, or tools like KivyMD (for material design). These frameworks allow you to write Python code and deploy it to Android and iOS.

# Example: Installing Kivy

pip install kivy

# Example: Creating a basic Kivy app

python -m pip install kivy

-Desktop Development

For desktop applications, frameworks like PyQt or Tkinter are commonly used. PyQt is a set of Python bindings for the Qt application framework, and Tkinter is the standard GUI toolkit included with Python.

# Example: Installing PyQt

pip install PyQt5

# Example: Creating a basic PyQt app

Set Up the Development Environment

Install a code editor or Integrated Development Environment (IDE) suitable for Python development, such as VSCode, PyCharm, or Atom.

Learn the Basics of Python

If you’re new to Python, it’s essential to familiarize yourself with the basics of the language. Understand data types, control structures, functions, and object-oriented programming concepts.

Explore Framework Documentation

Depending on the framework you choose, explore the official documentation to understand its features, components, and best practices.

Write Code

Start writing the code for your application. Follow the structure and conventions of the chosen framework.

Test Your App

Write unit tests to ensure the functionality of your application. Use testing frameworks like unittest or pytest.

# Example: Installing pytest

pip install pytest

# Example: Running tests with pytest

pytest

Optimize and Debug

Optimize your code for performance and resolve any bugs or issues. Use debugging tools provided by your IDE or use print statements to troubleshoot.

Package and Distribute

Depending on the type of application, use tools like pyinstaller or cx_Freeze to package your application into executables for distribution.

# Example: Installing pyinstaller

pip install pyinstaller

# Example: Creating an executable with pyinstaller

pyinstaller your_script.py

Deploy

Deploy your application on the desired platform. For web applications, you might need to set up a server. For mobile apps, you can distribute through app stores, and for desktop apps, users can download and install the executable.

Maintain and Update

Regularly maintain and update your application based on user feedback, feature requests, and bug reports.

Remember that Python is a versatile language, and the specific steps may vary depending on the type of application and the chosen framework. Always refer to the official documentation for the tools and frameworks you are using.

How much does it cost to develop a Python app?

The cost and time required for developing a Python app can vary significantly based on several factors, including the complexity of the app, its features, the development team’s experience, and the project’s scope.

Additionally, the security of a Python app depends on various factors, including the implementation of security best practices, the use of secure coding techniques, and the careful consideration of potential vulnerabilities. 

Let’s explore each aspect of the cost of Python Application Development. There are several factors Influencing Cost.

App Complexity

The more complex the app, the higher the development cost. Simple applications with basic features will generally cost less than complex applications with advanced functionalities.

Design Requirements

The complexity and uniqueness of the app’s design can impact development costs. Custom designs and user interfaces may require more resources and, consequently, increase costs.

Integration with External Services

If your app needs to integrate with external services, APIs, or third-party components, development costs may increase based on the complexity of integration.

Development Team Location

The geographical location of the development team influences costs. Development teams in regions with higher living costs may charge more for their services.

-Cost Ranges

  • Simple Apps: $10,000 – $50,000
  • Moderate Complexity Apps: $50,000 – $150,000
  • Complex Apps: $150,000+

How much time will it take for Python mobile app development?

The time required for Python app development can vary significantly depending on various factors, including the complexity of the app, the scope of features, the size and expertise of the development team, and the overall project requirements.

Here’s an overview of the factors that influence the time it takes to develop a Python app.

Project Scope and Complexity:

-Simple Apps

Basic applications with limited features can typically be developed in a shorter timeframe. Examples include simple calculators, to-do lists, or basic informational apps. Timeframe: 1 to 3 months.

-Moderate Complexity Apps

Apps with moderate complexity, involving more features, user interactions, and potentially backend functionality, will naturally take longer.

Examples include content delivery apps, inventory management systems, and small e-commerce platforms. Timeframe: 3 to 6 months.

-Complex Apps

Highly complex applications with advanced features, intricate business logic, extensive databases, and integrations will require a more extended development period.

Examples include enterprise-level systems, social media platforms, or comprehensive e-commerce solutions. Timeframe: 6 months and beyond.

Team Size and Expertise:

-Individual Developers

A single developer working on a project will generally take longer compared to a team due to the limited resources. The development time is also influenced by the developer’s experience and expertise.

-Small to Medium Teams

Having a small to medium-sized development team allows for better collaboration, parallel task execution, and potentially faster development.

The collective expertise of the team members can also contribute to efficient problem-solving. Timeframes depend on the specific project requirements.

-Large Teams

While larger teams may have the advantage of parallelizing tasks, communication overhead can become a challenge. Efficient project management practices become crucial in large teams to avoid bottlenecks and ensure effective collaboration.

Prototyping and Testing

  • The time invested in prototyping and testing is crucial for delivering a high-quality app. Rushing through these phases can lead to issues later in the development process.
  • Adequate time should be allocated for testing various aspects of the application, including functionality, security, performance, and user experience.

External Factors:

-Third-Party Integrations

If the app requires integration with external services or APIs, the development time may be influenced by the availability and compatibility of those services.

-Regulatory Compliance

Apps that need to adhere to specific regulations or compliance standards may require additional time for implementing and testing the necessary features.

-Platform-Specific Requirements

Developing cross-platform apps or ensuring compatibility with different devices and operating systems may affect the overall development timeline.

Agile Development and Iterative Processes

  • Adopting Agile development methodologies, such as Scrum, allows for iterative development and continuous improvement. This approach can lead to quicker releases of minimum viable products (MVPs) and ongoing enhancements.

Time Estimates

  • Simple Apps: 2 – 4 months
  • Moderate Complexity Apps: 4 – 8 months
  • Complex Apps: 8 months and beyond

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How secure is Python for mobile app development?

The security of a Python for mobile app development project is influenced by various factors, including coding practices, adherence to security best practices, the use of secure libraries and frameworks, and the overall approach to addressing potential vulnerabilities.

Here are key aspects that contribute to the security of Python app development.

How secure is Python app development

Python’s Security Features

  • Built-in Security Libraries- Python provides built-in libraries and modules for implementing security features. For example, the cryptography library is commonly used for encryption and hashing.
  • Web Framework Security- Popular web frameworks like Django and Flask include built-in security features to protect against common web vulnerabilities. Django, for instance, protects against Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF) attacks by default.

Best Practices for Secure Python App Development:

-Input Validation

Validate and sanitize user inputs to prevent injection attacks, such as SQL injection and command injection.

-Authentication and Authorization

Implement secure authentication and authorization mechanisms to control access to resources. Use strong password hashing algorithms.

-Secure Communication

Use secure communication protocols (HTTPS) to protect data during transmission. Ensure that sensitive information is not transmitted in plain text.

-Regular Code Audits

Conduct regular code reviews and security audits to identify and address potential vulnerabilities. Automated tools and manual reviews can be employed for this purpose.

-Update Dependencies

Keep third-party libraries and dependencies up-to-date. Regularly check for security updates and apply patches promptly to address known vulnerabilities.

-Data Encryption

Encrypt sensitive data at rest and in transit. Encryption protects data from unauthorized access, especially in storage and communication.

-User Session Management

Implement secure session management practices to prevent session hijacking. Use secure and random session identifiers.

-Cross-Site Scripting (XSS) Protection

Protect against XSS attacks by validating and sanitizing user inputs and encoding output. Avoid using user input directly in HTML or JavaScript.

Secure Package Management:

-Pip and Package Integrity

Verify the integrity of packages installed via pip to prevent the installation of compromised or tampered packages.

-Use Virtual Environments

Use virtual environments to isolate project dependencies. This helps prevent conflicts and ensures that the project uses specific, known versions of libraries.

Security Tools

Employ security scanning tools and static code analyzers to identify vulnerabilities and potential security issues in the codebase.

Security Community and Updates

Stay informed about security best practices, emerging threats, and updates in the security community. Regularly review and update security measures based on new information.

Authentication and Authorization

Implement Multi-Factor Authentication (MFA)- For applications handling sensitive data, consider implementing multi-factor authentication to add an extra layer of security.

What are the top applications developed using Python?

After learning about so many of Python’s capabilities, let us now understand how this top programming language is used. Python is a helpful programming language for creating many of the well-known apps listed below.

