Pros and Cons of Python Programming Language

As a widely popular programming language, Python has been taking the programming world by storm with its continued growth in popularity year after year. In 2023, it was ranked by Stack Overflow as the third most popular programming language and was rated first by IEEE.

However, popularity isn't everything, when it comes to choosing a programming language for your app. With so many programming languages available, it can be challenging for business leaders to determine which language is the best fit for their organization.

In this article, we'll explore Python advantages and disadvantages to help you decide if it's the right language for your business needs.

What is the Main Use of Python?

Python is a versatile programming language that can be used for a wide range of applications. According to the results of the Python Software Foundation and JetBrains 2022 survey of Python users, the most common uses of Python are for data analysis, web development, machine learning, and DevOps. Python can also be used for scientific computing, artificial intelligence, scripting, automation, and more.

Python's easy-to-learn syntax and large collection of libraries make it a popular choice for beginners and experienced developers alike. Its simple yet powerful features enable it to handle complex tasks efficiently, making it a popular language for creating software prototypes, building scalable web applications, and performing data analysis and visualization.

Additionally, Python is widely used in the development of games, mobile applications, desktop applications, and network programming.

What are the Pros of Python?

Python's ease of use, versatility, and large community of developers make it a great programming language for businesses looking to develop software quickly and efficiently. Here are some of the key pros of Python:

Large Community

As a free and open-source language, Python has a large and active community of developers. This means there are plenty of resources, libraries, and tools available to support its use. Additionally, Python's standard library is extensive, offering a wide range of built-in functions and modules.

One of Softjourn's Python software engineers, Volodymyr Miroshnychenko, said that a major advantage of Python is its community. "There are tons of very useful third-party libraries that you can use in your projects," he stated.

It's Versatile, Easy to Use, and Fast to Develop

Python's syntax is designed to be intuitive and easy to learn, making it a great language for beginners. Its clean and readable code helps reduce the time and effort required for software development.

It's also a versatile programming language that can be used for a wide range of applications, from web and mobile app development to data analysis, scientific computing, artificial intelligence, and machine learning.

Volodymyr Miroshnychenko, Python software engineer at Softjourn said:

The biggest advantage of Python is its relative ease and speed of learning. When compared to other popular languages, the effort and knowledge required to become a decent developer in Python are much lower.

Python Enhances Productivity

Python's focus on simplicity and ease of use means that developers can be more productive with less code. This can help reduce development time and costs, making it a cost-effective choice for businesses.

Cross-Platform Compatibility

Python is compatible with multiple operating systems, including Windows, Linux, and macOS. This makes it a versatile language for developing applications that can run on different platforms.

Machine Learning

Its versatility, ease of use, and large community of developers make Python an excellent language for machine learning (ML) applications.

One of the main advantages of Python for ML is the availability of a rich collection of libraries designed specifically for ML. TensorFlow, PyTorch, Keras, and Scikit-Learn are some of the most popular libraries that offer a wide range of tools and functions for building, training, and deploying machine learning models.

Python's simple syntax and ease of use make it an ideal language for beginners to learn machine learning. Software engineer, Volodymyr Miroshnychenko, said that he likes Python's syntax because "It forces you to write readable code and there are a variety of Python enhancement proposal documents that can be used as guidelines for writing code."

Additionally, the language's clear and readable code can help developers build and maintain ML models with greater ease.

Since the language is versatile, developers can use it for various stages of the machine learning pipeline, from data preprocessing and feature engineering to model training and deployment.

Python can easily interface with other programming languages, such as C++ and Java, allowing developers to use Python to build the high-level logic of their machine learning models while relying on other languages to handle lower-level tasks such as data processing or hardware optimization.

With Python's powerful machine learning libraries and intuitive syntax, you can feel confident that complex models can be built and deployed with ease.

Extensive Libraries

The sheer amount of libraries is a major advantages of the language, particularly for machine learning and data science applications. The libraries offer pre-built functions and algorithms that developers can use for their projects, which saves time and effort compared to writing these functions and algorithms from scratch.

Additionally, the libraries are often designed to solve specific problems, such as image recognition or natural language processing, and provide specialized tools that make these tasks easier to accomplish.

