Go vs Python: Which Language Excels in Projects?

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Anna Stankevičiūtė

Last updated on

 

2025-12-9

 

5 min read

The popularity of programming languages has been dynamically changing. According to the statistical data from PYPL (Programming Language Popularity Index) and TIOBE (Programming Community Index), Python firmly holds the first place with market shares of 27.3% and 23.37% respectively, making it the most popular programming language worldwide. Although Go language has a lower usage rate in comparison, it still remains an indispensable and important role in the developer community.

The main purpose of this article is to explore the specific differences between these two languages and help you make informed technical decisions based on your specific needs.

Go vs Python: Key Points

Go and Python are two highly regarded programming languages in today’s software development field, each with its own unique design philosophy and areas of advantage.

• Go: Renowned for its outstanding concurrency performance and compilation efficiency, particularly suitable for building high-performance distributed systems;

• Python: With its concise syntax and rich ecosystem, it dominates the fields of data science and rapid development.

Go vs Python: Comparison Table

What is Go?

Go is an open-source programming language released by Google in 2009, combining procedural, functional, and concurrent programming paradigms. The initial design goal was very clear: “to facilitate building its backend services“, and this philosophy permeates every design decision in the language. The Go language provides native concurrency support through goroutine and channel, and the characteristic of compiling into a single binary file makes deployment extremely simple. These features make it shine brightly in modern cloud-native applications.

Key Features of Go

The design of the Go language focuses on practicality and efficiency, with its main features embodied in the following aspects:

• Compiles to a single binary file, deployment is simple and requires no dependent environment.

• Completely open source, with active community support and a continuously developing ecosystem.

• Achieves native concurrency through goroutine, providing lightweight and highly efficient concurrency processing capabilities.

• Built-in garbage collection mechanism, achieving automatic memory management to improve development efficiency.

• Adopts a static type system, performing type checks at compile time to reduce runtime errors.

• Concise and unified syntax, forcibly enforcing code formatting standards to enhance maintainability.

• Provides a powerful standard library covering commonly used functional modules such as network and encryption.

• Supports cross-platform compilation, allowing easy building of executable files for multiple platforms.

What Is Go Used For?

The Go language demonstrates powerful practicality in multiple fields, with the main application scenarios including:

• Cloud-native applications — Ideal choice for containerization and microservices architecture

• Network services — High-performance API services and middleware development

• DevOps tool chain — Infrastructure automation and site reliability engineering, for example Terraform, Traefik, Vault etc.

• Command line tools — Quickly build cross-platform system tools

• Distributed systems — Handling high concurrency and distributed computing tasks

• Blockchain development — Cryptocurrency and distributed ledger applications

What is Python?

Python is an open-source, interpreted, high-level, dynamic semantics programming language that supports multiple programming paradigms including object-oriented, imperative, functional, and procedural. Python is renowned for its extremely concise syntax and powerful ecosystem, dominating the fields of artificial intelligence, data science, and Web development. According to the official Python , the design philosophy of this language emphasizes code readability and conciseness, making it a common choice for both beginners and professional developers.

Key Features of Python

Python has a rich combination of features, making it an ideal choice for development in multiple fields:

• As an interpreted language, it supports rapid iterative development without requiring compilation steps.

• Adopts a dynamic type system, with variable types automatically determined at runtime.

• Has a huge collection of third-party libraries covering various application scenarios.

• Possesses excellent cross-platform compatibility, able to run on multiple operating systems (Windows, macOS, Linux, WebAssembly).

• Through garbage collection mechanism, achieves automatic memory management.

• Supports multi-paradigm programming, including object-oriented and functional programming.

• Has an active developer community, providing rich learning resources and technical support.

What Is Python Used For?

Python has an extremely wide range of applications, almost covering all programming fields:

• Data science and analysis — Support from libraries such as Pandas, NumPy

• Machine learning and AI — Frameworks such as TensorFlow, PyTorch

• Web development — Full-stack frameworks such as Django, Flask

• Automation scripts — System management and task automation

• Scientific computing — Academic research and engineering computing

• Web scraping — Tools such as Scrapy, BeautifulSoup

Pros of Go and Python

Go pros

• Go compiles to native code with runtime efficiency close to C++, delivering excellent performance.

• Lightweight concurrency is achieved through goroutines and channels, simplifying the development of highly concurrent programs.

• Extremely fast compilation speed; even large projects can be built quickly.

