Difference Between Python and Java: Key Features | Hacker Noon

June 19th 2020

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Python or Java, which is better? This question has triggered lots of heated discussions around the global dev community. 

Beginner developers might be in doubt as to which of the two they should master. Startups and companies might be wondering which one would work better for their next project. 

And it comes as no surprise as both of these languages can solve many tasks with equal efficiency. Yet, in certain cases, one beats the other by a wide margin. 

In this post, we’re going to analyze their strengths and weaknesses based on numerous aspects. This comparison will come in handy to anyone who has difficulties with pinning down the right technology for their tasks and goals. 

Python vs Java: Facts and Statistics

Before diving deep into the difference between Python and Java, let’s get up to speed on the current trends. 

PYPL statistics are derived from Google Trends to determine the frequency of online learning resources being looked up on a monthly basis. The stats in May 2020 reveal that Python is in lead by far and away, with Java being the runner-up. It’s no wonder, as having plain English-like syntax, Python offers a much shorter learning path. 

A survey conducted by the Association for Computing Machinery (ACM) proved that Python is the top go-to option to get beginners started on programming. Besides, Python has a wide application in advanced fields like web development, automation projects, AI solutions, machine learning, and data science.

The TIOBE index collects statistics from 25 search engines to capture the popularity of programming languages. As confirmed by the May 2020 stats, Java is ranked among the top three options slightly overtaken by C. So, despite the hot competition, it maintains its high position amid the most in-demand languages. 

Offering robust security and reliability features, Java is employed in a variety of applications from mobile development to scalable web and enterprise solutions. What’s more, it’s recognized as the official language for the native Android apps, so it has extensive support from Google. Today, a vast number of apps in the Google Play store are built in Java. 

Python vs Java: Jobs and Salaries

Being highly popular, both Python and Java offer lots of career opportunities. 

On average, around 60K of job postings for Java and Python software developers are available on the USA job market. So if you master either of these languages, you won’t be out of the job for sure. As for salaries, Java developers can earn around $102K per annum, whereas Python developers can count on $123K. 

There is no doubt that job prospects and salary size play an essential role when choosing your occupation. But when all is said and done, you need to love what you do. So when deciding on a language to learn, pursue the one that sparks your interest and imagination the most. 

Apart from becoming a Software Developer, you can always consider such career paths as Data Scientist, Data Analyst, or Product Manager, where programming skills can come in handy too.

Python or Java: Key Factors to Influence Your Choice

Python and Java are pretty much alike in many respects. Both are suitable for cross-platform development. Both come with rich standard libraries. Both are compiled to bytecode, although Python’s bytecode compilation happens at runtime. Both originate from the Algol family, although Java has much more similarity with C/C++ than Python does.

So when facing a dilemma of Python vs Java, how should you go about it? 

To draw a clear line between the two, we’ve prepared the following table that compares and contrasts their essential features. This comparison will help you choose the right programming language for the right tasks.


  • Java code is characterized by complexity and verbosity. Curly braces are obligatory to set the boundaries between code blocks. Your code won’t work if you miss a curly brace.
  • Python code is succinct. It requires neither curly braces nor semicolons. However, indentation is mandatory to divide code into blocks. Indentation provides a major advantage of improved code readability though.


  • Java is a compiled language. So translation into bytecode happens at compile-time, and not at runtime. 
  • Python is an interpreted language. So translation into bytecode happens at runtime. 

Execution Speed

  • Due to prior compilation, Java has faster execution speed.
  • Python is slower than Java, as it’s an interpreted language, and the determination of data types happens during code execution. 

Multiple Inheritance

  • Java provides only partial support of multiple inheritance through interfaces. 
  • Python delivers both single and multiple inheritance features. 


  • In most cases, Java is faster than Python, as was proved by Debian Benchmarks. It can tolerate heavy loads.  
  • Python shows lower performance speed than Java does. It isn’t a good option for memory-intensive tasks. 


