5 Common Python Issues Developers Face and How to Deal With Them

 

As with any programming language, Python poses its own unique set of challenges. From performance issues to data management problems, there are many pitfalls you’ll need to sidestep as you continue developing your app or software. These aren’t deal-breaking roadblocks — they’re standard growing pains that every developer experiences in their coding journey. Even the brightest coders encounter bugs and errors from time to time, but that shouldn’t discourage you from continuing to build on your Python skillset. It’s important to know what to expect when working with Python so you can tackle these issues head-on before they have a chance to grow into something bigger than they need to be. Here are 5 common Python issues that top python developers face and how you can deal with them...

Python’s Growing Pains

As you continue developing your application, you’ll notice that certain tools and libraries that work seamlessly with other programming languages may cause friction when used in Python. Some of the most common issues arise from Python’s complex type system, the fact that it’s a hybrid language, and the way it’s been implemented as an interpreter rather than a compiler. Any time you’re dealing with different types, you’ll run into problems when trying to integrate these with other code that expects a different type of system. This is especially common with Python’s complex types like lists and dictionaries, which don’t always match up with how other languages expect data to be organized. Hybrid languages like Python that mix elements of both dynamic and static typing create challenges at compile time. You’ll often see issues like mismatched parameter types that are only caught at runtime when your code is running. On the other hand, Python’s interpreter implementation can cause issues at run-time with slow startup times and potential problems with memory management. This can be especially challenging if you are working with large data sets.

Slow Runtime Speed

Python is a great language for prototyping and getting an app up and running quickly. It’s also well-suited to scale as you bring your application to market. But if you’re working with a large dataset that needs to be processed in real-time, you may notice that your Python code is lagging behind. This may be because Python is an interpreted language rather than a compiled one. When a program is compiled, the code is converted into another language that the computer can read and understand natively. This means that the computer can run the program much more quickly because there’s no need to interpret the code each time it’s run. If you’re dealing with larger data sets and need to process this data in real-time, you’re less likely to see these kinds of issues with compiled languages like C or C++.

Data Management Issues

It’s also important to anticipate data management issues when working with Python. One of the challenges you’ll face is that Python’s default data type is the list, not the array like in many other languages. While this can be helpful in certain situations, it can create issues when you need to store and access data in a specific order. This is especially problematic if you’re building an app that relies on a database to store data. If you’re storing your data in a list, you’ll need to convert it into a database-friendly format before you can use it with a tool like SQL. You’ll also need to anticipate challenges when working with binary data. Python doesn’t have a native binary data type, so you’ll need to use a tool like NumPy or pandas to convert it to a list. This may not be a big deal if you’re only storing the data in memory, but if you plan to write it to a file for long-term storage, you’ll need to create a new, binary format for the data before saving it.

Debugging Challenges

Python’s interpreter implementation can also pose challenges when debugging code. When you’re first building your application, you can run into challenges when trying to step through your code line by line to find out where it’s getting hung up. This can be especially challenging if you need to debug code that’s running in a separate process, like code that’s being used to interact with an API. While you can work around some of these challenges by using a tool like PDB, which lets you set a breakpoint in your code and then look at the values of each variable as you run the code, you may find that it’s easier to debug when using a compiled language like C++.

Limited Language Support for Libraries and Tools

The Python ecosystem is vast, but it’s not as mature as some of the other programming languages that have been around for decades. You’ll find that some of your favorite tools and libraries that are used in other languages may not be available for Python. This may not be a deal breaker if you’re only building an internal application, but if you plan to release it to the public, you may want to consider tools that have more robust support for Python. For example, Python’s standard library is robust, but it doesn’t have the same level of support as the libraries that are available for other languages like PHP or Ruby. You may find that you need to use a third-party tool like AWS that has robust support for Python when building your application.

Conclusion

As you can see, Python comes with a unique set of challenges that you don’t experience when coding in other languages. Because of this, it’s important to anticipate these growing pains as you continue developing your application. You may run into issues when trying to debug your code, when working with data, or when trying to integrate your application with third-party tools. It’s important to recognize these issues and know how to deal with them so you can successfully build your application without getting slowed down by these challenges.