As with every technology stack, Python has its advantages and limitations. The key to success is to use Python at the right time and in the right place.
So, in this guide, we’ll talk about what as a product owner you should be prepared for when you take on a Python project. We’ll explain when to use Python and share the best Python practices for developing different types of products with Python.
7 Best Cases of Using Python for Software Development
Let’s begin by exploring popular use cases where Python can demonstrate its advantages and enhance a project. Additionally, we’ll provide you with valuable insights drawn from MobiDev’s extensive experience in various domains and working with diverse types of products.
1. Python for Web Development
Python is an excellent choice for web development, and it has gained favor for several compelling reasons:
- Python offers a wide array of web frameworks that simplify the development process. Depending on your technical requirements, you can choose the framework that fits your needs better. For example, if you need to create your proof-of-concept quickly or check if some specific features can be developed, Flask is the most suitable choice. There is also Django, which is better for more scalable projects. Django offers some modules for example authorization and admin panels by default. It helps to significantly save time and reduce development costs as developers don’t need to create these elements from scratch.
- Python has robust support for integrating with databases. You can utilize the Python Database API (Python DB-API) to connect to popular databases like MySQL, PostgreSQL, SQLite, and more. Some of the web frameworks, such as Django, have a built-in ORM. It allows you to communicate with databases by writing python code. The classes represent the database tables, and the framework transforms the desired manipulations into the most efficient SQL queries.
- Python web frameworks often come with template engines that allow you to separate the presentation layer from the application logic.
- Python web applications can be deployed using various deployment options, such as traditional servers (e.g., Apache, Nginx), cloud platforms (e.g., AWS, Google Cloud), or containerization technologies (e.g., Docker, Kubernetes). However, it is important to consider scalability requirements and then choose appropriate deployment strategies.
At the same time, Python web applications may face scalability challenges when handling high traffic or extensive processing requirements. While Python web frameworks can handle moderate loads effectively, scaling Python applications to handle millions of concurrent users may require additional measures, such as load balancing, caching, asynchronous processing, or utilizing distributed systems. However, this question is more about the project architecture and the expertise of the development team as experienced Python developers can find a solution that will meet your project needs better.
Case Study #1: Building a Web-Based SaaS Solution with Python
Let’s look at a real example. We helped a Belgian company called Resolved to create a SaaS solution which aimed to structure hiring processes and deliver a problem-free, data-based hiring process to customers.
Our development team embarked on a journey to create this product entirely from the ground up, opting for Python, specifically Django, as our development framework. The prior version served as more of a proof of concept, and the decision to transition to Python was prompted by the intricate server-side calculations required.
Anticipating the client’s future need to harness the power of data science, Python’s versatility and rich ecosystem made it the natural choice.
Employing a microservice architecture was instrumental in enhancing the modularity and adaptability of the application. It enabled us to offer discrete modules as autonomous services. For example, our vacancy simulation modules seamlessly integrate with various Human Resources Information Systems (HRIS), amplifying the product’s usability and integration potential.
The true complexity of this product emerges from its intricate calculations, specifically those designed to scrutinize and align applicants with suitable vacancies. To tackle this challenge, our team delved deep into the realms of advanced mathematics and algorithm development. This dedication allowed us to craft and rigorously test the sophisticated functionality that defines this application.
Here is what the project’s architecture looked like:
Read also:
Python-Based HR Analytics Product2. Python for Artificial Intelligence
The rise of Python is primarily associated with its increasing use in Artificial Intelligence and Data Science. Most Data Science libraries and models are now developed in Python, making their integration with a Python backend much easier than with other technologies.
Python offers several popular libraries, such as TensorFlow, Keras, and PyTorch, which provide robust tools for implementing machine learning algorithms and models. These libraries offer various functionalities, including data preprocessing, model training, evaluation, and deployment. Python’s ease of use and extensive documentation make it a preferred choice for machine-learning tasks.
