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Jupyter VS ParseHub

Compare Jupyter VS ParseHub and see what are their differences

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Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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  3. » Reviews
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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.

ParseHub logo ParseHub

ParseHub is a free web scraping tool. With our advanced web scraper, extracting data is as easy as clicking the data you need.
  • Jupyter Landing page
    Landing page //
    2023-06-22
  • ParseHub Landing page
    Landing page //
    2021-09-12

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.

ParseHub features and specs

  • User-friendly Interface
    ParseHub offers a point-and-click interface that makes it easy for users to extract data from websites without needing any coding skills.
  • Advanced Features
    The tool supports complex data extraction tasks, including handling AJAX, JavaScript, infinite scroll, forms, and CAPTCHA.
  • Cross-platform Compatibility
    ParseHub is available as a web app and a desktop application, making it accessible on multiple operating systems.
  • API Integration
    ParseHub provides an API that allows for easy integration with other applications, enabling automated data extraction workflows.
  • Schedule and Automate
    Users can schedule their data extraction tasks to run at specific intervals, which is useful for keeping datasets up-to-date.
  • Cloud Storage
    Extracted data is stored in the cloud, allowing easy access and management of large datasets without consuming local storage resources.
  • Free Tier
    ParseHub offers a free tier that allows users to perform a limited number of data extraction tasks, suitable for small projects or initial testing.

Possible disadvantages of ParseHub

  • Learning Curve for Complex Tasks
    While the basic interface is user-friendly, advanced data extraction tasks may require a steep learning curve to master.
  • Monthly Limits
    The free tier and lower-tier plans have limits on the number of tasks and the amount of data that can be extracted per month, which could constrain heavy users.
  • Pricing
    Higher-tier plans can become expensive, especially for businesses that require extensive data extraction capabilities.
  • Performance Issues
    Users have reported occasional performance issues and bugs when dealing with very large or complex websites, which can affect the reliability of the data extraction processes.
  • Limited Export Formats
    While ParseHub supports common formats like CSV, JSON, and Excel, it lacks support for some specialized or less common file formats.
  • Customer Support
    Some users have reported that customer support can be slow to respond to issues, which could be problematic for time-sensitive projects.
  • Privacy Concerns
    Since the data extraction occurs on ParseHub's servers, there could be privacy concerns related to the handling of sensitive or proprietary data.

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

ParseHub videos

+ Add

ParseHub Tutorial: Scrape Ratings and Reviews from a Website

More videos:

  • Tutorial - ParseHub Tutorial: Scraping Product Details from Amazon

Category Popularity

0-100% (relative to Jupyter and ParseHub)

Jupyter

ParseHub

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Web Scraping
0 0%
Web Scraping
100% 100
Data Dashboard
100 100%
Data Dashboard
0% 0
Data Extraction
0 0%
Data Extraction
100% 100

User comments

Share your experience with using Jupyter and ParseHub. For example, how are they different and which one is better?
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Reviews

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

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

ParseHub Reviews

Best Data Scraping Tools
Parsehub is a fantastic tool for people who want to extract data from websites without coding. It is used widely by data analysts, journalists, data scientists, and many fields. Parse Hub is easier to use; you can click on the data that you are working on to build a web scraper, which then exports the data in excel format or JSON.
Source: pamelaewallaceu.medium.com

Social recommendations and mentions

Based on our record, Jupyter seems to be a lot more popular than ParseHub. While we know about 216 links to Jupyter, we've tracked only 3 mentions of ParseHub. 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.

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 / about 2 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 / 3 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 / 4 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 / 8 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 / 11 months ago
View more

ParseHub mentions (3)

  • Home Depot price data using IMPORTXML?
    I've heard some folks have success with "parsehub.com", though I once tried it for a project and found it a bit intimidating... Source: over 3 years ago
  • Free for dev - list of software (SaaS, PaaS, IaaS, etc.)
    Parsehub.com — Extract data from dynamic sites, turn dynamic websites into APIs, 5 projects free. - Source: dev.to / almost 4 years ago
  • Turn any website into an API with no code
    Parsehub is a powerful web scraping GUI tool for efficient fetching and manipulating data from any webpage. It helps you create an API output for a given website. You can even sanitize your content by using regex or replace function. So the input is a URL and the output is a structured json file. - Source: dev.to / about 4 years ago

What are some alternatives?

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

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.

import.io - Import. io helps its users find the internet data they need, organize and store it, and transform it into a format that provides them with the context they need.

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

Octoparse - Octoparse provides easy web scraping for anyone. Our advanced web crawler, allows users to turn web pages into structured spreadsheets within clicks.

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

Apify - Apify is a web scraping and automation platform that can turn any website into an API.

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