Excellent for Data Analysis

Python is an excellent programming language for data analysis, with a range of libraries available for working with data. The most popular of these is Pandas, which offers powerful tools for data manipulation and analysis.

It's Easy to Test

In general, Python is considered to be a language that is easy to test, thanks to a number of features and libraries that support testing and test-driven development (TDD).

For example, the built-in unit test library provides a framework for writing and running tests, while the PyTest library offers a more flexible and powerful testing framework with a range of plugins and features.

Python is a Portable Programming Language

One of the benefits of Python is that it is a portable language, meaning that Python code can be run on different platforms without needing to modify the code. This is because Python's interpreter is available on a wide range of platforms, including Windows, Mac OS, and Linux.

As a result, developers can write Python code on one platform and then deploy it on another platform without needing to make any changes to the code.

Python's portability is particularly useful for large, distributed projects that run across multiple platforms. By using Python, developers can write code that works seamlessly across different systems and devices, which can save time and effort compared to developing separate code bases for each platform.

Additionally, Python's portability makes it easier to migrate code from one platform to another as needed. If a project needs to be moved from one platform to another, the code can be simply transferred without requiring any significant changes.

This can be especially useful for organizations that need to migrate their software to a cloud environment or new platform.

Great for Prototypes - You Can do More With Less Code

Python is an ideal choice for developing prototypes due to its ease of use, extensive library support, and rapid development capabilities.

Its simple syntax and dynamic typing make it easy to write and modify code, while its large collection of libraries and frameworks provide pre-built functions and tools that can be used to quickly solve common problems.

Python's interpreted nature also means that prototypes can be developed quickly and tested immediately without needing to spend time compiling the code, which enables developers to iterate rapidly on their prototypes until they have a functional proof-of-concept.

Python software engineer, Volodymyr Miroshnychenko, posits that Python can be an ideal choice for quickly getting started on an MVP. "However," he said, "rewriting or refactoring may be necessary in the future since Python does not require the learning of certain concepts, techniques, and patterns found in other languages."

Python is Dynamically Typed

Python is a dynamically typed language, which means that variables do not need to be declared with a specific data type. This allows for more flexible and agile programming, as developers can change variable types on the fly without needing to modify the code or worry about type mismatches.

For example, in a statically typed language like Java, a variable must be declared with a specific data type and cannot be changed to a different data type later in the code. However, in Python programming language, variables are dynamically typed and can be changed to a different data type as needed.

The Cons of Python Programming Language

While Python is a popular and powerful programming language, it may not be the best choice for every use case. Developers should consider the potential disadvantages of Python before choosing it as their programming language of choice, and evaluate whether its strengths outweigh its weaknesses for their particular project.

Some of the disadvantages of Python include:

Python has Speed Limitations

Python is an interpreted language, which means that it can be slower than compiled languages like C or Java. This can be a problem for certain types of applications that require high performance, such as real-time systems or heavy computation.

"From the performance perspective, Python is horrible, as it is very slow." said Softjourn's Python software engineer, Volodymyr Miroshnychenko. This may not be a total deal breaker though, as Miroshnychenko mentions, "Keep in mind that all the libraries for ML and data analysis are fast and can be used in Python (since they are written in C)."

Python is Not Designed for Building Mobile Applications

Python is not a native language for mobile app development, meaning that it is not specifically designed or optimized for building mobile applications. While it is possible to use Python in the context of mobile computing, it is not typically considered to be the primary language for mobile app development.

This can be particularly problematic for mobile applications that require high levels of performance, such as games or graphics-intensive applications. In these cases, using a compiled language or a language that is more optimized for mobile development may be a better choice.

Additionally, while there are frameworks and tools available for using Python in mobile app development, the mobile development ecosystem is largely dominated by other languages and frameworks, such as Java and Kotlin for Android and Swift for iOS.

This means that developers may need to spend extra time and effort learning how to integrate Python into the mobile development workflow, which can add complexity to the development process.

Python Consumes a lot of Memory Space

Python uses more memory than some other programming languages due to its dynamic typing and garbage collection mechanisms. This can be an issue when working with large datasets or complex algorithms.

Python can Have Runtime Errors

Python's dynamic typing can be a double-edged sword. While it can make programming more flexible, it can also lead to more runtime errors due to issues with type mismatches or incorrect variable assignments.