These libraries offer efficient data structures and functions for handling large datasets, performing mathematical operations, and conducting exploratory data analysis, which are essential steps in AI app development.
| # | Technology | Description | Domain | License* |
|---|---|---|---|---|
| 1 | TensorFlow | One of the most popular ML Python frameworks. Training DL models, inspection & model serialization. | Machine learning, deep learning, computer vision, natural language processing, etc. | Apache 2.0 |
| 2 | Keras | High-level, deep learning framework developed by Google for implementing neural networks. Fast experimentation with deep neural networks.. Focused on being user-friendly, modular and readable. | Deep learning, computer vision, natural language processing, etc. | Apache 2.0 |
| 3 | PyTorch | One of the fast-growing ML frameworks used for creating deep neural networks. Built for speeding up the process between research prototyping and deployment. PyTorch is great for its excellent support for GPUs. | Machine learning, deep learning, computer vision, natural language processing, etc. | BSD |
| 4 | scikit-learn | Focuses on data modeling, has a lot of built-in classification, regression, and clustering algorithms. This framework is well-documented and easy to use. Has excellent integration with other Python libraries, such as Pandas, NumPy, Plotly, etc. | Machine learning, data mining, data analysis, etc. | BSD |
| 5 | Pandas | One of the most popular frameworks for data manipulation and analysis. Assists with data reshaping and preprocessing, dataset joining, data filtration, alignment, & handles missing data, etc. | Data analysis, data manipulation, data visualization | BSD |
| 6 | NumPy | One of the main libraries for working with numerical data. It supports multi-dimensional arrays, and matrices, including a lot of other mathematical functions. It is a math powerhouse, supporting Fourier transforms, linear algebra, & more. | Scientific computing, linear algebra, numerical analysis | BSD |
| 7 | MXNet | Deep learning framework for training and deploying deep neural networks on a wide variety of platforms. MXNet is lightweight and can scale on multiple GPUs on multiple machines. Supports Java, C++, Scala, Go, R, & more. | Deep learning, computer vision, natural language processing, etc. | Apache 2.0 |
| 8 | NLTK | One of the most popular frameworks for working with textual data. It has an easy interface. NLTK provides tokenization, stemming, tagging, parsing, classification and more. | Natural language processing, text analysis, text preprocessing | Apache 2.0 |
| 9 | SpaCy | Framework for more advanced natural language processing. SpaCy provides pretrained pipelines, fine-tuning options, tagging, parsing and so on. It also supports tokenization and training for around 70+ languages. | Natural language processing, NLP deep learning models training, inference | MIT |
| 10 | SparkML | Framework for scaling machine learning pipelines. It also supports a wide range of popular ML features and models, but the main, primary difference – is division into slices & distributing computation across multiple machines. | Building, training, & deploying and scaling ML models and pipelines | Apache 2.0 |
| 11 | Plotly | Python library that is used for fancy interactive visualizations. It supports over 40 unique chart types in different areas such as financial, statistical, geographic and many more. Plotly can be used not only on Python, but also on R, JavaScript and Julia, etc. | Data visualization | MIT |
*The BSD and MIT licenses have minimal restrictions on the modification and distribution of the software code. Apache has more conditions and restrictions on this.
Python is an excellent choice for developing chatbots and AI assistants due to its simplicity, availability of libraries, and natural language processing (NLP) capabilities. Python frameworks like Rasa offer a complete solution for building chatbots with dialog management capabilities. Rasa provides tools for intent recognition, entity extraction, dialogue flow management, and response generation. It also allows developers to create chatbots that can handle multi-turn conversations, maintain context, and provide meaningful responses based on user inputs.
Case Study #2: Face & Voice Recognition Solution
This case is connected with developing an enterprise verification-as-a-service (EVaaS) solution for securing access to sensitive data. We decided to use microservice-based architecture and WebRTC and employ machine learning for biometric recognition. Python was the main part of the technology stack here. Our team also offered some valuable product ideas to achieve the best results. What was done?
- Utilizing a Microservice-Based Architecture with WebRTC Integration
In our approach, each microservice takes on the role of a distinct subproject, each with its own dedicated functionality. This modular structure simplifies the development, maintenance, and enhancement of each microservice. It also grants us the flexibility to choose optimal solutions and frameworks tailored to the unique objectives and characteristics of each microservice.