 

What are the top applications developed using Python

Pinterest

It is an incredible site for sharing pictures. The programming languages utilized to create Pinterest are Python and Django. Given that Pinterest has more than 442 billion users, we may infer with great certainty how popular Python development is.

Instagram

Another highly well-liked app, Instagram has one billion users. The modern digital world has been changed by this software. Python’s value is further demonstrated by this program.

Spotify

Everyone is aware of the fantastic music streaming service Spotify. This app was created using Python because developers wanted to create apps more quickly.

Additionally, Python’s readability makes it faster to develop apps. So it’s ideal for hastening the development of the Spotify app. Python is the only viable solution to manage the list of 70 million songs.

Uber

Uber is the top cab booking app with services available worldwide. Additionally, it provides services like meal delivery and bike sharing, among others.

Uber has more than 100 million customers worldwide and operates in 285 major cities. Python is the only option that can handle Uber’s enormous amount of data, making it the best choice for this and other well-known apps.

Netflix

The biggest OTT platform for users to watch movies, web series, and other serials. This application was made using Python.

Disqus

Python assists the Disqus app in engaging users in discussions and intelligently guiding them through comment moderation. That is the main factor for this smartphone application’s popularity. Numerous sign-in choices, cross-site notifications, and improved security are further advantages.

Dropbox

Dropbox, a file-hosting service, is another well-known mobile application created in Python. It just switched from Python 2.7 to Python 3.

It is one of the most well-known desktop mobile programs worldwide and is also available for installation on the macOS, Windows, and various variations of Linux operating systems.

Python’s portability and compatibility with a wide range of platforms—from Linux and PC to PlayStation—is one of its best qualities.

Reddit

Although Python wasn’t the first choice for the Reddit mobile app, the social news aggregator has changed its mind due to the greater accessibility to code libraries, scalability, and flexible development of this programming language. After that, it quickly gained popularity and now has more than 600 million visitors each month.

What kinds of applications can be built using the Python Framework?

Blockchain Applications

Blockchain is one of the hottest trends in technology at present. This specific technology is supported by Python and has established itself in the market.

Users can interact with the Blockchain online thanks to support frameworks like Flask, which also enable the creation of endpoints for unique functions that can be executed on several machines.

Command-Line Applications

Console applications are another name for command-line applications. Python is primarily used to create console apps since it makes it simple for programmers to assess the language and find new opportunities.

Game Development Applications

Python development has a lot of things in store for its users. We all are aware by now that Python as a language is used in various languages, but it’s also a fact that it is used in game development as well.

One of the main reasons for this is Python’s ability to be utilized in real-time applications, which can then be used to develop a variety of games.

Audio and Video Applications

Python also supports developers in both audio and video applications. The language has libraries such as OpenCV and Pydub, that aid in the effective completion of app development. 

System Administrator Application

We are aware of how tiresome system administration may be, hence Python is currently one of the top-billed tools for creating programs and scripts that automate tiresome system administration activities.

Machine Learning Application

Python is blessed with Scikit and Panda libraries. These are used for the analysis and manipulation of data that is to be supplied to the machines.  

Business Applications

Python offers real agility to help developers create numerous ERP and eCommerce applications.

AI and ML Apps

Python’s extensive ecosystem plays a pivotal role in AI and ML applications. NumPy and SciPy facilitate numerical computations, while Pandas simplifies data manipulation.

Scikit-learn offers a variety of machine learning algorithms, making it accessible for developers implementing models for classification, regression, and clustering. For deep learning tasks, TensorFlow and PyTorch provide powerful frameworks.

Python’s syntax and readability expedite the development process, attracting researchers and engineers to leverage its capabilities in the rapidly evolving field of artificial intelligence.

RPA Automation

Python’s role in Robotic Process Automation (RPA) is growing, thanks to its readability and ease of integration with popular RPA platforms. Automation Anywhere, UiPath, and Blue Prism support Python scripting, enabling developers to automate repetitive tasks efficiently.

Selenium, a widely used tool for web automation, integrates seamlessly with Python, allowing for the creation of sophisticated automated workflows for web-based RPA scenarios.

Python’s versatility makes it a valuable asset in the automation landscape, contributing to increased productivity and efficiency.

Cloud Deployment Automation

Python is instrumental in automating cloud infrastructure deployment and management. Boto3, the Python SDK for AWS, facilitates interaction with various AWS services, enabling developers to automate resource provisioning and configuration.

Similarly, Google Cloud Client Library and Azure SDK offer Python bindings for Google Cloud Platform and Microsoft Azure.

Additionally, infrastructure as code (IaC) tools like Ansible and Terraform, with Python support, empower developers to define and deploy cloud infrastructure using code.

Python’s role extends beyond just application development, contributing significantly to the automation of cloud-related tasks.

Web Development

Python is a popular choice for web development, offering robust frameworks such as Django and Flask. Django, a high-level web framework, follows the MVC pattern, streamlining the development of complex web applications.

Flask, a lightweight micro-framework, provides flexibility for smaller projects. FastAPI, another modern framework, excels in building APIs using Python’s type hints. Pyramid and CherryPy offer additional options, catering to diverse web development needs.

Python’s emphasis on readability, coupled with these frameworks, enables developers to create scalable and maintainable web applications efficiently, making it a preferred language in the web development landscape.

Having a scalable and open-source language is essential since advanced business applications span the spectrum of e-commerce, ERP, and much more. Businesses may create scalable, fully functional apps with Python straight out of the box. Business analysts can create and deploy Python apps using tools like Tryton.

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What is the future of Python app development?

The future of Python relies on various factors like technological progress, community involvement, and evolving industry needs.

Python has consistently grown and is now widely used globally, thanks to its readability, versatility, and strong community support. This trend is likely to continue due to these strengths.

Rising Python ecosystem

The Python ecosystem keeps advancing with improvements in libraries, architectures, and tools, especially in areas like web development, data science, machine learning, and artificial intelligence. Expect new tools and libraries to emerge, addressing evolving challenges and requirements in these fields.

Enhanced web development

In web development, frameworks like Django and Flask are expected to evolve to meet modern web application demands.

Focus areas may include abandoning asynchronous programming models, improving performance, and providing enhanced support for microservices infrastructures.

Data science, AI, and machine learning dominance

Python’s dominance in data science and machine learning is set to continue, with ongoing advancements in existing libraries and the emergence of new ones.

Its simplicity makes it ideal for AI and automation, and its usage in these fields is anticipated to grow as technologies advance.

Python’s readability and ease of learning contribute to its popularity in education and community engagement. Initiatives to teach programming with Python are likely to persist, supporting the language’s ongoing growth. The Python community’s emphasis on collaboration and inclusivity is expected to remain strong.

Quantum computing

As quantum computing continues to be explored, Python may play a role in this domain. Quantum programming architectures and tools could emerge, enabling developers to work with quantum computers using Python.

Cybersecurity

In the field of cybersecurity, Python’s utility for scripting, automation, and penetration testing is well-recognized. As the importance of cybersecurity grows, Python is likely to maintain its position as a valuable tool in this field.

What are Python App Development Challenges?

Python is a versatile and widely used programming language, but like any technology, it comes with its set of challenges. Here are some common challenges faced in Python app development.

What are Python App Development Challenges

Performance Limitations

Python is an interpreted language, which can result in slower execution speed compared to languages like C or Java. While this might not be a critical issue for many applications, it can be a challenge for performance-sensitive tasks or applications with high computational requirements.

Global Interpreter Lock (GIL)

The Global Interpreter Lock in CPython (the default and most widely used Python implementation) can limit the execution of multiple threads concurrently. This can impact the scalability of multi-threaded applications, especially in scenarios where parallel processing is crucial.

Mobile App Development Challenges

Developing mobile apps using Python can be challenging due to limited support from major mobile platforms. While frameworks like Kivy and BeeWare exist, they might not provide the same level of native performance or access to platform-specific features as languages like Swift (for iOS) or Kotlin/Java (for Android).

Dependency Management

Python uses package management tools like pip, but managing dependencies and ensuring compatibility between different versions of libraries can be challenging. Dependency conflicts and version issues might arise, leading to unexpected behavior.

