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PHPUnit VS Jupyter

Compare PHPUnit VS Jupyter and see what are their differences

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PHPUnit logo PHPUnit

Application and Data, Build, Test, Deploy, and Testing Frameworks

Jupyter logo Jupyter

Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
  • PHPUnit Landing page
    Landing page //
    2023-08-26
  • Jupyter Landing page
    Landing page //
    2023-06-22

PHPUnit features and specs

  • Comprehensive Testing
    PHPUnit provides a wide range of tools and functionalities for unit testing, allowing developers to thoroughly test their PHP code.
  • Command-Line Interface
    PHPUnit includes a robust CLI that facilitates the running of tests, which can be easily integrated into automated build and deployment processes.
  • Integration with CI/CD
    PHPUnit integrates seamlessly with continuous integration and continuous deployment pipelines, enhancing the DevOps workflow.
  • Mock Objects
    The framework provides built-in support for creating mock objects, which can simulate the behavior of complex dependencies, making unit tests more isolated and reliable.
  • Rich Documentation
    PHPUnit has extensive documentation and a strong community, offering a wealth of resources and support for developers.
  • Code Coverage Analysis
    PHPUnit can be used with Xdebug or PHPDBG to generate detailed code coverage reports, helping identify untested parts of the codebase.

Possible disadvantages of PHPUnit

  • Steep Learning Curve
    For beginners, PHPUnit can be daunting due to its comprehensive set of features and conventions, requiring a significant time investment to master.
  • Performance Overhead
    Running a large number of tests with PHPUnit can introduce performance overhead, making test execution slower especially in larger projects.
  • Complex Configuration
    Setting up PHPUnit in a complex development environment can sometimes be tricky, requiring careful configuration and maintenance.
  • Limited Functional Testing
    PHPUnit is primarily designed for unit testing and may not be as suitable for functional or end-to-end testing, necessitating additional tools for comprehensive test coverage.

Jupyter features and specs

  • Interactive Computing
    Jupyter allows real-time interaction with the data and code, providing immediate feedback and making it easier to experiment and iterate.
  • Rich Media Output
    It supports output in various formats including HTML, images, videos, LaTeX, and more, enhancing the ability to visualize and interpret results.
  • Language Agnostic
    Jupyter supports multiple programming languages through its kernel system (e.g., Python, R, Julia), allowing flexibility in the choice of tools.
  • Collaborative Features
    It enables collaboration through shared notebooks, version control, and platform integrations like GitHub.
  • Educational Tool
    Jupyter is widely used for teaching, thanks to its easy-to-use interface and ability to combine narrative text with code, making it ideal for assignments and tutorials.
  • Extensibility
    Jupyter is highly extensible with a large ecosystem of plugins and extensions available for various functionalities.

Possible disadvantages of Jupyter

  • Performance Issues
    For larger datasets and more complex computations, Jupyter can be slower compared to running scripts directly in a dedicated IDE.
  • Version Control Challenges
    Managing version control for Jupyter notebooks can be cumbersome, as they are not plain text files and include metadata that can make diffing and merging complex.
  • Resource Intensive
    Running Jupyter notebooks can be resource-intensive, especially when working with multiple large notebooks simultaneously.
  • Security Concerns
    Because Jupyter allows code execution in the browser, it can be a potential security risk if notebooks from untrusted sources are run without restrictions.
  • Dependency Management
    Managing dependencies and ensuring that the notebook runs consistently across different environments can be challenging.
  • Less Suitable for Production
    Jupyter is often considered more as a research and educational tool rather than a production environment; transitioning from a notebook to production code can require significant refactoring.

Analysis of PHPUnit

Overall verdict

  • PHPUnit is a good choice for testing PHP applications. Its strong reputation in the PHP community and its extensive capabilities make it a valuable tool for ensuring code quality and reliability.

Why this product is good

  • PHPUnit is widely regarded as a robust and reliable testing framework for PHP. It is well-documented, actively maintained, and integrates seamlessly with various development tools and environments. PHPUnit's comprehensive feature set, including support for test-driven development (TDD) and behavior-driven development (BDD), makes it a popular choice among PHP developers.

Recommended for

  • Developers looking to implement test-driven development practices in their PHP projects.
  • Projects requiring a mature, stable, and well-supported testing framework.
  • Teams that benefit from built-in support for continuous integration workflows.
  • Developers who need to perform unit testing, integration testing, or acceptance testing for their PHP code.

PHPUnit videos

+ Add

PHP Unit Testing with PHPUnit | Automated PHP Testing Tutorial [2021]

More videos:

  • Review - DrupalCon Dublin 2016: Automated Testing: PHPUnit all the way
  • Review - Our first PHPunit test in Drupal 8

Jupyter videos

+ Add

What is Jupyter Notebook?

