Python vs Golang

Python and Golang are both powerful programming languages with distinct strengths and applications. In this blog, take a look between the similarities & differences of Python & Golang.

GraphQL has a role beyond API Query Language- being the backbone of application Integration
background Coditation

Python vs Golang

Python and Go (Golang) are both popular languages used for backend development. Both languages are Open Source and powerful. Languages have their pros and cons and their use cases vary according to the needs of a project. Before we talk about the similarities and differences, let us briefly look at them individually.

Python
Developed by Guido van Rossum, Python is a popular & general-purpose language for programming. It is in demand with most developers with straightforward syntax. A broad selection of projects & systems uses Python due to its various libraries & community support.
Instead of curly braces, Python prefers indentation for blocks. The standard indentation recommended is to use four spaces for a standard python code, in which both tabs & spaces are supported.

Golang
Launched by Google in 2009, Golang is an open-source language designed to reduce the complexity to manage the codebases & infrastructure within Google. It was developed to make languages such as C++ easy to manage. It is short with a few things but can resolve many issues. Having said that, it does not have an extensive library or object-oriented programming support but it does have strong security, Goroutines & some standard libraries.

Let’s look at the similarities between Python and Go:
Similarities between Python & Go
There are also key differences between Python and GoLang:
Differences between Python & Go

When it comes to ease of usability, Python wins the round. Python’s syntax makes it easier to learn and understand, and hence it is a popular language among first-time coders.

Let’s look at the classic Hello World example in both languages:

Hello World in Python & Go

From the above example, the simplicity of Python can be seen. This simplicity also brings disadvantages which were reflected in the difference between the languages.
When it comes to Web applications, both Python and Golang are great, and both have respective frameworks that help the developer write applications with ease. Python has frameworks like Flask, Django, FastAPI, web2py, and lots more. Go has frameworks like Gin, Fiber, Echo, and many more. Go often prevails in web development compared to Python due to its built-in concurrency model.

Let us take a look at their disadvantages:
  1. Python
  • In many projects, speed is an important aspect. Python is a dynamically-typed and interpreted language, which is responsible for the slow speed. With its line-by-line code execution, often the execution is slow
  • The slow processing power and poor memory management leads to Python being weak in mobile computing. It is not usually used on the client-side or mobile applications. It is generally used in programming done on the server side.
  • Python’s interaction with databases lags compared to popular technologies such as ODBC & JDBC. The database layer is primitive and underdeveloped.
  • It is unsuitable for memory-intensive tasks as it uses a large amount of memory due to the flexibility of data types.
  1. GoLang
  • GoLang has fewer drawbacks. The lack of frameworks for developers is one of them. As compared to other languages such as PHP has laravel and Python has Django.
  • It lacks generic support and it is majorly due to the repetition of codes with different data types for the same operations. Furthermore, a lot of the data structure cannot be implemented due to the same.
  • Golang is still in the development stage due to which developers usually find it difficult to extract the best from its libraries.

To summarize, Python and Golang are both powerful programming languages with distinct strengths and applications. Python is an excellent choice for projects that require a significant amount of data manipulation or machine learning, whereas Golang is an excellent choice for developing large, scalable systems. Finally, the choice between the two languages will be determined by the project's specific requirements, the peers you are working with, and the architecture where the project must run.

Hi, I am Sanjeev Kumar Bharadwaj. I am a Python developer specializing in Flask Framework and lately have been getting experience with Go Lang for backend development. I have fun exploring new technologies, integrations, and bots.

Want to receive update about our upcoming podcast?

Thanks for joining our newsletter.
Oops! Something went wrong.

Latest Articles

Implementing Custom Instrumentation for Application Performance Monitoring (APM) Using OpenTelemetry

Application Performance Monitoring (APM) has become crucial for businesses to ensure optimal software performance and user experience. As applications grow more complex and distributed, the need for comprehensive monitoring solutions has never been greater. OpenTelemetry has emerged as a powerful, vendor-neutral framework for instrumenting, generating, collecting, and exporting telemetry data. This article explores how to implement custom instrumentation using OpenTelemetry for effective APM.

Mobile Engineering
time
5
 min read

Implementing Custom Evaluation Metrics in LangChain for Measuring AI Agent Performance

As AI and language models continue to advance at breakneck speed, the need to accurately gauge AI agent performance has never been more critical. LangChain, a go-to framework for building language model applications, comes equipped with its own set of evaluation tools. However, these off-the-shelf solutions often fall short when dealing with the intricacies of specialized AI applications. This article dives into the world of custom evaluation metrics in LangChain, showing you how to craft bespoke measures that truly capture the essence of your AI agent's performance.

AI/ML
time
5
 min read

Enhancing Quality Control with AI: Smarter Defect Detection in Manufacturing

In today's competitive manufacturing landscape, quality control is paramount. Traditional methods often struggle to maintain optimal standards. However, the integration of Artificial Intelligence (AI) is revolutionizing this domain. This article delves into the transformative impact of AI on quality control in manufacturing, highlighting specific use cases and their underlying architectures.

AI/ML
time
5
 min read