Limited Mobile and Web Frameworks

While there are frameworks like Django and Flask for web development and Kivy for mobile development, the Python ecosystem has historically been stronger in certain areas (e.g., data science, machine learning) compared to web and mobile development. This might limit the choice of frameworks available for specific project requirements.

Async Programming Complexity

Asynchronous programming in Python, while powerful, can be complex. The introduction of async/await in Python 3.5 and later versions has improved this, but writing and maintaining asynchronous code can still be challenging, especially for developers new to this paradigm.

Limited Support for Some Libraries

Certain specialized libraries or frameworks might not have strong support or might be less feature-rich in Python compared to other languages. This can be a challenge when working on projects that require specific tools or libraries.

Packaging and Distribution

Packaging and distributing Python applications, especially for desktop applications, can be a challenge. Different operating systems might require different packaging formats, and ensuring a smooth installation experience for end-users can be complex.

Security Concerns

While Python itself is secure, poorly written code can introduce security vulnerabilities. Developers need to be diligent in following security best practices to prevent issues such as injection attacks, data breaches, and other security risks.

Despite these challenges, Python remains a popular choice for many developers due to its readability, versatility, and extensive libraries.

Many challenges can be mitigated with good development practices, experience, and the use of appropriate tools and frameworks.

Which is the best company for Python App Development?

To determine the best company for your Python app development project, consider the following criteria.

Experience and Expertise

Look for companies with a proven track record in cross platform app development with python. Check their portfolio, client testimonials, and case studies to gauge their experience and expertise.

Technological Proficiency

Ensure that the company is well-versed in the latest Python frameworks, libraries, and tools relevant to your project. A strong understanding of Django, Flask, or other frameworks can be crucial.

Client Reviews and Testimonials

Read client reviews and testimonials to understand the experiences of previous clients with the company. This provides insights into their work quality, communication, and project management.

Industry Experience

Consider companies that have experience in your industry or a similar domain. Familiarity with industry-specific requirements can contribute to a smoother development process.

Project Management Approach

Evaluate the company’s project management approach. Agile methodologies, effective communication, and transparency in project handling are essential for successful development.

Scalability and Flexibility

Assess whether the company can scale the team or adapt to changing project requirements if needed. Flexibility is crucial for accommodating unforeseen changes during the development process.

Cost and Budget Considerations

Obtain detailed quotes and proposals from potential companies. While cost is a factor, it should be balanced with the company’s capabilities and the quality of its work.

Security and Compliance

Ensure that the company follows industry best practices for security and compliance, especially if your app deals with sensitive data or has specific regulatory requirements.

Communication and Collaboration

Consider the company’s communication channels, response times, and overall collaboration approach. Clear communication is vital for the success of any development project.

Post-Launch Support

Inquire about the company’s post-launch support and maintenance services. A reliable support system ensures that any issues or updates can be addressed promptly.

Before making a decision, it’s advisable to reach out to multiple companies, discuss your project requirements, and gather detailed proposals. This allows you to make an informed choice based on your specific needs.

Remember that the “best” company varies depending on individual project characteristics and priorities.

Embrace next-gen Python App Development with OnGraph

Python app development can be used to create applications in the upcoming years on both an enterprise and product basis because it has recently attained larger heights and is overgrowing.

Furthermore, Python has demonstrated that it is a capable language that is simple for beginners to learn but strong in the hands of experts.

Many large corporations, like Google, have adopted Python as one of their primary languages as a result of its versatility.

Python offers huge benefits at both a low and high level, whether you’re building web applications using practical Python frameworks or employing data science and machine learning to optimize your company’s outcomes and strategy.

You can enlist the aid of a reputable Python app development company if you want to see your idea come to fruition. With exceptional Python app development services, we can assist you in developing advanced, robust, and scalable apps to help you reach new heights in this constantly changing digital world.

Ruby Vs. Python: Which Will You Choose for Your Next Project?

Ruby Vs. Python

In modern programming, two languages often find themselves in the limelight of debates and comparisons Ruby vs. Python. Both revered for their simplicity and readability, they’ve powered some of our generation’s most iconic web platforms and tools. 

Yet, developers often find themselves at a crossroads when choosing one for their next project. Each language has its own strengths, applications, and staunch advocates, which makes the decision far from straightforward. In this blog, we will dive deep into the nuances, compare the capabilities, and provide insights to help you make an informed choice between Ruby and Python for your upcoming endeavor.

Before getting into the ruby python comparison 2024, let’s have a quick introduction about them.

Introduction to Ruby:

Born in the 1990s, Ruby is a versatile programming language crafted by Yukihiro Matsumoto. Inspired by languages like Perl and Smalltalk, it was shared with the world in 1995, aiming to make coding more adaptable and efficient.

Ruby is a vibrant, freely available programming language emphasizing clarity and efficiency, ideal starting point for beginners. Its consistent structure and abundant resources and documentation simplify the learning curve. 

Prioritizing developer efficiency, Ruby offers libraries for task automation and promotes clear code, enabling newcomers to develop applications swiftly. The supportive Ruby community offers guidance through forums and study groups, enhancing the learning experience. Its versatility spans web development to data analysis, offering novices varied programming avenues. 

Furthermore, frameworks like Ruby on Rails guide beginners in web development, streamlining the creation of web-based projects.

For What Ruby is Used

Core Ruby Features:

Below are the core features of the Ruby programming language.

1-Clean Syntax-

Boasting a syntax mirroring natural language, Ruby ensures code clarity and ease of maintenance, which expedites development.

2-Metaprogramming

Ruby’s robust features enable the dynamic creation of classes and methods at runtime. With functionalities like define_method, Ruby can intuitively produce and adapt code based on real-time data or logic, leading to concise and DRY (Don’t Repeat Yourself) code.

3-Object-Centric-

Everything – from strings and numbers to booleans – is treated as an object in Ruby. This inherent object orientation paves the way for class definitions, inheritance, polymorphism, and encapsulation, promoting modular programming.

4-Community Support-

With a thriving developer community, Ruby offers extensive libraries and frameworks. Notably, the Ruby on Rails framework, grounded in Ruby, stands out for building robust web applications.

5-Rapid Prototyping-

Ruby’s adaptable syntax dynamic characteristics and extensive library offerings expedite development. Furthermore, its metaprogramming capabilities facilitate swift advancements and prototype modifications, optimizing time and effort.

Advantages of Ruby:

These advantages have made Ruby a preferred choice for many startups and established companies looking to build robust web applications efficiently.

1-Easy Learning Curve-

Ruby’s syntax is intuitive and mirrors natural language, making it accessible for newcomers and allowing seasoned ruby developers to grasp its nuances quickly.

2-Faster Time-to-Market-

With frameworks like Ruby on Rails and a wealth of ready-made libraries (gems), Ruby facilitates rapid development, ensuring your applications and websites can be launched swiftly.

4-Cost-Efficient Development-

Ruby’s open-source nature, combined with many free libraries and tools, means businesses can reduce development costs. Additionally, the development speed translates to lower labor costs and quicker returns on investment.

Limitations of Ruby:

While Ruby () has numerous advantages, like any framework, it has limitations. Here are 3-4 core limitations.

1-Performance Overheads-

Ruby, the language on which Rails is built, is often considered slower than languages like C++ or Java. While Rails is sufficient for many applications, extremely high-performance applications might find it less optimal.

2-Niche Usage-

While Ruby shines in web development, its adaptability for diverse applications might be somewhat restricted.

It’s important to note that while Ruby has these limitations, it remains a powerful tool for many use cases, especially web applications that benefit from rapid development cycles.

Where to use Ruby?

Given its dynamic, object-oriented nature and strong ecosystem, Ruby is well-suited for several scenarios. Here are some instances where using the Ruby programming language might be a great choice.

Ruby on Rails

MVPs and Startups-

If you’re a startup looking to quickly prototype or build a minimum viable product (MVP), Ruby’s ease of use and rapid development capabilities make it a solid choice.

E-commerce Sites-

Platforms like Shopify, built on Ruby, showcase its strengths in developing scalable and user-friendly e-commerce solutions.

Content Management-

If you want to create a content-driven site or a custom content management system, Ruby offers several gems (libraries) and tailored tools.

Custom Database Solutions-

Ruby can effectively craft solutions involving intricate database work, given its robust ORM (Object-Relational Mapping) capabilities.