- Leveraging WebRTC for Biometric Authentication
Our biometric authentication flow encompasses facial capture and voice recognition. To effectively process media data on the server, we implemented a readily available WebRTC service. This choice aligned with the specific requirements of media data processing.
- Harnessing Machine Learning for Comprehensive Biometric Recognition
The product’s mandate includes the capability to identify users based on their voice, photographs, and responses to questions. To achieve this, we embarked on the crucial task of acquiring valid data. Initial datasets containing voice and photo entries were meticulously collected. We also conducted a thorough evaluation of more than 10 available solutions for validating US driver’s licenses and selected an Optical Character Recognition (OCR) module.
Validating the accuracy of these solutions in real-world scenarios was of paramount importance. After rigorous assessment, we opted for Google Vision as the most fitting choice for seamless integration with our product.
- Fostering Innovation within the Development Team
Despite its enterprise origins, infusing a startup mindset into the product’s development proved indispensable. Our team’s unwavering commitment to the project led to the generation of pioneering ideas and the discovery of practical solutions. Features such as face anti-spoofing, question generation using Natural Language Processing (NLP), and the expansion of voice and photo datasets all originated within the team and seamlessly integrated into the product’s framework.
3. Python API Development and Integrations
Python is also widely used for API development. With frameworks like Flask or Django, you can create RESTful APIs that enable communication between different systems and applications, allowing data exchange and integration. Here are several important aspects to pay attention to:
- Python’s FastAPI framework allows for quickly building APIs or facilitating API integration. This framework is also an excellent choice for creating Proof of Concepts (PoC) since you don’t need to deploy a large framework or incorporate additional modules.
- Additionally, Python has extensive support for various tasks, such as data manipulation, authentication, database integration, and more, through libraries like requests, SQLAlchemy, and OAuthLib. This vast ecosystem reduces development time and effort by providing pre-built solutions for common API requirements.
- Python is good for API integrations like payment systems, communication between parts of an application, GIS, and data scraping. It provides excellent support for working with databases, messaging queues, external APIs, and other services.
4. Python for Сloud Services
Python is a popular and versatile programming language for building and interacting with cloud services and cloud providers such as AWS, Azure, Google Cloud, and others. It allows you to automate tasks, manage cloud resources, and leverage cloud services offered by various providers. Here’s how Python is commonly used in the context of cloud services:
- Most cloud providers offer software development kits (SDKs) and APIs that allow you to programmatically interact with their services. These SDKs and APIs provide Python bindings, allowing you to manage resources such as virtual machines, storage, databases, and networking components. You can use these libraries to create, query, update, and delete cloud resources.
- Python enables serverless architecture which allows you to save money on the server and pay only when this or that service is used. Cloud providers offer serverless computing platforms like AWS Lambda, Azure Functions, and Google Cloud Functions. These platforms allow you to run code without provisioning or managing servers. Python is a supported language for serverless functions, and you can write functions using Python to perform specific tasks or respond to events in the cloud environment. This is a great way to optimize development costs within the scope of PoC development, for example.
When working with Python and cloud providers, it’s essential to understand the specific APIs, SDKs, and services provided by the cloud provider you are targeting. Each provider has its own set of libraries and documentation that you can refer to for more details on how to interact with their services using Python.
Case Study #3: Human Pose Estimation Platform Connected to AWS with Python
BeONE Sports, a sports technology startup, is on a mission to extend specialized training opportunities to athletes worldwide, regardless of their geographic location. Through the utilization of cutting-edge computer vision technology, they offer data-driven training programs designed for both athletes and coaches. Our team was tasked with delivering end-to-end iOS app development services, encompassing processing data obtained from BeONE Sports’ existing Machine Learning models, video rendering, and incorporating in-app purchasing capabilities.
BeONE Sports initially launched its MVP as an iOS app, with plans to expand its availability to Android and web platforms in the future. By leveraging MediaPipe, they’ve streamlined the development process by employing a single codebase that can seamlessly run across various operating systems. This approach not only reduces development time significantly but also guarantees consistent functionality across different platforms.
