This article was written by a guest author, Eric Kahuha. Eric is an accomplished data scientist and an experienced technical writer whose work appears on many blogs. His writing is highly technical yet easy to understand for beginners and experts in the technology field.
Code reuse involves applying preexisting code you’ve written before or external code written by someone else to your current project. You can reuse the native code for its initial function or repurpose it to perform a different role. The goal of code reuse is to reduce redundancy and increase reliability by relying on previous work rather than duplicating it.
It is appropriate to reuse code if it is high quality and well-suited to your needs. To reuse code, you must understand what the code is doing. It must be well organized and easy to read and understand. You'll have difficulty reusing the code if it is messy or poorly documented.
Among other benefits, reusing code instead of writing from the start saves you time and resources for building and maintaining models. This article introduces the concept of code reusability in data science, shows its value for data scientists, and compiles the best practices to consider while writing code optimized for reuse.
Code Reuse in Data Science
Data scientists spend most of their time doing repetitive tasks such as data finding, preparation, and cleaning. These tasks significantly decrease efficiency and productivity, increasing costs due to time spent. However, cleaning and preparation are not tasks to rush or outsource — improperly completing these steps can lead to errors in the analysis process.
Data scientists can share and reuse code snippets to speed up data preparation, reduce costs without skimping on quality, and avoid rework in AI projects.
If you want to write reusable code effectively, ensure the native code is clean, well-structured, documented, and generalizable. This makes it easy for others to understand its properties and reuse it on different platforms.
How to Become a Wizard at Code Reuse
There is a lot of value in code reuse. Let’s explore the main benefits you’ll gain from reusing code.
Reduced Development Time and Lower Costs
Reusing existing code instead of writing new code to perform the same function saves time and effort, decreasing overall development time.
Particularly in highly regulated environments, where feature engineering steps and reference datasets may have to undergo a series of checks and approvals for compliance purposes, using pre-approved code and feature definitions save you the time of an entirely new sign-off process.
When working with large amounts of data, it’s easy for tasks to become tedious and time-consuming. For example, repetitive data analysis, modeling, and visualization tasks typically take a lot of time.
Reusing code can reduce time spent on these tasks, allowing you to decrease costs. Using high-quality, well-tested code instead of starting from the beginning can also help reduce the cost of testing or solving issues that arise from not testing thoroughly enough.
Reduction in Codebase Size
Reusing code can reduce the lines of code you must write for your new project, decreasing the size of your model’s source codebase. A smaller codebase is easier to maintain. It can make it easier to find bugs, especially when working with large projects.
Improved Reliability
Reusing trusted code can also improve reliability. When you reuse code that has already been thoroughly tested and proven to perform the desired function, you save time testing for bugs and resolving issues. This is especially true when reusing code you or your team wrote for a related project. When reusing relevant and reliable code, the chances of the code introducing new errors into your project are minimal.
Code Reuse Best Practices
When writing reusable code, consider the following best practices.
Documentation
Developers don’t always write software, ML, and AI projects from nothing — most projects use existing code libraries or components. However, this is only possible when the reusable bits of code are well-documented and easy to use.
Therefore, proper documentation is critical for any piece of reusable code. Other developers may have difficulty understanding how to use the code and its limitations without it. Make sure you write comments into your code and prepare readme files with clearly presented information that helps others understand how to use the code easily and identify potential issues quickly.
Think of Reuse While Writing
If developers haven’t designed their code for reusability, using it can create problems for your project’s development cycle. The best way to deal with this problem is by thinking about code reuse during the planning stage of your project and applying some best practices as you write code.
The next time you’re writing new or modifying existing code, consider how you might want to reuse its functionality in other projects down the road. Taking this into consideration ensures that your work is modular and reusable in the future.
The bottom line is that reuse should not be an afterthought but something you consider while writing new code for new and future projects that might need similar functionalities.
Keep Track of Dependencies
The first thing you should do before reusing any code is to ensure that you can account for all its dependencies. Accounting for dependencies means you must know what other code needs to be available for your code to run correctly. For example, if your code depends on being able to connect to a database server, then you cannot reuse it until this dependency has been satisfied.
If a developer knows what other components they need to use your code, they're more likely to go out and get them before they start using your code in their project. Keep track of dependencies and provide this information for other developers who may want to reuse your code.
Ensure a Team Approach
Your entire team should be involved with reusing code rather than just one person. Involving everyone ensures that all understand how the system works and what they need to make changes accordingly.
Involving everyone also ensures that everything remains consistent throughout the development process. Having multiple people review the code early on means finding bugs or issues before they become a problem later down the road when you're integrating code into production environments.
Enhance Your Efficiency With Reusable Code
There are many benefits of reusing code in software development and data science. By reusing code, you can reduce the codebase size, improve the quality of your project, and reduce development time and costs.
To make the most of reusing code, plan for ways to reuse code when writing it. Create your own libraries, document thoroughly, and keep track of dependencies. By following best practices, you can enhance the efficiency of your data science projects.