Web Scraping-

If you need to extract large amounts of data from websites, Ruby, combined with libraries like Nokogiri, can be a powerful tool for web scraping.

Automation and Scripting-

Ruby’s expressive syntax and extensive standard library can be beneficial for automating repetitive tasks or building utility scripts.

API Development-

If your project involves creating a RESTful API, frameworks like Sinatra or Rails can be used to design efficient and scalable APIs in Ruby.

Integration Projects-

Ruby’s vast library ecosystem can aid in projects where you must integrate different software components or services.

Cloud and DevOps-

For infrastructure automation, configuration management, or other DevOps-related tasks, tools like Chef or Capistrano, built with Ruby, come in handy.

You may like to know: The Definitive Guide to Python App Development in 2024

Companies Using Ruby:

companies using ruby

Introduction to Python:

Python, one of the most versatile and accessible programming languages, has carved a significant niche in the tech world since its inception by Guido van Rossum in the late 1980s. With a syntax that emphasizes readability and a design philosophy that champions code simplicity and clarity, Python has become the go-to language for beginners and seasoned developers alike. 

Its vast array of applications, from web development and data analysis to artificial intelligence and scientific computing, showcases its adaptability. Today, Python stands as a language and a community-driven movement, powering some of the most innovative projects in the digital landscape. With many plugins, tools, and libraries, Python is widely used in creating complex apps leveraging AI, ML, and other technologies. 

There is no doubt about the growing popularity of Python.

python popularity

Image Credits- Statista

Core Python Features:

Python is renowned for its myriad features that cater to beginners and seasoned developers. Here are 5-6 core features of Python.

1-Readable Syntax-

Python emphasizes code readability, using indentation to define code blocks. This makes the code clean and easy to understand, often resembling plain English.

2-Dynamic Typing-

Variables in Python do not require an explicit type declaration, and their types can change over their lifecycle. This provides flexibility in coding but also requires developers to be cautious.

3-Extensive Standard Library-

Python boasts a rich standard library that covers a range of modules and tools, reducing the need for external libraries and making many tasks more straightforward.

4-Interpreted Language-

Python is interpreted, meaning it executes code directly, line-by-line, which aids in easier debugging and rapid prototyping.

5-Cross-platform Compatibility-

Python is inherently portable. Code written on one platform, be it Windows, macOS, or Linux, can typically run on any other platform without modification.

6-Support for Multiple Paradigms-

While Python is primarily object-oriented, it also supports procedural and functional programming paradigms, offering flexibility in how developers approach problems.

The combination of these core features and the vibrant community supporting it makes Python a top choice for various applications, from web and software development to scientific research and AI.

Advantages of Python:

Python has become one of the leading programming languages in the tech industry, and its widespread adoption can be attributed to several benefits.

1-Beginner-Friendly-

Python’s clean and readable syntax makes it a favorite for beginners. The simplicity aids in quick learning and understanding of programming concepts.

2-Efficient Development-

Python’s rich standard library and vast ecosystem of third-party packages mean developers can accomplish a lot with fewer lines of code, leading to faster development times.

3-Flexibility-

Python is versatile and supports multiple programming paradigms, including procedural, object-oriented, and functional programming.

4-Robust Community-

A vibrant community backs Python. This ensures a constant influx of tools, libraries, tutorials, and forums to help developers at every skill level.

5-Cross-Domain Application-

Python finds utility in diverse domains- web development (Django, Flask), data science (Pandas, NumPy), artificial intelligence (TensorFlow, PyTorch), automation, gaming, and more.

6-Portability & Scalability-

Python applications can run seamlessly across different platforms, and with the right tools and libraries, it can scale to handle large-scale applications.

7-Integration Features-

Python integrates with other languages and technologies, facilitating features like web services, database connections, and integration with C/C++ libraries.

8-Strong Support for Testing-

Python offers tools and libraries for robust testing, enabling a test-driven development approach, which results in fewer bugs and more stable products.

9-Cost-Effective-

Being open-source, Python reduces development costs. Additionally, its efficiency and ease of integration can reduce time-to-market, leading to cost savings.

These benefits collectively make Python a compelling choice for organizations and individual developers seeking an efficient, reliable, and versatile programming language.

Limitations of Python:

While Python offers many benefits, it has certain limitations. Here are the critical limitations of Python.

Speed Constraints-

Python is an interpreted language, and thus, its execution is generally slower than compiled languages like C++ or Java. While this difference in speed isn’t noticeable for many applications, it can be a limiting factor for compute-intensive applications.

Mobile Development Limitations-

While Python can be used for mobile app development, it’s not the go-to choice. Languages and frameworks like Swift (for iOS) and Java/Kotlin (for Android) are commonly used in mobile development.

Despite these limitations, Python’s versatility, simplicity, and extensive libraries often outweigh its drawbacks for many applications. Still, it’s essential to consider these limitations when determining the best fit for specific projects.

Where to use Python?

Where to use Python

Python’s adaptability makes it suitable for a wide array of tasks. Here are 6-7 use cases where you can employ Python.

1-Web Development-

Using frameworks like Django or Flask, developers can design and deploy intricate web applications ranging from blogs to e-commerce sites.

2-Data Analysis & Visualization-

With Pandas for data manipulation and Matplotlib or Seaborn for visualization, Python is a top choice for data scientists to dissect information and generate insightful visual representations.

3-Machine Learning-

By leveraging libraries such as sci-kit-learn, TensorFlow, and PyTorch, researchers and developers can craft predictive models, neural networks, and more.

4-Automation & Scripting-

Python’s concise syntax makes it perfect for writing scripts to automate repetitive tasks, from managing files to automating emails or web browser sessions.

5-Cybersecurity-

Python’s flexibility enables security professionals to write penetration testing scripts and network scanners or even develop full-fledged cybersecurity tools.

6-Game Development-

Though not its most common use, Python’s Pygame framework allows for creating simple video games.

7-Internet of Things (IoT)-

With platforms like Raspberry Pi, Python becomes a bridge to control and gather data from embedded devices, making smart machines more intelligent.

These use cases showcase Python’s wide-ranging capabilities and vast potential in various fields and industries.

 

Top 10 Companies Using Python

companies using python

Similarities between Ruby & Python:

While Ruby and Python have their own unique characteristics and are often used for different purposes, they share top 6 core similarities.

1-High-Level Languages-

Both Ruby and Python are high-level, interpreted languages. This means they abstract away most of the complex details of the computer’s operation, allowing developers to write applications using easily understandable syntax.

2-Object-Oriented-

Both languages primarily adopt an object-oriented approach. This means they treat data as objects with associated attributes and methods, making organizing and managing code easier.

3-Dynamic Typing-

In both Ruby and Python, variables are dynamically typed. This means that a variable’s type is determined at runtime and can be changed as the program executes.

4-Standard Libraries-

Each of these languages comes equipped with extensive standard libraries that cater to a wide range of functionalities, allowing developers to perform many tasks without needing external packages or tools.

5-Community and Open Source-

Ruby and Python have solid and active communities. They are open-source languages, meaning that their source code is freely available. This has led to a rich ecosystem of tools, libraries, and frameworks built around them.

6-Platform Independence-

Both languages are cross-platform, meaning that, in most cases, code written in one operating system (like Windows) can be run on another (like macOS or Linux) without any modifications.

While these similarities exist, it’s essential to remember that each language has its strengths and ideal use cases. The choice between Ruby and Python often comes down to the specific requirements of a project or personal preference.

Python vs. Ruby: Detailed Comparison:

Having understood the foundational attributes, nuances, and commonalities, let’s delve into the contrasts between Ruby and Python. In the following sections, we’ll explore their standing in the tech world, application scenarios, employment prospects, and other factors to assist you in determining which language—Ruby or Python—best aligns with your needs.

1. Ruby vs. Python: Popularity

When comparing Ruby and Python based on their popularity, various metrics, including developer surveys, job postings, community contributions, and more, can be considered. As per w3techs, below are the current scenarios between Ruby and Python usage over the last year.

Based on TIOBE’s data, Ruby accounts for a mere 0.94% of the global market, landing it in the 18th spot on the list of the Top 20 most-used technologies worldwide.