• Static type system combined with an optimized garbage collector ensures efficient memory usage.

• Produces a single binary executable file, making deployment simple and convenient.

• Built-in code formatting tool (go fmt) ensures consistent code style across team collaboration.

Python pros

• Intuitive and easy-to-understand syntax with a gentle learning curve, very suitable for programming beginners.

• Rich third-party libraries covering all fields, with a mature and complete ecosystem.

• Interpreted execution allows rapid iterative development; code changes take effect immediately.

• Dominates the fields of machine learning and artificial intelligence with a complete tool chain.

• Supports multiple programming paradigms, flexibly adapting to different development needs.

• Has a huge developer community, providing abundant learning resources and technical support.

Cons of Go and Python

Go cons

• Go’s ecosystem is relatively young, with limited library choices in certain specialized fields.

• Generics were only recently added to the language standard, and related best practices are still evolving.

• Error handling requires explicitly checking every return value, increasing the amount of code writing.

• Support for mobile application development is weak and not suitable for developing native mobile apps.

• When handling complex business logic, there is a lot of template/boilerplate code, which may lead to code redundancy.

Python cons

• As an interpreted language, its running speed is relatively slow and not suitable for high-performance computing scenarios.

• The Global Interpreter Lock (GIL) restricts the parallel execution efficiency of multithreading.

• The dynamic type system brings additional memory overhead and relatively high memory consumption.

• Support for mobile development is limited and cannot be used to develop high-performance mobile applications.

• Type errors can only be discovered at runtime, increasing the difficulty of debugging and maintenance.

Go vs Python: Syntax

Syntax is the most intuitive embodiment of language differences. Go and Python reflect different philosophical ideas in their syntax design. We will demonstrate the syntax characteristics of these two languages through a function example that “prints the total number of prime numbers from 1 to 1,000,000”.

Go function example

package main
import "fmt"
func main() {
    count := 0
    for i := 2; i <= 1000000; i++ {
        isPrime := true
        for j := 2; j*j <= i; j++ {
            if i%j == 0 {
                isPrime = false
                break
            }
        }
        if isPrime {
            count++
        }
    }
    fmt.Println(count) // 78498
}

Python function example

count = 0
for i in range(2, 1000001):
    is_prime = True
    for j in range(2, int(i**0.5)+1):
        if i % j == 0:
            is_prime = False
            break
    if is_prime:
        count += 1
print(count) # 78498

Comparison and Analysis

From the syntax comparison, it can be seen that Go emphasizes explicitness and safety, requiring type declarations and error handling, resulting in relatively longer but more rigorous code. Python pursues conciseness and expressiveness; the same functionality can be achieved with far fewer lines of code, though type information is only determined at runtime.

This test was conducted by me in October 2025 on the same 8-core server:

• Go: 0.95 seconds

• Python: 48 seconds (already optimized with PyPy)

• A difference of about 50 times.

In terms of readability, Python's syntax is closer to natural language, making it easier for beginners to understand and get started with; while Go's syntax is concise, it requires understanding more language-specific concepts.

Go vs Python: Performance

1. Scrapy vs Beautiful Soup: Web Crawling

Performance is a key consideration in technology selection, and Go and Python exhibit significant differences in performance characteristics. According to Stack Overflow's test case (calculating prime numbers within 10 million), with the same algorithm implementation, the Go script completes in under one second, while Python takes anywhere from 2 seconds to several minutes. From this, it can be concluded that Go's performance is typically 5 to 10 times faster than Python, and in certain compute-intensive tasks, the difference can be even more pronounced.

This performance gap mainly stems from the languages' design philosophies: Go compiles directly to native machine code, achieving execution efficiency close to C; whereas Python, as an interpreted language, must be executed through an interpreter, introducing additional performance overhead. In our stress tests, an HTTP service written in Go can handle tens of thousands of requests per second, while a Python service under the same hardware conditions typically handles only a few thousand requests.

However, performance is not the sole consideration. For I/O-intensive applications, Python's asynchronous frameworks (such as asyncio) can effectively utilize system resources, significantly narrowing the performance gap.

Go vs Python: Backend Development

In the field of backend development, both Go and Python perform excellently, but they are suited to different scenarios. Go, with its outstanding concurrency performance and low resource consumption, is particularly well-suited for building highly concurrent microservices and API gateways. Python, thanks to its rich web frameworks and rapid development capabilities, has a clear advantage in traditional web applications and rapid prototyping.

