Simplicity, readability, and impressive community support have made Python one of the top choices for back-end development. Its extensive ecosystem of libraries and frameworks covers everything from interacting with databases and APIs to making calculations and analyzing data. But with so many tools available, where do you even begin?
In this blog post, we will talk with Andrii Kovalov, our seasoned Python Engineer, to uncover how Python libraries can speed up your development process, discuss their benefits and potential drawbacks of heavy reliance on them, and provide insights into choosing the right libraries for your project.
How do Python libraries streamline back-end development compared to writing everything from scratch?
One of Python’s core principles is to avoid “reinventing the wheel.” The language has a large community behind it, providing many libraries and frameworks that serve as pre-made solutions for most common back-end needs. Using these tools is beneficial for several reasons:
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Time savings. Implementing libraries often takes routine, repetitive coding off your shoulders and frees up time for creative problem-solving aspects of development. Instead of writing code for the features that already exist, you can dedicate your energy and time to the core logic and unique functionality of your application.
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Quality. Popular Python libraries often have large and active communities dedicated to their improvement. They are regularly updated, thoroughly tested, well-documented, and follow consistent coding standards. This means that when you integrate these solutions into your project, you can be confident that they are stable and secure.
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Faster development. Using a well-maintained library is definitely faster than writing everything from scratch. For example, instead of coding user authentication from the ground up, you can integrate a library that offers pre-built, secure authentication solutions. This way, you avoid writing and debugging standard features, which leads to faster development cycles and quicker time to market.
What are the potential drawbacks of relying heavily on external libraries?
Despite their numerous benefits, Python libraries have their downsides that need to be considered before implementation. One common issue is the Common Vulnerabilities and Exposures (CVEs) that can affect external libraries just like any other software.
These vulnerabilities can introduce security risks to your application, especially if the library is not actively maintained or patched promptly. Delays in applying security patches can leave your application vulnerable until a fix becomes available. In addition, updating libraries can be time-consuming, especially for projects with many dependencies.
However, if a library is useful for your application and well-maintained, there’s often no reason not to use it. Just be sure to select a reliable well-maintained tool.
Which Python libraries do you consider essential for back-end development?
Let’s take a practical approach and imagine we are going to build a REST API application. To get this project off the ground, we’ll need a web framework like Flask, FastAPI, or Django, which will provide the necessary foundation to handle HTTP requests and route them appropriately.
For validating the inputs and outputs of our API requests, libraries like Marshmallow or Pydantic will be extremely helpful. They ensure that the data flowing through your API meets the expected format and reduce the risk of errors.
Lastly, to store data in a relational database, we will use an ORM tool. Popular options include SQLAlchemy, Peewee, and Django ORM. These libraries simplify database interactions and make it easier to manage your data models and perform database operations.
Each of the mentioned tools is popular, robustly tested, and widely used in production projects. Using them saves you significant development time and promotes cleaner, more maintainable code.
How would you approach selecting the appropriate library for a specific back-end task?
When selecting the right Python library for a project, I follow these steps:
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Define the problem. Usually, the task or problem dictates what kind of tool is required. The understanding of a problem you need to solve helps identify the exact functionality you need and narrows down the range of libraries that might be suitable for your needs.
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Evaluate the tool landscape. Research the available options that solve the problem. I recommend opting for libraries with large user bases and active communities. Popular libraries are more likely to be reliable, regularly updated, and properly tested.
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Consider ease of use. A perfect library is easy to integrate and use within your project. Therefore, assess the learning curve associated with the library (especially if your team is new to Python) and determine how difficult it will be to pick up and implement it into your project.
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Documentation quality. Detailed and well-written documentation is a developer’s best friend. Having clear explanations, code examples, and tutorials helps you better understand how to use the library’s functionalities and troubleshoot issues that arise.
Why is a strong community and active support around a library important for back-end development?
No single person can handle all the tasks and features on their own. An active community contributes to the continuous improvement of the library, ensuring that it evolves to meet the needs of its users.
When many developers are involved, bug fixes get implemented faster, new features are rolled out quicker, and performance optimizations happen on a larger scale. This collective effort ensures that the library remains up-to-date, reliable, and high-quality. Plus, it speeds up your development process and boosts the overall quality of your software.
Are there any common mistakes to avoid when starting with Python libraries?
Certainly, here are a few pitfalls to watch out for:
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Not evaluating the need. Don’t implement a library just because it’s available or popular. Take a critical look at whether it genuinely adds value to your project. Unnecessary dependencies only complicate your project and increase the maintenance burden.
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Ignoring popularity. While popularity isn’t everything, selecting well-established libraries with active communities offers several advantages: better documentation, numerous online tutorials and examples, and a higher chance of finding solutions to potential issues. However, be aware of “doppelgängers” that mimic the functionality of popular tools; always research and compare options to find the one that best fits your project’s requirements.
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Overlooking ease of use. If you find yourself spending more time figuring out how to use a library than actually getting things done, it might not be the right choice. A library should simplify your development process, not add unnecessary complexity.
Bottom line
Python libraries can significantly optimize back-end development. Offering pre-built solutions for common tasks, they save you loads of time, streamline development processes, and ensure high-quality, reliable code. By carefully selecting the right libraries—considering their value, popularity, and ease of use—you can substantially enhance your productivity and focus on what makes your project unique.