On the other hand, Python claims around 12% of the worldwide market share. It has experienced a remarkable ascent from the 8th position in 2013 to clinch the top spot among programming languages, a surge primarily driven by the consistent advancements in AI.

Ruby vs. Python Popularity

Image Credits- jaydevs

2. Python or Ruby on Rails for Web Development

Python and Ruby are employed in app creation, refining, and automating DevOps activities.

In particular, these languages are adept at crafting scripts that simplify processes such as building, deploying, and managing infrastructure. Platforms like GitLab, Jenkins, and Ansible effortlessly accommodate scripts from both Python and Ruby, ensuring uninterrupted development, even among geographically dispersed teams.

While both have their merits, a distinguishing feature of Python within project frameworks is its prowess in interfacing with other coding languages. Often dubbed a “bridging language,” Python seamlessly connects with other technologies like C++, Java, and Go. This characteristic becomes especially significant in a microservice setup, allowing different app “modules” to be constructed using various technologies.

3. ML & Data Science

Python for ML & Data Science-

Python has become the dominant language in Machine Learning (ML) and Data Science. This dominance is primarily attributed to its rich ecosystem of data-centric libraries such as NumPy, pandas, and matplotlib for data manipulation and visualization, as well as TensorFlow, Keras, and scikit-learn for machine learning. 

Additionally, the community support for Python in the ML and data analytics spaces is unparalleled, which ensures continued growth, updates, and readily available resources. The vast array of tools and extensive documentation make it easier for newcomers to dive into data science projects with Python.

You may like to know: How Machine Learning is Reimagining User Experience?

Ruby for ML & Data Science-

While Ruby is a powerful and elegant language, it has yet to see the same level of adoption in the ML and Data Science sectors as Python. Nevertheless, libraries like Ruby Numo for numerical computations and Rumale for machine learning show the language’s potential in this domain. 

The Ruby community’s emphasis on developer happiness and productivity makes it an attractive choice for various applications, but when it comes to ML and Data Science, its ecosystem is not as expansive as Python’s, making it less of a go-to choice for these fields.

4. DevOps

Ruby for DevOps-

Ruby, mainly due to the success of Chef and Puppet, has been a significant player in the DevOps world. These configuration management tools, written in Ruby, have set a strong precedent for infrastructure as code, enabling IT automation at a grand scale. 

The Ruby ecosystem promotes a ‘convention over configuration’ philosophy, seen notably in Ruby on Rails, which translates well to the DevOps practices of consistency and automation. Consequently, many DevOps professionals have become acquainted with Ruby due to its association with these influential tools.

Python for DevOps-

Python’s versatility has also made it a popular choice in the DevOps space. Tools like Ansible, a robust IT automation tool, and SaltStack, designed for configuration management, are Python-based and have garnered substantial attention. 

Python’s extensive standard library and the vast availability of third-party packages make it conducive for scripting and automation, both critical components in DevOps. Moreover, Python’s readability and straightforward syntax make it easy for DevOps professionals to write and maintain scripts, fostering a more collaborative and efficient environment.

5. Automation

Ruby for Automation-

with its expressive syntax and powerful metaprogramming capabilities, Ruby offers a robust automation platform. Its dynamic nature and flexibility, combined with the ‘principle of least surprise’, make it relatively straightforward to understand and write scripts for various automation tasks. 

Gems like “Capybara” and “Watir” showcase Ruby’s strength in web automation, allowing for efficient testing and web scraping. Moreover, tools like “Thor” and “Rake” are popular in the Ruby community for building command-line applications and task automation, respectively. This ecosystem, centered on developer happiness, ensures Ruby remains a strong contender in the automation domain.

Python for Automation-

Python has long been lauded as the “Swiss army knife” of programming languages due to its versatility, which extends prominently into automation. With its clear and readable syntax, Python scripts are often self-explanatory, making automation tasks more maintainable and collaborative. 

Libraries like “Selenium” for web automation, “PyAutoGUI” for GUI automation, and “Fabric” for system administration showcase the breadth of Python’s capabilities. Additionally, Python’s expansive standard library and modules such as “os”, “sys”, and “subprocess” offer native tools that make system automation more accessible. This richness and breadth in its ecosystem ensure Python is often the first choice for various automation challenges.

6. Testing

Ruby for Testing-

Ruby has a strong testing culture, with the Ruby on Rails framework significantly emphasizing Test-Driven Development (TDD). The Ruby ecosystem boasts a variety of robust testing tools and libraries. “RSpec” stands out as a popular Domain Specific Language (DSL) for writing readable tests, while “Capybara” and “FactoryBot” streamline integration and unit tests, respectively, for web applications. “MiniTest”, another versatile tool, offers traditional assertion-based and spec-style tests. 

Ruby’s metaprogramming capabilities also provide a dynamic means to generate test cases, mock objects, and craft flexible test setups. This rich landscape emphasizes readable, maintainable, and comprehensive test suites.

Python for Testing-

Testing in Python is supported by various tools, each catering to different testing paradigms and needs. The built-in “unittest” module provides a traditional xUnit-style framework, ensuring that even without external libraries, Python developers have testing capabilities at their fingertips. Tools like “pytest” elevate the testing experience by offering a no-boilerplate approach and feature-rich plugins. “Nose2“, building on the legacy of “nose”, further extends Python’s testing capabilities. 

For web testing, “Selenium” integrates seamlessly with Python, and for mocking and patching, “unittest.mock” offers a range of options. Python’s focus on readability and simplicity is mirrored in its testing tools, emphasizing clarity and thoroughness.

7. Community Growth

The presence of a robust community and a supportive network is pivotal when harnessing any technology for a project. The bigger the community, the smoother the project’s progression. With an active community, developers can.

  • Seek guidance from the pooled wisdom on online platforms, forums, and community-led websites to tackle coding hurdles.
  • Utilize open-source resources to amplify application functionalities and hasten the development process.
  • Stay abreast of recent innovations and churn out cutting-edge solutions. Additionally, the community can act as a reservoir of potential talent. By participating in community events or engaging online, businesses can spot and recruit proficient professionals to enrich their teams.
  • Ruby’s community is home to approximately 2.4 million dedicated developers. They benefit from a close-knit ambiance, top-notch documentation, and supportive peers, ensuring projects are executed adeptly.

Regarding community strength, Python outshines many of its counterparts with a staggering community of around 17 million enthusiasts. It offers a wealth of educational materials and a suite of ready-for-action tools.

8. Security Aspect

Python and Ruby are recognized for their security strengths. Data from WhiteSource indicates that only 6% of total vulnerabilities are linked to Python, while Ruby accounts for 5%. In contrast, the C language is associated with 47% of reported vulnerabilities. When considering more severe threats, 19% of vulnerabilities found in Ruby are highly severe, compared to 15% in Python.

Most security issues in applications built with Python or Ruby arise from components written in other languages. Common vulnerabilities include SQL injection and XML file parsing. Other areas of concern include Cross-site Scripting (XSS) and authentication protocols.

The proactive involvement of the community bolsters the security of both languages. Ruby actively engages in the HackerOne bounty program, ensuring rapid response and resolution to reported vulnerabilities, with patches released at a minimum semi-annual frequency. Python’s security can be assessed using community-crafted tools such as Python Taint, Tinfoils, and Pyntch.

9. Multithreading

Effective multi-threading is vital for maximizing computer resources. These applications have segments operating simultaneously, tackling tasks like reading files or managing API connections. Designing such applications is intricate, demanding deep expertise to navigate issues like race conditions and deadlocks.

Python uses its ‘threading’ package for parallelism, with the Global Interpreter Lock (GIL) managing concurrent execution. While GIL ensures data consistency, it can sometimes slow down processes.

On the other hand, Ruby initiates threads with Thread.new. Exceptions don’t typically halt the program; the thread quietly ends. Thread pools can limit active threads, and the Mutex class manages access to shared resources. Deadlocks are addressed with condition variables, and gems like ‘parallel’ and ‘workers’ can expedite multi-threaded application development in Ruby.

10. Ruby vs Python Performance

While both Python and Ruby exhibit impressive performance, Ruby often processes computer programs at a speed roughly double that of Python. In a head-to-head comparison using benchmark programs, Ruby finishes tasks in nearly half the time it takes Python. For instance, while Python requires 567.56 seconds to run an n-body program, Ruby accomplishes it in just 232.08 seconds.