More videos:

  • Tutorial - Jupyter Notebook Tutorial: Introduction, Setup, and Walkthrough
  • Review - JupyterLab: The Next Generation Jupyter Web Interface

Category Popularity

0-100% (relative to PHPUnit and Jupyter)

PHPUnit

Jupyter

Development
100 100%
Development
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Automated Testing
100 100%
Automated Testing
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare PHPUnit and Jupyter

PHPUnit Reviews

We have no reviews of PHPUnit yet.
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Jupyter Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Once you install nteract, you can open your notebook without having to launch the Jupyter Notebook or visit the Jupyter Lab. The nteract environment is similar to Jupyter Notebook but with more control and the possibility of extension via libraries like Papermill (notebook parameterization), Scrapbook (saving your notebook’s data and photos), and Bookstore (versioning).
Source: lakefs.io
7 best Colab alternatives in 2023
JupyterLab is the next-generation user interface for Project Jupyter. Like Colab, it's an interactive development environment for working with notebooks, code, and data. However, JupyterLab offers more flexibility as it can be self-hosted, enabling users to use their own hardware resources. It also supports extensions for integrating other services, making it a highly...
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Jupyter Notebook is a widely popular tool for data scientists to work on data science projects. This article reviews the top 12 alternatives to Jupyter Notebook that offer additional features and capabilities.
Source: noteable.io
15 data science tools to consider using in 2021
Jupyter Notebook's roots are in the programming language Python -- it originally was part of the IPython interactive toolkit open source project before being split off in 2014. The loose combination of Julia, Python and R gave Jupyter its name; along with supporting those three languages, Jupyter has modular kernels for dozens of others.
Source: searchbusinessanalytics.techtarget.com
Top 4 Python and Data Science IDEs for 2021 and Beyond
Yep — it’s the most popular IDE among data scientists. Jupyter Notebooks made interactivity a thing, and Jupyter Lab took the user experience to the next level. It’s a minimalistic IDE that does the essentials out of the box and provides options and hacks for more advanced use.
Source: towardsdatascience.com

Social recommendations and mentions

Based on our record, Jupyter should be more popular than PHPUnit. It has been mentiond 216 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

PHPUnit mentions (33)

  • 19+ Laravel Best Practices for Developers in 2024
    Laravel also has out-of-the-box testing tools like Pest and PHPUnit and functionalities to enable expressive testing. It also supports executing automated testing sessions that are more precise than manual ones. - Source: dev.to / 3 months ago
  • Focusing your tests on the domain. A PHPUnit example
    The example is built over a Symfony environment and using the PHPUnit library, but the idea is valid for any language or framework. - Source: dev.to / 9 months ago
  • Run PHPUnit locally in your WordPress Plugin with DDEV
    Okay, I am digressing; the focus here is PHPUnit for plugins. As with many of my other articles, my goal is to create a reference for myself to use when I need it in the future. - Source: dev.to / 12 months ago
  • Wordpress tests with Pest and WP Setup
    Today, I finished the first implementation of this environment, adding Pest and PHPUnit in v10.5, which is currently not supported by default with WP Env. - Source: dev.to / about 1 year ago
  • Laravel Fundamentals: A Cheat Sheet for Rapid Development
    Laravel integrates with PHPUnit for unit testing out-of-the-box. Tests are stored in the tests directory of your Laravel application. - Source: dev.to / about 1 year ago
View more

Jupyter mentions (216)

  • The 3 Best Python Frameworks To Build UIs for AI Apps
    Showcase and share: Easily embed UIs in Jupyter Notebook, Google Colab or share them on Hugging Face using a public link. - Source: dev.to / 3 months ago
  • LangChain: From Chains to Threads
    LangChain wasn’t designed in isolation — it was built in the data pipeline world, where every data engineer’s tool of choice was Jupyter Notebooks. Jupyter was an innovative tool, making pipeline programming easy to experiment with, iterate on, and debug. It was a perfect fit for machine learning workflows, where you preprocess data, train models, analyze outputs, and fine-tune parameters — all in a structured,... - Source: dev.to / 4 months ago
  • Applied Artificial Intelligence & its role in an AGI World
    Leverage versatile resources to prototype and refine your ideas, such as Jupyter Notebooks for rapid iterations, Google Colabs for cloud-based experimentation, OpenAI’s API Playground for testing and fine-tuning prompts, and Anthropic's Prompt Engineering Library for inspiration and guidance on advanced prompting techniques. For frontend experimentation, tools like v0 are invaluable, providing a seamless way to... - Source: dev.to / 5 months ago
  • Jupyter Notebook for Java
    Lately I've been working on Langgraph4J which is a Java implementation of the more famous Langgraph.js which is a Javascript library used to create agent and multi-agent workflows by Langchain. Interesting note is that [Langchain.js] uses Javascript Jupyter notebooks powered by a DENO Jupiter Kernel to implement and document How-Tos. So, I faced a dilemma on how to use (or possibly simulate) the same approach in... - Source: dev.to / 9 months ago
  • JIRA Analytics with Pandas
    One of the most convenient ways to play with datasets is to utilize Jupyter. If you are not familiar with this tool, do not worry. I will show how to use it to solve our problem. For local experiments, I like to use DataSpell by JetBrains, but there are services available online and for free. One of the most well-known services among data scientists is Kaggle. However, their notebooks don't allow you to make... - Source: dev.to / about 1 year ago
View more

What are some alternatives?

When comparing PHPUnit and Jupyter, you can also consider the following products

PHPTester.net - PHPTester.net gives developers and learners the ability to write their PHP code and get the output online.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

JUnit - JUnit is a simple framework to write repeatable tests.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

Cucumber - Cucumber is a BDD tool for specification of application features and user scenarios in plain text.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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