Packaging and Distribution

While Python has a large and vibrant community of developers and libraries, packaging and distribution of Python applications can be more challenging than with other languages. This is partly due to the many different package management tools and distribution mechanisms available, which can make it difficult to ensure that applications run consistently across different environments.

Pros and Cons of Python: Garbage Collection

Garbage collection is an automatic memory management feature in Python that helps manage the allocation and deallocation of memory in a program. It allows developers to allocate memory for objects without having to manually manage the memory allocation or deallocation process.

While garbage collection is a useful feature, it can also be a potential downside of using Python. Python's garbage collector works by periodically scanning memory for objects that are no longer being used and freeing up the memory they were occupying. However, this can potentially cause performance issues, as the garbage collector can consume a significant amount of system resources during its scans.

Additionally, Python's garbage collector may not always free up memory immediately when an object is no longer being used, which can result in memory leaks if the program continues to consume memory over time. This can be especially problematic for long-running programs or applications that process large amounts of data.

Pros and Cons of Python: Multithreading

Multithreading is a programming technique that allows multiple threads of execution to run concurrently within a single process. Python provides support for multithreading through the threading module, which allows developers to create and manage threads in their Python programs.

One of the benefits of using multithreading in Python is that it can help improve the performance of CPU-bound tasks by allowing them to be executed in parallel across multiple threads. This can be particularly useful for applications that need to perform complex calculations or data processing operations.

However, it is important to note that Python's support for multithreading is limited by the Global Interpreter Lock (GIL), which is a mechanism that ensures that only one thread can execute Python bytecode at a time.

Additionally, managing multiple threads can introduce additional complexity and potential issues, such as race conditions, deadlocks, and synchronization issues. Developers should be aware of these potential challenges and use best practices such as locking and synchronization to manage multithreaded programs effectively.

Pros and Cons of Python: Interpreted Language

Python is an interpreted language, which means that it does not need to be compiled before running. Instead, Python code is interpreted by the Python interpreter at runtime. This comes with both advantages and disadvantages. When a Python program is executed, the interpreter reads each line of code and immediately executes it, without first converting the entire program into machine code.

There are several advantages to using an interpreted language like Python. First, it makes development and testing easier and faster because there is no need to compile the code after every change. Developers can simply make changes to the code and run it immediately to see the results. Python's interpreted nature is advantageous for beginners as it eliminates the need for compilation and allows for easy experimentation.

However, interpreted languages can be slower than compiled languages, resulting in longer execution times, especially for complex or computationally intensive programs. Debugging errors can be more challenging since they are not detected until runtime.

Frameworks

Libraries

Why do Businesses Choose Python for their Software Development Projects?

We asked our Python software engineer about the use cases he most often sees for clients who decide to use Python for their projects.

He described the three most common reasons:

For Cloud

"Nowadays, businesses try to use a serverless approach for their products. In the projects I work on, we use AWS services, specifically AWS lambda. Since we need our app to start separately on each request, interpreted languages, like Python, are perfect here."

Easy Adaptability

"If you already have ML engineers and data analytics that use Python, it is relatively easy to assign them as Python web developers. Even engineers that use other programming languages can pretty quickly learn Python and start using it. This means Python works great when businesses need to pivot or scale quickly."

Python is Just Popular

"Since Python is such a popular language, there are so many more engineers in the market searching for work with this technology, in comparison to other languages. Plus, Python can be used anywhere from web development to ML to data analytics and beyond, so if your project might become complex, you have a language that covers your bases."

Is Python Right for Your Next Project?

It is difficult to say whether Python is better than other programming languages as it largely depends on the specific use case and project requirements.

Python has some unique strengths, such as its ease of use, versatility, and a large library of pre-built functions and tools. This makes it a popular choice for tasks such as data analysis, machine learning, and web development.

However, other languages may be better suited for other tasks, such as C++ for system-level programming, Java for large-scale enterprise applications, or JavaScript for web front-end development.

Ultimately, the best programming language for a particular project depends on a variety of factors, including the development team's skills and expertise, the project requirements, and the available resources.

If you are looking to hire Python developers or other experienced tech talent, Softjourn has Python developers with niche-industry expertise in finance, payments, M&E, and ticketing.

See our guide on the most popular programming languages for 2023 and tips for selecting the best one.