However, this advantage in speed for Ruby comes with a trade-off in memory usage. Running the n-body program consumes 22,968 memory units in Ruby, whereas Python uses a more modest 8,076 units.

While loops-

While loops

Image credits- Quora

Ruby takes 3.82 seconds,

Python takes 24.04 seconds.

Winner: Ruby.

Bigint Multiplication-

Bigint Multiplication

Image credits- Quora

Ruby takes: 23.503788766s

Python takes: 40.691734s

Winner: Ruby.

Eratosthenes-

Eratosthenes

Image credits- Quora

Ruby takes 2.394614951s

Python3 takes: 3.111802s

Winner: Ruby.

11. Average Developer’s Salaries

The varying popularity and demand between Ruby on Rails and Python shape developers’ salaries, with differences often based on geographical regions. Here’s a general salary comparison between Ruby developers and Python developers for informed hiring decisions.

Average Developer’s Salaries

Image credits- stackoverflow

  • With its expansive application in web development, data science, AI, and machine learning, Python has seen a surge in demand for skilled developers.
  • As of the data from the StackOverflow Developer Survey in 2020, the median global salary for a Python developer stood at $59k.
  • The versatility of Python means that specialists in fields like data science or AI might command even higher salaries due to the specialized nature of their skills.

Ruby (particularly Ruby on Rails)-

Ruby (particularly Ruby on Rails)-

Image credits- Stack Overflow

  • Ruby, mainly when associated with the Ruby on Rails web framework, has been a choice for many startups and tech companies.
  • The median annual salary for Ruby developers globally, according to the same 2020 survey, was $71k, placing it higher than Python in this context.
  • The potentially higher salary reflects the scarcity of seasoned Ruby on Rails developers compared to the broader pool of Python developers.

While Ruby developers, on average, had a higher median salary as of the 2020 data, it’s essential to consider the specific role, specialization, geographic location, and industry demand when evaluating these figures.

Ruby on Rails (ROR)

12. Latest Trends in 2024

In the dynamic landscape of web development, staying updated is paramount. Whether you’re already harnessing the power of Ruby on Rails (RoR) or contemplating using Python, being in tune with the newest developments is vital. As we venture into 2024, let’s delve into the emerging trends to redefine web development’s horizon.

Ruby Trends to Watch in 2024:

Rails 7-

Eagerly awaited by the RoR community, Rails 7 promises streamlined web development with various enhancements.

API-Only Applications-

API-first approaches dominate 2023, with RoR ideally suited for crafting backend APIs for diverse front-end solutions.

Frontend Diversification-

RoR’s move from server-side leans towards integrating frontend frameworks like React and Vue.js for dynamic UIs.

Containerization and Orchestration-

RoR applications in 2024 will frequently utilize container solutions like Docker and orchestration via Kubernetes for scalability and maintenance.

Microservices Architecture-

Monolithic apps are fragmenting into microservices, with RoR aiding in efficiently constructing and managing these more minor services.

Performance Optimization-

RoR will witness advanced optimization techniques in 2024, focusing on database queries, caching, and HTTP/2 adoption.

Enhanced Security Practices-

With evolving cyber threats, RoR will emphasize fortified security, incorporating regular audits and advanced authentication.

Sustainability and Green Computing-

With environmental awareness, RoR developers will prioritize energy-efficient, environmentally friendly application designs.

Serverless Integration-

RoR’s adoption of serverless platforms, like AWS Lambda, streamlines development by shifting focus from infrastructure to pure coding.

Python Trends to Watch in 2024:

Python 3.12-

comes with Improved error messages and offers suggestions and guidance. Python’s PEG parser now supports enhanced f-strings. There are speed optimizations, including inlined comprehensions. A new syntax has been introduced for annotating generics with type variables. Additionally, Linux users can utilize the robust perf profiler.

Surge in AI, ML, and Data Analysis-

Python is the preferred language for AI, ML, and data science due to its user-friendly nature and abundant libraries.

Python in Web Development-

Frameworks like Django, Flask, and FastAPI amplify Python’s footprint in web development.

Rising Asynchronous Programming-

Asyncio’s emergence is streamlining concurrent programming, fostering scalable and efficient applications.

Embracing Type Annotations-

Adopting type hints in Python elevates code clarity and minimizes runtime glitches.

Evolution via PEPs-

Python Enhancement Proposals (PEPs) continue to refine the language, targeting features, performance, and security.

Python in Serverless Architecture-

The growing appeal of serverless computing sees Python rising as a top choice, credited to its versatility.

Python’s Foray into Robotics and IoT-

Frameworks like MicroPython boost Python’s adoption in robotics and IoT, catering to limited-resource settings.

Pursuit of Performance with Cython and PyPy-

Initiatives like Cython and PyPy are harnessed to supercharge Python applications, meeting high-performance demands.

Hybrid Development-

Momentum Blending Python with languages like Rust and C++ is becoming prevalent to leverage the best of diverse tech stacks in projects.

Ruby vs. Python: Tabular Difference:

Ruby vs. Python Tabular Difference

Make the Right Choice with OnGraph Experts:

When faced with “Ruby vs. Python, which should I choose?”, aligning with your project’s unique needs and objectives is essential.

Ruby excels in web app development, offering rapid prototyping, end-to-end action, and cost-effective solutions for business projects. On the other hand, Python opens doors to diverse applications, from embedding machine learning algorithms to streamlining DevOps tasks within projects.

Regardless of your choice, ensuring access to competent developers is crucial. If your team lacks expertise, consider recruiting remote professionals as a cost-effective and flexible solution. Partnering with OnGraph can connect you with vetted Python and Ruby experts skilled across various fields!

For more details, go through our portfolio page to see how we have helped companies develop leading web apps using Python and Ruby.

Top 15 Python Interview Questions With Answers

Top 15 Python Interview Questions

Python will continue to be one of the most widely used and popular programming languages in the business, regardless of how many languages a programmer learns. It’s a popular choice among both new and experienced programmers.

We’ve put together a list of the most often asked Python Interview Questions and Answers to help you prepare for the Python interview questions you might be asked during your interview.

We’ve covered all of the most commonly asked basic and advanced Python programming questions in our list of Python Scripting interview questions, along with extensive solutions to help you breeze through the employment interview.

To assist you in preparing for your upcoming interview, we have covered practically all of the relevant Python interview questions for freshers and experienced applicants.

This comprehensive list of Python interview questions will help you ease your Python programming job interview.

1. What is your understanding of Python?

Python is a scripting language that is extremely comprehensive, interactive, and object-oriented. It was created with the intention of making the text more readable for internet users. Apart from punctuation, it uses a variety of keywords in other languages. Unlike other languages, it contains fewer syntactic structures. Python is used to create AI-based intuitive web and mobile app development.

2. What are the benefits of using Python?

Python is a programming language that features objects, modules, threads, exceptions, and memory management that is automatic. Its advantages are well-known. It is simple and straightforward to use, portable, extensible and has a built-in structure while being open-source. 

3. What is pickling and unpickling?

Pickle takes any Python object and converts it to a string representation, which it then dumps into a file using the dump function. Pickling is the name for this method. Whereas, the process of recovering original Python objects from a stored text representation is known as unpickling.

4. How is Python interpreted?

Python is an interpreted programming language. Python programs are compiled from source code and run directly. It translates the programmer’s source code into an intermediate language, which is then translated into machine language, which must be executed.

5. How is memory managed in Python?

Memory management is handled via Python’s own heap space. All Python objects and data structures are stored in a private heap. This private heap, which is controlled by the Python interpreter, is not accessible to the programmer.

Allocating heap space for Python objects is handled by Python memory management. Programmers can use the core API to access some programming tools.

Python has a built-in garbage collector that recycles all unused memory, freeing it and making it available to the heap.