  • Java updates provide backward compatibility that results in a huge legacy codebase. Today, a large percentage of enterprise legacy platforms are coded in Java.
  • You won’t find many enterprise legacy solutions written in Python. There are several reasons for this, including its scripting nature and rapid overall language updates. However, it’s not uncommon for Python to be used as a “glue” layer that connects different entities of legacy platforms. 

Cross-Platform Support

  • Java Virtual Machine is responsible for the execution of Java bytecode. JVM is available on various platforms allowing Java to be a solid choice for cross-platform development.
  • It’s possible to execute Python code on any platform where there is a Python interpreter installed. However, Python is not well-suited for native mobile development. 


  • With the Java Database Connectivity (JDBC) API, Java provides effective database access features. It also offers object-relational mapping (ORM) frameworks like Hibernate and JPA. 
  • Python’s database API is independent of any database engine. Various libraries deliver DB connectivity including psycopg2, SQLAlchemy, and others. Django ORM and PonyORM provide object-relational mapping (ORM) capabilities. 

Backend Frameworks

  • When opting for Java, you’ll be fully equipped with powerful web development frameworks. The most popular options include:
    • Spring: an open-source framework primarily used in the development of enterprise-level applications.
    • Hibernate: a framework that implements the Java Persistence API (JPA) to empower applications with an ORM database; struts: a fully-featured framework supported by the Apache Software Foundation (ASF). It allows implementing easy-to-maintain enterprise applications.
    • Struts: a fully-featured framework supported by the Apache Software Foundation (ASF). It allows implementing easy-to-maintain enterprise applications.
  • Python frameworks are overtaking Java ones in their popularity and wide-spread usage. The following ones are especially difficult to compete with:
    • Django: an open-source full-stack framework. Its aim is to deliver all the essential features out-of-the-box instead of providing them as a set of libraries.
    • Flask: a web development framework provided under the BSD license. Flask is aimed at building solid foundations for web applications.
    • FastAPI: a framework designed to enable rapid and efficient implementation experience. It enables speedy development of network APIs (including GraphQL) with Python 3.6+.

Development Speed

  • Due to higher code complexity, Java applications take more time to develop. Implementation of an MVP version can easily take a couple of months. Though Java applications are considered to be robust and highly secure, so they are well worth their time and costs. 
  • Python’s natural simplicity enables rapid development. MVP versions can be implemented within a couple of weeks. So when having a tight schedule and a task at hand that can be well-handled with Python, don’t hesitate to go for it. Apart from that, Python development is more time- and cost-efficient. 

Machine Learning Libraries

  • Though Python is the most popular language in the Machine Learning landscape, Java offers a lot of handy ML libraries with rich capabilities as well. For instance:
    • Deeplearning4j
    • Neuroph
    • Weka
    • Mallet
    • MOA
  • Being the primary language in the Machine Learning industry, Python offers a wide array of ML libraries. The most popular ones include:
    • TensorFlow
    • PyTorch
    • Keras
    • Scikit-learn
    • NLTK

Python or Java: Key Takeaways

Now that we’ve compared Python and Java, it’s clear that they have their similarities and differences. Let’s briefly summarize when it might be better to prefer one over the other:

  • If you’re new to the programming world, then go for Python. Its simplicity and English-like syntax will enable you to have a head start on your programming journey. 
  • If you’re an experienced professional and development speed is your top priority, then you should opt for Python.
  • If application security and robustness matter above everything else, don’t hesitate to pick Java.  

Ultimately, you need to choose based on your requirements, your budget, and the type of project you have at hand. Sometimes, you’ll need both languages for different tasks in your project to achieve the best results. So keep your mind open when it comes to making a decision!

At HuskyJam, we are skilled at Python and Java development. Take a look at our portfolio to see examples of projects implemented with these languages.
You are also welcome to visit our blog and read an extended article about Python vs Java. We hope this will further help you make the right choice!


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