One of the standout features of BeONE Sports is its offline video processing capability, and MediaPipe plays a pivotal role in optimizing resource utilization for handling high-dimensional data. This optimization ensures that the app performs smoothly even on devices with limited resources.
Additionally, the implementation of MediaPipe has led to a noteworthy improvement in data processing speed, slashing the processing time to half of the video’s duration. Consequently, users typically receive video analysis results within a matter of seconds, elevating the overall responsiveness and user experience of the app.
The Python-based backend solution is responsible for data processing, storage, as well as data upload and download operations. The Data Science (DS) components were also developed using Python.
We utilized the AWS SDK for Python, known as Boto3, to establish connectivity with AWS services. Boto3 offers a broad spectrum of functionalities for interacting with various AWS services. We employed it for uploading and downloading files, such as videos and images, among others. This expedited the establishment of interactions with AWS services and optimized the development timeline.
Boto3 offers extensive documentation that details how to use the library to interact with specific AWS services. This includes code examples, API links, and tutorials. Relying on the documentation and our own experience, we managed to quickly achieve the set technical tasks.
In cases where the ready-made functionality provided by cloud SDKs is not enough, we can add a custom solution to meet the needs of a specific business case.
Case study:
Human Pose Estimation Platform for Athletes5. Python for Internet of Things (IoT)
Python’s compatibility with different platforms and operating systems makes it suitable for IoT projects targeting diverse hardware and environments. Python runs on popular IoT platforms such as Raspberry Pi, Arduino, and BeagleBone, allowing developers to leverage their capabilities for building IoT applications. Python’s compatibility with various data formats, such as JSON and CSV, enables seamless integration with cloud platforms, data analytics tools, and other IoT infrastructure components.
Python is able to work with IoT systems on both Linux and Windows, but since most libraries on Linux are Python-based, Python interacts more seamlessly with these systems. On Linux, everything runs smoothly with Python without the need for additional configurations, significantly simplifying the development process.
Working with Microsoft systems can be a bit more challenging, and you may encounter certain limitations. In this case, the native .Net platform for Windows allows for more effective operations.
Case Study #4: Modernizing IoT Access Control System
In this case, we dealt with a legacy app modernization project. MobiDev’s engineers embarked on this project when the client already had an existing desktop solution but required the addition of a web module and several new features. Upon conducting an audit, we encountered legacy code and technical constraints that hindered the incorporation of fresh functionality into the system.
The primary technical challenges included:
- Improving Code Quality
The product was Python-based, featuring a legacy graphical user interface (GUI). During the audit, it became evident that the code was monolithic and lacked coverage for numerous edge cases. There were frequent instances of calls to non-existent methods, executed based on common logic (e.g., a set of rules involving _getattr_). Furthermore, error handling was absent, significantly complicating the debugging process. Overall, the code’s structure hindered integration with the backend and proper data storage.
To prepare the product for future development, we undertook a code refactoring effort. The situation was further complicated by the absence of technical documentation. Nevertheless, our engineers successfully addressed all bugs and enhanced the code’s readability and quality.
- Rewriting Project Architecture
The original architecture of the product presented limitations, notably a lack of synchronization with the database and mixing of logic with UI redraw functions. This complication arose as the same code triggered unintended UI alterations.
To resolve these issues, our team embarked on an extensive rewrite of the architecture, preserving only essential components. We implemented synchronization between the application and the server, with data stored in the cloud. Additionally, we introduced remote control capabilities through a web-based administrative panel. Engineers separated the UI from the logic, establishing transparent communication via WebSockets.
In pursuit of modernizing the desktop application’s frontend, we adopted a custom approach. Instead of utilizing Python-based GUI components, we initiated a browser in kiosk mode at system startup. This approach facilitated the creation of an adaptive design, compatible with screen sizes ranging from 5″ to 12″. Moreover, it enabled the use of contemporary JavaScript libraries, resulting in a superior UI, an achievement unattainable within the constraints of Python.