6. What are Python namespaces?

In Python, a namespace refers to the name that is assigned to each object. Variables and functions are the objects. Each object’s name and space (the address of the outer function in which the item is) are created as it is formed. The namespaces are stored in Python as a dictionary, with the namespace as the key and the object’s address as the value. In Python, there are four different types of namespaces:

  1. Built-in namespace– These namespaces contain all of Python’s built-in objects and are accessible at all times.
  2. Global namespace– These are the namespaces for all of the objects created at the main program level.
  3. Enclosing namespaces– These namespaces belong to the higher or outer function.
  4. Local namespaces– These namespaces are found in the inner or local function.

7. What are decorators in Python?

Decorators are used to change the appearance of a function without changing its structure. Decorators are usually defined prior to the function they will be enhancing. To use a decorator, we must first specify its function. Then we write the function to which it will be applied and simply place the decorator function above the function to which it will be applied. The @ symbol is used before the decorator in this case.

8. What is the difference between a list and a tuple?

The difference between a list and a tuple is that a list can be mutated, whereas a tuple cannot. A Tuple can be hashed as a dictionary key, for example.

9. How are arguments passed by value or by reference?

In Python, everything is an object, and all variables are references to objects. The functions determine the reference values. As a result, you won’t be able to change the value of the references. If the objects are mutable, you can change them.

10. What built-in type does python provide?

Python provides two built-in types: 

1) Mutable and 2) Immutable.

Mutable built-in types are:

  • List
  • Sets
  • Dictionaries
  • Immutable built-in types
  • Strings
  • Tuples
  • Numbers

Immutable built-in types are:

  • Strings
  • Tuples
  • Numbers

11. What is slicing in Python?

Slicing is a technique for gaining access to specific bits of sequences such as lists, tuples, and strings. [start:end:step] is the slicing syntax. This step can also be skipped. When we type [start:end], we get a list of all the elements in the sequence from the beginning (inclusive) to the end-1 element. It means the ith element from the end if the start or end element is negative i. The step represents the jump or the number of components that must be skipped. If there is a list, for example, [1,2,3,4,5,6,7,8]. Then, by printing every second element, [-1:2:2] will return elements from the last to the third element. [8,6,4] is an example.

12. What are python modules? Name some commonly used built-in modules in Python?

Python modules are executable files that contain Python code. Functions, classes, and variables can all be used in this code. A Python module is a .py file that contains code that may be executed.

The following are some of the most often used built-in modules:

  • os
  • sys
  • math
  • random
  • data time
  • JSON

13. Is indentation required in python?

Indentation is required in Python. It designates a coding block. An indented block contains all of the code for loops, classes, functions, and so on. The most common method is to use four space characters. Your code will not execute correctly if it is not indented, and it will also throw errors.

14. What is self in Python?

A class’s instance or object is called Self. This is explicitly supplied as the first parameter in Python. However, in Java, where it is optional, this is not the case. With local variables, it’s easier to distinguish between a class’s methods and attributes.

In the init method, the self variable refers to the newly created object, whereas it relates to the object whose method was called in other methods.

15. What are negative indexes and why are they used?

In Python, the sequences are indexed and include both positive and negative numbers. The positive numbers utilize ‘0’ as the first index and ‘1’ as the second index, and the procedure continues in this manner.

The index for a negative number begins with ‘-1,’ which is the last index in the sequence, and ends with ‘-2,’ which is the penultimate index, and the sequence continues as it does for a positive number.

The negative index is used to remove any new-line spaces from the string, allowing it to accept the S[:-1] character as the final character. The negative index is also used to represent the index in the correct order of the string.

I hope this set of Python Interview Questions will help you in your interview preparation. Best wishes!

 

If you are looking for Python developers for your next project, get in touch with us.

Also, if you have a question for us? Please let us know in the comments box, and we’ll get back to you as soon as possible. 

List of Top 10 Python Frameworks for App Development in 2024

Python Frameworks

Top 10 Python App Development Frameworks

The latest Statista research shows that over 48% of developers worldwide use Python app development frameworks for software development. Python is among the most popular programming languages, with major firms like IBM, NASA, Spotify, and Instagram using them. Many developers use Pyjnius. Pyjnius is a Python library for accessing Java classes. It either starts a new JVM inside the process, or retrieves the already surrounding JVM (for example on Android).

The popularity of Python is because of its vast range of libraries, packages, and frameworks. A Python application framework is a collection of modules and packages that allows developers to build apps through a pre-built implementation of redundant functions. They release developers from minor issues like thread management, sockets, and protocols.

Several best Python frameworks for mobile app development and web app development enable seamless, efficient, and swift application development. We’ve listed the best frameworks in this blog.

Why Should I Go for Python App Development?

Python is best suited for creating dynamic applications integrated with CRUD operations and avoiding repetitive coding for activities like authentication and sessions. It also provides an execution system when you’re building a separate UI with associated logic. 

Python is also the best option when you have strict deadlines and time and resource constraints. From full-stack and DevOps to AI/ML, data engineering, and data science, Python offers all-around Python App Development options. 

Here are some reasons why Python is the king of programming languages:

  • Python’s vibrant range of frameworks, libraries, and tools enables swift development and gives access to code templates.
  • Python is a high-level programming language and quite close to English, hence it has a lower learning curve compared to other coding languages.
  • Python has a large developer community and rich documentation with new frameworks and libraries being added quite often.

Types of Python Frameworks for App Development?

There are three types of Python application development frameworks: Microframework, Full-stack, and Asynchronous Framework.

1-Micro Framework

Micro Framework

A Python micro-framework is easy to use with basic capabilities or features required for web development and does not have added functionalities like form validation and data abstraction layers. They are lightweight and useful for building small applications.

Some popular examples of Micro Python frameworks include CherryPy, Bottle, Falcon, and Dash.

2-Full-Stack Python Framework

Full-Stack Framework

A full-stack framework is an end-to-end solution for web and app development with functionalities like template layouts, form validation, form generator, and so on. It is the most convenient and popular Python framework since it offers features of back-end functions, front-end interfaces, and databases. 

Some popular examples of Full-stack Python frameworks include Django, Pylons, CubicWeb, and Giotto

3-Asynchronous Python Framework

Asynchronous Framework

The asynchronous framework empowers developers to work on large amounts of concurrent connections, databases, web servers, connection libraries, high-performance networks, and so on. It is gaining widespread popularity recently since the development of the Asyncio library.

Some examples of Asynchronous Python frameworks are Tornado, Growler, AIOHTTP, and Sanic.

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How to Choose the Right Python Framework For App Development?

The most essential factor before selecting the right framework for development is the nature, size, and complexity of the project. For example, you can opt for a micro-framework for a smaller project while considering full-stack frameworks for larger projects. 

Also, here are some examples of frameworks based on the project’s nature.

Python application development

Other factors that you can consider before selecting a Python app development framework for your project are

  • Scalability to handle traffic load when they run on several servers
  • Core libraries like authentication, data validation, URL mapping/rewriting, and templating.
  • Comprehensive documentation with tutorials, sample codes, blogs, and snippets.
  • Easy installation of the preferred Python framework.

10 Most Popular Python Frameworks for App Development

Here’s a list of the most popular python app development frameworks:

1-Django

django

Django is a high-level development Python framework for creating both mobile and web apps that operate on a power ORM and follows the Model View Controller (MVC) pattern, making the code reusable and readable. It has built-in tools like URL routing/parsing, authentication system, form validation, template engine, and caching to ensure a swift development process.

Django follows the DRY (Don’t Repeat Yourself) concept and focuses on rapid app development with a neat design. It is the first choice of developers for any Python project due to its versatility, customization, scalability, deployment speed, simplicity, and compatibility with the latest Python versions.

According to a Stack Overflow survey of 2020, Django and Flask are the most popular Python frameworks. Popular examples of apps built with Django include Instagram and Spotify.

Key Features of Django:

  • Enables execution of automated migrations
  • Robust security
  • Enhanced web server support
  • Comprehensive documentation
  • Vast add-ins with SEO optimization

2-Flask

Flask Framwork

Flask is an open-source, lightweight, and the best Python microframework with a built-in development server, debugger, HTTP request processing, file storage, and support for establishing client-side sessions. It has a modular and adaptable design and added compatibility with Google App Engine.

Besides Django, Flask is another popular Python framework with the Werkzeug WSGI toolkit and Jinja2 template. Flask comes under the BSD license and it is inspired by the Sinatra Ruby framework. Popular examples of apps developed with Flask include Lyft and Netflix.