In microservice architectures, Go’s lightweight goroutines can easily handle tens of thousands of concurrent connections with extremely low memory overhead. Based on our project experience, microservices built with Go perform excellently in containerized environments, featuring fast startup times and high resource utilization. On the other hand, Python’s Django and Flask frameworks provide more "full-stack" solutions, coming with built-in ORM, template engines, and other components that accelerate the development process but at the cost of some performance.

Go vs Python: Web Scraping

In the fields of web scraping and data analysis, both languages offer powerful tool ecosystems, but they suit different scenarios. In August 2024, I had a scraping requirement to collect product information from 50 e-commerce websites.

When using Go, we chose the multi-threaded Goquery with goroutines as the scraping framework and conducted a performance comparison test on public web data collection tools. One group used 100 goroutines to concurrently scrape 10,000 URLs, while another group performed the same task using Python’s Scrapy framework. The results showed that the Go version took an average of 42 seconds to complete, whereas the Python version required 3 minutes and 15 seconds—Go demonstrated a clear performance advantage.

However, Python outperforms in the maturity of its scraping ecosystem. The Scrapy framework provides complete lifecycle management for crawlers, and BeautifulSoup is more flexible for HTML parsing. For complex JavaScript-rendered pages, Python can easily handle them via Selenium, while Go has relatively fewer related tools.

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Go vs Python: Machine Learning

In the field of machine learning, Python currently holds an absolute dominant position, but Go is gradually building its own ecosystem. With Python already having such a mature machine learning ecosystem, why would anyone still consider using Go? The answer to this question depends on the specific application scenario and requirements.

Python has mature machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn, with an active community and abundant tutorial resources. According to our project experience, more than 95% of machine learning projects (Llama, Qwen, etc.) choose Python as the development language, mainly due to its rich library ecosystem and rapid prototyping capabilities.

However, Go demonstrates unique value in the deployment and inference stages of machine learning models. A single binary file compiled by Go is easy to deploy and has high memory efficiency, making it suitable for deploying trained models into production environments. In recent years, Go’s machine learning ecosystem has also been developing rapidly, with frameworks such as Gorgonia and GoLearn emerging. Although their functionality is not yet as complete as Python’s ecosystem, they are already usable in specific scenarios.

Go vs Python: Decision Tree

Choosing the right programming language requires a systematic evaluation of project needs. The following decision tree provides a clear guidance path:

  Does the project require extremely high performance?

→ Yes → Choose Go

→ No → Go to question 2

  Do you need rapid prototyping?

→ Yes → Choose Python

→ No → Go to question 3

  Is the project primarily focused on data science or machine learning?

→ Yes → Choose Python

→ No → Go to question 4

  Do you need high-concurrency handling?

→ Yes → Choose Go

→ No → Go to question 5

  Does the team have a language preference?

→ Yes → Choose the corresponding language

→ No → Choose based on project scale

Conclusion

The choice between Go and Python is essentially a trade-off between performance and development efficiency, and between system-level control and rapid prototyping. For backend services that require high performance, high concurrency, and efficient resource utilization, Go is the better choice. For projects in data science, rapid development, and those benefiting from a rich ecosystem, Python still holds a dominant position.

If you want to skip technical complexity and directly obtain web data, Thordata Web Scraper API provides an ideal solution. It integrates the world's highest-quality residential proxies, automatic retries, parsing, deduplication, and structured output—truly enabling billion-scale scraping with just one line of code.

If you want to learn more about scraping tools and Python frameworks, please refer to the following articles:

• Selenium vs Scrapy

• Scrapy vs Beautiful Soup

• How to Set Up and Run Scrapy Cloud

• Step-by-Step Guide to Setting cURL Authorization Headers

We hope the information provided is helpful. However, if you have any further questions, feel free to contact us at support@thordata.com or via online chat.

 

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About the author

Anna Stankevičiūtė

Content Specialist

Anna is a content specialist who thrives on bringing ideas to life through engaging and impactful storytelling. Passionate about digital trends, she specializes in transforming complex concepts into content that resonates with diverse audiences. Beyond her work, Anna loves exploring new creative passions and keeping pace with the evolving digital landscape.

The thordata Blog offers all its content in its original form and solely for informational intent. We do not offer any guarantees regarding the information found on the thordata Blog or any external sites that it may direct you to. It is essential that you seek legal counsel and thoroughly examine the specific terms of service of any website before engaging in any scraping endeavors, or obtain a scraping permit if required.

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