Finally, we optimized the development process, allowing developers to test code on standard working computers without the need for constant desktop app updates to assess its appearance.
Following the initial upgrade, the client approached us with a fresh request to incorporate additional features. As the project continued to evolve, it required greater adaptability to function across various screen types. Furthermore, a pivotal requirement emerged: the need to establish a system capable of instantly responding to suspicious user behavior.
This undertaking posed significant challenges because the product transcended the realm of a typical web application, essentially resembling a modernized operating system. We had to register all hardware components within AWS IoT, facilitating continuous data exchange between the project’s hardware components and the server portion via the MQTT protocol. Additionally, we implemented internet connection monitoring to promptly detect any disruptions or issues with the devices.
Upon conducting a thorough technical audit, it became evident that the existing libraries used in the project no longer aligned with the new requirements. Consequently, the technology stack expanded to encompass AWS IoT, the Django framework, PostgreSQL, and WebSockets.
Engaging in such innovative projects is always simultaneously challenging and captivating. At present, the project enjoys significant recognition in the market and stands as one of the premier offerings in its niche. We successfully achieved the client’s objectives, enhancing the application’s stability and performance through the integration of modern technologies.
6. Python for Automated Testing
Python provides numerous libraries and frameworks for testing, greatly simplifying both the testing process for developers and QA specialists. With Python, it’s easy to outline various testing scenarios and automate them by writing small scripts. Among other benefits of Python for automated testing are:
- Python’s versatility and flexibility make it suitable for automating various types of tests, including unit tests, functional tests, integration tests, load tests, and more.
- Python integrates well with other tools and frameworks commonly used in test automation.
- Python’s ability to interact with system processes, file systems, and databases allows for comprehensive test coverage.
- Python seamlessly integrates with popular CI/CD tools like Jenkins, Travis CI, and GitLab CI/CD. These tools can be configured to execute Python test scripts automatically whenever changes are pushed to a repository, ensuring that tests are run consistently as part of the development process.
7. Python for Mobile Development
When we are talking about mobile, we typically refer to simply writing an API. Nowadays, you can write practically all the code in Python, including logic and the interface, and have a small wrapper that converts this code for mobile. Instagram is an example of an application entirely written in Python. This allows for quick development of solutions within a single stack with a consistent architecture.
Native mobile app development for platforms like iOS and Android often relies on languages like Swift, Objective-C, or Kotlin. However, frameworks like Kivy or BeeWare allow Python to be used for cross-platform mobile app development.
It’s important to note that when using Python for mobile development, you may encounter certain limitations compared to native languages. Performance, access to platform-specific features, and user interface design are some aspects that may require additional attention and consideration. If you have a strong preference for Python or want to leverage existing Python skills and codebases, exploring the above options can help you develop mobile apps with Python.
However, judging from our experience, we recommend using native languages for iOS and Android to achieve the best performance, security, and scalability.
When Python is Not the Best Choice
Python is a fairly universal programming language, but in several cases today there are better technology options for implementing business and technical tasks. Among such cases, we would like to highlight the following:
1. Front end development
Python has Jinja templates and can generate html pages, but still, it’s not as convenient and powerful as some JavaScript Frameworks. Jinja is well-suited for traditional server-rendered web applications. However, if you are building a single-page application (SPA) where most of the rendering and UI updates happen on the client side, Jinja may not be the ideal choice. SPAs often require a more robust front-end framework like React, Angular, or Vue.js, which offer advanced features like virtual DOM diffing, component-based architecture, and state management. It’s better to use languages that were created specifically for front-end development.
2. Real-time video processing
Python has limited support for WebRTC formats, making it not the best choice for projects involving video conferencing and streaming platforms. While it does have libraries, they currently do not match the capabilities of languages like Java and Node.js, for example. Python can work with video frames, but Java, for instance, can handle such tasks more efficiently and offer more options. We’ve had cases where real-time video processing was built with Python, but it came with challenges.