Key Features of Flask:

  • Jinja2 templating and WSGI compliance
  • Unicode-based with secure cookie support
  • HTTP request handling capability
  • RESTful request dispatch handling
  • Built-in server development and integrated unit-testing support
  • Plugs into any ORM framework

3-Web2Py

Web2Py

Web2Py is an open-source, full-stack, and scalable Python application framework compatible with most operating systems, both mobile-based and web-based. It is a platform-independent framework that simplifies development through an IDE that has a code editor, debugger, and single-click deployment.

Web2Py deals with data efficiently and enables swift development with MVC design but lacks configuration files on the project level. It has a critical feature, a ticketing system that auto-generates tickets in the event of issues and enables tracking of issues and status.

Key Features of Web2py:

  • No configuration and installation needed
  • Enables use of NoSQL and relational databases
  • Follows MVC design with consistent API for streamlining web development
  • Supports internationalization and role-based access control
  • Enable backward compatibility
  • Addresses security vulnerabilities and critical dangers

4-TurboGears

TurboGears

TurboGears is an open-source, full-stack, data-driven popular Python web app framework based on the ObjectDispatch paradigm. it is meant to make possible to write both small and concise applications in Minimal mode or complex application in Full Stack mode.

TurboGears is useful for building both simple and complex apps with its features implemented as function decorators with multi-database support. It offers high scalability and modularity with MochiKit JavaScript library integration and ToscaWidgets for seamless coordination of server deployment and front end.

Key aspects of TurboGears:

  • MVC-style architecture
  • Provides command-line tools 
  • Extensive documentation
  • Validation support with Form Encode
  • It uses pylons as a web server
  • Provides PasteScript templates

5-Falcon

Falcon

Falcon is a reliable and secure back-end micro Python application framework used for developing highly-performing microservices, APIs, and large-scale application backends. It is extensible and optimized with an effective code base that promotes building cleaner designs with HTTP and REST architecture.

Falcon provides effective and accurate responses for HTTP threats, vulnerabilities, and errors, unlike other Python back-end frameworks. Large firms like RackSpace, OpenStack, and LinkedIn use Falcon.

Falcon can handle most requests with similar hardware to its contemporaries and has total code coverage.

Key Features of Falcon:

  • Intuitive routing with URL templates
  • Unit testing with WSGI mocks and helpers
  • Native HTTP error responses
  • Optimized and extensible code base
  • Upfront exception handling support
  • DRY request processing
  • Cython support for enhanced speed

6-CherryPy

Cherrypy

CherryPy is an object-oriented, open-source, Python micro framework for rapid development with a robust configuration system. It doesn’t require an Apache server and enables the use of technologies for Cetera templating and accessing data.

CherryPy is one of the oldest Python development frameworks mainly for web development. Applications designed with CherryPy are self-contained and operate on multi-threaded web servers. It has built-in tools for sessions, coding, and caching.

Popular examples of CherryPy apps include Hulu and Juju.

Key features of CherryPy:

  • Runs on Android
  • Flexible built-in plugin system
  • Support for testing, profiling, and coverage
  • WSGI compliant
  • Runs on multiple HTTP servers simultaneously
  • Powerful configuration system

7-Tornado

Tornado

It is an open-source asynchronous networking Python framework that provides URL handling, HTML support, python database application framework support, and other crucial features of every application.

Tornado is as popular as Django and Flask because of its high-performing tools and features except that it is a threaded framework instead of being WSGI-based. It simplifies web server coding, handles thousands of open connections with concurrent users, and strongly emphasizes non-blocking I/O activities for solving C10k difficulties. 

Key features of Tornado:

  • Web templating techniques
  • Extensive localization and translation support
  • Real-time, in-the-moment services
  • Allows third-party authorization, authorization methods, and user authentication
  • Template engine built-in
  • HTTP client that is not blocking

8-AIOHTTP

AIOHTTP

AIOHTTP is a popular asynchronous client-side Python web development framework based on the Asyncio library. It depends on Python 3.5+ features like Async and Awaits. 

AIOHTTP offers support for client and server WebSockets without the need for Callback Hell and includes request objects and routers for redirecting queries to functions.

Key highlights of AIOHTTP Python framework:

  • Provides pluggable routing
  • Supports HTTP servers
  • Supports both client and WebSockets without the callback hell.
  • Middleware support for web servers
  • Effective view building

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Popular Cross-Platform Python Frameworks for Mobile App Development

Here are the two main cross-platform Python mobile app frameworks: Kivy and BeeWare

9- Kivy

Kivy

Kivy is a popular open-source Python framework for mobile app development that offers rapid application development of cross-platform GUI apps. With a graphics engine designed over OpenGL, Kivy can manage GPU-bound workloads when needed.

Kivy comes with a project toolkit that allows developers to port apps to Android and has a similar one for iOS. However, porting Python apps to iOS currently is possible with Python 2.7.

Features of Kivy:

  • Enables custom style in rendering widgets to give a native-like feel
  • Enhanced consistency across different platforms with a swift and straightforward approach
  • Well-documented, comprehensive APIs and offers multi-touch functionalities

10-BeeWare

BeeWare

BeeWare is another Python framework for Python iOS development & mobile app development that enables developers to code apps in Python and cross-compilation for deployment on several mobile platforms and OS like Android, Linux GTK, iOS, and Windows.

The primary repository of BeeWare gets updated often with added commercial support including technical support, consulting, and training.

Features of BeeWare:

  • Enables code reusability
  • Provides consistency across apps with the native look and feel
  • Includes tools like Briefcase and Toga that are designed for precise functions
  • Agile developer community

Still Confused? Partner with OnGraph for Python App Development

Python has several app development frameworks and selecting the perfect fit can prove tough with several factors to note from budget and complexity to scalability, additional features, learning curve, and so on. 

Fortunately, you can get the right pick for your project with OnGraph, a leading Python Development Company that offers tailored Python development solutions with proven expertise in various Python application frameworks like Django, Flask, TurboGears, Kivy, and Tornado.

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FAQs

 Q1. How do I choose the right framework for my project?

Choosing the right framework depends on your project’s specific requirements. Consider factors like project complexity, scalability needs, and your team’s expertise. Django is excellent for robust, feature-rich applications, while Flask is lightweight and suitable for smaller projects. FastAPI is a good choice for building APIs with high performance, and Pyramid offers flexibility for diverse project structures.

Q2. What are some popular Python libraries?

Python boasts a rich ecosystem of libraries. Some popular ones include NumPy and Pandas for data science, requests for HTTP requests, Flask for web development, Django for full-stack web applications, Matplotlib for data visualization, and TensorFlow for machine learning.

Q3. Can Python replace Java?

While Python and Java serve different purposes, Python’s simplicity and versatility have led to increased adoption. Python is often preferred for quick development and readability, but Java remains a strong choice for large-scale enterprise applications and performance-critical systems. The choice between them depends on the specific requirements of your project.

Q4. Can Python be used for enterprise application development?

Yes, Python is suitable for enterprise application development. Its simplicity, extensive libraries, and frameworks like Django make it a viable choice. Python’s readability and ease of maintenance contribute to its growing popularity for developing scalable and robust enterprise applications.

Q5. Is Flask better than Django?

The choice between Flask and Django depends on your project needs. Flask is lightweight and modular, suitable for small to medium-sized projects. Django, on the other hand, is a full-stack framework with built-in features, making it ideal for larger, more complex applications. The decision should be based on project requirements, development speed, and scalability.

Q6. Which Python framework is mostly used for app development?

For app development, both Flask and Django are widely used. Django is often chosen for its built-in features and conventions, making it suitable for rapid development of full-stack applications. Flask, being lightweight and modular, is preferred for smaller projects or when developers want more flexibility in choosing components. The choice depends on the scale and requirements of your app.

Q7. What are some GUI frameworks for building mobile apps with Python?

GUI framework is an open-source Python package that allows you to quickly build a demo or web application for your machine learning model, API, or any arbitrary Python function. Below are the top 10 GUI frameworks.

  • Gradio
  • Streamlit
  • Kivy
  • Tkinter
  • PyQt
  • PySide
  • wxPython
  • PyGTK
  • wxPython
  • PySimpleGUI