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

Optimizing Databricks Spark jobs using dynamic partition pruning and AQE

Learn how to supercharge your Databricks Spark jobs using Dynamic Partition Pruning (DPP) and Adaptive Query Execution (AQE). This comprehensive guide walks through practical implementations, real-world scenarios, and best practices for optimizing large-scale data processing. Discover how to significantly reduce query execution time and resource usage through intelligent partition handling and runtime optimizations. Perfect for data engineers and architects looking to enhance their Spark job performance in Databricks environments.

time
8
 min read

Implementing custom serialization and deserialization in Apache Kafka for optimized event processing performance

Dive deep into implementing custom serialization and deserialization in Apache Kafka to optimize event processing performance. This comprehensive guide covers building efficient binary serializers, implementing buffer pooling for reduced garbage collection, managing schema versions, and integrating compression techniques. With practical code examples and performance metrics, learn how to achieve up to 65% higher producer throughput, 45% better consumer throughput, and 60% reduction in network bandwidth usage. Perfect for developers looking to enhance their Kafka implementations with advanced serialization strategies.

time
11
 min read

Designing multi-agent systems using LangGraph for collaborative problem-solving

Learn how to build sophisticated multi-agent systems using LangGraph for collaborative problem-solving. This comprehensive guide covers the implementation of a software development team of AI agents, including task breakdown, code implementation, and review processes. Discover practical patterns for state management, agent communication, error handling, and system monitoring. With real-world examples and code implementations, you'll understand how to orchestrate multiple AI agents to tackle complex problems effectively. Perfect for developers looking to create robust, production-grade multi-agent systems that can handle iterative development workflows and maintain reliable state management.

time
7
 min read