Real-time video processing typically requires high-performance computations and efficient memory management, which can be better achieved with lower-level languages. The overhead introduced by Python’s interpreter can make it challenging to achieve the real-time processing speeds required for video applications.
3. IoT devices on Windows
Python has good cross-platform support and can run on Windows as well as other operating systems. However, it may have some limitations in terms of low-level hardware access and real-time performance, which can be crucial in certain IoT scenarios.
The .NET ecosystem offers powerful tools and libraries for Windows development, including IoT-specific frameworks. .NET, particularly with C#, has strong integration with Windows and provides excellent support for developing IoT applications on Windows. So, it’s the best solution for most cases like this.
Comparison of Python to Other Backend Technologies
Python is an interpreted, dynamically typed language. Syntax is easy to read and no extra preparation is necessary to start the project. Moreover, Python also has a huge community and thousands of ready-to-use open-source libraries. This allows you to build an initial app in short terms and verify that your idea is possible and perspective.
Python like Ruby, Node.js, PHP, and Java can be used to build web application development. The most appropriate is a comparison of technologies for a specific technical task because all projects are unique and have their own features that each technology can meet differently. For example, in terms of speed, Node.js can be faster than Python and more suitable for I/O type of tasks, when no DS or complicated calculations are required. Java can be faster than Python, but with Python, it still takes less time to build an app.
Python Development Challenges and Best Practices
When selecting a technology stack, you’ve likely encountered a list of challenges that can arise during Python application development. From my own experience, I can attest that not all of these challenges will necessarily surface in your project. Even if they do, the experience and skills of your development team play a crucial role. Let’s examine some common challenges and MobiDev’s practices for addressing them.
- Scalability
You may have heard about Python’s scalability limits, but in reality, scalability challenges extend beyond the choice of programming language. The key lies in the product’s architecture. With Python, you can build diverse architectures, including serverless and microservices. Optimizing code, enhancing algorithm efficiency, and adopting sound system architecture practices can significantly improve scalability in Python applications.
For instance, in a microservices architecture, the system comprises various services, each capable of scaling independently based on the load they experience. This allows us to optimize performance effectively.
Read also:
Guide to Migration to Microservices- Performance
Performance limitations are often associated with Python, especially in older versions. However, we leverage the latest and most stable Python versions, such as Python 3.10 and 3.11, which have seen substantial improvements in processing speed.
If you already have a Python-based product, we can upgrade it to the latest versions or provide ongoing support on the current versions if everything is functioning well and meeting your business needs. Our experience enables us to update applications while preserving data security and system functionality.
It’s worth noting that Python’s performance limitations can be mitigated to some extent through performance optimization techniques. These include profiling to identify performance bottlenecks, optimizing critical sections of code, using efficient algorithms and data structures, and leveraging specialized libraries or tools designed for high performance in specific domains.
Onboard Python Developers at MobiDev and Succeed
Whether you opt to initiate a project with an internal development team or explore outsourcing options, it’s imperative to grasp the most effective methods for team organization and project management when embarking on Python product development.
MobiDev boasts an in-house team comprising exceptionally skilled Python developers, complemented by a flexible development approach. We are well-equipped to seamlessly integrate into your existing team or assume full responsibility for the end-to-end Python software development process. Drawing from our extensive 14+ years of experience, we have the capability to craft a diverse range of Python solutions, all underscored by a commitment to guaranteed delivery. Our expertise spans from crafting concise MVPs to transforming them into production-ready applications capable of handling autoscaling and high loads.
We possess the proficiency to undertake projects of varying complexities, commencing with MVPs and seamlessly scaling them into fully-fledged products within the Python stack. Furthermore, our capabilities extend to the seamless integration of Data Science models to enhance your solutions.
Adding an extra layer of efficiency to our project handling, each project at MobiDev is overseen by a dedicated project manager and an account manager who serve as the vital bridge between the development team and our esteemed clients. This ensures that you can immerse yourself in the development process to the extent that aligns with your preferences. Rest assured, our communication channels are transparent, affording you regular updates on the project’s progress.
If you are seeking for experienced Python developers (with strong Python skills), feel free to contact us or schedule a call.
